AN ABSTRACT OF THE THESIS OF

Richard Roger Hagestedt for the degree of Master of Science

in Forest Management presented on

Title: Spatial Allocation of Land Uses in Land Use Planning

Abstract approved: Signature redacted for privacy.Pame1J. Lase

The purpose of land use planning, as conducted by the

Forest Service, is to allocate land uses. The techniques

employed in the current planning process fail to take location of

the land allocations into account in any systematic manner. The

resulting solutions may be inconsistent with planning goals; the

land use patterns produced may not provide the maximum value of

goods and services possible while protecting long-term biological

prodicti.vity of the Forest.

This study examines the impact of location on land allo-

cation decisions, developing a strategy and set of techniques for in-

corporating spatial factors into the allocation process. Three

spatial factors affect land allocion decisions: 1) the size of a

land unit required to make management of a use practical,

conflicts caused by the adjacent location of specific uses, and

the need to organize ises across the landscape to take advantage

2

of certain characteristics of the planning unit.

Three promising strategies are investigated: 1) an opti-

mizing algorithm, 2) an efficient solution algorithm, and 3) an

assignment algorithm. The optimizing algorithm replaces the

linear program currently employed in the planning process with an

integer program able to consider location of land units in the allo-

cation process. The efficient solution algorithm uses an integer

program to create a land use pattern from the linear program

acreage allocations. Computer core size limitations and the size

and complexity of the planning problem prevent application of these

strategies. The assignment approach overcomes these difficulties

with a heuristic algorithm designed to locate linear program

allocations on the platining unit.

The computer programs required to support the spatial

allocation strategy include: 1) a computer mapping program,

2) a detail reductioti program, 3) an adjacency program,

4) the heuristic program, and 5) a conflict detection program.

The mapping program creates land units and keeps track of

their location. The detail reduction program eliminates some

complexity from the land base data. The adjacency program

identifies adjacent land units. The conflict detection program

detects conflicts caused by uses located adjacent to each other

and violations of minimum land unit size.

The spatial allocation strategy and its associated tools

are tested on the Clackamas Planning Unit of the Mt. Hood

National Forest. Results of this case study indicate that the

approach is workable with minor modifications.

Spatial Allocation of Land Usesin Land Use Planning

by

Richard Roger Hagestedt

A THESIS

submitted to

Oregon State University

in partial fulfillment ofthe requirements for

the degree of

Master of Science

Commencement June 1980

APPROVED:

Signature redacted for privacy.

Professor of Fopst Management in Charge of Major

J

Signature redacted for privacy.

H?'adof Department of Forest Management

Dean of Graduate School

Date thesis is presented

Typed by S. Hagestedt for Richard Roger Hagestedt

ACKNOWLEDGMENT

I would like to express my appreciation and gratitude to

the following people. It was through their assistance, support,

and understanding that this thesis was possible. My thanks to:

Pan-i Case,

Bill Ferrell,

Dave Butler

Jeff Arthur,

Doug Brodie,

John White and the Mt. Hood National Forest Planning

Staff,

my fellow graduate students,

my parents, sisters, brother, and my dear wile, Sarah.

TABLE OF CONTENTS

I. Introduction 1

The Spatial Problem 2

The Present Study 4

U. Location and Land Allocation 6

Linear Program Models of the PlanningProblem 7

Current Forms of Spatial Allocation 13

Types of Spatial Factors 15

Promising Algorithms 20Limiting Factors in the Search for an

Algorithm 21

Promising Algorithms 23An Optimizing Algorithm 23An Efficient Solution Algorithm 26An Assignment Algorithm 27

Problem Formation for the HeuristicAlgorithm 29

Summary 33

The Spatial Allocation Strategy 35Tools Needed for the Procedure 35

The Computer Mapping Routine 35The Detail Reduction Program 38The Adjacency Program 41The Spatial Assignment Process 41The Conflict Detection Program 41

The Procedure 44Inputs to the Spatial Allocation Procedure 44Development of Management Units . . . 46Development of Spillover Matrix 48Activity Assignments 49Detrimental Spillove r s and Minimum

Management Unit Size 52Readjustment Procedure 54

V. Spatial Allocation of the Clackamas PlanningUnit 56

The Clackamas Planning Unit 57The Spatial Allocation Procedure 58

Inputs to the Spatial Allocation Pro-cedure 60

Construction of Management Units . . . 64Construction of the Spillover Matrix . . 66Assignment of the Activities 69Detrimental Spillovers and Minimum

Management Unit Size 70Discussion of Results 75

VI. Future Research Steps 85Difficulties in Application 86Tool Improvements and Subjects for Further

Investigation 88

Bibliography 94

AppendicesAppendix A 97Appendix B 101Appendix C 137Appendix D 142Appendix E 146Appendix F 149

LIST OF ILLUSTRATIONS

TABLE

I The Linear Program Model of theAllocation Problem

II Formulation for the Linear ProgramAlgorithm

PAGE

10

11

III Linear Program Solution to the Allocation 11

Problem

IV Mapping Routine Functions 39

V The Spatial Allocation Procedure 45

VI Activity Information for Clackamas 62Planning Unit

VII Acreage Allocations 65

VIII Allocation Achievement Table 71

IX Allocation Achievement Table 72

X Allocation Achievement Table 73

XI Acreage Breakdown Between Activities 83

XII Expected Values for Map 1 84

XIII Expected Values for Map 3 84

LIST OF ILLUSTRATIONS

Figure Page

A Typical Land Type Map 12

Spillover Matrix 49

A Digitized Map 61

The Original Expanded Legend 67

Spillover Matrix for Case Study 68

Detrimental Spillovers 74

Detrimental Spillovers 76

Violations of Minimum Management 77Unit Size

SPATIAL ALLOCATION OF LAND USESIN LAND USE PLANNING

I. INTRODUCTION

The U. S. Forest Service currently is engaged in compre-

hensive planning for use and management of National Forest lands.

The plans produced for each Forest within the National Forest

System are changing the pattern of land use throughout the System.

Changes in land use will change the mixture of goods and services

flowing to the public from these lands for the foreseeable future.

The significance of the changes in goods and services which

might be made as a result of the planning process has led the Forest

Service to design its planning system as carefully as possible. The

system emerging from early trials is an assembly of highly technical

components, each for analysis of some aspect of land capability:

ecological effects of alternative management regimes, social con-

sequences of shifts in land use patterns, or the achievement of

budgetary and operational criteria under alternative plans.

The basic task of the planning process is to allocate land

to uses. The plan produced must achieve two important objectives:

it must supply a mixture of goods and services to the public and it

must maintain the long-term biological productivity of forest Land.

The mixture of public goods and services is determined by maxi-

mizing the total value of the consumer products and recreational

opportunities which the Forest could provide. Protection of long-

term productivity is achieved by: a) identifying the kind of environ-

mental impacts produced if a land use were to be imposed on a given

area of land, b) identifying acceptable levels of these impacts and

then, c) constraining the allocation process so that excessive

impacts are avoided.

The Spatial Problem

The location of land uses on the planning unit can cause

undesirable effects. For example, land uses, such as timber pro-

duction or developed recreation, which are located on land types

consistent with their physical and vegetative requirements, may

sometimes have adverse effects on some other resource, such as

when they intrude upon the movement of wildlife from summer to

winter range. Location also affects the supply of forest products

realized by the land use allocations. Timber production, for

example, could be assigned to one area of the planning unit on the

basis of compatibility with land type, but its location within the

planning unit, in terms of road access or size of the unit, could

make the assignment impractical or uneconomic. In a related

example, land uses, such as timber production and developed

recreation, which are consistent with the ecological requirements

of the land types to which they are assigned, become incompatible

if located next to each other.

The size of a land unit required to make management of a

use practical, the problems of conflicts between adjacent land uses,

and the need to organize uses across the landscape to take advantage

of roads or protect streams are all examples of the importance of

spatial factors in land allocation decisions.

Spatial factors are excluded from the most critical phase

of the land allocation process in the current system of land use

planning. Land allocation is performed in a single phase, in this

system, with use of either a linear or goal program. Land use

assignments, using this technique, are made on the basis of:

1) causing minimal disturbance to the land type to which they could

be assigned and, 2) achieving the maximum product value possible.

Spatial factors are treated only as a second step when the linear

program output is transferred onto a map of the planning unit.

These factors are treated only in cursory fashion: they are con-

sidered only if the individual doing the mapping recognizes that the

location of a land use will cause some type of resource management

problem.

Resolution of any recognized spatial problem usually is left

to the professional discretion of the individual doing the mapping.

In dealing with such problems, land use planners often are forced

to modify the initial land allocation produced by the linear program.

Modification of these results undercuts their Toptimalityhl in ways

that probably are not clearly understood and which probably vary

from planner to planner.

The Present Study

4

The general purpose of this study is to develop a strategy

and set of techniques for incorporating spatial factors into the

allocative phase of the planning process.

The general problem of location and land allocation is

discussed in Chapter II. Three primary spatial factors are isolated

and identified during this discussion. The chapter concludes with

a description of the effects of these factors on the land allocation

process.

The third chapter contains a review of the most promising

strategies and techniques which might be used for spatial allocation

of land uses. Criteria for a useful technique are described and

each tool is evaluated with respect to these criteria. One technique,

a heuristic search algorithm originally developed by J. P. Ignizio

(1978), satisfies most of the listed criteria.

The fourth chapter describes adaptation of this algorithm

to the spatial allocation problem. The problem itself is broken

down into discrete components and its overall structure is described.

The tools and procedures necessary to apply the heuristic algorithm

are also described.

The fifth chapter describes application of these tools and

procedures to a real problem. The Clackamas Planning Unit of

the Mt. Hood National Forest possesses many of the features of a

typical problem and is used to illustrate the application. This

chapter concludes with a description and discussion of the practical

difficulties encountered in the application of this strategy.

The final chapter summarizes the work completed in this

thesis and concludes with a suggestion for the next research steps

needed in managing the spatial allocation problem.

5

II. LOCATION AND LAND ALLOCATION

Only recently have spatial factors been excluded from the

land allocation process while planning for use of the National

Forests. Originally, site capability was the primary determinant

in the location of a land use. Land use decisions were made by pro-

fessional foresters who matched use to site. The decisions were

based on professional understanding of resource management

requirements and rules describing the Thighestit or best? use of

the land.

Three social changes altered traditional decisionmaking.

First, the absolute number of land use demands has increased

dramatically since the end of World War II (Public Land Law

Review Commission, 1970). Since the land base remains the same,

increased demands have resulted in more intense competition for

the limited resources of the forest.

Second, the scale of planning and decisionmaking expanded

to include larger areas and longer time periods. Land use

decisions in one area more and more frequently began to be

affected by the decisions made on neighboring areas (Hirsch, 1970;

Hufschmidt, 1969). These mutual effects increase the need for

coordinated land use decisions between areas and the consideration

of larger areas in the planning process. Similarly, longer planning

6

horizons result from the realization that environmental impacts of

many land uses extend far into the future.

The third change, probably an inevitable consequence of

increased competition and expanded scale, was in the decision

process itself. The first two changes require an individual to

consider more information when making land use decisions. Pro-

ces sing this information in any systematic way exceeds human

capabilities. Synoptic tools, such as mathematical programming

algorithms, and small-scale analytical models, such as computer

simulations, are more and more frequently replacing the in-

dividualts professional judgment (House, 1976).

The present land use decision is no longer made as a

single choice, a result of the professional's judgment about the

correspondence between land type and land use activity. Rather,

land use decisions are aggregates of choices, many made with

synoptic tools and models. The decision process itself is shaped

more by the strengths and weaknesses of the available tools than

by conscious structuring of choices and design of tools to assist

with particular problems.

Linear Program Models of the Planning Problem

The allocative phase of current Forest Service planning

7

processes is structured to a considerable extent by the use of linear

8

programming. The purpose of the land allocation process is to

create a land use pattern which maximizes the value of the consumer

products and recreational opportunities provided by the land while

minimizing the associated environmental impacts. A linear pro-

gramming algorithm is used by most Forest planning teams to

create this pattern.

Linear programming and the problems it addresses are

best summarized by Hillier and Lieberman (1974).

Briefly, linear programming typically dealswith the problem of allocating limited resourcesamong competing activities in the best possible(1. e., optimal) way. This problem of allocationcan arise whenever one must select the level ofcertain activities that compete for certain scarceresources necessary to perform those activities.

In the case of land use planning, the "limited resource is

the productive capability of the forest land. The productive capability

of a forest depends upon its physical and vegetative characteristics.

Since the forest's physical and vegetative characteristics vary a

great deal from place to place, forest land is sub-divided into land

type units, each with unique physical and biological characteristics

(Bell, 1976). These land type units become, in effect, the limited

resources of the forest.

The Itcompeting activities" in land use planning are manage-

ment practices which will occur when the land is assigned to a

certain use. Each land use will have a unique set of management

9

practices associated with it. Since an area of land often can be used

for more than one purpose at a time, the 'competing activitie&T are

often sets of management practices designed to produce certain

land use combinations.

Management activities of any sort produce consumer pro-

ducts, rec reational opportunities, and env-ironmental effects.

The allocation problem is regarded as one of assigning the limited

acreage in each land type to the competing activities so that the

maximum amount of consumer products and opportunities are pro-

duced while environmental disturbance is minimized.

An example of the land allocation problem formulated for

the linear programming algorithm and the type of solution produced

are shown in Tables I through III. As Tables I and II show, all

the acreage of a given land type is grouped together to form a

sum total of acreage for that type. In actuality, however, the

acreage for each land type is scattered in different sized parcels

throughout the planning unit (Figure 1). Therefore, the linear

programming model is insensitive to location.

TABLE I. THE LINEAR PROGRAM MODEL OF THE ALLOCATION PROBLEM

LT1 LTZ LT3Management Activities

Al AZ A3 AZ A3 Al A3

Variable (1) (2) (3) (4) (5) (6) (7)

units per acre

Goal 1 10 ZO 15 40 maximum

GoalZ 5 10 = 5,000

Goal 3 15 20 10 20 = 12, 000

Goal4 1.0 0.5 1.0 2.0 = 2,000

LT 1 (acres 1 1 1 = 1,000

LT 2 (acres) 1 1 = 500

LT 3 (acres) 1 1 = 800

Output Land Types Goals andConstraints

TABLE II

FORMULATION FOR THE LINEAR PROGRAM ALGORITHM

MaximizeZ = lOX2 + 25X3 + 20X4 ± lsX5 + 40X7

Subject to

5X1 + lOX2 + lOX6 5,000

15X1 + 20X2 + lOX4 + 20x6 - 12,000

TABLE III

LINEAR PROGRAM SOLUTION TO THE ALLOCATION PROBLEM

11

Acreage Allocations Goal Levels

X1 = 400 acres Goal 1 44, 000 units

X2 = 0 acres Goal 2 = 5, 000 units

= 600 acres Goal 3 = 15, 000 units

X4 = 300 acres Goal 4 1,900 units

X5 = 200 acres

X6 = 300 acresX7 = 500 acres

l.0xl + 0.5x3 + l.0x5 + 2. OX7 = 2,000

xl + X2 + X3 = 1,000

x4 + x5 = 500

X6 + X7 = 800

xl , . . . , x7 = 0

LT2

LT3

LT1

LT2

LT3

Figure 1. A Typical Land Type Map

LT1

12

Current Forms of Spatial Allocation

Current forms of spatial analysis in the land use planning

process typically begin with the linear program output and a land

type map of the planning unit. The land allocations produced by

the linear programming algorithm must be transferred to the planning

unit, creating a land use pattern. Since the linear program output in-

cludes the acreage allocations without reference to the location of

this acreage, special procedures must be taken to locate the

activities on the planning unit.

The process of mapping the activities onto the planning unit

is done entirely by humans. A plaimer must locate acreage of a

particular land type on the map and assign one of the allocated

activities to it. He must do this for the entire planning unit,

assigning every allocation while taking into consideration the acre-

age and location of each assignment. As the pattern of land uses

develops, the planner must consider each new assignment in the

context of those made previously. This consideration is crucial if

the final pattern is expected to achieve the value of forest products

and maintain the level of environmental impacts indicated by the

linear program solution.

The basic problem with the procedure is that humans cannot

optimize in problems of this complexity. They can't create an

13

14

optimal pattern for three reasons. First, all of the needed infor-

mnation can't be considered by a single individual at once. Second,

the individual often is selectively sensitive to the needs of land

uses or restrictions on resources which are most familiar to him

because of professional training. That is, selective perception

often causes an individual to overlook some spatial conflicts among

certain resources. Third, the individual has inadequate time to

search every possible combination of activities and compare every

potential solution. Optimization requires comprehensive consider -

ation of all the factors in the decision problem.

The overall result of planners manually locating the land use

assignments is to undercut the optimality attained by the linear

programnrning algorithm. The assignments made by the planner

may meet the acreage allocations specified by the linear program

solution but, in many cases, will not produce the effects predicted

by this solution.

The scope of the problem becomes most apparent after the

pattern of activities is completed for the first time. A careful

examination of the pattern will turn up areas where conflicts between

activities will prevent achievement of the linear programming goal.

This inspection compounds the problem for the planner; he must

rearrange the assigned activities to eliminate this condition, not

create additional location problems, and still correctly assign the

acreage allocated by the linear program. In many cases, the

planner may not be able to anticipate all the consequences of any

possible rearrangement, forcing him to settle on a pattern which

fails to achieve the optimal condition.

Types of Spatial Factors

To discover what kinds of spatial factors intrude upon land

allocation decisions, a number of Forest Service planning documents

were examined and the land use planning staff of several National

Forests in Region Six were interviewed. Most of the staff members

had never considered the spatial allocation problem in any depth and

therefore had limited perception or knowledge of the spatial factors

crucial to a land use allocation process. This fact eliminated any

possibility of using a survey of planners to develop a description of

the crucial spatial factors.

However, these discussions, combined with my study of

the Environmental Impact Statements (the land use plans), did

produce a number of examples of spatial problems in land allocation.

Examination of their common characteristics indicate that they could

be grouped into three basic types about which generalizations could

be made.

The three underlying spatial variables affecting land

allocation decisions are:

15

16

management unit size,

adjacent-use spillovers,

collocation patterns.

In the land allocation process, one of the critical spatial

factors is the size of an individual management unit. Certain

activities require a minimum number of acres grouped together

before they can be imposed on the land.

Examples of activities which require a minimum manage-

ment unit size include some wildlife habitats, timber harvest units,

and wilderness areas. The wildlife habitat for some species, such

as pileated woodpeckers, must have a minimal acreage of a par-

ticular land type to maintain the species (biological requirement).

The timber harvest process requires management units of sufficient

size to offset the fixed costs associated with access and management

of the timber (administrative requirement). Originally, by

Congressional designation, wilderness areas had to contain a

minimum of 5, 000 acres (administrative requirement).

The management unit must contain sufficient acreage so

that the activity assigned to the site can meet the applicable bio-

logical, physical, or administrative requirements. Since the reason

for the requirements varies from activity to activity, the minimum

management imit size varies for each activity. In addition, the

appropriate minimum unit size also depends on the land types

17

invoLved in the allocations. Some land types are able to satisfy the

requirements for a particular activity with fewer number of acres.

Therefore, the minimum mangement unit size depends on the

activity under consideration, the applicable requirements, and the

land types involved.

Another critical spatial factor is the 'spilloverT caused

by locating some activities adjacent to one another. Adjacent land

uses may produce both positive and negative spillovers, depending

on the activities involved. A planner must decide whether the

aggregate impact of these spillovers produces a desirable or an

undesirable result. If the total effect is desirable, then deliberate

steps should be taken in the allocation process to locate these

activities adjacent to each other. Undesirable spillover effects

require relocation of the activities to avoid their adjacency.

An example of a desirable grouping arises in the establish-

ment of wildlife habitat. Some wildlife species, such as elk, require

a certain combination of vegetative habitats for forage, shelter,

and reproductive functions. An example of an undesirable grouping

is the placement of timber management next to developed camp-

grounds. Timber harvesting activities often create noise, hazards,

and undesirable scenery. The result of such placement would

produce a land use pattern which would fail to provide the recre-

ational opportunities dictated by the linear programming solution.

18

The third prominent spatial factor in the allocation process

is collocation. "CollocationT refers to the process of deliberately

locating activities on the planning unit so that they form a particular

pattern across the face of it. The pattern to be formed depends

upon the special requirements of some activity or upon the spatial

organization of the planning unit as a whole. For example, two

prominent physiographic features frequently involved in the location

of activities are road systems and streams. Developed campgrounds

usually are assigned to the land next to the road system. In order

to minimize the environmental effects created by automotive camping

and protect the aesthetic attractions of the campgrounds, it is

desirable to space the campgrounds some distance from each other.

However, it is also important to consider travel distance and access

in designing developed recreational opportunities for an urban

public

Collocation and adjacent-use conflicts obviously have much

in common and may not actually be mutually exclusive variables.

It may be that adjacent-use conflicts simply depend more on the

nature of the activity than the land type while the reverse is true

for collocation problems. Or, the two factors may vary only

according to the scale of the spatial factors involved in a specific

planning problem. For the purposes of this study, however, the

magnitude of differences between these factors and the fact that

they must be treated in different ways, seems to warrant treating

them as two distinct spatial variables.

19

III. PROMISING ALGORITHMS

There are two major flaws in the current methods of trans-

lating linear program allocations onto maps of planning units.

First, perception of location conflicts and judgment about their

resolution vary from individual to individual. Second, individual

planners are unable to carry in their minds all the information

needed to make synoptic location decisions. The first flaw can

be overcome if planners know what spatial factors to take into

account, if they have a systematic procedure for making activity

assignments and, if they have a uniform set of rules for resolving

conflicts in the location of activities. Yet, the amount of detailed

information to process in the spatial analysis problem still would

prevent planners from finding the most efficient land use pattern.

The spatial allocation problem, even though extremely

complex, is one of decisionmaking under the condition of certainty.

That is, the effects of assigning any activity to any location can be

known. The feature of certainty in the problem implies that there

is an optimal or most efficient land use pattern for any set of

activity-location assignment values. The troublesome problem is

how to design a procedure or system of decision rules (i. e. , an

algorithm) for finding the most efficient land use pattern.

A computerized algorithm capable of searching for the

20

21

optimal pattern would improve the present situation in several ways.

Its use would decrease the selective perception and variability

between people, since the technique would standardize treatment

of the spatial factors in the assignment procedure. An algorithm

increases the efficiency of the user in two ways. It drastically

reduces the time required to solve highly detailed problems. More

importantly, it allows the user to focus his attention on the value-

judgment components of a problem, rather than on its computational

aspects. Finally, because the decision rules are publicly available

in the algorithm, they can be scrutinized over time, compared with

others, and amended as knowledge of spatial influences on land use

decisions grows.

Limiting Factors in the Search for an Algorithm

A great number of promising algorithms exist in the field

of operations research (Phillips, et al., 1976; Ackoff and Sasieni,

1968). These include the specialized procedures of mathematical

programming, which are designed for problems of resource

allocation under conditions of certainty (Hillier and Lieberman,

1974; Simmons, 1972; Taha, 1971; Wagner, 1970). Two elements

of spatial allocation problem greatly narrow the search for an

algorithm among the mathematical programming techniques.

The first restricting element is the choice of principal

22

criterion to be used in the algorithm. There are two broad options.

One is to strive for the optimal solution to the total problem. In

this case, the linear program would be replaced with some other

optimizing technique. The algorithm would find the location for

all activities which would maximize the value of consumer products

and opportunities developed in the planning unit while meeting

environmental constraints. The alternative is to retain the linear

programming technique and use the results it produces as the

criterion for location decisions. Some special algorithm could

be devised for making location assignmeits: these assignments

would have to sum so as to match the acreage allocations first

provided by the linear program solution. The second option

implies that the criterion of Ttefficiency would be substituted

for "optimalityt' and that ?Iefficiencyll would be judged in relation

to the linear program solution.

The second limiting factor in the search for an algorithm

is the size of the problem. Size is expressed by the number of

units of analysis and the number of variables in the problem.

Essentially, the greater the size of the problem, the more

numerous the decisions to be made in assigning a land use activity

to a land type.

Size of the problem determines whether it can be handled

by a computerized algorithm or not. The more numerous the

An Optimizing Algorithm

A strong argument can be made for adopting optimality as

the principal criterion for the proposed algorithm. The linear

programming model of the planning unit does not take the spatial

organization of land type units into account, yet spatial factors

will affect the products and opportunities which can be developed

in the unit. The linear program model may be unrealistic and,

therefore, the "optimal solution' it produces may also be un-

realistic. A genuinely optimal solution could be developed if the

linear programming technique were replaced by another optimizing

algorithm which coild take location into account.

An integer programming algorithm in place of the linear

program might overcome the difficulty associated with locating

23

decisions to be made, the less likely the problem will fit within

existing computer capabilities. The larger the problem, the less

likely that an optimizing algorithm can be used (since these require

simultaneous consideration of a great deal of information), and

the more likely the problem must be broken into sub-parts in

order to solve it. As indicated above, the larger the problem,

the more likely that an efficiency solution algorithm criterion

must be selected.

Promising Algorithms

24

acreage allocations on the planning unit. The problem formulation

for the integer program is similar to the linear programming

formulation except that the acreage in the planning unit would be

divided into "management units" rather than land type units. While

the linear program allocates acreage from each land type to the

various activities, the integer program allocates each management

unit to a single activity.

Acreage in the planning unit could be divided along land

type boundaries to create the management units. The larger blocks

might be split into two or more management units in order to

allow the algorithm to allocate this acreage to more than one

activity when optimal. Another possibility would be to create a

management unit out of the acreage of two or more land types.

This tactic would be useful when there is insufficient acreage of any

one type to support a specific allocation.

Since the allocation of a management unit to a particular

activity is essentially a yes-or-no decision, a zero-one integer

programming algorithm could be used. This type of algorithm

examines the contribution which the assignment of each activity to

a particular management unit would provide to the value of consumer

products and recreational opportunities, then selects the assignment

which meets all the constraints (environmental or spatial) and

contributes the most value (Geoffrion and Mar sten, 1972; Hillier

25

and Lieberman, 1974).

Since the algorithm assigns an activity to a management

unit and the location of that management unit is known, the operator

can now consider the relative location of activities in the allocation

process. That is, the operator can include additional constraints

in the problem formulation to control the allocation of management

units based on their location.

The most common use of this constraint would be to make

the assignment of an activity to a particular management unit

depend partially on the activities assigned to adjacent management

units. Constraints of this form either strictly prohibit adjacent

assignment of certain activities or place a penalty on the occurrence

of this condition.

The zero-one integer programming formulation of the land

allocation problem with location constraints is presented in

Appendix A. This information can be compared with that provided

for the linear programming solution technique in Tables I through

III.

The major advantage of the integer programming algorithm

is that it would consider both minimum management unit size and

adjacent-use conflicts in producing an optimal assignment pattern.

However, the algorithm in this form cannot consider the collocation

factor. Unfortunately, the algorithm also runs into a number of

26

practical disadvantages.

The problem formulation for most planning units would

probably exceed the computer core size required to solve the

problem. Any attempt to make the problem fit the computer core

size, by reducing the number of management units and increasing

their size, affects the precision of the solution and may make it

invalid. A number of runs usually are required in the allocation

procedure to determine solution bounds under various constraints

and also to produce alternative allocations from which to select.

The total cost of all these runs may be prohibitive. So, although

a zero-one integer programming algorithm may be a good

theoretical replacemeiit for a linear program, practical limitations

hinder its application.

An Efficient Solution Algorithm

The practicality of a pure optimization algorithm is limited

by the size and complexity of the spatial allocation problem. The

alternative is to seek an efficient, rather than an optimal solution.

The integer programming technique also might be used for this

purpose. Most of the problem formulation would remain as above,

but the integer program would contain only three types of con-

straints. These would be: a) the acreage assigned must be equal to

the linear program acreage allocations, b) only one activity could be

Z7

assigned to each management unit, and c) the assignment of one

activity would depend on the assignment of other activities. This

problem formulation is also presented in Appendix A.

Dividing the spatial allocation problem into two smaller

problems permits both the linear and integer programming to fit

within existing hardware capacity. However, a number of linear

program runs are required to bound the allocation problem and

create alternative solutions. Plus, the zero-one integer program

must be run a number of times for each alternative linear program

allocation. Each of these runs include a different composition of

spatial constraints. Some constraints are relaxed and others are

added until all important spatial factors have been considered in

the final assignment pattern. The problems are still so large that

the cost of making several runs of each algorithm is prohibitive.

Few National Forests would find the solution costs for a normal

problem to be a reasonable investment.

An Assignment Algorithm

A more promising possibility employing the efficiency

criterion is to substitute an assignment algorithm for an optimizing

algorithm. Ramalingam (1976) notes that assignment algorithms

are particularly compatible with the efficiency criterion in resource

allocation problems. They,

. . arise in the context of associating eachof the N requirements to each of the N avail-able means of satisfying them (requirements).

. . One of the important characteristics ofthe assignment problem is to allocate resourceson a one-to-one basis. . Another character-istic is that the total number of requirementsequals the total number of resources. Theeffectiveness coefficients indicate what mightresult from allocating each requirement toeach resource. . . . The objective is toestablish the assignment that minimizes the sumof effectiveness coefficients of the selected com-binations.

Assignment algorithms overcome the size problem, but at

the expense of finding an optimal solution. Assignment decisions

are made in a step-by-step fashion. At each step, after the

assignment is made, the size of the problem is reduced for all

subsequent steps. The algorithms consider the current assignment

and its affect on future options, but can't consider possible adjust-

ments to earlier assignments which could improve the final

solution. Essentially, assignment algorithms can overcome the

size problem because they do not consider all possible land use

patterns.

Ignizio (1978) has developed an assignment algorithm which

can be adapted to the spatial allocation problem. His algorithm

searches the array of options created by potential assignments

and selects that assignment (land use activity location) which

maintains the greatest number of future possibilities. Accordingly,

28

his algorithm, 'Tthe heuristic search algorithm, can deal very

well with the adjacent-use spatial factor. Although the algorithm

cannot handle the collocation factor directly, the problem formu-

lation could easily be structured so that the desired collocation

pattern appears. The algorithm does not deal with the problem of

minimum management unit size, but the procedure in which the

algorithm is embedded also could be developed to take this factor

into account.

Problem Formulation for the Heuristic Algorithm

Il the heuristic technique were to be used, the formulation

of the spatial allocation problem would have the following

characteristics:

There must be a finite number of management units in

the planning unit.

Each management unit can be assigned to only one of a

finite number of activities.

Because of the characteristics of adjacent activities,

assignments will be promoted where a favorable condition arises

or avoided where an adverse situation appears.

The acreage of the management units assigned to each

activity should equal the acreage allocation determined by the

linear programming algorithm.

29

30

(5) The primary objective will be to assign activities to

management units in such a way that the impact of each adjacency

condition is considered and the acreate allocations are met.

The heuristic approach requires the computer to make a

number of conditional checks; each of which narrows the selection

process substantially and allows the activity assignments to be made.

The procedure followed by this approach is briefly outlined below.

The planner must arrange the management units in some

order. Since the computer algorithm will assign the management

units in that order, the planners may wish to place certain units

near the beginning or order them by decreasing acreage.

For all adjacent management units, the planners must

indicate the occurrence of any favorable or unfavorable conditions

which will result if certain activities are assigned. With this

information, the computer algorithm starts the assignment process

by considering the first management unit. The computer checks

its land type, acreage, and all linear program acreage allocations

of that land type. The acreage of that land type may be allocated to

any number of the activities. The computer program compares

the management unit acreage to the acreage allocations to determine

which activities are eligible for assignment to this management

unit. If the management unit acreage is the smaller acreage for

a particular activity, then that activity is considered for possible

31

as signment.

The computer repeats this comparison for each activity

until it has identified all eligible activities. The computer program

then checks the impact each possible assignment has on future

assignments to adjacent management units. The favorable and

unfavorable conditions created by each possible assignment are

compared and the activity which creates the most desirable situation

is selected. When the assignment of two or more activities are

equally desirable, the activity with the largest remaining acreage

allocation is selected. The acreage of this management unit is

deducted from the appropriate acreage allocation.

The updated acreage allocations represent a running tally

of the remaining acreage of each land type to be assigned to each

activity. The algorithm must consult this tally to determine the

eligible activities for each management unit and to assure that the

algorithm is attempting to meet the linear program acreage

allocations.

The same assignment procedure is repeated for each

management unit, except now, the algorithm also must consider

previous assignments made to adjacent management units. The

favorable and unfavorable conditions created by these activity

assignments will further restrict the selection process. The

algorithm continues through the list of management units, attempting

32

to assign an activity to each unit.

However, in some cases, no activity will be eligible for

assignment. This occurs because of two constraints which any

activity must satisfy before it can be assigned to a management

unit. The assigned activity must not: 1) create a conflict with any

previously assigned adjacent activity and 2) cause the acreage

assigned to exceed the linear program acreage allocation by more

than a specified tolerance. After assigning all management units

possible with these constraints in effect, the algorithm stores the

assignments and a matrix indicating how close to the acreage

allocations the assignment process came. Then, the algorithm

allows the user to terminate the assignment process or continue it

with one or more of the constraints relaxed.

Subsequent runs assign the remaining management units

based on the activities previously assigned. The constraints are

relaxed one after another in the following order: 1) strict prevention

of adjacency conflicts with previously assigned activities is

eliminated and activities are selected which simply minimize all

possible (current or anticipated) adjacency conflicts, 2) the tolerance

level for achieving the acreage allocation is eliminated bit an activity

still cannot be assigned if that allocation was previously satisfied,

and 3) all attempts to meet the acreage allocations are dropped

and the activity is selected which minimizes all possible adjacency

conflicts. Since the results of each run are stored, the user can

examine the assignments at each step to get a feel for the location

of conflicts or select one of the intermediate runs and assign the

remaining management units manually.

A more detailed description of how the computer algorithm

carries out the above procedure and an example of its operation

is presented in the following chapters.

Summary

Inability to treat spatial influences in land allocation

decisions in any systematic way has caused them to be ignored in

the allocation process itself. A computer algorithm which could

take these factors into account would be a useful tool for the land

use planner and would help reduce the present variability in

allocation decisions among National Forests. Unfortunately, the

size of the spatial allocation problem prevents us from making use

of an optimizing algorithm, such as integer programming. This is

so even when a new unit of analysis, the management unit, is

created to replace the land type unit and reduce the size of the

problem.

The best alternative seems to be to sub-divide the problem

into smaller components. Sub-division allows us to retain the

linear programming tool and devise a specialized algorithm for

33

34

making location assignments. Sub-division also implies that the

objective of the spatial allocation problem is to find the most

efficient approximation to the linear programming solution.

The collection of assignment algorithms is compatible with

this objective. Ignizio's heuristic technique, in particular, provides

a simple and effective way of taking the two most difficult spatial

factors into account when assigning activities to management units.

This algorithm has several advantages over the optimizing strategies

explored. It is relatively simple to understand and inexpensive to

use. Unlike the optimizing routines considered here, it can be

modified to take the collocation factor into account. The major

difficulties with the algorithm are that it does not produce optimal

solutions and it may not produce consistently efficient solutions

over a wide range of spatial allocation problems. Optimal solutions

cannot be developed because the algorithm cannot search all possible

improvements to the first assignment. The consistency of the

algorithm is unknown at this time and will have to be tested before

the algorithm could be widely used.

IV. THE SPATIAL ALLOCATION STRATEGY

A number of computer programs were needed to perform

certain tasks in the spatial allocation process. The tools developed

include: a) a computer mapping routine, ) a detail reduction

program, c) an adjacency program, d) a spatial assignment pro-

cedure, and e) a conflict detection program. The mapping routine

creates management units and keeps track of location. The detail

reduction program eliminates some of the complexity in a land type

map. The adjacency program detects adjacent management units.

The assignment procedure utilized in the spatial allocation process

is the heuristic approach presented in the previous chapter. The

conflict detection program is a computer routine designed to detect

activity assignments which produce detrimental spillover effects

when they are located next to each other. It also assists the planner

in identifying assignments which violate the minimum mangement

unit size rule. This chapter describes the tools in greater detail.

It focuses on their functions in the overall process and the linkages

between them.

Tools Needed for the Procedure

The Computer Mapping Routine

The spatial arrangement of activities on a planning unit

35

36

affects achievement of the goals described by the linear program

acreage allocations. Some device is needed for keeping track of

the locations of the management units, and later, of the activity

assignments.

A number of computer mapping routines are available which

are able to keep track of location. In most cases, they are unable

to provide all of the mapping functions needed or handle the type of

data used in land use planning. The E-ZMAP (Child and Rollin,

1976), IMGRID (Sinton, 1976), and SYMAP (Dougenik and Sheehan,

1976) computer mapping systems each contain many of the required

mapping functions but they are unable to handle the amount of data

needed. That is, the number of symbols needed to display land

types, management unit5, or activities usually exceed the symbol

list available in each of these mapping systems.

Some mapping systems may satisfy both requirements but

were not available for use in the present research. The RAP mapping

system, for example, is recommended for adoption throughout the

Forest Service for use in land use planning, but is currently in-

compatible with Oregon State Universityts computer system. The

Mt. Hood National Forest, the only Forest in the Pacific Northwest

to make use of a computerized mapping system, is currently using

a modified version of R3MAP. This program contains functions

necessary to process and display planning unit data. The functions

37

include display of maps and their legends, an ability to combine

maps, and an ability to aggregate symbols. These functions are

used to develop maps of land type units by combining maps of in-

dividual forest resources. The modified R3MAP program uses

some computer system features not currently available on Oregon

State University's computer.

The computer mapping routines discussed above all use

cellular mapping techniques. The map information is displayed in

square cells of a fixed acreage. The symbol assigned to each cell

represents some characteristic of the acreage of that cell. All of

the acreage inside the cell boundaries is assumed to have similar

characteristics. Since the characteristics of the planning unit are

not laid out in neat squares of fixed acreage, each cell symbol

represents the dominant characteristic of the acreage in that cell.

Mapping routines, like R3MAP, have standard functions

which are used by planners to create land type maps. The current

land allocation process limits the mapping routine to this contribution

and possibly to a display of the final land use pattern. However, the

proposed spatial allocation procedure requires additional information

and data processing which only an expanded computer mapping

routine can provi.de. The activity assignment procedure requires

the planning unit to be broken down into management units and

information provided on their acreage, land type, and the adjacent

38

location of other management units. The conflict detection program,

which detects detrimental spillover effects and provides information

on management unit size, also requires additional information not

available from conventional mapping routines. This information

includes each management unitrs acreage, land type, and adjacent

management units, as well as the activities assigned to it and the

adjacent units.

In light of these requirements, a greatly expanded version

of the R3MAP routine was built to support the spatial allocation

procedure. The functions provided by this mapping routine are

listed in Table IV along with a short explanation of their purpose.

A program listing of the mapping routine is presented in Appendix B.

The Detail Reduction Program

The land type map of many planning areas may contain a

great deal of complexity. That is, in addition to a large number of

land types, each land type is broken into small parcels and these

are scattered across the planning unit. Each parcel of each land

type must be treated as an individual management unit when the

mapping routine creates the management unit map. A large number

of single-cell management units will result; pushing the total

namber of management units beyond that which the computer memory

can contain or which can be handled at reasonable cost.

SUBROUTINE PURPOSE

DIRECT

EXCESS

TABLE IV. MAPPING ROUTINE FUNCTIONS

39

The map directory. This mapping routine is aninteractive computer program which allows up totwenty maps to be available to the operator duringa single run. The directory lists and keeps trackof the available maps, aiding in the selection ofmaps for other mapping functions.

Handles the map overflow. When the number ofmaps input or created exceeds the twenty allowed,this subroutine locates one map on the recodeddata file.

COPY Transfers maps onto the recoded data file.

RECODE Recodes map symbols into a standardized numericcode and collects statistics on the occurrence ofeach symbol.

IN Inputs maps in either of two formats.

SELECT Selects and stores maps on the recoded data fileat the completion of a run.

OUT Displays maps at the terminal or outputs them ona line printer file.

COMBO Combines the characteristics on several mapsinto a set of symbols on a single map. Eachsymbol on the new map represents a uniquecombination of the original characteristics. Thelegends of the original maps are also combinedto create a legend which describes each uniquecombination.

AGGASS Aggregates or assigns cells of each symbol intogroups and documents those groups. Each groupconsists of any number of the original symbols.

Table IV. Mapping Routine Functions (Cont.)

SUBROUTINE PURPOSE

Reorders the symbol list and legend of a givenmap based on a selective order or by decreasingacreage size.

Searches a map for symbols which share one ormore common characteristics and lists thedocumentation associated with these symbols.

Detects and groups cells of the same symbolwhich are adjacent to one another.

Changes the titles and/or legend of any map.

Manipulates map cells: moves individual cellsfrom symbol to symbol, aggregates cells of onesymbol with another, and splits cells of a singlesymbol into groups of several symbols.

40

The number of management units must be reduced to an

acceptable level without reducing the accuracy of the final land use

pattern. The easiest and most practical strategy is to group the

isolated single cells of each land type with the adjacent group of

cells exhibiting the most similar characteristics. The detail

reduction program (REDUCE) is designed to perform this operation

on a land type map before it is broken into management units by

the mapping routine. A program listing of REDUCE is presented

in Appendix C.

REORDER

HILITE

PARCEL

UPDATE

MANIP

The Adjacency Program

The spatial assignment procedure and conflict detection

program both require a list of adjacent management units. The

assignment procedure uses the adjacency information to avoid

assigning activities next to each other which produce detrimental

spillovers when located on adjacent management units. The conflict

detection program uses information about adjacent management

units to identify any detrimental spillovers which appear in a land

use pattern.

The adjacency program (ADJ) examines a map of marage-

ment units, identifying the management units adjacent to each unit

and recording the number of times each adjacent pair occurs. The

program listing of ADJ is presented in Appendix D.

The Spatial Assignment Process

41

The heuristic approach described earlier in Chapter III is

used here. The program listing of the heuristic (HEURIST) is

presented in Appendix E.

The Conflict Detection Program

The conflict detection program is a computer program which

provides information about the management unit size of assigned

activities and detects detrimental spillovers created by the assign-

42

ment of activities to adjacent locations on the planning unit. This

program simply provides the planner with information which he can

use to make a quick check for violations of minimum management

unit size or the location of any adjacent activities producing detri-

mental spillovers.

The size of a management unit is checked after activity

assignments are made. A single activity may be assigned to

adjacent management units, not necessarily of the same land type,

to produce a larger management unit for that activity. This is done

by ignoring the old management unit boundaries and creating new

management units based on adjacent cells assigned to the same

activity. The mapping routine carries out this process. First, it

assigns the activities to the land type map, erasing the management

unit boundaries. Then, adjacent cells assigned to the same activity

are grouped into a parcel or !mnewhl management unit. The adjacency

program detects the activities assigned to locations next to these

management units.

This information is transferred to the conflict detection

program. The computer program examines each management anit,

checking its acreage against the minimum acreage figure provided

for management units assigned this activity. Lf the acreage is

smaller than the minimum required, the program lists the assigned

activity, the management unit acreage, and the activities assigned

43

to adjacent management units. This information, plus a map display

of the assignments, allows the planner to detect violations of

minimum management unit size.

Detection of detrimental spillovers can use either the fold?1

management units or the TtnewT ones developed in the above pro-

cedure. The Holdu management units will pinpoint the location of

those management units which should be reassigned by the spatial

assignment procedure in the next run. However, the tmnewfl manage-

ment units indicate the true acreage assigned to an activity. The

"true" acreage of a management unit is important since sufficient

size often overcomes detrimental spillovers by providing a buffer

for the activities. However, if a detrimental impact still exists

(as shown by the "new" management unit), the planner must refer

back to the uoldtI management unit map to locate those units needing

reassignment. Having both options available allows the planner to

select the information important in different circumstances.

The planner loads the conflict detection program with:

a) information on the activities assigned, b) the management units,

and c) a matrix indicating which activities will produce detrimental

spillovers and the severity of these spillovers. For each management

unit, the program checks the activities assigned to adjacent manage-

ment units. The spillover matrix tells the program if a detrimental

spillover exists between the activity assigned to the management unit

44

being considered and those adjacent to it. If a detrimental spillover

is found, the program lists the severity of the spillover, the

activities involved, and information about the management units

involved.

The conflict detection program provides the planner with

information about the land use pattern which is difficult to detect

with only a simple visual examination of an activity assignment map.

The information can be used by the planner to improve the spatial

assignments made in a subsequent run. A further explanation of

this process is described in the next section of this chapter. A

listing of the conflict detection program (DETECT) is located in

Appendix F.

The Procedure

The procedure can be described most easily by referring to

its major stages. These stages are shown in Table V.

Inputs to the Spatial Allocation Procedure

The spatial allocation procedure is only one step in the

entire land use planning process. Rather than reconstruct the

entire process here, the results of intermediate steps in the

planning process will be treated as inputs to the spatial allocation

procedure. The two inputs are: 1) a complete land type map with

TABLE V. THE SPATIAL ALLOCATION PROCEDURE

INPUTS TO THE SPATIAL ALLOCATION PROCEDURE

REDUCEPARCELMANIP

Land type map.Linear program acreage allocations.

DEVELOPMENT OF MANAGEMENT UNITS

Eliminate isolated single cells.Break into management units.

*Modify size or shape of management units.

*DEVELOPMENT OF A SPILLOVER MATRIX

ACTIVITY ASSIGNMENTS*Preassign activities to some management units.

REORDER *Place management units in order.ADJ Find adjacent management units.

Input linear program allocations.Input spillover matrix.

HEURIST Make activity assignments.AGGASS Transfer assignments to the map.OUT Display activity assignments.

Check allocation achievement table.

*These steps can be modified in a readjustment procedure.

45

DETRIMENTAL SPILLOVERS AND MINIMUM MANAGEMENT UNITSIZE

Input spillover matrix.Input adjacent management units.Input activity assignments.Input management unit map documentation.

DETECT Detect detrimental spillovers.Input activity assignment maps.

PARCEL Break assigned acreage into management units.ADJ Find adjacent management units.DETECT Detect detrimental spillovers.

Input minimum management unit acreage s.DETECT Detect minimum management unit size violations.

legend and 2) the linear program acreage allocations.

The land type map is developed from inventory maps

representing the characteristics of the planning unit. The

characteristics contained in the final land type map are those

which affect management for the selected group of activities. That

is, the characteristics in the land type map indicate the acreage

which is suitable and unsuitable for each activity. The linear pro-

gram allocations indicate the acreage of each land type which is to

be managed for each activity. The spatial allocation procedure

attempts to locate the acreage allocations on the planning unit;

using the locations indicated on the land type map as a guide.

Development of Management Units

Management units are areas of similar land type to which

activities eventually will be assigned. They are composed of a

number of adjacent cells in the digitized map which have the same

or similar land type characteristics. Each management unit is

identified by its: a) land type, b) acreage, c) shape, and

d) location in the planning unit.

Three computer programs (or subroutines) were created

for forming management units. These programs were designed

to manipulate the cells of a land type map so that the management

units developed could be assigned without serious complications.

46

47

There are almost always a large number of isolated single

cells of each land type in a planning unit. For example, the

original Clackamas Planning Unit land type map contains 1,944

such cells, or 11.72 percent of the total number of cells. If each

isolated single cell were to be treated as an individual management

unit, the number of units would exceed the information storage

capacity (core size) of the computer. A special computer program,

REDUCE, was developed to aggregate the map further. This

routine groups the isolated single cells with the adjacent group of

cells displaying the most similar characteristics.

The PARCEL subroutine is part of the computer mapping

program. This subroutine simply groups adjacent map cells of

the same land type into single units. For obvious reasons, PARCEL

creates the bulk of the management units.

The third routine, M.ANIP, is another mapping program

subroutine developed to make adjustments in the management units

created by the PARCEL subroutine. MANIP allows the planner to

make changes in management units which are too small, too large,

or the wrong shape for the assignment procedure.

The number of management units needed in the planning unit

presents the planner with a dilemma. Since each unit must be

described by: a) its land type, b) its acreage, and c) the adjacent

management units, a large number of units will exceed the core

48

size of even a large computer. A large number of units will also

increase computational time or the expense of each assignment run.

On the other hand, the more management units there are in the

problem, the more options are available for assigning activities

to u.nits. The planner must consider the balance between flexibility

and expense of problem-solv-ing when determining the number of

management units with which to work.

Development of a Spillover Matrix

The spillover matrix reflects the desirable and undesirable

impacts caused by assigning each pair of activities to adjacent

management units. A spillover matrix is shown in Figure 2. The

values in this matrix represent relative levels of desirable and

undesirable impacts which are displayed by a range of numbers

from positive to negative three. A spillover value of zero represents

no spillovers or a neutral impact.

The development of the spillover matrix is important since

creation of the final land use pattern is directly affected by the

values it contains. The assignment procedure attempts to locate

those activ-ities with positive spillover values on adjacent manage-

ment units. It also attempts to prevent the adjacent location of

activ±ties with negative spillover values. At this time, the assign-

ment procedure is unable to distinguish the relative level of positive

ACTIVITIES

ar negative spillover values; concerning itself, instead, with the

absolute differences represented by the positive, negative and zero

values.

The conflict detection program, on the other hand, does

distinguish between the relative levels of undesirable spillover

impacts. It lists all adjacent assignments with undesirable spill-

overs, classifying them according to the severity of the impact.

This classification allows the planner to rapidly identify the most

severe, therefore most unacceptable, spillovers and take steps to

correct the assignment.

Activity Assignments

The heurisitc algorithm, HETJRIST, assigns activities to

49

1 2 3 4 5

A 1 0 0 -1 0 +1CT 2 0 0 0 -3 0I

VI 3 -1 0 +2 0 -2TI 4 0 3 0 +1 +3ES 5 +1 0 -2 +3 0

Figure 2. Spillover Matrix

the management units. This algorithm allows the planner to pre-

assign activities to some management units and then assigns

activities to the remaining units. The praassignment feature

fixes activities to specific locations on the planning unit, forcing

the algorithm to fit the other acreage allocations around these

locations.

The order in which management units are assigned affects

the final land use pattern. The heuristic algorithm assigns an

activity to a management unit only if it: 1) helps to meet the

acreage allocations produced by the linear program, 2) does not

create an undesirable spillover condition with an activity which was

previously assigned to an adjacent management unit, and 3) pro-

vides the maximum number of future assignment options by mini-

mizing the potential spillovers with the adjacent unassigned manage-

ment units. Each assignment restricts the activities which can be

assigned to subsequent management units by: 1) meeting part of

the acreage allocation and 2) blocking future assignments which

produce undesirable spillovers on adjacent management units.

Subroutine REORDER in the mapping program permits

arrangement of the management units in any selected order, will

arrange the units by decreasing acreage, or will allow a select

group to be placed first with the remainder arranged by decreasing

acreage. Arrangement by decreasing acreage causes the larger,

50

51

more critical management units to be assigned first, leaving the

smaller units to adjust and fine-tune the final pattern.

The heuristic algorithm, as previously stated, is concerned

with activities assigned to adjacent management units. Therefore,

the computer program ADJ is used to detect all adjacent manage-

ment units and this information is input to the heuristic algorithm.

Other inputs to the heuristic algorithm include the linear

program acreage allocations and the spillover matrix. The

function of both in the assignment procedure was also discussed

earlier.

The heuristic program attempts to assign activities to

management units in such a way as to meet the three conditions

previously listed. The first two are constraints: 1) restricting

each assignment on the basis of previous activity assignments to

adjacent management units and 2) preventing the assignment of an

activity which will exceed the acreage allocations by more than a

specified tolerance. With these constraints in effect, the assign-

ment procedure usually is unable to assign every management unit.

Subsequent runs relax each of the constraints, one at a time, until

activities can be assigned to all of the remaining management

units.

The results produced by each run can be checked by the

planner. The heuristic program stores the assignments made up

to that point and updates their effect on the achievement of the

acreage allocations. This last piece of information is presented

in an allocation achievement table which the planner uses as one

measure of the success of this assignment run. The assignments

for each run also can be displayed on the planning unit map through

the use of the mapping program. The subroutine AGGASS transfers

the assignments to the management unit map and the subroutine OUT

displays the assignment map at the terminal or places it on a line

printer file.

Detrimental Spillovers and Minimum Management Unit Size

The conflict detection program, DETECT, provides two

important functions to the spatial allocation procedure. It checks

the map of activity assignments for undesirable spillovers and

violations of minimum management unit size requirements. This

program simply detects and lists the occurrences of these

situations; it does not include procedure to correct them. How-

ever, this does not diminish its importance. This program

detects those undesirable spillovers which could not be avoided

in the assignment procedure, listing them so that the planner can

take steps to eliminate the unacceptable spillovers in a later

readjustment. It also provides the only systematic list of those

assignments of insufficient acreage; the only other option being

53

an individual inspection of the assignment map.

There are two strategies for the detection of undesirable

spillovers. Both require the input of the spillover matrix, but

differ in the rest of the required inputs. Both strategies list certain

basic information: 1) the severity of the detrimental spillover,

2) the activities involved, and 3) location on the map. One

strategy focuses on management units of a single land type and

includes the land types of the management units involved in the

spillover. The other strategy focuses on management units of a

single activity and the acreages provided reflect the true I size of

the management units involved.

The first strategy, emphasizing land types, requires the

input of activity assignments, the list of adjacent management units,

and the documentation of the management unit map. The conflict

detection program uses this information, plus the spillover matrix,

to list every undesirable spillover on the planning unit.

The second strategy, emphasizing the htruel? management

unit size, requires a number of additional steps. The activity

assignment map is input and subroutine PARCEL groups the

assigned acreage into a new' set of management units based on

the activities. The adjacency program, ADJ, finds the adjacent

management units in this new map. The conflict detection program,

in this case, uses the spillover matrix, management unit map of

54

the assigned activities, and list of adjacent management units to

detect the undesirable spillovers.

The conflict detection program also uses the newly developed

management unit map of assigned activities, the list of adjacent

management units, plus a list of minimum acreages to identify

all management units which violate minimum management unit

size. The program screens each management unit against the

minimum acreages, listing the location, activity, acreage, and

adjacent activities of any management unit which is smaller than

the minimum.

Readjustment Procedure

Major readjustments of the land use pattern can be

carried out in additional runs of the spatial allocation procedure

by changing certain inputs. The spillover matrix can be altered

to promote or prevent a particular adjacent activity assignment

in subsequent runs. The order in which management units are

assigned can be changed or some of the management units can be

preassigned activities. In some cases, a further step back in

the process may be required. The management units developed

from the land type map can be changed, with additional aggregations

of smaller units or divisions of the larger units. All of these

readjustments should cause changes in the final activity assignments.

The selection of which adjustments to make has to be based on

experience with the assignment algorithm and an inspection

of the assignments produced with the current set of inputs.

55

V SPATIAL ALLOCATION OF THE CLACKAMASPLANNING UNIT

This chapter describes the application of the spatial

allocation procedure developed earlier to a representative planning

unit of the Mt. Hood National Forest. Illustration of the procedure

through a case study probably is not necessary to provide an

understanding of its underlying strategy. However, the general

complexity of the procedure seems to warrant the time and

expense involved in the development of a case study. In any newly

developed procedure with the complexity this one contains, there

may be hidden flaws or unforeseen details in the planning unit

which prevent the procedure from being fully utilized. It seemed

wise to see whether the spatial allocation strategy outlined could

survive the test of workability.

As anticipated, there were difficulties which required

modification of the original spatial allocation procedure. The

major complication was that the size of the problem, or the

complexity of the land types in the planning unit, exceeded most

expectations about them. This chapter, in addition to describing

the planning unit and illustrating use of the procedure, describes

the difficulties presented by such complexity in the planning unit.

56

The Clackamas Planning Unit

57

The Clackamas Planning Unit is located on the Mt. Hood

National Forest in Oregon. It consists of 353, 779 acres of forest

land oii the western slopes of the Cascade Range, south of Mt.

Hood. There are three vegetative zones on the planning unit as

designated by the dominant climax tree species: 1) western hemlock

(with Douglas-fir as sub-climax species), Z) Pacific silver fir,

and 3) mountain hemlock. Within these three zones are 51

distinct forest plant communities. The unit also contains 1, 500

miles of rivers and streams, plus 160 ponds, lakes, and reser-

voirs. The diverse habitats found within the vegetative zones

support a variety of wildlife including 73 species of mammals, 150

species of birds, and 25 species of reptiles and amphibians.

The current use pattern includes management for timber,

developed recreation, dispersed recreation, and wildlife. Timber

harvest is a dominant activity on this planning unit, providing 187

MMBF/year or fifty percent of the total output from the Mt. Hood

National Forest. The biological potential for the unit with pre-

commercial thinning, genetic stocking, and full stocking level con-

trol is 199 MMBF/year. The major commercial species include

Douglas-fir, western hemlock, Pacific silver fir, noble fir, and

grand fir. The Clackamas Planning Unit also contains Z4 camp-

grou.nds with 417 camp units and 106 picnic areas. There are two

roadless areas in the unit, Bull of the Woods (43, 735 acres) and

Olallie (8,673 acres). There is also a total of 180 miles of trail

throughout the planning unit.

There are two reasons the Clackamas Unit was selected to

illustrate the procedure. First, the Mt. Hood Planning Staff

volunteered both the problem and their assistance in developing the

case study. 1 Second, the planning unit is representative of both

the size and diversity of the Forest-wide planning units to be used2in the near future. Physical characteristics and current use

patterns are typical of the majority of National Forests in the

Pacific Northwest Region.

The Spatial Allocation Procedure

The six stages of the spatial allocation procedure presented

1The Planning Staff provided all the data needed for thisstudy and served as a sounding board whenever unforeseen diffi-culties arose.

2The Clackamas Planning Unit acreage is actually threetimes the size of the typical planning unit used during the last fiveyears. However, recent changes in land use planning regulationsinstruct the Forest Service to regard National Forests, ratherthan their sub-divisions, as the proper unit of analysis. MostNational Forests have begun the shift to Forest-wide planning.The Clackamas Unit is actually a composite of three earlier,unfinished units and is being treated by the staff as a prototype forthe entire Forest.

58

in the previous chapter are:

inputs to the spatial allocation procedure,

construction of management units within the planning unit,

construction of the adjacent-use spillover matrix,

assignment of activities,

detection of detrimental spillovers and violations of

minimum management unit size,

readjustment of assignments.

Two variations were made from the procedure shown in

Chapter IV. First, the information received from the Mt. Hood

Planning Staff was not in the correct form to be input into the

procedure. It contained some detail unnecessary to an illustration

while lacking other data important to the demonstration. The

information submitted was modified to an acceptable form. The

second variation has to do with the last stage of the procedure.

Readjustments to the land use pattern were not made in the case

study as they might be in a real planning problem. The case

study is designed to demonstrate the procedure; there is no

need to correct any unrealistic results.

59

3Additional information on each of these stages is providedin Table V and the descriptions contained in Chapter IV.

Inputs to the Spatial Allocation Procedure

The information received from the Mt. Hood Planning Staff

includes a digitized land type map of the Clackamas Planning Unit

and a list of activities for which the unit can be managed. A portion

of the digitized map is shown in Figure 3. The activity list

describes twenty-seven activities: six of these were chosen for

purposes of demonstration in this study. These representative

activities are:

reserved sites,

developed recreation,

dispersed recreation,

commercial timber,

visual timber,

wildlife.

Each activity contains statements of the impacts it will

have on the total Forest and products it will provide if assigned

to the Forest. The physical characteristics of any land type unit

which would limit or prohibit its assignment to that land type are

also described. An example of an activity description and site

identification is shown in Table VI.

A seventh activity, lakes and streams, was added to the

list. Thj addition was made so that the lakes and streams would

60

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19

Identifiers:(Also RAU Criteria)

Unroaded Area

Soils

Ecoclas s

Geologic Conditions

RAM Site Class

Physiography

Accessibility

TABLE VI. ACTIVITY INFORMATION FOR CLACKAMAS PLANNING UNIT

DEVELOPED RECREATION (D - LAND MANAGEMENT CATEGORY)

Site Identification Activity Description

D-2

Not applicable

Slight erosionpotential

Excludes meadows,wetlands, & RAREcommunities

Stable Conditions

Non-limiting

Less the 30% slope

(see transportation)

Subactivitie s

Recreation Experience

Facilities

Timber Mgt.

Visual Sensitivity

Fire Mgt.

Transportation

Range Mgt.

Wildlife

D-2

Level 2

Rustic (Pit toilets, shel-ters, Rock Fire Rings,Rustic Tables)Extended rotation;regulated, specialLevel 2Less than 100%disposal (80%)

Primitive roads limitedmaintenance, unsurfaced,level

Exclude

Haras smentLevel moderate

63

appear on the activity assignment map, rather than being lumped

into an unassigned acreage category.

The land type map provided by the Planning Staff contained

the physical characteristics which would limit or prohibit as sign-

ment of all twenty- seven activities. Since only six representative

activities were selected, the land type map was simplified by

aggregating those physical characteristics in the map which do not

limit or prohibit the selected activities. The selection of six

representative activities and subsequent modifications to the land

type map simplifies the spatial allocation problem. This simplifi-

cation makes observation of the procedure easier and the case study

more clear.

Two activities, developed recreation and visual timber, are

sensitive to the location of roads on the planning unit. However, the

land type map did not contain any information about roads and

access. A simple road system was added to the land type map so

the spatial allocation procedure could demonstrate the way in which

it assigns activities relative to the roads.

Both modifications mentioned were accomplished using the

computer mapping system. After identifying the physical character-

istics which did not limit assignment of the six activities, the sub-

routine AGGASS was used to aggregate those land types with others.

The road system was added to the land type map with the subroutine

64

MANIP which singled out all the cells forming the road corridor.

The Mt. Hood Planning Staff was unable to provide the

linear program acreage allocations because they had not reached

that point in their own planning process. Instead of waiting until

the allocations became available, a dummy set of acreage allocations

was created. These dummy acreage allocations were devised so as

to keep the case study simple while completely demonstrating the

spatial allocation procedure. The acreate allocations used for this

case study are presented in Table VII.

Construction of Management Units

The first step in the construction of management units is

the elimination of isolated single cells by the detail reduction pro-

gram. As stated before, the Clackamas Planning Unit contained

1,944 isolated single cells. The detail reduction program, REDUCE,

grouped 1, 916 of these cells with adjacent cells. The remaining Z8

isolated single cells belong to lakes and streams which the program

was instructed to ignore. The land type map produced at this point

in the procedure is displayed as Map 1 in the accompanying map

packet.

The next step involved breaking the acreage of each land

type into management units. The subroutine PARCEL in the

mapping program carried out this step automatically, creating a

TABLE VII. ACREAGE ALLOCATIONS

01

LAND TYPES ACTIVITIES

RES[RVEO LAKES AND OESELOPEO DIIPIRSED CONI4(RCO*L VISUAL WILDLIFE51 TE SEPEAPIS RECREATION RECREATION c100ER TI NOES

LANO TYPE I 0.0 0.0 3176.0 0.0 16236.0 0.0 0.0LAND TYPE 2 0.0 0.0 0.0 6.0 3327.6 0.0 0.0LANa TYPE 3 0.0 0.0 0.0 0.0 16070.0 0.0 0.0LAND TYPE 6 0.0 0.0 096.0 0.0 2t30.0 0.0 0.0LAND TYPE 5 0.0 6.0 0.0 0.0 533.0 0.0 0.0LANa TYPE 6 0.0 0.0 j50.O 0.0 0.0 1693.0LAND TYPE 7 0.0 0.0 5023.0 0.0 11656.0 0.0 21129.0LAND TYPE 0 0.0 0.0 0.0 0.0 3756.0 0.0 0.0LAND TYPE 9 0.0 0.0 0.0 0.0 08010.0 0.0 0.0LAND TYPE to o.o 0.0 0.0 0.0 0.0 0.0 619.0LAND TYPE 11 0.0 0.0 0.0 0.0 2026.0 0.0 0.0LAND TYPE 12 0.0 0.0 0.0 0.0 761.0 0.0 0.0LAND TYPE 13 0.0 0.0 0.0 0.0 363.0 0.0 0.0LANT TYPE 16 0.0 0.0 0.0 0.0 0.0 0.0 0155.0LAND TYPE 15 0 0.0 0.0 0.0 0.0 0.0 0.0LAND TYPE 16 0.0 1130.0 0.0 0.0 0.0 0.0 0.0LAND TYPE 1? 6166.0 0.0 0.0 0.0 0.0 0.0 0.0LAND TYPE 10 0.0 0.0 0.0 3002.0 0.0 0.0 0.0LAND TYPE jq 0.0 0.0 0.0 3690.0 0.0 0.0 0.0LAND TYPE 20 0.0 0.0 0.0 650.0 0.0 0.0 0.0LAND TYPE 21 0.0 0.0 0.0 11612.0 0.0 0.0 0.0LAND TYPE 22 0.0 0.0 0.0 33160.0 0.0 0.0 0.0LAND TYPE 23 0.0 0.0 0.0 12I.5F.0 0.0 0.0 0.0LAND TYPE TI. 0.0 0.0 0.1 0.0 0.0 0.0 1173.0LAND TYPE 25 0.0 0.0 107.0 0.0 0.0 107.0 0.0LAND TYPE 28 0.0 0.0 0.0 0.0 0.0 0.0 0.0LANa TYPE 29 0.0 0.0 0.0 0.0 0.0 0.0 0.0LAND JYPE 30 0.0 0.0 317E.0 0.0 0.0 2033.0 0.0LAND TYPE 31 0.0 0.0 0.0 0.0 0.0 66.0 0.0LAND JYPE 32 0.0 0.0 0.0 0.0 0.0 3050.0 0.0LANO JYPE 35 0.0 0.0 0.0 171.0 0.. 0.0 0.0LAND TYPE 36 690.0 0.0 0.0 0.0 0.0 0.0 0.0L*ND JYPE 3? 0.0 0.6 0.0 6.0 0.0 0.0 0.0LAND JYPE 30 0.0 0.0 0.0 0.0 0.0 S.D 0.0LAND JYPE 39 0.0 0.0 0.0 277.0 0.0 6.6 8.8

map with 1,292 management units. The legend of this map was

run through subroutine UPDATE, reducing the original six lines

of legend down to a single line indicating the land type number.

This reduction of the legend saves storage space and processing

cost in subsequent map operations. A portion of the original

legend with six lines of description is shown in Figure 4. The

management unit map with the reduced legend is Map 2 in the map

packet.

No attempts were made to modify the size or shape of the

resultant management units with the subroutine MANIP. The

management unit map produced by the PARCEL subroutine was

input directly into the activity assignment stage.

Construction of the Spillover Matrix

The activities being assigned by the spatial allocation

procedure were examined and given the spillover values indicated

in Figure 5. Examples of how to interpret the spillover matrix

follow.

Select two activities, for example, commercial timber and

developed recreation. The spillover value of -3 indicates the

occurrence of a very severe undesirable impact when these

activities are assigned to adjacent management units. The spillover

value for developed recreation and lakes and streams is a +3,

66

MAP PARCELS - MANAGEMENT UNIT MAP

LANE1 TYPES

ROAD MAP

ECOCLASS MAP

SOIL GROUP MAP

SLOPE MAP

SITE INDEX MAP

67

TRUE FIRSOIL GPOUPS AA,00,CC,00.EELESS THAN 30 PERCE'IT SLOPESITE INDEX - 'lOT APPLICABLE OR AVAILABLE

69 3 6'.0 1.0 LAND TYPE IUMQOA CEOTRUE Fl0SOIL GROUPS AA.88,CC,00,ELESS THAN 30 PERCENT SLOPESITE INDEX - NOT APPLICABLE CR AVAILARLE

A0 I'. '9R.6 .1 LAND TYPE 1UNROA OE')TRUE FTPSElL f,POIJP5 AA,09,CC.CU.EE

Figure 4. The Original Expanded Legend

SYMOL NO. CELLS ACREAGE PCI. LEr,Ifl

Al 30 639.9 .2 LAND ¶P I

UNROA DECTRUE FIPSEll. GROUPS AA,00,CC,Ofl,EELESS THAN 30 PEPCE4T SLOPESITE INDEX - NOT 3ppLrCAnLc OR AVAILABLE

A2 IS. 1.0 LAND TYPE I

U N PC DECTRUE FIPSOIL GROUPS AA,BB,CC,00,EELESS THAN 30 PERCENT SLOPESITE INDEX - NOl APPLICABLr OR AVAILABLE

AS 5 106.7 0.0 LAND ITRE I

U MR CTRUE FIREdt GROUPS AA,BA,CC,Dfl.E!LESS THAN 30 PERCENT SLOPESITE INDEX - NOT APPLICABLE OP AIAILARLE

AN 7 t9.3 0.3 LAND TYPE IUNOOA OEDTRUE FIPSCIL GROUPS AA,B9,CC,00,EELESS THAN 30 PERCENT SLOPESITE INDEX NOl APPLICABLE OR AVAILABLE

AS Ii 31,.6 .1 LAND TYPE I

UNROA DECTRUE FIRSOIL GROUPS AA,BB,CC.UU,EELESS THAN 30 PERCENT SLOPESITE INDEX - NOT APPLICAPIS OP AVAIIABI.E

AF, 3 SN.0 A.0 LAND TYPE I

UMROA BEDTRUE FIRSOIL GROUPS AA,00,CC,CO.FELESS THAN 30 PERCENT SLOPESITE tNOE - NOT APPLICABLE OR AVAILABLE

A? 11 23N.6 .1 LAND TYP I

UMRDA CEOTRUE FIRSElL GROUPS AA,B0,CC,00,E!LESS THAN 30 PERCENT SLOPESITE INDEX - NOT AP'LICI.E OR AVAILABLE

AR 1'. I LANO TYPE IU NP OA CEO

68

indicating the beneficial impact produced when these activities are

located adjacent to each other. Wildlife and dispersed recreation

have a spillover value of 0. This value indicates a neutral impact

when these activities are assigned to adjacent management units.

ACTIVITIES

ACTIVITIES

1 Reserved Sites

2 Lakes and Streams

3 Developed Recreation

4 Dispersed Recreation

5 Commercial Timber

6 - Visual Timber7 Wildlife

Figure 5. Spillover Matrix for Case Study

1 Z 3 4 5 6 7

1 0 0 0 0 0 0 0

AC Z 0 0 +3 +3 -3 -Z 0T

3 0 +3 0 0 -3 -1 -3

IT 4 0 +3 0 +1 -Z 0 0

IE 5 0 -3 -3 -Z +1 0 -1S

6 0 -Z -1 0 0 +1 0

7 0 0 -3 0 -1 0 0

Assignment of Activities

In this case study, no activities were preassigned to

management units. This allowed the heuristic program to assign

activities to all of the management units without influencing the

assignment procedure through an initialization process.

The first step of the assignment procedure, therefore, was

to reorder the management units with the subroutine REORDER.

Since every attempt is being made to avoid influencing the

assignment procedure, the order of management units was based

on decreasing acreage only with no single management unit

picked out and placed in a special assignment position. The

reordered management unit map is Map 3 in the map packet.

The list of adjacent management units is created by

running the reordered management unit map through the adjacency

program, ADJ.

The inputs to the heuristic assignment program, HETJRIST,

include: 1) the reordered management unit map, Z) the list of

adjacent management units, 3) the linear program allocations, and

4) the spillover matrix. In the first run through the heuristic

program, with all constraints in effect, 618 management units

(or 47. 8 percent of the management units) were assigned,

accounting for Z50, 906 acres of the planning unit (or 7Z. 6 percent

69

70

of the acreage). The second run, which relaxed one constraint,

assigned activities to all of the remaining management units but

one. The third run, with two constraints relaxed, then assigned

this last management unit. The allocation achievement tables for

these three ruiis are shown in Tables VIII through X. The acreages

presented in these tables represent how well that assignment meets

the linear program acreage allocations. A positive number means

the algorithm assigned that many acres over the allocation level.

Negative numbers indicate the number of acres which need to be

assigned to meet the allocations.

The subroutine AGGASS was used to transfer the activity

assignments of each run to the management unit map. The first

and third activity assignment maps were displayed by subroutine

OUT and are shown in the map packet as Maps 4 and 5.

Detrimental Spillovers and Minimum Management Unit Size

Both strategies to detect detrimental spillovers were

implemented. A portion of the information provided by each

strategy is shown. The first strategy, which emphasizes infor-

mation about the management units involved, requires no additional

data manipulations, only an input of previous results. The in-

formation from this strategy is shown in Figure 6. The second

strategy, emphasizing the 'true acreage assigned, requires

TABLE VIII. ALLOCATION ACHIEVEMENT TABLE(No Constraints Relaxed)

LAND TYPES

RESERVEOSITES

LAKES ANDSTREAMS

DEVELOPEDRECREATION

ACTIVITIES

OTOPERSEDRECREATION

CONNERCEAI.TINDER

VISUALTIMOER

WILDLIFE

LAND TYPE 1 0.0 1.0 69.2 0.0 -361.9 0.0 0.0

LAND TYPE 2 0.0 0.0 0.0 0.0 -255.0 0.0 0.0

LAND TYPE 3 0.0 0.0 0.0 0.0 767.0 0.0 0.0

LAND TYPE S 0.0 0.0 -277.3 0.0 21.5 0.0 0.0

LAND TYPE 5 0.0 0.0 0.0 0.0 -92.9 0.0 0.0

LAND TYPE 6 0.0 0.0 -1219.3 0.0 21.3 9.0 -1219.7

LAND TYPE 7 0.0 0.0 59.1 0.0 SF9.2 0.0 -23526.7

LAND TYPE 0 0.5 0.0 5.0 0.5 -511.9 0.0 0.0

LAND TYPE 9 0.0 0.5 0.0 0.0 -1363.5 0.0 0.0

LAND TYNE IS 0.0 0.0 5.5 0.0 0.0 0.0 -517.3

LAND TYPE 11 0.0 0.0 9.0 0.0 -010.0 0.0 0.0

LOUt) TYPE 12 0.0 0.0 0.0 0.0 -AS.A 0.0 0.5

LAND TYPE 13 0.0 0.0 0.0 0.0 ..3 0.0 0.0

LAND TYPE 16 0.0 0.0 0.0 0.0 9.0 0.0 -7913.7

LAUD TYPE 15 -.3 9.0 0.0 0.0 0.9 0.9 0.0

LAND TYPE IT, 0.0 967.A 0.0 0.0 0.0 0.0 0.0

LAND TYPE 17 .6 0.0 0.0 0.0 0.0 0.1 0.0

LAND TYPE 19 0.0 0.0 0.0 -1117.7 0.0 0.0 0.0

LAND TYPE 19 0.0 0.9 0.0 -1239.3 0.0 0.0 0.0

LAID TYPE 20 9.0 0.0 0.9 -6'.5A. 0 0.0 0.0 0.0

LAND TYPE 71 0.0 0.9 0.0 -6969.7 0.0 0.0 0.0

LAND TYPE 72 0.0 0.0 0.9 -30005.9 0.0 0.0 0.0

LAND TYPE 79 0.0 9.0 0.0 -6016.9 0.0 0.0 0.0

LAND TYPE 26 0.0 0.0 0.0 0.0 0.9 0.0 -1173.0

LAND TYPE 76 0.9 0.0 -197.0 0.0 0.0 21.F 0.0

LAND TYPE 70 0.0 0.9 0.0 I. 0.0 0.0 0.0

LAND TYPE 29 0.0 0.0 0.0 0.5 0.0 0.0 0.0

LAND TYPE 30 0.0 0.0 .3170.0 0.0 0.0 62.6 0.0

LAND TYPE 31 0.0 0.0 9.0 0.0 0.0 0.0 0.0

LAND TYPE 32 0.0 5.0 0.0 9.0 0.0 .5 0.0

LAND TYPE 35 0.0 0.0 0.0 -120.3 0.0 0.0 0.0

LAND TYPE 36 -.9 0.9 0.0 0.0 0.0 5.0 0.0

LAND TYPE 37 0.5 0.9 0.5 0.0 0.0 0.0 0.0

LAND TYPE 30 0.0 0.0 0.0 0.5 0.0 0.0 0.0

LAND TYPE 39 0.0 0.9 0.0 -277.5 0.0 5.0 0.5

TABLE IX. ALLOCATION ACHIEVEMENT TABLE

N)

(One Constraint Relaxed)

LAND TYPES ACTIVITIES

RESERVEI)SITES

LAKES ANDSYPOKAIPS

DEVELOPEDRECREATION

DISPERSEDRECREATION

COPINERCIALTINDER

VISUAL710075

NILOLIFE

LAND TYPE I 0.0 0.0 69.2 5.0 -63.2 0.0 0.0

LAND TYPE 2 0.0 5.0 0.5 1.0 1.0 0.0 0.0

LAND TYPE 3 5.0 5.1 0.0 5.0 1.5 0.0 0.0

LAND TYPE 5 0.0 5.1 -21.1 II 21.5 0.0 0.0

LAND TYPE 5 0.0 0.0 S.D 5.5 .3 5.0 0.1

LAND TYPE 5 0.5 0.0 21.0 5.5 21.3 0.0 -52.3LAND TYPE 7 0.0 0.0 65.6 0.5 075.2 0.0 -936.0

LAND TYPE B 0.0 5.5 5.0 5.0 .5 5.0 0.0

LAND TYPE 9 0.0 0.0 0.0 5.0 1.5 0.0 0.0

LAND TYPE 10 5.0 0.1 0.0 5.0 0.0 0.9 -.5LAND TYPE II 5.9 5.0 0.5 5.0 .6 5.0 0.1

LAND TYPE 17 0.0 0.0 0.0 0.0 -.5 P0.0 0.0

LAND TYPE 13 0.9 0.0 0.0 0.0 -.3 0.0 0.0

LAND TYPE 15 0.0 0.0 0.0 0.0 0.0 0.0 1.2

LAND TYPE 15 -.3 0.5 0.0 0.0 0.9 0.55 0.0

LAND TYPE 16 0.0 -.2 0.0 0.0 0.0 0.0 0.0

LAND TYPE 1 .6 0.0 0.0 0.9 0.0 0.0 0.0

LAND TPE 10 0.0 0.0 0.0 .0 0.0 0.0 0.0

LAND TYPE 15 0.0 0.0 0.0 .3 0.0 0.0 0.0

LAND TYPE 20 0.0 0.0 0.0 .3 0.0 0.0 0.0

LAND TYPE 21 0.0 0.0 2.0 1.3 0.0 0.0 0.0

LAND TYPE 22 0.0 0.0 0.0 2.2 0.0 0.0 0.0

LAND TYPE 23 0.0 0.0 0.0 .9 0.5 .5.0 0.0

LAND TYPE 25 0.0 5.0 0.0 0.0 0.0 0.0 .3

LAND TYPE 25 0.0 0.0 -107.0 120.0 0.0 -21.7 0.0

LAND TYPE 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 29 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 30 0.0 0.0 -52.2 0.0 0.0 52.6 0.0

LAND TYPE 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 32 0.0 0.0 0.0 0.0 0.0 .5 0.0

LAND TYPE 35 0.0 0.0 0.0 -.3 0.0 0.0 0.0

LAND TYPE 36 -.5 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 37 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 30 0.0 0.0 0.0 0.0 0.0 0.5 0.0

LAND fyP( 39 0.0 0.0 0.0 .3 0.0 0.9 0.5

TABLE X. ALLOCATION ACHIEVEMENT TABLE(Two Constraints Relaxed)

LANO TYPES

RESERVEDSIRES

LAKES ANDSYREANS

DEVELOPEDRECREAT 109

ACTIVITIES

DISPERSEDRICREATION

COMMERCIALTINDER

VISUALTINDER

WILDLIFE

LAND TYPE 1 0.0 0.0 00.2 0.0 -03.0 0.0 0.0

LANO TYPE 2 0.0 0.0 0.0 0.10 1.0 0.0 0.0

LAND TYPE 3 0.0 0.0 0.0 4.0 1.0 1.0 0.0

LANO IYPE 4 0.0 II -21.1 0.0 21.4 0.0 0.0

LAND TYPE 5 0.0 0.0 0.0 0.0 .3 0.0 0.0

LAND TYPE 5 0.0 0.0 21.0 0.0 30.3 0.0 -02.3

LANO TYPE 7 0.0 0.0 04.1 0.0 070.2 0.0 -930.0

LAND TYPE A 0.0 0.0 0.0 0.0 .4 1.0 0.0

LAND TYPE 9 0.0 0.0 0.0 0.0 1.0 0.0 0.0

LANO TYPE 10 0.0 0.0 0.0 0.0 0.0 0.0 -

LANO TYPE II 0.0 0.0 0.0 0.0 .6 0.0 0.0

LAND TYPE 12 0.0 0.0 0.0 0.0 -.9 0.0 0.0

LANO TYPE 13 0.0 0.0 0.0 0.0 -.3 0.0 0.0

LAND TYPE 4 0.0 10.10 0.0 0.0 0.0 0.0 1.2

LANO TYPE 15 -.3 0.0 0.0 4.0 0.0 0.0 0.0

1090 TYPE II. 0.0 -.2 0, 0 0.0 0.0 0.0 0.0

LAND TYPE 1, .6 0.0 0.0 0.0 0.0 0.0 0.0

LAND TYPE 10 0.0 0.0 0.0 .0 0.0 0.0 0.0

LAND TYPE 19 0.0 1.0 0.0 3 0.0 0.0 0.0

LANO TYPE 20 0.0 0.0 0.0 .3 0.0 0.0 0.0

LAND TYPE 21 0.0 0.0 0.0 0.3 0.0 0.0 0.0

LAND TYPE 22 0.0 0.0 C.0 2.2 0.0 0.0 0.0

LAND TYPE 23 0.0 0.10 10,0 .9 0.0 10.0 0.0

LASt) TYPE TO 0.0 0.0 0.0 0.0 0.0 0.0 .3

1090 lOPE 25 0.0 0.0 -101.0 0.0 0.0 -21.7 0.0

LAND tYPE 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LASt) TYPE 29 0.0 0.0 00.0 0.0 0.10 0.0 0.0

LAND TYPE 30 0.0 1.0 -47.2 1.0 0.0 02.0 0.0

LAND TYPE .01 0.0 0.0 0.1 0.0 0.0 0.0 0.0

LAtH) TYPE 32 0.0 0.0 0.0 0.0 0.0 .5 0.0

L000 TYPE 35 0.0 0.0 0.0 .3 0.0 0.0 0.0

LAND TYPE 36 -.4 0.0 0.0 0.0 0.0 0.0 0.0

LANO IYPE 31 0.0 0.1 0.0 0.0 0.10 0.0 0.0

LAND TYPE 30 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LAND IYPE 39 0.0 0.0 1.0 .3 0.0 1.0 0.0

DETECTION OF UEIRIMENIAL SPILLQVES

*SSIGN1IENI - REORUEREL) MANAGEjENT UNIT MAPLAND rOPES

OETR1MENTL SPILLQVERS NuN SEVEUT! 3

SEIIERITY 3 3OUNDARY LENGTH IMANAGEMENT UNIT A0 10) OE4ELOPEU ECREAT1ON

220 CELLS L.b9Z. ACRES 1.3 PERCENTLAND IYPE 7

MANAGEMENT UNIT FE C 7) COMMERCIAL TIMOER.3 CELLS .0 ACRES 0.0 ERCENT

LAND FYPE 2

SEVERITY 3 BOUNDARY LENGTM 13

MANACEMET UNIT A0 C 10) OEELOPEO RECREATION220 CELLS .ô92.8 ACRES 1.3 PERCENT

LAND TYPE 7

MANAGEMENT UNIT 30 C 23) COMMERCIAL TIMOERt33 CE..LS 23A. A..RES .8 'ERCENT

LAND TYPE I

SEVERITY 3 aouNoaRY LENGTH IMANACEMET UNIT A0 I i) OEVELOPEO RECREATION

220 CELLS .o92. ACRES .3 PERCENTLAND IY'E 7

M4rAGEMENT UNIT 3 1 23) COlMEtC.LAL TIMBER118 CELLS 2516.9 ACES .7 PERCENT

LAND TYPE 3

SEVERITY 3 8UUNOARY LENGTH

MANAGEMENT UNIT A0 C lii EELJPEU RECREATiON220 CELLS .62.6 ACRES 1.3 PERCENT

LAND TYPE 7

MANAGEMEIT UNIT FC I 393) COMMERCIAL TIMOER7 CELLS IL.9.3 ACRES 0.3 PERCENT

LAND TYPE

SEVERITY = 3 9OUNOARY LENGTH 2

IAtIAGEMENT UNIT AO I 13) L)EELUPEO RECREATION220 CELLS .692.8 ACRES 1.3 PERCENT

LAND TY'E 7

MANAGEME'4T UNIT IN C 777) COMMERCIAL TIrIOER3 CELLS 6'.0 ACRES 0.0 PERCENT

LAND TYPE

Figure 6. Detrimental Spillovers(Strategy One)

74

75

additional data manipulation. The activity assignment map is input

and subroutine PARCEL breaks it into a new set of management

units. The program ADJ detects adjacent management units in

this map and all this information is input into the conflict detection

program, DETECT. Some of the information generated by this

strategy is shown in Figure 7.

Violations of the minimum management unit size were

detected in the same run with the second detrimental spillover

strategy. Detection of minimum-size violations requires the

inputs in that form plus minimum acreages against which the

management units are screened. Some of the results of this pro-

cedure are shown in Figure 8.

Discussion of Results

The results of the case study can be examined to determine

whether or not the spatial allocation technique developed is worth-

while. Tests can be constructed to determine: a) how close the

acreage assignments come to the linear program allocations,

b) whether or not the algorithm creates the pattern it is directed

to in the spillover matrix, and c) how successful the heuristic

is in preventing adjacent-use conflicts. Each of these indicators

is examined for the purpose of measuring the performance of the

spatial allocation strategy and of learning what value it may be

OETECTIQIi OF ETP1EtTL SPILLOVE S

PARCELS CTIV1T AS5IGNMEMI3 - O coNsTRiNrS LAXEJ

oErR1c1ENTL SILLQVERS WITH SEEtT1 2

SEVERITT 2 BOUNOAR' LEMGTI 3

MANAEKEMT UNIT FO 60)20 CELLS

IANaEMET UNIT JO ( IOU)2230 CELLS

t)ISPERSE RECREATION+2b.6 ACRES .2 PERCENT

OMHERCIAL TI1eER475.ALkES I3.+PECENT

SEVERITY 2 OUNUARY LEqGT-I 2

MANAGEKE4T UNIT Gi C 61) EJI5PERSEU ECREATtUN1 CELLS 5.3 4CRES 'ERCE?IT

I4N4GEME4T UNLT JO ( 100) CIHERCIA&. Tt1ER2230 CEL&.3 7515. 4(PES PERCEMT

SEERZT = 2 3OUNOART LEIGT1 IM4NAGEME1T UNiT 3 C 63 QISPERSED RECREATION

ti CELLS 23'i.ä ACES .1 PERCENT

l4N4EME4T UHT J9 C )) CMHERCIAL TIM8E8Z2 CELLS 17533.3 6. PERCENT

SEIERITY OUNORY LE4GT1 3

MAN4L,EKE?T UlIT I1' ( T.l ISE?.SEO RECREATIONt5 CLLS 32a.o ACRES .1 'ECENT

NAGEME4r UNIT 05 t5) COMMERCIAL TI13ERCE.LS 192.0 CES .1 ERET

SEIERITY 2 3OIJNUART .E?GT-4 3

lA1A(,EME'4T UNIT 46 C 76) ULSPERSEQ ECEATLN2 CELLS 1ó.& ACRES .2 ERCENT

r1AM4GElINT UNIT 06 C 1'&) OMMERcI4L TIH3E22S CE&.LS ACE3 1.) PERCE4T

SEVERITY 2 OUMOAY LF'4GTH 8

IANAGEME?IT UNiT H9 i 7J OISPERSEO ECPE4TIO1t CE&.LS 2ó.ó ACrES .1 PERCE'IT

MA4EIET UNIT 5 ( 135) OMHECI4L TI1ER2139 CELLS ACRES 17.7 PERGET

Figure 7. Detrimental Spillovers(Strategy Two)

76

DETCTXON QF MANAGEMENT UNIT SIZE VIOLATICNS

PARCELS - CTIVtT ASSIGNMENTS - 2 CONSTRAINTS RELAXED

MINIMUM ACREAGE ACTIVIT(RESERVEO SITES

10.0 LAKES ANO STREAMS25.0 DEVELOPED RECREATION

250.0 OISPERSEO ECEATIUNZ50.O COMHERCIAL TI1ER100.0 VISUAL TIMBER20fl.0 WLLOLF

MAMAEME1T UNIT (5 ( LOS) OISPERSED RECEAT ION2 CELLS L2.7 ACRES 0.0 PERCENT

AOJACE4T MANAGEIIENT UNITS NO THEIR 33IG?1EO ACTIVITIESiA C 33I COMMERCIL TI1BE

IAN4GME1T UNIT K6 C 106) OISPERSEO RECREATtOMZ CELLS Z.? ACRES 0.0 PERCENT

AOJACE1T fANAGEME4T U4tTS ND THEIR ASS1GNEO ACTIVITIESHI ( i5Q) WILDLIFE

GW ( 335) CDMIERCIAL TIMBER

$ANAGEENT UNIT X C 336) GMMERCIL TIM6ER3 CELLS ACRES 0.0 PERCENT

ADJACENT MANAGEIIEMT U4ITS NO THEIR ASSIGNED ACTIVITIESC 10) DISPERSEO RERETION

A3 3) RESERVEO SITESI i) RESERVEO SITES

3 ( 103) DISPERSED ECRE#TION

MANAIEME.4T UNIT C( C 337) CONMERCIAL TIMBERi CELLS &..0 ACRES 0.0 PERCZNT

ADJACEMT NANAGEMEMT UNITS NO THEIR ASSIGNED ACT IIIIESK3 ( 103) DISPERSED EEAT ION

MAMAGEI1T UNIT GA C '*17) COMMERCIAL TIMBER6 CELLS L28.0 ACRES 0.0 PERCENT

ADJACE?IT MANAGEMENT UNITS NO THEIR ASSIGNED ACTIVITIESP ( 3Z) OISPERSEU EEATONU ( 333 OISPERSE RECEATIOH

IANA(.EMEMT UNIT GE ( 21) LSU4L TIMBER3 CELLS 6L..0 ACRES

AIJJAL.ENT MAhAGEMENT UNITS NO THEIR ASSIGNEDQ9 ( 1'9) QISPERSE ECREATIQNJO I 3) COMMERCIAL UMBER

S ( 357k COMMERCIAL UMBERG9 I 69) OEVELOPEO ECEATON

IAHAGEME1T WIlT GH ( Z.2.) VISUAL TI19ER2 CELLS .2.7 ACRES

ADJACZNT MANAGEHE1T UNITS NO TIEIR A3IGNEO180) OISPERSE RECREATION

(a ( 537) WILOLIFE

MANAGEMEMT UNIT ID C '.'6) WILULIFECELLS L2.7 CRES

ADJACENT MANAGEMENT UNITS .NU THEIR A3IGNECV ( 33i) CONMERCIAL T'I8E

MANAGEHEMT UNIT HE ( I.17) lILOLIFE6 CELLS L2.0 ACRES

ADJACENT AN4GEMENT uPITS NO THEIR A3IGNED(3 1 103) OISPERSE ECEATIONCW I 335) COMHERCIAL TPIBER

IA4(.EME1T UNIT HF ( .B) WILOLIFE7 CELLS 1'9.3 AC.ES 0.11 PERCENT

AOJACIT MANMGEIIENT UNITS NL) THEIR ASSIGNEG ACTIVITIESCW ( 335) COMMERCI4L TIIBER

0.0 PERCENTACT IVt TIES

0.G PERCENTACT IVtTIES

0.0 PERCE4TACT IVI TIES

0.0 PERCENTACT IVI TIES

Figure 8. Violations of Minimum Management Unit Size

77

78

to a planner.

The heuristic program produces an Allocation Achievement

Table. ' This table allows comparisons to be made between the

acreage assigned by the heuristic algorithm and the acreage

allocations produced by the linear program.

After completion of the activity assignments (with two

constraints relaxed) in the Clackarnas Planning Unit, the heuristic

algorithm had assigned an activity to every management unit.

Table X shows how close the assignments came to the original

allocation levels. The assignments were 1,123.6 acres over and

1,235.8 acres under the linear program acreage allocations.

(The difference occurs because the allocation acreages are not

exactly equal to the planning unit acreage. ) The combined acreage

variations represent 0. 667 percent of the total acreage to be

assigned. There is, therefore, an extremely small difference

between the linear program allocations and the heuristic allocations.

Development of the spillover matrix requires the planner

to specify which land uses should be brought together and which

kept apart from each other. These specifications can be treated

as predictions for the purposes of testing the effectiveness of the

heuristic algorithm. That is, one can compare the expected and

actual locations to test the performance of the algorithm. In-

spection of the adjacent conditions shown in the maps can be

79

summarized as follows.

The assignment algorithm avoided placing timber activities

on management tmits adjacent to lakes and streams when all of the

constraints were in effect. However, in two cases, visual timber

was assigned next to a stream which followed the road corridor.

Fifteen adjacent commercial timber assignments were also made

after the constraints were relaxed.

The algorithm grouped 69 percent of the total developed

recreation allocation into four management units, each containing

more than 1, 000 acres. This action was taken by the algorithm

in an attempt to keep developed recreation away from timber ac-

tivities and wildlife. This was an unexpected result, but satisfied

the instructions given to the algorithm.

The dispersed recreation assignments all were made as

expected. Large areas were created near lakes and streams,

away from roads and timber activities.

The algorithm assigned the majority of the commercial

timber units as expected with all the constraints in effect. The

linear program acreage allocations fixed visual timber activities

to the road corridor as expected. No visual timber assignments

appeared elsewhere in the map.

Wildlife areas were scattered across the planning unit

adjacent to roads, developed recreation, and commercial timber.

80

Wildlife management units generally were not grouped together,

as might be necessary to create range for large game animals.

Comparison of the expected pattern with the pattern

produced by the heuristic algorithm produces two major differences.

These differences are associated with developed recreation and

wildlife. First, the acreage assigned to developed recreation

appeared in large blocks away from the road system. The algo-

rithm grouped developed recreation units together to offset the

conflicts resulting when these are assigned next to commercial

timber, visual timber, or wildlife units. The conflict with visual

timber (which is associated with the road corridor) forced

developed recreation units to be located away from the road.

This result could be avoided by changing the value assigned

to visual timber and developed recreation in the spillover matrix

or by preassigning the most suitable areas of the planning unit to

developed recreation. The algorithm would then fill in around

these assignments.

Wildlife units were scattered around the planning unit and

made adjacent, in many places, to activities which created

negative spillovers. There seem to be two reasons for this

result. One reason may be that commercial timber assignments

were made first by the algorithm since these units were to be

allocated to large management units. This left smaller acreage

openings into which wildlife could be fit to satisfy the allocation

goal.

Wildlife assignments also were made without consideration

of roads. Wildlife could be made sensitive to road location by

placing roads in a special category similar to lakes and streams.

A negative value then could be given to the adjacent location of

roads and wildlife in the spillover matrix. An alternative tactic

would be to assign negative spillover values to the adjacent

location of wildlife and those activities associated with the road

corridor (i. e., visual timber and developed recreation).

Success of the heuristic algorithm in preventing adjacent-

use conflicts also can be determined by comparing the number of

expected conflicts in a random assignment process with the number

actually produced. This comparison was made for assignments

with no constraints relaxed (Map 1) and assignments with two

constraints relaxed (Map 3). The actual number of adjacent-use

conflicts produced in each case was determined from a tally of the

detrimental spillovers listed by the conflict detection program.

Map 1 contained 41 detrimental spillovers while Map 3 contained

1,551.

The expected number of conflicts produced in a random

assignment process was determined by weighting the values in

the spillover matrix with the percentage of acreage assigned to

81

82

each activity (Tables XI through XIII show the computations for

this process). Table XI shows the percentage of acreage assigned

to each activity in Maps 1 and 3. Using these percentages as

weights on the expected occurrence of adjacent locations, com-

putations of these expected values were constructed for Maps 1

and 3 (Tables XII and XIII). The values in parentheses are linked

to the negative values in the spillover matrix (Figure 5) and, when

totalled, represent the total percentage of adjacent conditions

resulting in conflicts.

The calculated percentages are 11.37 for Map 1 and 44.31

for Map 3. The total number of adjacent conditions (6, 120) is

obtained from the adjacency program. Multiplication of the total

number of adjacent conditions by the percentages produces the

number of conflicts which can be expected from random assignment

of acreage equivalent to that shown in Maps 1 and 3. The expected

number of conflicts are 696 for Map 1 and 2, 712 for Map 3. Com-

parison of these values with the actual number of conflicts (41 and

1,551) indicate that the spatial allocation strategy performs better

than a random assignment procedure.

TABLE XI

ACREAGE BREAKDOWN BETWEEN ACTIVITIES

Activities Percentages

83

Mapl MapZ

1 - Reserved Sites 2.1 2.1

2 - Lakes and Streams 0.2 0.3

3 - DevelopedRecreation 2.8 4.1

4 - Dispersed Recreation 5.0 20.4

5 - Commercial Timber S9.6 60.8

6 - Visual Timber 1.5 1.5

7 - Wildlife 1.4 10.8

7 0. 029 0. 003 (0. 039) 0. 070 (0. 834) 0. 021 0. 020

TABLE XII. EXPECTED VALUES FOR MAP 1

ACTIVITIES

TABLE XIII. EXPECTED VALUES FOR MAP 3

ACTIVITIES

84

1 2 3 4 5 6 7

0.044 0.006 0.086 0.428 1.277 0.032 0.227AC 2 0.006 0.001 0.012 0. 061 (0. 182) (0. 005) 0.032TI 3 0.086 0. 012 0. 168 0. 836 (2. 493) (0. 062) (0. 443)VI 4 0.428 0.061 0.836 4. 162 (12. 403) 0.306 2.203TI 5 1.277 (0.182) (2.493) (12.403) 36.966 0.912 (6.566)ES 6 0. 032 (0. 005) (0. 062) 0.306 0.912 0.023 0. 162

7 0. 227 0. 032 (0. 443) 2. 203 (6. 566) 0. 162 1. 166

1 2 3 4 5 6 7

0.044 0.004 0.059 0. 105 1.252 0.032 0.029

VI. FUTURE RESEARCH STEPS

The spatial allocation problem involves three important

spatial factors: 1) management unit size, 2) adjacent-use conflicts,

and 3) collocation patterns. Construction of strategies which

optimize land allocation locations, when these factors are taken

into account, generally are prohibited by the size of the allocation

problem. Even when the problem is sub-divided and an efficiency

criterion substituted for optimization, optimizing algorithms

either exceed computer memory size or are prohibitively ex-

pensive. Assignment algorithms are more useful and are suitable

for the objective of finding the most efficient approximation of the

linear program solution.

Igniziots (1978) heuristic technique, a search and assign-

ment algorithm, can be modified so as to treat the three spatial

factors as variables in the assignment problem. Before the

technique can be used, the problem must be broken into several

components, then tools developed to handle each component. The

tools and procedures developed through this research are not

completely satisfactory, although they are workable and make it

possible to deal with spatial factors in a systematic manner.

85

Difficulties in Application

86

A number of difficulties are encountered when applying the

spatial allocation procedure to an actual planning unit. These

difficulties include: 1) the amount of detail, 2) computer core

limitations, 3) algorithm efficiency, 4) the amount of information,

5) the number of interactions, and 6) the lack of exact rules for

making tradeoffs. Each of the difficulties will be discussed in a

general manner.

The amount of detail refers to the complexity of the

planning unit. The diversity of land types plus their location

creates a complex and detailed pattern. This complexity forces

solution techniques away from mathematical programming algo-

rithms (which can only handle a limited number of variables)

toward algorithms with more flexible limits. It also strains

computerized data manipulation routines, such as the mapping

routine developing management units.

The computer core limitations refer to the amount of

information which the computer can load and process at any one

time. These limitations become important in problems where a

large number of factors must be considered at the same time. Any

mathematical programming technique attempting to include location

in its allocations encounters this limitation. Several operations

87

in the mapping program also face this problem. However, in the

mapping program, this limitation is overcome by sub-dividing

the problem. This strategy also can be applied to mathematical

programming problems, but formulation is difficult and multiple

rutis make a final solution quite expensive.

Algorithm efficiency is reflected in the cost of obtaining

a solution. The solution cost is affected by: 1) the amount of

information considered by the algorithm, 2) the required pre-

cision of the solution, and 3) the number of runs necessary to

calculate a solution. Mathematical programming algorithms

considering location and factors associated with location in their

allocations must sub-divide the problem into a number of smaller

problems, each of which must be solved. This, coupled with the

requirement of an Moptimalhl solution for each sub-problem, usually

creates a solution with high costs. Cost also may be prohibitive

for other types of algorithms depending on their design, their

solution requirements, and the amount of information they process.

The amount of information included in an allocation or

assignment algorithm must be examined in light of computer core

limitations and solution cost. These factors sometimes require

a simplification of one or more aspects of a particular problem.

In the spatial allocation problem, the linear program acreage

allocations and adjacent activity assignments were considered

88

directly in the assignment process while minimum management

unit size and collocation requirements were encouraged with the

spillover matrix but only checked after assignments were made.

The number of interactions between the information

examined when making assignments adds to the complexity of any

assignment algorithm. When a tradeoff exists between two factors

affecting the solution, the algorithm must consider this special

case (and every other special case) or must have rules to avoid

these complications. Consideration of every case will increase

the amount of information to be examined, possibly causing com-

puter core or cost problems.

The lack of exact rules occur in the case of interactions

and tradeoffs. The problem arises because of the complexity of

the spatial allocation process and the number of factors involved in

development of a land use pattern Attempts to anticipate every

tradeoff and develop a rule for each are virtually impossible and

create additional complexity in solution procedures. The best

approach may be a simplification of the problem with a standard

procedure which ignores tradeoffs.

Tool Improvements and Subjects for Further Investigation

This study has exposed the problem of spatial allocation in

forest land use planning. There is room for improvement in the

89

tools developed. The problem requires greater study in its own

right and the heuristic technique is in need of reliability and efficiency

te sting.

The adjacency program, for example, is limited to working

with only those management units sharing a common boundary. Ex-

amination of spillover impacts are restricted to those located along

a boundary. The adjacency program, as a result, cannot account

for the effects of a single activity assignment on the overall land

use pattern. A useful improvement to the procedure would be a

program which kept track of the relative location of all management

unit assignments. This program would allow the proximity of

other activities to be considered in each assignment and, thereby,

allow the collocation factor to be treated directly in the procedure.

The spillover matrix, or the values in it, require further

study. The values in the matrix represent the spillover effects

expected if two activities were to be located on adjacent manage-

ment units. The matrix is a useful device for making identification

of spillovers and the judgments about their importance explicit.

However, the values necessarily are a simplification of several fac-

tors. These include: 1) the type of effect expected from proximity

of two activities, 2) the impact of this effect on production of

consumer goods and services, 3) the sensitivity of each activity

to the type of spillover, and 4) the relative importance of the types

90

of spillovers identified for the matrix. Very little is known about

these spillovers and their effects on production of goods and services

from a planning unit. Even though explicit in the allocation process

developed here, the value judgments in the matrix remain sub-

jective.

A related task is to more thoroughly investigate the spatial

factors involved in land use allocation on National Forests. Three

factors were identified here. It seems unlikely that these are the

only spatial factors or that they are the most important ones. It is

not even certain that the three identified here are mutually

exclusive factors. Further research into the identity of spatial

factors and their influence on land use allocations seems warranted.

On another tack, planning problems vary in size and com-

plexity. It would be useful to know how size of the problem affects:

a) computational efficiency, b) the flexibility of assignment options

mentioned earlier, and c) the sensitivity of the solution given by

the heuristic technique. If the problem is very large, the planner

will make simplifications in variables and reduce the level of

detail in the units of analysis. Similarly, it will be important to

know how such reductions, when made, are likely to affect the

accuracy of the final solution.

The most troublesome shortcomings are in the heuristic

technique. Since the 11ocation algorithm (or strategy) seems

91

relatively independent of the kind of spatial factors involved in the

problem, high priority should be placed on further study of this

strategy.

The heuristic search algorithm looks to future assignment

options and considers previous assignments when selecting an

activity to assign to a management unit. However, the algorithm

is unable to reassign management units even if such an action were

determined to be the most beneficial to the final land use pattern.

The heuristic search algorithm could be modified so that

it is able to reassign management units. This modification should

permit an improvement in the land use patterns which is currently

prevented by the algorithm.

The heuristic algorithm assigns activities to as many

management units as possible without violating the values in the

spillover matrix. To assign the remaining management units,

the spillover constraint is subsequently relaxed and activities are

assigned which fulfill the acreage allocations and minimize the

detrimental spillovers. However, there is no assurance that a

land use pattern would not have been created with fewer detrimental

spillovers had this relaxation always been in effect.

Detrimental spillovers block assignment options in the

heuristic search algorithm. There is probably a range of detri-

mental spillovers which is acceptable in the heuristic algorithm.

92

Outside of this range the spillovers disrupt the algorithm, per-

mitting assignments to only a few isolated management units. The

acceptable range of these spillovers would be useful information

to a planner who may wish to select only the most severe conflicts

in order to create a land use pattern.

The tradeoff between computational efficiency and assign-

ment options caused by the number of management units should

be investigated. There is a need to establish a range in the number

of management units which fits the computer core limitations, can

be manipulated with some degree of efficiency, and provides

adequate flexibility in the assignment of activities to management

units. The range would assist planners in determining the amount

of detail the land type and management unit maps could contain,, if

the hearistic assignment algorithm was being used in the spatial

allocation procedure.

Finally, the reliability of the heuristic algorithm is un-

known. It seems likely that the ability of the technique to produce

the most efli.cient possible solution is affected by the number of

variables in the problem and by the range in these variables.

Tests should be conducted to determine how sensitive the algorithm

is to: a) a change from the use of positive, negative, and zero

values from the spillover matrix to the use of a full range of

values from +3 to -3, b) variation in the spillover matrix values,

and c) the order in which management units are scheduled for

assignment.

93

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Phillips, D. T., A. Ravindran, andJ. J. Solberg. 1976. Oper-ations Research: Principles and Practice. Wiley & Sons,Inc., NewYork. S85 p.

94

Public Land Law Review Commission. 1970. One Third of theNation's Land. Government Printing Office, Washington,D. C. 342 p.

Rarnalingam, P. 1976. Systems Analysis for ManagerialDecisions. Wiley &z Sons, Inc., New York. 607 p.

Simmons, Donald M. 1972. Linear Programming for OperationsResearch. Holden-Day, Inc., San Francisco, Calif. 288 p.

Sinton, David F. 1976. An introduction to I. M. G. R. I. D.: aninformation manipulation system for grid cell datastructures. Harvard Graduate School of Design, Dept.of Landscape Architecture, Cambridge, Mass. 23 p.

Taha, H. A. 1971. Operations Research: An Introduction.Macrnillian Press, New York. 626 p.

Wagner, H. M. 1970. Principles of Management Science.Prentice-Hall, Inc., Englewood Cliffs, New Jersey.562 p.

95

APPENDICES

96

APPENDIX A

97

98

USE OF INTEGER PROGRAMMING FOR MAKING ALLOCATIONS

Problem FormulationI = activity

1j = management unit

Objective Function

M.AXZ

-WT (d++...+d+ +d +d+ +...+d+d+)2 1 m m+1 m+1 n n

subject to2Allocation Constraints

a11X11+a12X +... +a X .b12 lj lj

a X +a X +...+a.X.b221 21 22 22 2j. 2j

a. X. + a. X. + ... + a. .X.. = b.11 11 i2 i2 ij ij

Adjacent Conflict Constraints3

X11 + X2 + d - 4 = 1

x +x +d d+ =1m m

Management Unit Size Constraints4

X11 - X1 + d+i - d++l 0

+X -x :+ddn = 0

5Multiple Choice Constraints

xli + x2l + ... + xii = 1

xl2+x22-... +xi2 =1xii + x2 - ... + xii = I

Zero-one Constraints

Xfg = 0 or 1 for every f = 1, . .. , i and g = 1, .., j

1 The objective function maximizes one goal or product while

minimizing the adjacency conflicts and management unit size

violations. The weights are used to indicate the relative im -

portance of these two objectives.

2 The allocation constraints are similar to those in the linear pro-

gram formulation except each management unit is assigned in its

entirety to a single activity.

3The conflict constraints prevent two adjacent management units

from being assigned to conflicting activities. One constraint is

required for each pair of management units where the conflicting

situation may arise. The possible situations which can arise are:

1 + 1 + 0 - 1 penalty

0 + 0 + 1 - 0

1+0+0 -00 + 1 + 0 - 0

Since only the d+ variable is penalized in the objective function,only the first situation (where the conflicting activities areassigned to both management units) produces a penalty and is,therefore, avoided.

4The management unit size constraints require two adjacent manage-

ment units to be assigned to the same activity. The possible

situations are:1 - 1 + 0 - 00 - 0 + 0 - 01 - 0 + 0 - 1 penalty

0 - 1 + 1 - 0 penaltySince the same activity is to be assigned to both management unitsor neither, a penalty is attached to the situations where the activity

is assigned to only one of the management units. This situation

arises in the last two cases when either the d or d+variable takes

99

on a value other than zero.

5The multiple choice constraints assure that each management

unit will be assigned to one and only one activity.

6The zero-one constraints prevent the variables from taking on

any values other than zero or one.

USE OF INTEGER PROGRAMMING TO ASSIGN ALLOCATIONS

Objective Function'

MIN Z = WT(d +... + d + d + d +... + d + d)1 m m+1 m+1 n n

Allocation Constraints

a. X. + a. X. + ... + a. .X.. = b.ii ii iZ 12 1J 1J 1

Conflict Constraint3

Management Unit Size Constraints3

Multiple Choice Constraints3

Zero-one Constraints3

'In this use of the objective function, the algorithm must simply

minimize the spatial assignment penalties.

2The allocation constraints require the management units assigned

to each activity to meet the acreage allocations. These constraints

can also be formulated so that the allocations do not have to be met

exactly but a penalty will be paid for deviations from the allocation

levels.3The other constraints are identical to their previous use in the

integer program making actual allocations.

100

APPENDIX B

101

102

UVEL4Y(lA.S,0,0) I

PROGRAM NAIN(iNPUT6 ,QUTPUTb5,TuP 9ZNPUT, TAPE50OUTPUT, 23

2TAPEb6,TMPE7:5 ,TAo ,TAPE0b, ,TAPEtlo, 5

5 3TAPEtt,, , TAPEI2.b5,TAPE 136,, TAEt.o, TAPE1b5, 56

5TAPE21.,, i4pE22.o5,rApE23o,ApE2os,TApE2so5, 78

7TAPE3t.63.TE326S,TAPE33a5,TA?E365,TAPE3,S, 9

tO 8TAPE3oY,TAPt3765,TAPE3d:oS,TAPE3'O5,TAPE1.0o5, 13

9TAPE1.3:3,,TAPE...:b,.rAPE.5oS, 11ATAPE'.6S, TAPt.76,T4PE1.865) 12COMHOI/A/I1FILE,lF,LF 13

COMN//IJYIBL(21.Ao) I'.

t5 COM1ON/E/KUJE 15

DATA MFI.E/0/ 16C 17

C'C 19

20 C THLS PROGRAM IS A nAPPIG RCUTIME WHICH CARRIES OUT A NUMBER OF 20

U SPECIALIZEO FUMc.T1SNS. MAPS A'ii3 THEIR ASSOCIAIEO DOCUMENTATION 21C ARE iNPUT £f1j THE P GRA1 AN3 THE MAP SYMBOLS ARE RE000EO £NTD 22. A WUMEKICA.. CUJE siiIGM A.jOW FURTHER OPERATIONS ON THE MAPS. 23C.

2.25 C THESE )PEr,ATIONS INCLUDEI 25

C t) OUTPUT £N 4 TAHJARJIZED FORM AT THE TERMINAL OR ON A 26

C LINE 4TEH FI.E, 27C 2) COMBiNATIN OF MAPS TO CREATE A MAP WITH A NEW SET OF 28C SYMBO REPRESENTING EAOH UNIOLIE MAP COMBINATION, 29

30 C 3) AGGREOATION OF lAP SYMBOLS lc.IO ASOCIA1EO GROUPS, 30

C ) SYMBO_ OHAES FROM A MP ASiC.NMENT, 31

C 5) REOROER HAP SYMBOLS ANtO LEc.EMO 32C 6) A OIRICTOw.Y OF MAPS AMCLAB..E O THE USER, 33

C 7) HIOH.ZGrIf OF SOME CIA CrE.iSTICS OF THE OUCUMENTATICH. 3'.

35 C 8) UTQ1..TCC .OUPING OF ADJACENT CELLS OF THE SAME SYMBOL 35

C INTO ?AGELS, 36C 9) UPOAT: Or NAP TILES AND LEGE4OS, 37C tO) IANPJLATION OF MAP CELLS. 38O 39

1.0 C THESE 3PERATON, NIL 3E 3ETTER DESCRIBED IN EACH SUBROUTINE. .30 THIS POGRA1 uPE,ATE ON AN INTEAOTIAE AASIS THE USER RESPONDS '.1C TO QUESTIONS AND ON MOST GASES CAN SELECT AMY MAP OPERATION .2

C AFTER COMPLETIDN OF THE PREVIOUS PERATION.C 1.'.

1.5 C THE TAPE uSAGE FO. Trill PRO1SIAAM IS) 1.5

O TAPES 1-20 TITLES AND LEGENDS FOR 20 lAPS 1.6C TAPES 2t- MUTUAL MAPS MSSOOITEO WITH OUCUHENTATION ON .7

C TAPES t-20 1.8

C TAPE .J ..INE PRINTER OUTPUT FILE 1.9

50 C TAPE '.1. cCOOEJ DATA OUTPUT FILE 53O TAPE 1.5 RECODED DATA INPUT Fi.E ASSIGNMENT INPUT FILE 51

o TAPE .0 RAW DATA INPUT FILE, ASIGNMENT INPUT FiLE 52

C WING FILE 53C TAPE .7 SYMBOL LIST LNPUT FILE. WORKING FILE 51.

55 0 T..PE '.5 WOKING FILE 55C. TAPE 1. TERMIAAL INPUT FILE 50U TAPE 50 TERMINAL OUTPUT FILE 57C 58C 59

60 C 63WRITE(T0,1i301.J 61

tool FORMAT 4/,A,AMAPPING ROUT INE/CREATEO BY R. HAGESTEOTI 62toA,ASEPr. IB7t) 63

C 61.

65 wRIIE(50, 10321 651002 FDitMAT(/THE FOLLOWING FILE NUMBERS ARE SET./ 66

lATHE USER MUST OE CONSISTENT WTH THEIR OESIGNATION./ 072.3*,ALINE P..INTEP. OUTPUT Fi_E ('.31 Al33X,.tRECOOEU DATA OUTPUT FIiE ('.) Al 69

70 1.JA,tRE0OJEJ DATA iNPUT FILE ('.51(/ 7053A,ARAW (AIM t4PUT FILE (1.o) All 716AWNEN A AUE$TioN IS ASKED, ANSWER WITH A YES OP. NO.Al 727AWHEN M)(E TMMN ONE NUIIOER IS TO BE PLACED ON A SINGLE A, 73IALIME,t,ASEP..RAIE THEM HITH BLANKS OR COMMAS.AI 7'.

C 76CC THE MAPS WHICH ARE OUTPUT FROM THE PROGRAM CONTAIN A STANOAROIZED 78C SET OF 5YH3DLS. THE FO.LOWING COOE iNPUTS THIS SYMBOL LIST FROM 79

103

SO C TAPE'.?. IMIS FILE IS LATEI uSED 3! TiE PROCRAM AS A O.KING FILE. 80C 81

82C 63

REiINO .7 6'.

55 ,cEAOt.7,1033) (ISMAL(I),I1.2'.9o) 851003 FOKMAT(13A2)C 67

68C 89

90 C THE FOLLOiI CUOE SET U? AHiS CAL.3 THE INPUT £UOUTIHE. THIS 90

L SU0WUTNE MILL. INUT MAPS IN ET THE OR ECOOEO DATA FORM. 91C IN SwE CAI. 'IiIERE A PREIOU. RU JUST lADE AND THE MAPS ARE 92C ALEAOY P.ADED ON LOCAL FILE5 t-'.0 THE NUM3ER OF AAILMdLE MAPS 93C IS SUPPLIED AND TH INPUT P,OGEDURE CAN 3E SKIPPED.

95 C 95C 96C 97

RITE(5U, 100.) 98t001 FUKIAT(/tIS THEr.E MN! MAP DATA TO SE INPUT. 99

100 READ ('.9, 1005) KOCE1005 FO'.4AT(Al) 101

IFOOE.EQ.lHY) GO TO 100 102C 103

WRIrE(5u, bOo) 10'.105 1006 FORNAT(/tHOW MMt,Y MAPS AE AVAILABLE' V) 105

REA0(.9,) lFLE 106GO 10 113 107

C 108100 CALL OVEV.LA!(l.HMAPS,t,0) 109

110 C 110C 111C 112C THIS CODE ALLOWS SEtECTIN OF A MAP CPERATICN AND CALLS THE 113C APPOPIATE SU3ROUTIHE. 11'.

115 C 115C 116C 117110 WcITE(5Q,t007) 1161007 FORMAT(/tiST OF lAP OPERATIONS AND THEIR KES./ 119

120 AVTO A C.VEN 3PERTjN - TrP IN THE KEV.// 1203VLIST - TO GET THiS LISTV/ 121C*TERMIt.ATE - To TErNINATE NA? OPEIkATIDNS*/ 122UOUTPUI TO oUTPUT riAPSt/ 123EtGOMdZr4C - To COMBINE MAPS AND DDCUMENIATION*/ 12'.

125 FtAGGEA1E - TO AU,..,ECATE NAPS AND UPDATE 000UNENTAIIONX/ 125- TO iAKE lAP AS5ICNMtTS AND UPDATE DCGUMENTAIION/ 126

HtSEU,OEA. - TO EOOE THE lAP Stl8OLS AND LEGEND/ 127I*DIV.E,1(Y - TO EAANINE THE DLkEDTORVt/ 128JVHI,HLI,4T - 10 HI,HLiDMT CM MCTERISIIG(S) OF 0OCUP4ENTATI0N/ 129

ISO K:PACEL - 10 U.DUP AJONCENT CEL INTO PARCELSt/ 130LtUP)ATE - TO UPDATE MAP TTLE DR .GENO/ 131MIMANIPJ11E - TO IANIPULUTE PARCEL MAPS) 132

C 133120 IIE(5U,tQ0a) 13'.

135 1008 FOiATL//:,EECT A lAP OPERATIONs) 135REAO ('.9, 1009) KUOE 136

1009 FURlA1R3 137C 136

IF(KUOC.EU.3HLIS) GO TO 110 13011.0 IFKUUE.EQ.SHDU() DO TO 130 1.0

IF(KUOE.EU.3iCUH) CD TO 1'0 141IF(KUDE.LN.3H.UI CO TO 153 11.2

1F(KOOE.EU.3Hu) SD TO 150 1.3IF(KOOE.EQ.JNrEO) U TO 150 1'.'.

11.5 ZF(KOD.U.3HDIR0 GO To jc,JIF(KOO..E.3HIlCU0 GOb 170 1.6IF(KUOE.EU.3HPA) CU TO 183 1'.?IF(KOUE.EQ.SHUPO) CD TO L0 11.6IF(KOOE.EO.3H1AN) GO TO 20 11.9

150 IFOUE.EA.JlTER0 (J 10 210 150C 151

WRITE(50, 1010) 1321010 FCRMAT(f1rlA1S NOT ONE OF THE GHOICES? 153

GO 10 110 15'.

155 1.30 CALL DELAY('.P(MAPS,3,0) 155GO TO 120 186

11.0 CALL 0VELA!(1.P(MAPS,'.,0)TaO tO 120 158

104

150 CALL OLY(HMPO,5,3)C.O TO 123

toO CML. OIECT ttCo 10 120

170 C.L_ OVE.L.Y(,HMAPS.b,3) to3GO TO 123 tOL

165 180 (..LL O(LMY(M.iS.7,3 165GO TO 120 166t0 CALL OE(LAY(HMAPS,8,3 167GO TO 123 164

200 CALL 3V'LuY('HMAPS,9,3)170 GO 10 123 170

C 171172

C 173AFTER ALL IP J.Eb(4TLU H4T 3EEN C0IPLETEO OUT 0EFO1 PROGRAI t7'

175 (. TE,MZIsATLO, THE UOROUrLNE SELECT IS CALLEU. THIS U8OUTINE 175C 4LLON THE JE TO SELECT ECUJEO MAE'S TO 8E SEO IN THAT FO?i 176C FOR iNPUT Ol U8jEQUET L. THE SELECTEJ MAPS AE PLACEO 117C ON T4PE.',. 178C 179

180 C 180181.

210 CAL. OE.LAYL'MMAPS,2,0) 142C 153

WRITE(50,tatL) 13'.105 1011 HMPS HAVE 6EEN L4CEO ON FILE '.'./ 145

t$kEHdE TO VE THAT FILE/iTHE LINE PINTE. OUTPUT HAS 1862t8EEN PLCEJ ON FLLE '. /*(EMEMER TO OLTE THAT FILEt) 187

C 148STOP 139

190 190

105

t SU3UTjNE ECT t1uI1ENSIl Ti_E(6 192

193C 19'.

5 l5C t6C SI4CE T1E 'G*M IS INTEAC1IE, IT IS UIPORTAT HAT T Y POINT 17C IN Ji 1-sE KNOW HICl MAPS ME AILA3LE NICH MP 198c NUMBER C lrj 1T :OA)N. THiS IS C1IED aT uROuTINE 199

tO OECT. ir P00ES ri AP UME AO TITLE OF EACH AVAiLABLE MAP. 200C 201C 202C 203

WiITEI50,10Jfl 2U.t5 1001 FORlT(/OiET0RY FOi QV(.AYS, AGGREGATIONS, ANO C04IATIQNSt/) 205

C 20600 IQO J:1,PlFiE 207REWINO J 208

C 20920 EA3(J,10Ofl NT, (TIT.E(II ,I1, 6) 210

1002 FUk1.T(t!,A,610 211WkITE(53,100.3I J (TITL(I),I1,ô) 212

1003 T(/$P t,,ó1O 132t.

25 00 100 (1,NT Z15EAO(J,tQO (TITL(II,I1,6 216

100 WRITE(50, 1Q'.) TLTLEU) ,I1,6I 271001.Q Z19

30 RETURNNO Z21

106

SU9OUTtME EAES 222CO,/MFI.E.MF,LF 223COMMG4/)/1TLE(6) 22g.

C22S

S C225

C227

C THIS J3OJtit OWS FO. lOE THA TEMT'f LAPS REPLCIM& OE OF 22C ThE TwET ILT1 rHE EAE i.P. T1E PLACEO P CA 3E Li$IMATE 22C O MY 3E P_A.EJ u ECJQED MAP OUTPUT FiLE. THIS FILE MA(E$ 233

to c THE$ iAP$ FO LATER 23t232

C THE TITLE )F TIE NCOMiN& hAP 4M TPE OiETOY OF MAPS ELI&I8E 233C FO EPuCiENT AE 2'.1..

2515 23ô

C237

WRtT(5O,tO01) (TITLEtI) IL,S) 238lOut F1T(/.tT1I 1API ,ôAII/.l1tJST 3E wITTEM 3ER AN 23g

ItEAiSTI i14P..) 240c 2kt

MFILE:ZI 2.2CALL. OECT 2.3

C2+.

wIT(5Q, 1Q2)25 lOU .AT(/t.ELEL THE APPP,UAT lAP MUME fl 2+â

1F 27C

2'.8L 24.9C

30 C IF TE EAisr1N MAP IS TO d $VE SU3ROUTIME COPY IS LALLEO. 25tC IT WLL Pk)E TE EXTIG AP a&FCE TPE MEW ?AP £S P4ACEO 252C ON THE FI.E. 53

C2.255

35 CWRjTE(O,1OU3I 257

1003 FtATL/O OL wH TO PLACE T$E EXISTING ?IAP ON TIE RECODED t, 251IAP UT3UT FE4fl 259

EMC( t0..) (A 20'.0 1001. FUcMAT(ti 2t

IF 1YI GM. COPY 262C

23LF1F+2O - 2Sh.

EwINO 265IEINO L 26

C267

RETRM 26&END 269

107

U3kQUTI: COPY 270QtMEl$iJ4 TNS(L2) 27tCDMlON//MFX.i,MF,LF 272

C 2735 27.

C 275THIS uOJTI TiANSFES THE SPECIFIJ FILES OMT) THE RE.00E0 2TC MAP OUTPUT WILE (TPE.4). 277C 273

10 u 27C 2O

ML:IF 21tOO EwIMO 'I. 232C 23

15 il EA)(M,iOQl1 (TAMs(K,1,12)iUl F(lAT(1AiUI Z85IF(EOF(MI 1313,120 286

C Z7120 wRITEI,,,1OOI ITAN$Ii(),1,12 23

20 C3 13 jisi 28C 2O130 ML:Il.,2G 291

IFIML.LZ.0) O TO 100 22C 233

25 SETURNNO 295

t

5

to

15

20

25

30

35

*40

so

55

60

65

70

75

SUBROUTINE RECODEOiMENS1J P(3o),1G(a5J),.CT(t29,DOG(*41,TITLE(6)CU/A/i1FL..E ,CiON/3/iCjU1296i,NS,NC,TCO*110t4/ /KOOEOMTA Q. /21.33/,ICT/t296'3/

CCCC THIS SUKOuTIr4 15 CA..LEQ 5 OTHER jUROUTZNE WHEN THEY WANTC THE S'r430L5 UN 4AP ECODEO ACCOOZNO TO THE ODEk OF THEIRC OCCURrENCE ZN THE EGEUC. THIS URCUTINE ALSO kiCOkOS THEC CE*CE OF SYISOL IN THE HAP, so srrxsrios SUCH ASC NUHOER OF ACREAGE, AtD ERCETGE O ACREAGE CAN AEC C.UIPOT FOR EACH SYHAOL.CC1.

TOTAC=0.REWIND :

CCCC THE FULLCii CODE CHECKS EAC*i CEL IN EVERY MAP ROW AGAiNST THEC LEGEND OROEcING INFORMATION, RECODES THAT CELL ACCORDINGLY, AND1. WlITES THE CUUO QOW ON THE HAP FILE. A TALLY IS ALSO MADE OFC EACH SYMOUL OCCURRENCE.CCC100 READ (.o, loOt) £CO,IRUW, (MAP(I .1=1,36)1001 FORMATl2*.,36A2)

1F(IC.EQ.VV) GO TO 1901F(1Cu...T.t.ANO .MAP(t).E.2H AND.HARGIN(IROW).HE.ZH)

1 MAP(l):HAN(1ROW)IMMP :2H

CDO lQ 1 3oIF(HAPU) &.2H I GO TO 11)3IF('I.*P(I).E0.H) 6) TO 1.70IHAPMM?(i)GO TO 12)

110 IF(<OO.E.lHr) GO TO 160IF(LIAP.EU.2r1 P GO TO l0MAPI 11=1 lAP

120 DO 130 .l,IS13)3 1F(MAP(L).E9.ICOCE(Ll) GO TO 1*40

MAP Ci) -

Gj TO 15)1*40 1AP(II_

ICY C L)LT CL) '1150 T3Y0c.1)1AG1.

GO TO 150160 MAP(I)0

GO TO 15')170 IMAP2I

MAP(1):991Q CCNTZNUEC

IF(MAP(3,) .GT.0) MAGIN(IROW)ICOGE(MAP(36)ljF(lAPC3).EQ.-99A) IARGIN(IROW).2H'IF(IAP(35).EtO.-99d) IAHGIN(IROW)2H'4IF(H.P(3,) .Eo.0) HMA.CIN(1RDW)2H

WRITE(L, 1002) ICOL,IRO**,(HAP(I), It,36)10)32 FOW.MaT2UJ.,/a, 1I,)

GO TO 1)3)3C100 TOTAC=T)T4CAREA

WRITE(LF,1003) NS1003 FO.MA1(*41999,I*4)CC

THE FOLLOWING CuOE COMP,..ETES THE OOCCMENTATIUM FILE FOR THE MAP.C FIRST IHE TLT4.E5 MRE TRANSFERRED, T*iEN SYMSOL OY Yl8OL, THEC LEGNa IS IAO ACREAGE AND PERCENTAGE ACREAGE IS CALCULATED,C AND THE EPANOED DOGUMEHIMIION Z WRITTEN ON THE HAP OOCUHEMTATIOMC FILE.

108

296297293299300301302313330'.3053063073013313931.031131231.331'.31531.631731831932032132232332'.32532632732532933)3331.3323333 3'.

3353363373333393 '.0

31.13423*433 4

34,31.63*4731.83193503513323533 '.

3 63373533593603613623 6330'.3653 663673 683693703 71.

3 723 7:31l.

109

80 CCG

3 753 763 77

EWIN0 .1 378.cEWI90 IF 3 79

85 380FEuJ(,? 100.1 4T,G,(TjTLE(KI,t,6I 381100'. F04I.t (2Z2, *A oAO!3I 332Wp.ITE(l,jJJt,I NT,NC,N5,(TflLE(KI,t,6( 3 831005 3 8+

90 C 38800 200 t1 IT 386R3(71t00 (TITLE((I,<1,6) 387200 W'.ITE(M-,IOObI (T1TLE(K1,Kj,61 3 88IOOD F0vl..T(8,bA1Q1 389

95 39000 220 L1,N 3'91kEQ(.7 1031I l,(0QC(L1,L1,'.1 392

1007 F0.cIgT( 393C3.EAIr ( Ju.'Ej 39'.100 PCEE/ITAi.100.0 398W.iTE(I:,1oo8l 396

1008 Fglr(..,i8,F11.1,F,. t,I'.,'.A10 337IF(NC.E.11 0 TO 220 .3 98C 339

105 00 210 J:2,..Ei0('.71t0u1l (1,IQQC(L),_t,'.1 '.00

'.01210 w,xTE(M-, 13071 1, (i0C(L1 ,(i,'.) 4.02220 ICf IL '.03

'to CWkLTE(.1,10091 TOTAG

4.0'.4.08

1009 E0.MAI (:11.13C 4.07

iTJRN 4.08040 4.09

110

0ERt.AY(lAiS,L,0) '+10PKOCAM IN 1.11UIMEt.SIJM IRANS(12),000(l+) '.12CMON/A/IIFjLE,IF,LF '.1.3

5 COMMC1/3/ICOJE)l29,;5,NC,NT '41'.C0M'10N/O'1I1LZ(6 '415

CC 1+17C 1+18

1(3 C tHIS 1$ THE FiRST SU9.)u1IE CALLED WHEN THE PKOGkAM IS EXECUTED. l+1C IT KEA3S 'S IN 80TH RECjJEO AND KAW DATA FDvl FROM FILES .5 ANO '.6. w20C THE o0CUMENrATEO FC. IlIESE MAPS IS PLACED ON FILES 1-20 +NLLE THE .21C MAPS IHEMSEdES ARE P..ACEJ ON FILES 21-.0. '+22C 1.23

15 C THE kE000EJ M4PS Mr.E .NFIJT 5I+CE THE RECODED FORM 15 USEO IN '.2'.C THE Pr0CRA11 (r(L COOE SIMPLY TRANSFE.j THE MAPS F.OM THE RE000EOC DATA £NPUT IsE TU EriE APP PP.IATE FLEA (TAPES 123 AND 2t1+0J,C MAKANIA THEM u4ILA5LE TO THE USER. 1+27C

2!) C 1+29C 1+30100 REAJ(.5,tQ01I NT,NC,l5,(TITLE(I),It,6j '+3110(31 FIAT(212,I.,oAtO) '+32

IF(EOF('.5 I t..Q,tt0 '+3325 c

CCC THiS CODE IA USED HENEER THE NUMBER OF AAILA8LE MAPS IS 1.37C INCREASED (EITHEi A NEW MAP I it+PUI U ONE £5 CREATED 1 A '+38

30 C MuP OERATI.IN). THE JM5E OF AMALE MAPS 15 iNCREASED, 1.39C THE FILE UMBERS Fi.K THE NEW HAP ARE UESIGNATEO, AND A C$EC1(C IS MADE TO SEE IF THE MEW IA? PUSHES THE NUMBER UF AWAILABLE 1.41C MAPS OEYOND THE FILE SPACE FOR TWENTY MAPS. IF THE NEW luP 1+42C IS NUMBER IWENTY-UME, 5uOUTINE EXESA IS CALLED To HANOLE +43

35 C THE PROSLEM.C

'.1.6C110 MFILEP1Fj,_E#1

'+0 IFMFILELF'IF+20IF(lFI.E.GT.2UI CA..L EAIESS 1.

c '4,2C

'45 cWRITE(MF,1001I NT,NC,Ns,)T1TLEIl,Ist,6) '455NlDNT#NSNC#t '+5600 120 Jt,NAKEMC(l+,,1002) (TRN5I),I1,0)

50 1211 WPcITE(1, 1002) TRuNS(I) ,=t,81 '4591002 F0,..MAT(SAIOI '+bOC 1.61130 READ(.5t1003 ICOL,(TRWNS(1),It,12) '+62WRITELr,IQB.3) iCO,(TRAN5(I),j1,12) +03

55 1003 FUR'IAT(j,,12M10 '+6'.IFUC0L.El.9o99) GO TO 1110 '+o5GO To 1311 '+56

C

6(3 0 l+9C THE MAPS IN Ri.W OuTA FORM ARF. REO IN. THEIR SYMBOL LIST AND LEGENO '471C ARE kE000ED. ..NO THIS INFORMATION LA CARRIED TO ROUTIN RECOOE '+71C WiEA.E THE RIDDOING POCES IS COMPLETED. THESE MuPS ARE PLACED ON '+72C THE UNUSED FILES AMONG TAPES 1-20 ANO 21-'.!). .73

65 0 47'.C 175C '+7611.0 REAJ('+6,LQU..I (TITLE(I),I.t,6) '+771001. FuRMAT(510) '+78

7(3 IF(EOF('.61) 183,1511 '+79C '+80CS " I4

C '+82C THIS CODE 1 uSED Wi1ENEER THE NUMBE OF AAILA3LE lAPS IS '+83

75 C INCREASED (EITHER A t+W MAP IS INPUT OR ONE IA CREATED BY A '48'.C MAP OPERATION). THE NUMBER OF AV,I.ABLE MAPS IS INCREASED, +85C THE FILE NJMBRS FOR THE NEW MAP ARE DESIGNATED, uNO A CHECK 1.86C IS MADE TO SEE IF THE NEW MA? PUSHES THE NUMBER OF A'+AILABLE '+87C MAPS BEYOM3 THE FILE SPACE OR TWENTY lAPS. IF THE NEW MAP 1.88

111

81) C IS NUPI3E 1WEiTY-ME, SUOROIjTINE EXES £5 TO HANOLEC TH .00CC.C150 MFiLEHL+t

MFMFIL.E '.05LF1F+Q *06iF(lFI.G1.2U1 Cui EXCE5 '.07

C90 C '.09

C 300REWMO .7 501NSQ 532

C 503Os WRITE.1,10IJ5l (TITL(1 ,Z1 .61

1005 F1AT (10t0t,'.A,ORLAYt/.(,6A10) 505C 506160 .ENC('.ó.0061 £TYPE,HO, (OOC(L),t,'.1 50?106 FQkhTIO2,A,3,'.A1OI 508

tOO IF4EH.E.3.)1 GQ TO 170 509C 510

t.5'43+1 511ICOOE(N5=TYP 512hTE('.1,1QQ7) ,(J)C(L),L1,'.) 51.3

105 100? FOMA1(2A,I'.,kA10 51.'.

GO TO 15) 515C 516110 NCt 517

CALL 518110 GO TO t,1) 519

C 520180 RETURN 521

ENO 522

112

OvEc..Afl1MPS,2,3 523POCAI1 5EET 524OIMtSI3N Nt1Aa(20) 525

MION/6/MFIE,MF,...F 5265 C 527

C ' 525C 529C THIS SU53JTINE 4(S FO THOSE MAPS WrIM THE OPERATOR WISHES 5301 TO SAVE G r4 ,..ECGDEO HAP OUTPUT FILE. TillS FILE UAN BE INPUT 331

10 C Oil LATE udiou(INE co 15 IALEO TO MA(E THE ACTUAL 532C TANSFEk. 533C 53h4C 535C 53,

15 WV(ITE(50,IOOii 5371001 FQ,clAfl/UO Y0i WISH TO SAVE AN' OF THE RE000EO lAPS FOR 538

tFUTuE U.EetJ 539cEAO 140, t0')2J (A

1002 FOc'IAI(Ati 5.t20 IF(<A.E.1HI CO TO 110 5'.2

543wITE(0,i003 5+.

1003 FO,'IAT(/tUU You WISH TO SEE THE DLECTORY TO AID lil X 5.51rOU( E.ECTiUNe1J 5'+6

25 EA3(.9,t0U2 5 5L47IF(D.E.1Hfl CALL OIRECT

CWiITE(50. 100'.J 550

100'. FMlAt(/tHuW IIAHY RECOO.0 NAPS 00 YOU WISH TO SAVEefl 55130 EA0('9 J CIFC.FJ.O GO TO 110 55:3

C 5,heWItE(5J,i00

1005 FO T(/.IST EACH MAP OU WANT SAVEO ON A SINGLE LINEX/ 55635 IXWITM EACH MA NUl3E EPAATEO SY A COIIA.Xi 537

EA04I., (APUi,I1,I(Gi 5,5C 559

00 100 Ig1, 560MFMH4P(IJ 5ó1

'.0 100 QA.L. C0 5625,5till tUikN 5o.

END 565

113

OELAY(l4PS,3,0) 566PiOGl UT_ 567DIME ilL (20 .) ,MAP(3e) ,00C('.( .1 ITLE (6( 508CON,1N//iSYMth.(2L.9ó) 59

5 C 573571.572

C THIS SUAkCJTINE OUTPUTS MAPS iN A STANOAROIZEC FORM TO EITHER THE 573C TERML1AL O I 0 4 LINE PRiIE FILE. TiE MAPS ARE SPLIT RTICALLY 57'.

10 C INTO COLUMNS OF 3 CEL PE oW ETH COMTI.UATiON IN THE NEXT 575C COi.UMN. TrII ALLOWS iJISPLAY AT A TERMINAL WITHOUT WRAPARCUMO. 576C 577

578C 579

15 W.uTE(L.3,1000) 5801000 FORMAT(9XI 581.

C 582100 IEr.R0 583

ICO.2'O 58'.

20 KI50 585C 586

WRiTE) 50.1001.) 5371001 FURMAT(/0U YOU WISH TO SEE THE OIECTORY4) 538

kEJ(.9.1002) £5 58925 1002 FOR'4AT(01h 590

£F(tS.13.1rY) CALL OIRECT 591.C

592110 WTE(5Q. 1003) 5931003 FOic4AT(/WH,ECH MAP 00 YOL WISH TO SEE4X) 594

30 REAO(4.9.') Ic). 595IF(Ik.E.0) CO TO 230 596

C 57598

kEWINO ZR 5'935 REWLNO JR 630

C601

WRITE(50.100.) 6021.004. FOkMAT(/tWHE. IS THE OUTPUT TO SE OLSPLAIEOe/ 633

1X(1.)IMMEi4TE OISPLAI (2)LHE PHINTER OISPLAY) 60'.

READ('.9.) iT 635IF(IT.E9.2) K'.3 636

C o07C 638C 609

4.3 C THE FOLLO.1N0 COE PRO4IOES HEAOINc,i AT THE TOP OF EACH COLUMN OF 610C MMP OUTPUT ..MO .T THE TOP OF ThE MAP LECENO OUTPUT. 611C 12

C 13

C61'.

50 REAJL..,100) NT,MC,N,(TILE(I),I1.6) 6151005 FOR4AT(2j2,I.,bAIOi t6

00 1.20 I:1.NT 617120 REAJ(I.t03â) (TITL(I,J).J1.6) 61.8

1.006 FO.1i.T(3...410) 61.9

55 C620

130 READ (J, 1037) iCOL.IOW, ($3P (I) .1.1,36) 6211007 FCk,iAT(201./3A,181.) 622

IF(ICUL2.tiJ.ICL) CO TO 153 623C

624.

60 WRITE(Ki,1U0) IR,(TITLE(I),I1.,6) 6251008 FCkr1AT(:I$.P ,.2,'.X,â410) 626

00 14.0 £t.4T 6271.0 .LTE(KI,100S) (T1TL(I,J),J.1,6) 6251009 F0RMAT(t/tIJA,6At0) 629

65 £COL2IJL 630IF(ICO..EQ.9999) CO TO 200 631.WcITE(l(I,1IJ10) ICOL 632

1010 FQRMAT(1A//1,COLUMN ,IL./) 633C

63L.

70 C 635C

636C THE FOLLUWENt. COQE CONVERTS TuE MAP CELLS TO SIM3OLS USINC THE 637C S1MoQ LiST INPUT AT THE 4EGNNiNC OF THE PROCRAM, THEN WAjTES 638C THE MAP ON TH ELECTEO OUTPUT OEiCE. 639

75 C6'.0

C 6.1C

64.2150 00 190 1=1 36 64.3

jF(NAPU).E.0) 00 TO 160

114

80 IF(iP(1.EQ.-99gJ GO 10 170 óW5LFMP(1J Ei.-d1 GO TO 130 o..6MAP( iijYM3&.(MAP(j 647GO TO 130 5.48

160 MAP(I2185 GO TO 100

170 MMP(.)21' 551GO TO 131 652

180 HAP(I21ee 653634

90 190 ..CNTINUEC 055

WITE(KZ,1.01U 6571011 F0.i.MAT (1( ,.ZA,3oAZ,,t

GO TO 13395

i:. 661662, THE F)LLQWIG GOOE WRITES THE LEGENO ANO ASSOCI.ATEO DOCUMENTATION 6O3

C ON THE 3E_ETD uTPu( EI.CE. 86'.100 C 885

C 6o6i:. a7200 W.LTE(KII1O12 6681912 FMMAT(L// NO. GELS,'.X,AEAGE,5X,tPCT.t, 859

105 15A,tLEE' 570C 671

00 220 672REP.0(t,t013 673

1013 F0M.T (I,, i8,F11.1,F5.1,,4A10 674110 TEK1,jlj1.s SY.'l3L(J.ITC,GM,4CR,(OUCft),L=1,'. 875

101'. F AT(t/3.,AZ &A,8,1X,FI1.1, ,F5.1,5h,4AtQ 376IFLNC.E.0 GO fo 220 o77

C 67800 210 1Z,NC 675

115 .EA(jA.,L0l.$) (OOC(L,L1,4 6301015 Fo1AT32A 41o) 681.210 WTE(KI.1 LOOCt,L1'. 6321016 FOATtsA,4A10 633220 C0N1IUE 68'.

12) C 685IFUE.GE.0 R1TEI,101fl £ER 6361017 FQMAT (l.'./.3X ,2H4',OA,Z8,26.4,UOOCUMENTED 687C 638

READ(ISL013J TOTAC 689125 1018 FOkiATL1.1 650

WSITE(KI,1G19 TOTAC 651.1019 ACREAGE 652IFLI.1,.t E$I 69.3C 69'.

130 C 655C 636

WRITE(50,10201 6371020 FCRMAT(/0TMEl Ol.TPUTeJ 658RE0.3 10021 IU 655

1.35 IF(IU.E.1Hfl GO TO 100 700c 7012.30 RETURN 702

ENO 703

115

t 70L.PkOGQ H UM3O 75OIME4SI4 iMuP2Q),,l4Pj(3óJ,lAP(3),MAP3(3ô), 70677

5 2MUi.1123) ,MOC(2U) 7o709710

COMl//iYMr3L(2.ô 711CCMlONfl/TT..E() 712

tO C 713I;. 7t.C 715C Ti1L J3.OUTE CQM9I4E EEcAi.. HS INTO A SINGLE MAP Y 716C A3ir& NEW l3QL. TO ECi1 UNIQUE CJM0UATION CREATEU DURI4G 717

15 C T-4E PQCES. TilE EEJ5 OF T1E M..P IE LS COMINE, kESULTIN 718C IN A NW L.ENO WhiCh CJNTAINS ?lULTLNEJ UECkIPT1ONS FOk EACM 7tC SYM8O..C 71C 722

20 C 72310 WITE5O, LOOLJ 729lOol FQi..T(/tHu MANY NAPS O YU I3H TO COM1ME+fl 725EAO(9,) J. 7SIF(J.E.0 &.e TO .00 77

25 ciRLTE(5O.1U32

1002 FOiMT(/iJO YQU WLSM TO jE THE IETO.Y T AlO IN 7301YCU $E.ET4fl 731RE(,10Q3 J 73230 10J FOiMT(UJ 733

(J.E.1,iY CA.J. LRECT 73C 73

WR1TE(5l3,13.. 736100k F1MT(/tI EACh t4P £N THE UIWE OF CQM3LATION. 737

35 1PiJCE HE 4AP E5 ON A IE dITi1 EACH / 7382PlAP NUlE,k EAiMTJ a! OtiM. 739kEA3(',9,') (P(,Li.,J 740

C 741wRITE(0, 100 7.2

10115 FO4T(ItETE NAME 3F T1 C 1iNATIOl (5 CHARACTERS) 7.3REAO(49,10iI, (TITi(K),KL, 7.L

1000 FOi($AT(1OJC 7'.C 7.7C 7'.dC THIS COOE IS uEO WNEt1VE T1E UME F AiAE LAPS ZS 7+C ICREE (ELTHE. NE MP £5 INPUT O NE £ CETED A 70C MAP CPEs.ATLj. THE UN3E UF uIAE MAPS IS INCREASED 71

THE FILE NJlE FOR TilE $ 4P A ELNTE0, A3 A CIECK 752C 1$ MADE TO EE T1E Ew N.P PuHE.) TE PUE OF VILaLE 753

$AP EYON Tp,E FILE PAC O TEiTY MAPS. IF THE NEW MAP 7.C IS UM3E I TYO$, SUQUTIE ECES ZS CALLEO TO HANDLE 755C TH( PS3E1. 7

757C. 758C 7

HFiLEMFLE+I 763JCIFLLE 761MFMFIE 7,2

60 LFIF+fl 7iJ£F(lFIi..E.GT.20 CA..L EACS

C 75CC 7b7

65 O1LY TWO 1S ARE COrIIED IN ONE PS T}1OLGH THE SUROUTIS SO 7oC TIi FOLI O!I4& COL)E INiTLAl.I!E CONUITIONS FOR TH Two ips 8E1G 7C CJPtdLNEO II (H PASS. 770C 771.

70 77HL1:NM4'(1 77LtlLZNMA?(2) 77NLXil.1'O 776NL2ML20 777

75 JEZ 778C 779110 REWINO Mi..1 730

EWI4 12EWPU 'L& 782

116

80 EN) L2 73REWNO . 78.kEINO .7 75

C 7

EO(ML1,1007) NT1,NC1 7d785 rEA(ML2,1Q07) NTZ,NC2 7d

1007 FjRMAT(2flNTNTI.r 730CMC1+'42 71

N$0 72C 73

7g,.+

C 75t TIE FOL_OrG COsl3LiES TE CE.S OF 1W) lAPS, ChES TlAT 7i6C CMiATi)'4 AGAZ5T A CF PRV1U EL_ COMLTI)NS QOI1c3 77

95 IL) TIE LIST IF £T IS NEWI , COCE T?IE CELL COIG TO £TS PLME 7

C ON THE LIT ..N) ITE THE MAP INFI'1ATICN N A FORM WMICH CAMU$E r THE JOUTIE EC)E. 00

C 331C Q2to C120 E(N..1,1QO UL1,IO41,(P1(I),iI,3

RE4(N,1O1)3) CUL2,IRUW2,(MAP(I),I1,36i aU51006 FO4T(UI,/&,1I4) 8Q6

GO To OJ105 GO TO 13

U TO 31 839C 81131) 00 1O I1,Jb 11

£F(M4p1(:1 .E.-99.A 0. P2(1i.E.-9 G3 TO 17 l2110 GO TO 3U 8i3

0 TO 180M4P3(i114P1(j)'1U0OO4M4PI) 15LF(Ni.E.0 G& TO 1,000 1.J .:1,M a17

115 1.0 LF(MADJ(j.E(.ICOOE3(L)) GQ TO 160 d13150 NSMS+1 d19

1COUE3('1)14P3(II1sP3 (I)SVil(NS) 321GO To 1

t2 160 AP3(I)5YML(L) Ui&O TO 1 Ui

170 MAP3(I)H'&Q TO 13 U6

180 AP3(I)H125 l0 CONTINUE

C LTE(C+a,1039) ICL)Ll,LROW1(AP3(1),I1,36)1009 FCv14T(L4,3A2) 831

&U TO 12J 32130 833

0O 4RITE(.3,1013j N3 331010 FUT(.19,i' 835C 836

837135 C.

AFTEP. CETZNG T4E TIT.E F)R T4IS fE MAP, THE LEGEMO IS EVELPEO.T4 COQE IS ET UP TO NGE T4 EGENO It. ORD IT1 T1E LEGEND

C OF TIE EO'J NETE ITHIN ThE F$T. CE NUT EVERYCUM3INATIO' OF Q,EN 1Y EXZST £ THE MAP, TE EE0 IS OMPAREO 8.2

C TO TtE I$T u OINATIOS CREATED FM TE 4P ELL ANO THOSEC MXSSING 4kE SKIPPED THE REAIt'.E. OF T1E ..EGENO IS WR1TTN, ON A iOiK' Fj.E, TIlE INFORMsTOI 10 ECOJE THE MAP CELLS

C CONZSTN 4ZT T4E LEGEO T0RE), ANO SIJ8ROUTINE RECOOE ISC (A.LEO.15 CC(. 5a

WRITE(bP 10111 T,NC,(TITL(K) ,1 ) 851lOti £,51)

t50 O 210 J1,NT1 853AA CMLI, lO12 (TTL(I),I1,6i

210 WITE(il,1O1) (TIT(I),I1,6) 8551012 FQ1iT(4 A10J 856

00 220 NT2 87155 4(ML2,112) (TXTL(Z1,11,61 58

2Z0 WP.ITE('.,1U12J (TTL(J,I16 359C 460

WXNO . 8â1

117

1611

165

170

175

185

190

195

200

205

210

215

22)

225

230

235

00 230 IL.L.OW200 230 i1,z1C2REMO(ML2,10131 M0G2(Il,(OOC2lI,Jl,J1,4.I

1013 FOlAT(25 I,'AL01230 W,ITE(.8,1L.a I9,M0C2tI),(0OC2(I,,J1,'.11011. FOA1(L4,Z1.A,i.,l+A1fi)C

IP=0JP0C

00 380 I:t,NSiH=I.00EJ1J

C00 2,0 I41,NS£F(ZC00E3(Lt).jT.Ll1) GO TO 2'ê0

IQN21.0 COHIINUE

IG0DE(I0SM8L(IQlIG00E3Uli00O00000C

£AtM/10000IFZ9.E9.0 GO TO 210

250 jF(ZA.E9.P) CO TO 290EAG(MLL,l0t1.1 iF jGt(1l,(COC1(l,Jl,Jl,,lF(MC1.E3.I.) GO 10 25000 260 I2,0l

260 EA0(MLt,L01Jl MOCL(Il,(QCl(i,,Jl.,1.)GO Ti) 230

C210 00 26 j,tj28U iTE(*7,LQL5l 101015 FO..iAT(,,21.X,0000

GO 10 310C296 00 300 it,NCt300 wITE(I.7,101k1 MOC1(I),(0OG1(I,J),J1,1.lC310 RE.iNO 1.8

I3ZM.'L00001F(Z.E0.0e GO 10 31.0

320 IF(13.Ei9..JPI GO TO 36000 330 £'1.iC2

330 EAO41.8 L01a JP,MOCZ(I),(OCCZ(I,Jl,J:t,1.)Cu 1) 320

C3.0 00 350 t,t9C230 W11E(il,101,l 10

(0 10 30C

360 00 370 tL,NC2310 wkITE(il,1011.) 10,MOC2(i),(Q0G2(Zl,Jt,1.l380 CONIIMUEc

kEWINO .GALL EJ0E

COCC iF THE NM39. OF MAPS Ti) 3E OOMOINEO EXCEEOS TWO THIS SU6OU1NEO 1 P6SSED I iOu N AJJ1T0Qt9A TIME F3 EsGH IJiTIOHAL MAP. THEC PEV1CUSLY ATE0 OOM3INATiJi IS TEATED A. THE FLST MAP N0 THEO WELT MAP TO E CJMOINEO IS TETE0 95 THE SECJNO MAP. THE FOLLOWINGC COOE TESTS FO A T9.MLNAIION OF THE COMBINATION POCE$S ANO WILL SETC UP CONOITIOS FO ANY OF THE AOOITICNML RUNS THROUGH THE SU3ROUTINE.00C

IF(ML2.E.NMuPI.JAl) GO TO 1+06JEJE#1MLIMFML2r41A'(JE)NL1LFNLZZML220GO TO 110

C

C.390 WRITEISO,1016) lt.1,l1L2

8628638à.865866867

869870871.87287387k875876877878879880881.832883

885896887833889890891.89289389k895896897898839900901.90206390k90590693790890991.0911.91291391k91.591691.791891.9923321922923921.92592692792892993093193293393k93593693793893991.0

tOtb FOIlTL/f/CUMBZNATtQN TERMITEt/iMPS ,I2, 9..tis AND S AR 1QT IDENTICAL IN SIZES)

2.1 IFLJC..ZO) iFILEMFILEt 9.3C'.00 RETURN 9.5

EN 946

118

119

OVE9L4v(lAPS,S,Q) 97P,GRM A.GA 98U1MENSIOI, MAP(5Q,3OC(,IP(t29E1,NA?(t296,ICT1t296lCOMMON/A /MFL _E. HF, ..F 950

5 COMM //ISYMoL(2'.9b 951COM1ON/0/TITL(i 932COHIUN/E/KOOE 953COMMCH/F/ikDATA ARE/2t.33/,ICT/t2960/ 935

10 C 936C 337

C958

C THIS SU3ROUIINE ALLOWS A USER TO AGG.EGATE OR A5IGN THE CELLS OF 9591 EACH SYHAUL £N10 KQU?S AND 3GUMENt tHOSE GROUPS. THI3 INFORMATION 960

15 C CAM AE PROIUE'3 jTHER IMTEACTIVELY FROM THE TERMINAL OR FROM TWOC FILES. IF IHE INFORMATION IS ON Fl_ES. TAPE45 CONTAINS THE ACTUAL 962C ASSICHIENIG AMO TAPE'.ã CONTAtH THE GROUP OUCUMENTATION. IT IS 9631 £i4PUl(TANT IN OAES WHERE THE tFOHATiOt4 IS ON FILE THAT THE MAPSC OE INPUT U' A PvEVIQUS RUN AHU THIS SUBROUTINE 3E THE FIRST 935

20 C OPEATIUN IN THE CURiEHT RUN.CC

368

C969

TOTACQ. 970

25 MNAP0 971xQ2 972

CWRIIE(5Q,IQQ1) S

1001 FO A1(/tDO YO WISH TO SEE THE O1RECTORY) 975

30 REA3 p.3. 10321 £5 971002 FOK1AT(At 977

IF(Is.E.tHY) CMLL OIRECT 073

C S 979WR1E(S0,t003 080

35 1003 FORIAT(/wilIt.i$ MAP 0*) YOU 1t5H 13 5ELECT4) 981REAJ(*.9 ) 10.

992

IF(I.S.E.0I GO TO 310 983

CC

995

C936

THIS CODE £5 USED WHENEJER THE NUMAER OF AAIL4aLE MAPS 15 987C IMCREASEO (EITHEK A MEW MAP IS INPUT OR ONE IS LREATEO AY A 998C MAP OPEiATtUN. THE NUMBER OF AWAILASLE MAPS IS iNCREASED 939

C THE FILE MUM8ERS FOR THE OEM MAP ARE OESIGNATED. AND A LEK 990

145 IS MLUE 3 EE IF THE NEW MAP PUSHES THE NUMBER OF AVAILAbLE 991

C MAPS BEYUIO3 THE FILE SPACE FOP TWENTY MAPS. IF THE NEW MAP 992

C IS NUH3E9. TWEHTYONE. SUBROLTIHE EXCESS IS CALLEO TO HANDLE 933

C rHE PR3BLEI. 90+

C995

50996

C3)7

MFILE:Mt*_Et 998MF.1FI_E 939

LFMFI23 1000

55 IF(MFIE.GT.20) CALL EXCESS 1001C

1002C

1003C

100.C THE FOLLOWING CuDE CLL5 THE 3USRCUT1ME WHICH WILL REORDER THE 1005

60 C MAP SYM9OLS IN THE LEGEHO ACCOROING 13 USER PREFERENCE CR BY 1036

C LuRGESr ACREAGE FIRST. THE UEP THEN INPUTS INFORMATION 1007

C OIRECTtNG lIE ASSIGNMENT/AGGREGATiON PROCESS ANO THE NEW 1003

C MAP TITLE. 1039

C1010

65 C1011

C1012

IF(KOOE.EQ.4HREOI CALL ,EOFOER 1013JRIR20 1015

70 EWINO J 1013REWINO .7 1017REAO(LR,100141 NT,t3C 1018

1004. FOkMAT(2L2 1019IFIIcOOE.EU.3HAI.G) cO=l 1023

75 C1021

WRITE50,1005) 10221005 FOb,.*IAT(/tSELECT THE INPUT&EVICE (1) TEAMINAL (2 FILES/ 1023

iANO IN'JT THE NAME OF THE NEW HAP (i5 CMMACTERS)./ 10242XPLACE li.S INFORMATION ON ONE LIi0E./S1PARATE WITH 1025

120

80 COMMA 0 3LANK.t) 1026REAQ(.9, 1036) .,OC,lT1TLE(I(],K.t,5) 1327

1006 0pcMATUt,U,54t3I 1025.0 10 120 1029

C 1103085 C 1031

C THE F0L0WLC COOE E43S IKE hAP A5SI PlENIS/AGREAII0NS ANO THE 1033C NEW LEGENO .UM TAPES 43 A '.6. 103'.C 1035

90 C 1036C 1037

REAO(.5 1037) MNAP,NS 10381007 F0Khl41L..I 1039

00 100 Kt,NS 101.095 100 REAOL'.5,1007) 1GRP(),NMP(I() 10.1

C 1092EAO (.b, 1007) iNAP,NC 10.3NQiNAP4C 10.'.00 110 L1,NL 101.5

100 REAO(.ó,10081 hl,(000(J),J1,1.I 10.6110 W.cI1E(.7,1338) M,(OOC(JI,.4ó1,'.I 13471008 F0c1ATU'.,'.41lJ) 10.8

GO 10 183 101.0C 1050

105 C 1031C 1052C THE F0L0W1C COQE ALLOWS THE MAP GMMENTS/AGGkECATIONS AHO 1083C THE NEW L.EENO IU 3E INPUT FROM THE 1Eh1INAL. 105'.C 1035

110 C 10561. 1037120 WRITE(80,tQ0I NC 10581009 F0RiUr(/ENTEP. EACH GROUP NUrISEiR, ITS JOCUMENIATION , 1053

1ANJ THE AS5CIATE0 P1 .TH30L3./T1E EGENO FUR EACH t, t0Q115 2GiOUP MUST E £.IZ,* LINEt5I - 40 CHARACTERS PER LINE.t/ 1061

3tTHE P.)T YM8ULS SHOULO OF PLACEC £N QUOTATION MARKS.t/ 102'.WITH EA;H SE?AiRATEO OT A COMMA OR dLAHK.// 10635*MOW MA9 0RLiU*S 00 0U WIjH TO kEA1E4*I 106.REAO(49,) NO 1035

120 C 106thJi7

00 170 1068WRITE(50, 1013) 1069

1010 FORMAI(/.IEh4IER GROUP NUHOER ANO NUiI8EiR OF ASSOCIATED STMOOLS.*) 1070125 REAO(.9,') NP,ilP 1071

C 1072WRIIEtSO, 1011) NP 1073

1011 FUkMA1(/N1E.c LEGEHO FOR GROUP ,I3) 107'.00 130 £:t,f)C 1076

130 iREA3(.9,10tZ) (OOC(J),J1,i.I 10761012 F0.MAT(1.A13) 1077130 WiRIrE(.f,11008) NP,LOOC(JI,Jt,k) 1378C 1079

WI1E(50, 1013) NP 1080135 101 FO(I4T(/E1.(E PLOT STM3OI.S ASSOCIATEC WITH GROUP t,I3) 1081

iREA3(Le9,) ($AP(1),t1,IlP). 1032C 108.3

00 [60 t,MP 103'.00 1.0 J1 L29 1035

11.0 190 IFMAPI.Q..LSM3LLJI) GO TO 150 108615Q (rh(41 11087

1038160 NAP(hMP 1339

LF(NAP(R) .l.,T.MHAPI APHAPU() 109011.5 170 CONTINUE 1091

C 1092C 1033C 1094C THE FOLLOWInG COOE ASSIGNS EACH CEL IN THE MAP TO THE GROUP 1095

150 C WHICH WAS PREIQQL OESIONATEO. £r ANT CELI_ STMOOLS WERE 1096C II.LSSEO IN lIE INPUT POCES5 GiRCUP ASSIGNMENTS AHO OCCUMENTATIOM 1097C WILL 3E kEQUE1E0 ANO MUST PRQJISEO IMMEOIATELY AT THE 1098C TEiRMINAL. 1099C 1100

155 C 1101C 1102180 READ JR 1011.) ICOI.,LRON, (MAP (I),I=1,3S) 11331011. FQH.iAT(201s/8A,18j'.j 1101.

izi

IF(ICO..E.99J C,) TO 25Q 1105160 C 1106

00 4Q 1 3b 1107GO TO 2'.0 1108

IF1P(Z).E.-9) G TO 2'.00 1.0 _l ( 1110165 1 IF(P().k.RP(LJ) GO 10 220 11.11

11121113

WRItEO,jQ1) SY(MAP(IJ),?1U) 111'.1t5 Fc'UT/twNLGH GROUP DOES tiE Pi.T SYMBOL ,AZ,$ ($,11.,$I, 1115

L7JJ It EL0N T04*) IlibE(.9,) P(KI 1117

3.TO 210 11181119

IITE(50,lOh1) MAP((J 1.120

175 00 0 11Zt1J1J (O0(JI,J1 .1 1122

2I)Q djTE(4,1O]3J t44?KJ,(J),J1,.J 112321(3 112'.

ICT(P(<) )LCI(MAP(KJJ41 1125130 GO TO 23 1126

220 ti(P.) 1127jCT(NPL) ILCtN?.P(..J 1.1 112

230 T0TAGfltA.+t. 11292.O CONTINUE 113Q

1131WRITE(...,tQ1'.) ZCOL,IOW,(MA?(I),I1,36I 1132GO 10 133 1133

c 113g.25(3 WITE(L.F,t1b) MP4AP 1135

L913 tt6 FU1(9'',i'.J 11361137

C 1138C 1139C 1i'+J

195 THE FOQW TIT..ES TilE ,IMEMT OR AGC,iQ.EGATION, 11.11 c.ULATE ST1L.TiCS OR THE EW ilP, AO WRI1E tilE 11.421 UPOTE) 0 ME4TTI0N OM FILE. tt.3CC 11.5

200 11.óiF(<O.E.1 G To 2Q 11.7W.I1E(MF,1Q17J T,,1NAP,(TITLE(K),K:t,5J

1(317 FQAT(t2,j,S$LGl1ENT *,A10)GQ 10 2fl 1150

2J5 C 111.20 WITE(M,10td) NT,G,MNP,(TtTLE(X),Kt,5I 11521.318 FQ1MT(I,I., GE,ATIC4 - 113C270 00 .i1,NT 1155

210 .EA9(Ii,.L0Lfl (TTLE(II,I2t,J 11,62á0 wTE(lF,1)19) (TIT.E(II,I1.6) 1.157

1.019 F0iMuT,A1(3J 113c 115

.EWII4 7 11.60215 OU 300 L:1,1rP ltóI

CAEuIt(I).E 115211ô3

E4 (.7,1JI ii (OOJJ ,Jt .) 11a..kjTE(M,tOZO) ,IUT(II,C,PREA,l,(OOC(J),Jt,l4J 115

2213 1(320 FOT(Z.,L3,F1L.i,F5.t,14,4AIUJ 116ôiCT4I)0 1167jF(N.E.1) G3 TO SOO t100 2U 12 11à

(s7tt0IiSJ Ill, (i)OCJJ ,Jt,.) 117225 ITE(Pl',121J ?1M,OOC(J),J1,.J 1171

1021 F0T(2,I'.,..AtOI 1172300 COI4u Ills

1tP+WRITE(,1O2Z1 TOTAC 1175

233 tOZZ FQM.T(t1.1J 1176C 11773ta RETLIN 117

ENO 1179

122

SUdOUTItE (EOiECIIME?4SZJN jYM(2SO),IUi(2l i0ER(1296),GREA(12g6),t0OC(,)UCuH(t296,,TYPE(129âIC)M10M//iYM3L(24g6)

118011111d2lt3

5C 11

EwtNO iicEWtlJ .3 1.137REINO . 11.8

10 c 119WRITE(0,1OJ11

1Q01 F0 ATI'tFu tIUW 11 UG YOU WISH TO PW.EOETEIINE ,1TiE OE THE LENO4X)tit1.1.92REA(g,) RE tt3

15 IF(MO..cQ.U) GO TO 120 lt'+C 1i5

WPIT(0,1O02) tt20

1002 FOiAt(/tEETERc THE PLOT SYMBOLS (EiCH OE I PAENTHE3ES) OFIXTPIE EL TO E OOEE FZST.:/tSEPAATE EACH P.0T SIMBQL2tWITH A OrIlA R

,£,

11.711REgO(4,I (ISV ,I1,HO0EPi 12Q0

C t21O 110 I:1,1nQER 12)2

00 1.00 J1,t2 1.20325 100 tF(IYl(.EQ.Z$YMaL(JII GO TO 110 t2'.

110 IUl(I:J 125C 12S120 A)(I,1OU3I T,NC,N5 12071003 OlMATI,Z. 12

30 00 130 Zt,T 120g1J0 iEs3:k,1OO.I LWI 12101001. FO1f(tI 1211.C 1212

IF(NC.GT.1.I GO TO 210 1.21335 00 t0 L1,N4 t21.4O,,tUOI .Of3i(LI,CAEA(II,IT1PE(fl,(aQC(JI,Jz1,,J 1.215

1005 FQkiAT(,,h ,FL1.l,5.(,I. ,1UI 121NLT1PE(ZI 121.700 1.0 J:l,. 1218

1.0 04UM(N,JrQL(JI 121gC t22IF(0.EQ.OI GO TO 160 122100 150 i1,Ii)iER 1222

NiMUM( 1223J0L.E(fl 122+TrEA(Li 1225JrvPEIr1pE(:i 1226£0;CE(LI=LkOER(NI 1227CAREACZiCMM(NI 122SiT1PE(LiTfl'E(NI 1229

12301231

150 ITVPE(N):JTYE 1232C 1233

55 160 NOROE?t2 123.jF(MekE..NS.OR.NOgQE.GT.NSi GO TO 190 1235

C 123ô170 KO 123700 180 JO3E,NS 1238

60 £j-1 1239£FAkE.(Li.E.CAREA(JI GO TO 1O 12.OJUc)LOLi 12.1TM'E4(L 12.2JT1EITYPE(iI 12..3

65 IOE.ZiOEb(fl 12'.CM(IiCM(JL11PE(IiLT1iEJ12.,12.6

IOiOER(JiJO) 12.7CAE.iJi :TAEA 12.8

70 ITPELJiJTYPE 12K'(1 125U1O COITI1UE 1231

IF((.NE.UJ GO TO 170 1252c 1253

75 190 WRTE('.i,1O06I M5,$ 125WRTE.ô,1iJOb1 NS,NC 1255

1006 FQ.MAT(2L.1 1256C 1257

DO 200 Iat,MS 1258

12,3

MjOOEtjj t25NITVPEUi 12wRIrEc.;,Loobi l,i

ZOO wRrEc.o,Io7) N,(QQCUt1(N,Ji,Ji,4 12b2113J7 MT(I,,'.tO) 1263

a5 C 12'.EINO , t25

REWIND ,o 12oGo ro 22 1267

C 126d210 WRLTE(50, 1008) NC t2ag1QO FOMT(/tTMIS MAP CONTAINS ,2 LNE$ OF LEGEND FOR EACH 1210ItS! OL.t/THZ ROUT'4E CAN NLT OPE#TE WITH 1 LINE 1271

S!'130L.i 1272C 1273

95 220 ETRN 127'.ENO 1275

S

124

OVELAYiAPS,ó,O) 1276P'cQAM ii1T 1277OrIENIOM TZT..E46).TITLL2Q.o),JFji(2O),?(FIX(2t3),1DOCt20), 1278tJFLT(20p.0U0(2O,'.),FOOC(20,.i 1270

5 UCMMUN/lSY.(2.'0ó) 1230C 1281C 1282C 1283U INIC SU8kOJI1NE miLL MIOMLIGMI GEA1N ASPECTS OF A MAP'S 124

10 U QOCUMEMTAT ION. iN A LO jNATQN MAP, THE uSE CAN FIX lE 0i 1285U MUE ACTEi.T1CS WHICrI MArE UP THAI AP AND LET OE O MORE 1286U CHAAAUTErISILLS A,Y. THIS WILL PDOUUE A .EGENO OF MAP CELLS 1287C WHICH 4MVE TH FIAST SET OF CHARACTERISTICS I COMMON BUT A 1288C DIFFE,cENT SSUUNU ET. 1289

15 C 1290U 1291c 1282100 WRITE(50, 1001) 12831001 FQNlAT(/tOO IOU WISH TO SEE THE DIRECTORy TO *10 iN SELECTION : 129'.

20 1OF A M*.'4) 1295REAO'.9 1082) JA 1206

1002 FQRMAT(61) 1297IF(JA.E.1HY) ALL QIRECT 1298

C1209

25 WTcITE(50. 1003) 13001003 PG T(/WhCM MAP 00 YOU WISH TO SELECT4) 1301

J8 1302IF(J3.E.0.0) t.O TO 310 1383

C130.

30 110 NR 1305TACk0. 1336,JF.f) 1307JFT0 1318

C1309

35 EWfl10 il 1310MEWINO J3 1311

C1312

C1313

C131+

.0 U THE FOLLOWINO OUOE PROVIDES FOP. THE SLECTICN OF TiOSE 1315

C CHAPACTEkISIlUS ICM dILL 3E FIXEU AND THOSE WHICH WILL VARY. 1316C 1317

1318C 1319

hi REAOtJS,tU0'.) NT,NC,H,(TITLE(I),11,6) 1320

100k FOk?IAT(212,I*,bAlO) 1321DC 120 £:1,NI 1322REA) (J, 100,) (TITL(I,J) ,J1 ,6) 1323

1005 FORMArc3,bAt0) 132'.

50 120 WRITEtSO IJOb) I (TITL(I,J),Jt.6) 1325bOb FOMATU2,oA,óAt0) 1326C

1327WP.ITE(50.1007) 1328

1007 FIAT(/HOW MANY MAP CHARATER.STICS 00 IOU WISH TO FIX4Z) 132955 PEMO('.Ot') JFA 1330

IFUFi..3.0) CU TO 300 1331C

1332WRITE(50,tUOd) 1333

1006 F)rMAI(/tTU ELECT EACH CHA.ACTEP.ISTIO. TYPE THE MAP NUII8ER 1/ 133.60 tFQLL0WE3 Y PiE ORIGINAL lAP SYISL)L IN UUOIATIQH MARRS.X/ 1335

ZSE?ARATE THESE WITH A COMMA CR 8LANR.) 133600 1,0 J1,JFi 1337EAD(L.9,') JU,J0 1338JFIX(J):JC 1339

65 DO 130 £.1,300 13.0130 IFJO.E.1.YM8i.1)) (FIX(J)I 13+11s0 C0NINU 13'.2

C 1343150 00 170 £1,NC 13'..

70 00 160 rt.JF 13'+5

too IFtI.EQ.)FiXtP.)) GO TO 170 13.6JFTJFT't 13.7JFLT(JFT)I 13*8

170 CONTINUE 13'.9

75 C1350

C 1351

C 1352C THE FOLLOWIM)è 000E CHECKS THE ENTIRE MAP LEGEND AGAINST THE 1353C LIST OF FiEO CHARACIERISTICS. SELECTING OUT THOSE WHICH MATCH. 135b

125

80 . SZNc OrIutA STATISTICS 4 THIS PARTIAL AE AE !NTE, 13(. THE ACAGE iS 7ALIEU THE EEITE) LEGEPU IS ITTEN t356

A WO.KING LE. 1351c l35c t39c 13o0

00 21.0 t=1,NS I 131REuQ(J,10QI (00C(1,L),L1,) 13ó

1009 FOrcIAT(&, AtFL1.1,F,.1,I,t0) 13à3FLN..E.II u TO t85 t3k

9G C00 L0 z,ic l3oo

180 kEJ(J3,t01 I0(I),(OOCI,&,),.1,l.) 1311010 FOM1(2A,I,,.10) 1368C t369

95 185 00 1.0 Il,JF 1.370130 IF(<FX(i).1E.iOOC(JFX(I))) GO TO 21.0 1311

wITE('.,1O11J J,TC,CACR,PCACR t3fltoil FOl.T(Z.,,F11.j,F.1J 1.373

IF(JFT.E.0 GO 10 213 137.too c L375

30 200 t:1 .JFT 1.371,

200 wI1E.7,1i12 (QOG(JFLT(j),LJ,1,4.) 1.317

1012 FOR4AT(.j3 t378GO TO 220 1.379

105 c t33210 WjTE('.,10t31 13t1.313 FOkMAT(1) t3a2G 133220

hGTo 2.i 136

C 1.38700 230 Il,JFA 138AQ 230 (l,. 15

115 233 FDe(I,<)aUQC(JFtX(fl,K) 13024.e t1I1U 13)tC 1.3)2

ZF(.E.0 TO 20 t33iEA(J L01.) TOTG 1.39k

IZO 11's FOMuT(11.1)PTAcrR/rurAcloo.o 136(1:50 t37

C t33C t3

125 CC TNE FULLOWt, 3uE TIlE us&. ro SELECT OUTPUT TO TIE

TEMIMAL Q4 L4 PRITE OUTr'UT IEAON.S AE T1ENWpUTTE O4 rH ,ELEC1E oE.E. tkO3

ik4.IJO C

C t'4jIrEc5o. 1.01,) 1k07

1(11.5 F T(FEh IS T1E OUTPUT TO E ISP_AYEOe/htiE)TE LSPLA( (2)LINE PutrER OUrPUT FILEt)l REAO(.,) KJIF(KJ..2 c:,3 1jtEWIN0 .7

C t.t3wITE(K,l310 ia, (TiTLE(I) , It,I1 t'414

11.0 U31b Hj&LiHT FOR MAP t,I2/tX,ô10 jlj5

C tt600 253 I;L,r t.t7

2e dITE(KI,t17) (TITL(I,J),Jt,61(117 FrcMAT( o10 tIt'

1.5 wSiTE(KL,I0t) t.20101.8 FOt.A1(t/tA CUETATI)M HIC1 IS FIXEOfl t'st

00 260 E1,JX 1.22260 wITE(KL,t01 (FOOC(I,J1,J1,'s t2310i9 FOT(o(,.1fl t.2'.

15G Q t.254RITE(KZ,11120)

1020 FM4T(U//tA,tSYM3OL C,jX.xACEAGE,2X tTOTAL,2X9 t'.271'.28

C t.29155

Cjl3j

C THE FOLL0wLN COQE EAS T1E TOREO LEGENO OFF T1E OKIN FILES 1.32C CALCULTES OME AOOLTIONA. STTISTCS, O 4ITES ALL OF IHIS tl33

126

C 1NFOiM4TiO4 U THE PP(CPRIUE ouTPuT )EVICE.160 C +35

CC

00 280 tA=1.i t'.38

REA3('.7.tQlt)185 aGCG/Tt3O.0

pEAj(.7,t0t2 (00C(1,Lj,L:t,A+) 14+twirExz,I21J 1.+2

1021 FO MT(1/JA,.2,2X,Ji,tX,Fi1.t,2,F). L,4A,r5.I.X,+4IO)C 144'.

170 IF(JFT..E.i) O TO 230 1.,.5

UO 270 :2,JFT 14ÔE)('.7,tOI2) (D0C(I,L),Li,'4) t.47

270 WRIrE(Kt.1022i (00C(,Li,Lt,.)1022 FolMr(,A,'.,1 1+

175 283 C0li1tlWE

1023

180

CC'C

L5 2O wRIT(,O,1U2'.i1I. FO,14T(/tT1Z WEtE O CELLS IN TE O48IMATIN WITH THAT

ttFiAEC 3Cu.1aATIUM.)C i.e300 wITE(5U,1OZ5) t+a.

190 1025 FQkrIAT(ft)O Vol. WISh T lAi(E AO)&TZONAL RU F THIS OPERATIONe/ t+QS1t(3O (ii YES, FROM THE SA1E M.P (2)YS, FROM A OIFFENT iPX)RZ4(,j JZIF(JE-li 313,110,100

C15 1O RETURN i.73

11.71

WRITE(Z , 1O23 TOTAC,TAC,PTAC t4,2F T(t/t,XTOTA_ MCAGE QF T1E EMTIRE AE4 = t,F1I.i/ 1.5311A,TT4. CE.GE iF THE PMTIL AIEA :,F11.I/21,tPET OF THE EMTE EA M THE PAQ.TAL AREA z X,F5.1)&o To .3U

1.55

i4,

1Z7

OELAY(M.ePS 7,13) 1.12PcC.RAM cCE.. 1.73D1MEN3I i..p137) P37J,NMPt37),P2t37),?AGIN(25QI, 1+1.

1LPA.(2QI,.TYPE(250Q),C.0C.O,,iOU20).NARt.IN25O), 1.755 2JCO012bI

C3MION/./MF.E,MF,.F 1.77iU12NC.N1 1.78GOt1IUM/Y,ISL2.9bI 1.79COM1IOM/O1iTLE6) 1.30

10 C i'+ótc 1.82

C THI Su3Rour,E OETECTS Alu TEN 3UPS THOSE CEL.S Wl4ILH AEC. AJJACENT Tv UE ANUTHER NUT JIIGONAU uNi) ARE OF THE SAME 1..85

15 C. SYM9OL tHAE TrIE SAME G ,GTE,Q.ISTS). IF TWO CELLS SHAIE A 1+86C COIIMC:I 5YM3La Sul A AE NOT C ONNECT ED 5Y ADJACENT CEt. S OF THE 1.37C SAMI YM3O THESE TWO C.E.LS WILl. AE PLACED INTO DIFFERENTC. CiOUPS THIS PlL)CE55 WILL 3REA< THE ENTIRE MAP INTOC. PANCELS SOlE OF WHICH liAr SHARE CUIMUN ONAACTERISTICS SuT 1.10

213 . M,E SP.TIA..Y SEPARATEO.C 1.92CC. 1.0'.

1.9525 US0 1.06

C 11.97.kITE(50,i3Q1) 11198

11331 F.pelAI(/YOQ YOU WISH TO SEE THE D1RECTORY+*) 11109RE..C.0, 1002) j$ 1583

33 1002 FOlAT(1) 1501iF(IS.E0.IHY) CALL DIRECT 1582

C 1533WRITE(50, 1003)

1003 FU+MAT(/tWHICII MAP DO VOL WISH TO GROUP INTO PARCELS4Y) 150535 REu 118,) 1506

IFUA.E0.Q) CU TO 310 150?C 1508

159REWIND I 1510REWIND i 1511..EW4J +t, 1512REWINC + 1513

C 1)1111515

'IS C 1516rli:s C3OE tS USEC. WHENE9EP THE NUNSER OF AIAILAOLE NAPS IS 1517

C. INCREMSED EITliE9. A NEW MAP IM INPUT O ONE IS CREATED SY A 1518C MAP WEPTION). IHE NUHdER OF AVuIA8LE MAPS IS LNCREASEC, 1510C THE FILE IdRS FOR THE NEW IA? A DESIGNATED, AND A CHECK 1520

50 C IS MADE TO OEE IF EHE MEW MAP PUSHES THE NU1ISER OF AVAILASLE 1521C. MAPS EYUND THE FILE SPACE FD0 TWENTY MAPS. IF THE NEW MAP 1522C I NUMAER TWENTYONE, SUSROUTINE EADESS IS GAI.LEO TO HANDLE 1523C THE PRd3LEI. 152'.C. 1525

55 C 1526C 1527

MFILEMFILE+1 1528MF:MFILE 1529LFIF+Z0 1530

60 IFUIFILE.GT.20 CALL. EACESS 1531C 1532

1533C 153'.C THE FO1.LOWI;4G CODE CIIEDI<S EACH MAP DELL AGAINST THE CELLS ASOVE 1535

65 C AND TU THE ..EFT. IF IT lATCHES EITHER ONE IT IS PLACED IN THAT 1536t, PARCEL. IF IT MMTC.HES 00TH AND THEY ARE IN DIFFERENT PARCELS, 1537C THE GUNNECT ION £ NOTED AND THE GEL IS PLACED IN ONE OF THE 1533C PARCELS. TIE PARCEL NuIIOER OF EACH CELL IS WRITTEN OUT. 1539C 15.3

70 C 15+1C 154+2100 REA3LJ+. 10311) ICOL,IROW, HAP2U),IZ,37) 15.31001. FORMAT(3I./3X,i8I.)

IFUCO...EQ.9l,9) GO TO 22075 IF4ICOL.Ci.1) GO TO 135 i'.ó

15'.?NARGINURQW)Q 15.0

C 15.9135 MAPZi)'MARCINIROW) 1553

1Z8

NAP2W:1At(I4tIR0W) 1.551C 152

00 2O L2,JT 153IFIMMP2(I.EU.QI GO TO 1?IF(MMP2(I) .EU.-98I GO TO 18Q 155

85 GO TO i 15iC 157l8

£FiR0d.EQ.1) G TJ IIQ l55IF(?l..I(L) NE.M4iP1UI) O ro 15ó0

15ó11.10 IFb2(L).NE.MAP2(I-1)) GO TO 120 1Q2

C1.20 IF(K.E2.) GO TO IJG 15o5

IFIK.EQ.l) 2(I)IP4CL(NAP1(L)1 lb6IF(.EQ.) 1567

TO 135(O 10 2 15o

I; 15fl1tOO 13 N1I1 1571

ITYPEU.)lIAPZ(I)£PACE.('flN 1573NAP2Z)G TO 20) 1575

135 NPINAPICI)P2:4AP2 (jt) 15771.O IF(X..(cP1) .EQ.NPI) TO 1'. 17

1P1r ;E_ pij 15 gGO TO 1.J

t10 1'.5 IF(XP.UP2).EQ.N2) GQ TO 15Q 151NP2PE..(N'2)G) TO 1., 153

150 LF(NP112) 155,IoO,lôS i5+1,5 NAP2(i):4P1 185

t15 £PCEPZ)NP1Q TO OO 158,

ióO UAP2(X)P1GO TO 2Q 15o9

iô t4P2(I)1P2 1t1t2l IPAE.(eP1NP2 151

TO 2OJ 15170 NAP2(I)Q 193

co To 2180 N6P2(L)- 1595

US O T 2uJ 1561O NAP(I)- 157ZOQ 158C Mi(ILW)HMP2l31)

130 NLN()W)4AP(J7) 163130 21Q I1,37 1OMP1 U) '1.d'2(I) 1à33

210 4AP1(I)4MP2(I) 1SQ'+C 16a5

135 wI1EL,tOO.) ICOL,IROW,(NAP2W,2,37) IôOóTO 1 1b7

C 1Q220 ,ITE(..,1OI N 16o10U5 FOS,TLs,i.) j1

11.0 C 16111611b13

C THE FOLLu OOE CHECKS FOt ALL. CJNCTOS P1IOUSLY QETECTEO, 1ó1+C EAUS tHE PaCE. ANL <.LTES THE MP ONTO ORIG FIi_E 1615t5 ( MNY UiETED PARCELS THE A'1E SYML. THL SETS THE MAP 1616

UP TO 3E uutXE EGUE ACCOOIt To TE OROE OF 1617C THE LEGEND. 1618C 161.C 1o20

151) 16100 23 Ii,t 162IF(XPAL(I).NE.I GO TO 3O 1623NSM$I1 12JCOE(N) 165

t55 230 OT I4UE lb2óC 1627

REWINO i621.0 EAt.F,1OO.) £COL,OW,(MAP1(ii,X1,36) 1629

129

tooGO tO 310

c00 300 I1,36IF1(i).EU.OJ GU tO 270

163016311b321633

IF(M.t( ) .EQ.-98J oo to 280 163.IF(P1CZ1.E.-990) GO TO 1635

165 MP1:IP1(j) 136250 IFCIPALIIV'lJ.EQ.HPII GU TO 260 1637

MP1:Ip;E L( Met) 1638GO tO 253 1639

260 MAP1()L6YM8.(MPtJ170 Co tO 303 16.1

270 lAP1(Z)2H 16.2GO tO 303

280 MAPl(j12fl44GO tO 300

175 290 M4Pl(I)2Hsoo co1IMuE

1ó6

C.

WZ1E(*ó,1006J ICUL,LCOW,(MAPl(jJ,I1,3OI 16.91006 FOMAt(2L.,36A2J 1690

180 GO tO 28 l61l62

310 WITE6,l00J MS 1D53

I;185 C ls6

. THE FCJ(.LUI.LNG CODE SETS UP THE tZTEL, WRITES THE LEGENC FC EACH 1657C PARCEL. LN JOE O A wO.iNG FILE TOE3 THE INFGMA1IOM TO PROPERLY 1653L ECOGE tHE P, ANO CAL.5 SU0OutiNE ECOOE TO COMPLETE TIlE PARCELG EtIOM PCE. 1660

190 C 1661C 1o62C 1b63

EA3CIa 1007) MT,NC,MO1007 FOlAt(2O2 i'+) 1665

195 itE(50,10) 1661008 FOlt(/EfE, THE AME OF THIS MEH AP (50 CHARACTERS)J 1667

EA3(.9,10U9) CTT.EC),K1,5)1009 FQIAT(5eL0) 1669

WjtE(',7,1010J NT,MC,5,CTTLE(,Kt5J 1670200 1010 M (212, ,0PACELS - ,5.10) 1671

C 167200 320 :1,T 1673EA3(I 1311) (tITLE(KJ,I=1,ã) 167g.

320 WI(ItE('J,l0l1, (T1T_E(K),I:1,6) 1675205 1011 FO.lATt3,bA10)

C 177M:0 167300 360 L1,(U 1679E3(I,1012J ,3OC(1I,(OOC(1),L1,+) 1630

210 1012 FOT(j,2+A,j.,lO) 1ó31IFN..3.lJ ,U TO 3'.0 1o3200 330 J2,lC 1633

330 cE.3(iic,1U13) jUO(JI,(OOG(J,L),L1,) 163.1013 FOlATt23A,I,,,A10)

215 C 1636.5.0 00 360 <1,S 1637

JCO:jcQ3E(.) 1683IF(IC.ME.ZTYPE(JCO)) GO TO 360 1633

C 1690220 00 350 ,J1,;h. 1631

350 WRITE(.il,10121 JCO,I3OC(J),(G0CLJ,L),L1,.) 1692193ICOOE(M)IYMo..(JGO)

360 CONTINUE ló05225 C 1606

FcEWINO ó 1607CAL. EO3OE 1698

C 1609370 .ETUN 1700

230 ENO 1701

130

OLAY(1,ti1,0) t72P&M UPATE t7OUIMENI 1.70.COMM//t1FILE, MF,F i.705

5 CUUN//YMdL42.I t7bC t77C. 1.703CC THIS 3U8OJ1iE aLLOWS THE u.ER To PUATE THE TITLES AM/Q LEGES 1.71.0

lii C L.F M. THE UPOTE MAY INLUCE AN MEAE O DECREASE IM THE 1.71.1.C NUMEK OF E OF EACH MS 4CLI. AS A ME IN THEIR COHTENTS. 1.71.2

C 17LC; t7tC t7L,1.00 WTE(0,t001) i.Ttó1.001 F1AT(/U YOU 415H TO SZE IHE OIECTORe) 1.71.7

READ ('9, 1002) .S 1.71.8

1002 FOpMATRfl t7LIFtIS.E.tHfl CALL DIRECT 1.720

20 C 1721.WRITE(S0,1003) 1.722

1003 F M.1(/wH;cH 1AP 00 YOU WISH TO SELECT") 1.723kEO(.9,) . t72.lF(I.E..J) t;Q TO 350 t72

25 1726110 EiINC o 1.727

cEWL0 I 1.728C 172U t7U

30 C 1731C THE FQ.LQWIM( U0E ALLO.tS THE USEk TO SELECT AN UPOATE OPTIUN AMO 1732U ASp(S TO HME THt 4PPOPI4TE £NFTION LNPUT FRUM THE TEMI44L. 1.733C AN OUUME1ATIUN 4HH IS HOl TO jE OHA4E0 1.3 SIMPLY MOE3 INTACT j734

C 10 THE W3AItiC FILE. 173535 C 1.736

C4 1.737

C 1.738EA3UR 100.I NT,HC,N5,(TITLE(II,I=t.6) 1.739

100' FOpATth2.I4,oM10) 1740'.0 4F.I1E(,0,t00) iT,r 1741

taos FO.M.T(/TMEE AE CuREiT. TITLES AND ,I2, 174.2Lir,ES F Oa.W1ENTATION./WHCH 00 YOU 415i1 10 UPOATEe/ 1743

2t(0)NEIfriE 41.)TITLES (2)ENTRE .E,ENO L3)PAT OF LECEND/ j7444.SFTER rENT £Fth'(1ATICN IS LLTE0, ENTER UPDATED , 17i

'.5 INFOMLJ$.I 1.746QEMO('.4,) IT 1.74.7

IF(IT.E.0I GO TO 350 1.7.8

C 17.9NP1 t70

50 NQ=4T 175LNR4C 172

O 17,3Co 1.20 L1,20 t74.

1.20 IPOS (I) = 1 1 755'5 17,6

IF(IT.NE.t) GU TO 130 177WI1E(3,100I t78

1006 FOMATt/tH ioNY UPOATEG TITLES 00 YOU WATe) i7EAD(..) .4U 17ó0

17ó1C 171,2

130 IF(jT.NE.2) GO TO 10 173ITE(3,100?) 17o4.

100? FC1UT(/UU YQU W3aI SiMPLY TO REDUCE THE 4UMdER OF LINES LI t7565 1OF LE'U jTiIUT EiTE.IG 4E NFATIOM4) 1766

REAt. 1.002) IU 17ô7t76ô

1008 FQu1(/pt,4 1AY UPaATEU .NE$ OF LEGENO 00 YOU wANTed) 17iEA04,) Nk 1.770

TO IFtIU.E.1) GU TO 1',J 1.771

C 1.772MITE(50, 10391 1/73

1OO FO Tt/cE TH INEt3I TO 3E SELECTED LI t77tFt1 TIE 4riE iOSITjOh £N THE LEEM)+I 1.77579 EA3t.9.1O02) W 1.776

C 17771.0 WRI?E(+,iJ0.I Q,NR,NS, ¶TTL.E (1 ,Ial, jI 1778

lt]10) 1.7791010 FOblAT(/TIT.ESZ 178U

131

60 00 150 J1,TkE40(il011 (TITLE(I ,I:1, 6IFUT.N:.1 W.iTE(.6,10l1 (TLE,I1,6

17811782173

150 1+jTE(5J,10tlj TIT.E(fl,I1,b 178.11111 FOuT6,0AlI) 175

65IF(iT.E3.1 G TO 29) 1787IF(IT.E3.21 GI) TO toO 1783WTE(50. 1012J

1012 FOrJi.T(12HuW 1ANY LEGENOS 00 YOU WISH TO UPOATE4) 173090 EO(1+8. 1791

IF(L.E.0 U TO 3..0 1732C 1733

WjTE(0,t813 178k

951013 FOiMT(/tLjT THE SYi00L 45SQGITE0 .11TH THE .EGENOS TO

IdE UPUAIEU.t/YPLuGE EGH YHoO1. QUQT2TI3N lAi.KS/17951796

2t4t43 5E...uTE THE GYMOGL5 WITH A COjIMA O 0Li.Yl 1797Eu)(9.l LSYI(,I=1,NLI 1798

CU TO 183 1799C 1800

100 160 IF(IU.E.1HN) GO TO 180 1801jF(I.1.E.1Hfl GO TO 173 1802WIrE(,0,101.l NP 1303

tOIL. F MAT(fVELEGT THE ,I21t LINE(GI OF LEGENO TO AE RETAINEO./ 180'.ItTYPE 14 TMEI POGTLU N THE CUkTNT LEGENO.t/ 1805

105 ZtEP9kATE TH NUMOE'4.3 WLTH A GQMiA.l 1806EA0(1+9,1 (iPGi(,It,N1 1807GO TO 130 1803

C 1809170 WITE(5J,13151 NA. 181.0

110 1015 FO..aiAT(ItWHEN SELECTZNG THE .IHE(S) OF LEGENO,/ 18111TYDE IN TiE POSITION AFTER E..GH GUA.A.ENT LZGENO IS LISTEO./ 181.22SEPAR.rE THE Nui1i3E.S .ITH A COMIA.tI 1313

1. 181.'.160 00 260 (.t,NS 1815

115 lcEA)(I,tU11 J,ITC,CACA.,PGAC.,I3(t1,(OOC(t,Ll,LI,L.) 18101016 F.'tT(i.,i8tF1i.1 F5.t,Il.,At0) 1817

IF(NC.E3.1I ,O 10 203 131300 1.90 I2,4t 1819

180 rE(I,1U17l I0(I1,(OOC(I,L1,L1,'.l 1820120 1017 FOA.MAT(28X,14,4A101 1821

182200 IF(IT.E3.21 GO TO 223 1823

00 210 £1,NL 182.210 F(SYli_(J1.EO.ISYM(I11 GO TO 220 1825

125 GO TO 2] 18261827

220 IF(IU.E0.IHY.AHO.I.1.EQ.IHYI G.1 TO 263 182800 230 I14l; 1828

230 WA.ITE(51],t'J181 (G0C(I,L),Li.,L.1 1830130 1016 FO.1AT(L.4101 1831.

1332IF(1U.E.1HN1 Cu TO 21+0 1333REA)('.3,1 (iPQS(I),1,H44i t83.GO TO 20 1835

135 18362.0 00 250 I1,M.6 1837

I0(I1K 1838250 ..EuO('.9,t0181 (JOC(I,L1,t.1+1 1639C 13'.0

140 260 MIPO3(11 18.1W(1TEI+ó,1J161 J,ITC,CACR,i'CACR,IC(M1,(3OC(H,LJ,Lt,L.l 1842jF(N.E3.11 C.G TO 28k] 13.300 270 184'.M=IPOS(.L1 181.5

t'.5 270 WA.LTE(.j,10171 £0(M1,(OOC(M,L),LI,1.1 18.0280 CONTiNU 1847

GO TO 31.0 181+8C200 00 300 J:1,t4( 1850

150 EAJ(.r9,1019) lTITLE(I),I1,61 18t1019 FO.l.T(3A1J1 1852300 WLTE('.,1Ut11 (TITLE(I1,i=1,6) 1853C 185'.310 00 320 J1,NP 18o5

155 kE0(I.,10Z0) (TRAIII,1s1.,31 1856320 WITE('., 13201 (TANS1I1 .1=1,8) 18571020 FOHMT(8410) 1858C 1853

13Z

ttPLAGNG THE PCE. TO OE E.IMINTEJ FIST,/tF0LL0WO t, 20.3160 2tSY THE 'AiOE. IT IS TO SE INC.UEL) IN.S/ 2O3EPArE THESE WITH A C0M1 o 2O.5

00 230 £'ta( 20ôREAJ('.9,) ..(I),JPAM(II 20-7CO 230 J:1,1296 20.S

1.65 IF(iPAp(..I5Y15_(JH IPu.I)J 2O.9230 IF(JP4.I.EG.SYIlSLni JPALi1.J 2050C 2031

2052C 2053

1.70 C THE FOLLOiiNG 000E EAOS THE lAP, .00TE CELLS IN THE PAICELS TO 20.C SE ELIIINATEO, GHANI..E5 THOSE CE.L SYHSOi.S, ANO ITES THE MAP OUT 205

ON A W3pi(j'4, FILE. THE OUCUrENt4TI0i MIUS rHAT ASOCIATEO WLTH 2056C THE ELIMINATEu PACEj I. uLO T 5FEeEO TO OciNO FILE. 207C TMLS iNF3MTION IS P3VICEO TO S H.OuIINE EGOOE SO THE MAP 4r40 2053

1.75 C OUGuMENIATLON CAN SE UPO4IEO. 2053C 2050C 2061C 2062V.0 E.J(J,l0t2J 2063

18)3 1F(ICO_.E).995) GO TO 300 200L.C 20o5

00 290 1 3o 206IF(i4AP(I) .&Q.U) GO TO 270 2067IF(MAP(.EQ.-98) GO TO 260 2063

185 00 250 J1,:lPu. 2069250 IF(M(L).EQ..PA(J)I GO TO 260 2070

1AP( Z)zI51M9(MAP(I) I 2071GO TO 250 2072

260 MAP(i)isYM3L(JPAIJ1I 2073190 GO TO 20 207L.

270 ?4AP(I)2l 2075GO TO 200 2076

280 MAP(I)2l 2077290 CONIINUI 2076

195 C 2079ITE(.S,1Ot3) ICOL,ICW,MAP(I),It,36) 2010

GO TO 2.0 2061.C 2032300 NR9$ 2083

200 NS:NS?.I.. 208h.2015

WITE(.7,l01,I NT,NC,N, (TITLE (I) ,I1, 51 208600 311) J't,dT 2017iEAO(I, 1016) (TA.S(II ,I1,d) 2063205 310 8LTE(47,1U1I (TAHStI) ,I:1,8) 2089

C 209000 360 J1 Ni 200)..E0(I,t00) 1,14(1), (JOC(1,K) <j!) 2052

1020 F0,cIAT(,2'.,I.,.410I 2)53210 IF(MC.E0.1I CO TO 3.30 205'.

00 320 ..2,NC 2095320 E(s.t)21) IA(L),(OOG(L,),K1,'.I 2)961021 FOMAT(20A,I,..5101 2067C 2098

215 330 00 30 t,NuA 2099340 £F(I.EL1.ZPA.L)I GO TO 360 2100

00 350 ..'t,NC 21.01350 w&ITE(.7,10211 £A(J,(OOGL.,K),K1,4.) 2102

Mr1I1 210.3220 ICoOE(9):ISVMSL(I1 210.

360 CONtINUE 2105GO ro o..0 2106

C 2107C 2108

225 C 2109C THE FOLLOWI'G 00E AL.OW5 THE USE TO IP.]ICATE THE PMRCEL TO 9E 2110C SPLIT, THE NUNSEI% OF NEW PACEL )A4TE0, 5180 THE OI,ECTOt. OF TIE 2111.C iPLIT. TIlE MA? £5 THEN .EAO IN TO FINO THE LOCATION OF EACH CELL 2112O IN THE PIIEL. 2113

230 C 211'.0' " 2115C 2116370 W.ITE(5),1.022) 21171022 FC)14T(/tWMLCr$ PAiCEP. 00 YOU WANT TO OIVIGE, INTO HC t, 2118

235 ItMANY PIECES, 4180 IN WHAT OIECTICN.t// 21192tLIST THE PAHCE SVM3OL IN UUTATIJN MARKS, THE NUM0ER OF PIECES,t 21203/*ANO TIE OLREGTION OF 014i51018 (1) NorHsoUrH (2) EAST-WEST.t/ 2121

133

5EPAkAT1 EACH OF THESE WITH A COMMA O.R 3LAMR.) 2122RE..O('.9,') ISYM,NPC,IOIR 2123

V.0 00 3C0 J1,126 212.380 1F(1SM.EQ.I.VML(J)) GO TO 390 2125

0 pa=j 2126C 2127'.00 E7.O(J...,1012) IGOL,IROW,(M.PI),Ii,36) 2128

V.5 1F(I0O,..EQ.99) GO TO 3Q 212C 2133

00 '.20 t1,3ó 2131IF('1AP(t).NE.'(PA) GO TO '.20 2132IF(IOLR.EO.2) GO TO '.13 2133

250 J=UCU...-136.I 213'.LOC(J).3G(J) +1 2135CC TO '.23 2136

'.10 LOC(1R03)L30(IRCW)+L 2137'.20 C0NT1'4UE 2138

255 GO 10 2133C 21+0CC 212C THE FOLLCWLt3G COQE TRASFER THE 0000IEHTArION OF THE MAP AUOIMG 21'.3

260 C TO IT THE JO1TiON*L LIE5 CF LEEN FOR THOSE PARCELS CREATEO 3! 21..THE SPLIr. I.FURHTLOU IS AL$O TOE0 FOR USE IN UbR0UTIHE RECOOE. 21.5

21.6C 21'.7c

V.5 '.30 N='1$ 21'.92150

WITE('.7, 1015) NT ,NC ,US. (TITLE (1) .1=1,5) 215100 +'.0 .J:1,NT 2152RE..O(Ik, )01o) (TRA.S4I) .l1,d) 2153

270 '.J .ITE(.7,10io) (TRAHS(i),11,8) 215'.C 2155

00 510 1 N 2156IAII),(OOC(1,K),.(1,'.) 2157

IF(NC.E3.1) O O '.60 2158275 00 '.502,NC 21S

'.,O ,EMO(I,i02) A(L),(0OC1L,R),K1,'.) 210C

21,1'.oO 00 '.10 1,NC 21b2'.70 WcjTE(7,jQ2j) IA4L),(0OC(L,l,K1,'.) 2163

280 MM+ 216'.1CO0E(P1)1.YM3,.(I) 2165

C 2166IF(t.NE.<PAR) GO TO 513 2167

'.80 00 500 ._2,NPC 21o8285 00 '.90 ,NC 2169

'.90 1TE(.7,1021) £A(L),(OOC1L,I,K1,'.) 2170N0N.+L.. -1 2171(iM+1 2172

500 ICE1M).1Yl8L(NQ) 2173290 50 CONTINUE 217'.

C2175

C 2176C 2177C THE F0LLOIhG CO,,E USES THE CELL LOCATON PREIOUSL! COLLECTEO TO 2t78

295 C CALCULATE W-IErE T.iE PARCE.. SPLiTS '.I, SE MOE. I THEN REOOS THE 2179C MM? ANO E 5i4 THE CE.LS OF THE £NOIGMTEO PARCEL U5ING THE 2180C CALCULT1ON OF THE 5PL1T. 2181C

2182C

2183300 c 218'.

1=1 218500 520 3:2,250 2136L0C(J)..0 (j)+LOC41) 2187IF(Luc(J).Gr.a...Nu.ocJ).Eo.LoC(11) GO TO 530 2138

305 520 1:j 219C 2193530 h0I.O0(J)/NC 219k

REWINO J 2132C 2133

3L0 5'.0 REAOIJR, 1012) ICOL,IROW, (MAP(I) .1=1,36) 219'.IF(ICOL.E.3.9999) GO TO 20 2135

C 213600 610 Ii,36 2197IF(MAP(I).EO.0) GO TO 560 2198

315 ;F(MAP(I).Eu.-999) GO TO 570IF4MAPIL).EQ.'(PAR) GO TO 583 2200

134

5,0 MAP(I)IYML(MAP(LJJ 2201CO TO 611 2202

560 MAP(1)24 2203320 GO TO DL0

570 HAP(I)24" 2205GO TO otU 2206

530 LF(IOik.EO.2) G(i TO 590 2207JUCO.-ti'3b4I 2203

325 L011 O(J)/N)Il 2200GO TO 600 2210

590 LOLOII'JW)/NCI 2211600 IF(.Ji.E'..0) u TO 550 2212

2213330 1FI00.GT.) NaNS 221'.

M4Pt)lJYi8L(NQ 2215010 CONIZNOI 2216C 2217

WRITE('+6,1013) IGOL,IROM,tMAP(1),I1,36) 2218335 GO fo 5.9 2210

C2220

b20 W.ITE(*ô,101'.i iS 222100 ó30 I1.,250 2222

30 LOC(I):0 22233i.l) C

222'.2225

C2226

C THE SU0i3ufINE RECODE IS ALLEO FCR LL THREE .PERATIONS. IT 2227( CHANGES AC..E LAUSEO 8! 101 iNc. FOM PAEL TO PARCEL, 222

3'.5 C ELIM1NATE 00 .fATON FOR THOSE PARCELS AGOREC.ATED WiTH OTHERS, 2220C ANO EAPANOS uHENTATON FOP. THSE PMRCELS CREATED O'f A SPLIT. 2230C

2231.

C 2232C 2233

350 '.0 .ElNO .5 223'.

CALL REOOE 2235C

2236jRs1F 2237

2238355 C 2239

WRITECSO,1.02., 22'+0

1.024 FORMAT(YOO 'fOLj WISH TO PERFORM ANY MORE OPERATIONS X, 22'.1

lION THE ACELS OF THIS 22'.2

REAO('.0 1002) IS 22.3360 IF(IS,E.1e1'f) GO TO 103 224'.

C 22.5oSU RETURN

END 22'.?

135

OELAY(l,11,0) 1385

PNOGRArI lut4LP 1885OiIENSIOI8 1837

1888

5 COMM /9#MF_E,HF,LF 1839C3/3/IE(t23S,45,NC,NT 1390

COON/O/TIT-E(5) 1892OATA L3;/uao/ 1893

10 C139'.

C1395

C1896

C THIS su3oJrIiiE A...OWS THE USER TO MANIPULATE MAP CELLS AuT DOES 137C NOT ALLOW CONTENT GHACES TO TiE LECEN4O. THERE A,cE rHREE TYPES 18

15 C OF oPRArI3 PRUAIOEDI ZNOIIOUAL CEL.S CAN E lOVED FROM ONE 1393C PARCEL TO A9UTHER, CELLS OF ONE PAW,CEL CN dE ACOREOATEO WITH 1930

C THOSE OF A'4JTriE, ANU T1E CELLS IN ONE PARCEL CAN SE SPLIT INTO 1901

C TWO Ui MORE PARCE. 19(12

C1933

20 CC

1905WRIIE(50,1A01. 1906

1001 FO-.IAT(/tOU YOU WISrI TO SEE THE OIRECTORY4A( 1907

RE43(.9,1002( IS 1908

25 1002 FOMATU1( 1.909

IF(tS.E9.INY( CMLI. DIRECT 1910

C1911

WRITE(S0, 1.0031 191.2

1003 FOR1At(/WHIH MAP 30 YOU WISH TO SELECT4Y1 191.3

30 EA('.9 '1 IR 1.31.'.

jF(jR.E1.Ui GO TO b50 191.3

C131.5

C1917

C1313

35 C THIS CCOE IS uSED WHENEE THE NUMSER OF AVAILASLE MAPS IS 1919

C INCREA3EO (EITHE A NEW MAP IS INUT O OnE 13 CREATEO SY A 1920

C MAP OPENATJNl. TiE nIUMSER OF AILA3LE MAPS E.G INCREASED, 1321C THE FtLE MUM8E.S FOR THE NEW MAP aRC DESII.UATED, aND A CHECK 1922

C IS MACE TE SEE IF THE NEW MAP PUE3 THE n4UMSER CF AVAILASLE 1923'.0 C MAPS 3EYO) T4E FILE SPACE FOR TWENTY MAPS. IF THE NEW MAP 192'.

C 13 NUM3E TWEr.TYOME, SUSROLTI9E EXCESS IS CALZO TO HANDLE 1925

o THE PROSLEI. 1325

C1.927

C1923

'.5 C1929

MFLLEMFLLEG1 1930

MFMFI_ 1931.

LFMFG?J 1932IF(MFILE.CT.201 CALL EXCESS 13

50 C193a1935

C1935

WRITE(,0, 10051 1937

1005 FORMAT(/EiTE* THE NAME OF THE NEW MAP ('.5 CHAHACTERSI1 1938

55 (TITLE(,K1,Sl 10391006 FUrciAT(5101 19.3

C

100 REWINO60 REWZND J( 19..

REWIND 13.5EWINO .7 19'+ó

c65 REA3(IR,10071 r.T,NC,MS 13'.9

1007 FQIAT(212,IWI 1950c

1951WRITE(50,1004)

1.332

1000 FORIAT(#WHiCr4 OPERATID14 DO IOU WISH TO USEet/ 195370 lt(ti MOVE UOI4LOUAL ELLS FROM ONE PARCEL TO ANQTHER/ 195'.

2(21 AUGECTc QME PARCELS WITS OTHEW.SA/ 1955

3(3l OIZOE A CINCLE PAROE INTO TWO OR MORE PMRCELS) 19,6

REA3('.9,'l 101957

IF(1.O-21 110,220,370 1.958

75 C195919o0

C1961

ç THE FOLLOWING COOE ALLOWS THE oSER TO LOCATE INOIVIOUAL CELLS IN A 1962C MAP THEN IUMATE THE SYMOOLS FUH. THOSE CELS. 1963

135

SO C 19&.C 19,5C 1966110 WSITE(,Q,1039) 1967lOGY FCriAT(/ti1.jW MMNY It4QLVIDUAL CE..5 DO OU WANT TO 3E 19o8

55 ItMOVEO F.OM PARCEL TO PAACE.et) 1969RE..D('.Y,) ELL 1.973

C 1971WRIIE(53,jQlU) 1972

1010 FURMAT(/tL5T THE COUlN NU$BEF ROW MUM9EP., LOCATION t, 197390 hIM THE ç0W $/tANO THE MEW PAkC. YM50L (IN UuOTATIQN MAR5) t,

2tFO EO1 cL. 6EING MOVEO./ 13753t$EPAcAfE EAh OF 1HEE WITH A COMMA OR .t) 19l00 130 £1,ItE4j. 1977cEAD U.s. ) J..J.( II ,JAOI4( II ,JSPOT( I) ,JS(M (I) 1975

95 00 120 J1,1296 1979120 LF(JSVM(i) .EG.SYl8.(Jfl oo TO 130 1980130 JCELL(I:J 1931C 1962

100 CC THE FOLLOWING CODE REAOS THE MAP, ..00A1E3 THE INOICATED CELLS, 1935C CHANGES THDSE OEL. 5ya9J...5, ANO WRITES THE MAP OUT ON A WC(ING 196O FL.E. THE JouN64r4TijN IS A.GO 1M.3FER9.EO TO A WoW(ING FILE 1987C AND INF0RMArIOM IS PRUVIOEJ To ROUTINE RECQOE jO CHANE5 IN 1938

105 0 THE AC.EA)E FOR EAGH SYM8JL CAN SE JPOATEO. 1989C 1*90C. 1991C 19321.'.Q READ(JR 1012) LGOC,I.OW,(MAP(I),t.1,J6) 1993

113 1012 FOMMT(2Jj,/3A,L5I'+) 1994.IF(IOO.U.998) GO TO 190 1995

C 199600 150 1937

150 iF(JC(J).E4.O.ANO.JO(J1EQ.iROW) MAP(JSPOT(JI).JCELL(JI 1398115 C 1399

DO 180 t1 30 2000GO TO 160 2001

F(lAPIZ) .U.-999) 00 TO 170 e 2092MAI(L)I3YI1dL(ItA?(L) ) 2003

120 oo To too 230.160 MAP(1I.2l 2005

GO TO 139 2006170 MAP(j)r?'l' 2007180 CONTINUE 2003

125 C 2019WRITE(I.6,10131 ICOL,IRCW,(MAP(hi,I1.J6) 2010

1.313 FORMAT(2jd+,36A21 2311GO 10 1.0 2012

0 2313130 1'0 w.I1E(.3.101..) MS 201+

1014. FCRMAT(e199,I4.I 2315C 2316

W,iTE(4.711315) NT,NC,S (TITLEUI,.1,5) 23171315 FORlA1(22,j*,tMANIPULAIJN - *,5A19) 2015

1.35 NNNT+NON 201900 200 J1,NN 2920REAO (1k, 10 161 ITRAMS(I1 ,I1, 3) 2021

200 WRTEU.T,101ó1 (rRAN5(ZI,I1,81 20221016 FORIAT(8A13) 2023

14.0 C 202.00 210 I'1,M. 2025

210 ICUOE(I)ISYM(I) 2926GO TO 63 2327

C 23282029

C 2030C THE FOLLOWING CUOE ACLOWS THE USER TO INDICATE PARCELS TO GE 2031.C AGGREGATEO WITH UTHER PARCELS. 2032C 2033

150 C 203g.

C 2035220 WRITE(53,1017) 20361017 FOWMAT(/*HQW MANY PARCELS 00 YGJ WANT TO AOGREGATE WITH t, 2037

It0THERSb9I 2933155 READ(4.9.') NPAA 2030

C 204.0WRITE(53, 101) 234.1

1018 FORIAT(/tLISt EACH PAIR CF PAROE_ Sfl18OLS IN U0TATION MARKS./ ZQkZ

136

C" i8O160 C

. THE FO.L3'ZMC CQE SIIPLY TANFERS EVERY LINE OF THE WORIMG FILEC 0AC 0Mb rIE 0IiiL 1P 00uHEHT4T.OM FILE. t3C 1dÔ5

165 CEWINO .ô i8ó7

cEWLMO L I.d68c 1g

NPN+N.+2 i370ill CO 330 J.j,NP 171(TRMS(I) It,) ie7330 K.IbE(I(,1ozu) (TpAN3(fl.I.,8) 1873CC' 1d75

115 C.

3.0 wRITE(o.102t) j3771.021. FOMAT(UJ you WISH TO IA(E AOCLTIOHL UNS OF THZS OPEI4TIOHe/ 1878

i(0)N3 (1)VES, FRuM TIE $AMt P 2VES, F3M 01FFEEMT 1879JE 1880

1.60 IF(JE-1 30,t13,i00 t881C 1862350 .ETURN 1883

1.664

APPENDIX C

137

138

.kGAM E .I.ELMPUT,OUTPUT TPEIO,TAPEII,TAPE12,TAPE13,1T4pE1=OurpuT, LAPE15:IPUt,f,PE1o1

O1ME4SL3 MMP1(J6),,P2(I,1AP3(3a),<1t38è,2(381,(338),1Lt(2. 2,')) 2,5,25Oi,L4+I,M(4) i5YL(1Z6,NGELL(1296è,

1

234.

5 2ISyM(1?3I,rAN5(6),TITLE(a;,O4(2Q,.) 5GAlA 4i1/21..i3/ 6

cTCQ.SG0.

to 10It

uco. 12NQ3 13MEO 1'.

15 C 15EA3(1S,1001 (jSYM0(L;,I1,129ó 16

1301 FO-.1A (tOA2; 17EWLNG 18

C 1920 REAG(tO, 1302; MT,N.C,NS 20

1032 3r(?2,L.; 21CO 100 Lt,1 22

100 .E1D (10, ioo.s; 0UM 231003 F0R11T(1; 2'.

25 CCO 120 i:l M. 26EAJ(t0,l0l+1 J,MCEL.(J) 27

100'. C1T(Z.,i8) 28IF (3 .(.t. :E; NJ 29

30 iF(C.E.1J (C TO 120 .30

00 11.0 (2,CC .31110 RE13(1021.00.3 IGUM 32120 CO1 .3.3

C 3.35 cEA3(t0, tOOSi TOTAC 35

1005 FC.1.T(1t.1; .36

CWskITE(1..,100

37.38

4013Gb F AT(IXM..E rHEE A1V SYI3O_S wIIC1 SHOULO NOT BE

1tEJtJCJ N OETAI...efl3940

1007 FCi.IAtU1 +2

IFA.E.1r, G3 TO 150 1.3

C '+1.

1.5 wTE(14,1J0bJ 4+5

1308 FO A1(/tr4O' IIANY SYM0OS SHOL3*..O BE EMOE3 FRO),1 C3Si)ET.OMet)

46.7

..AO(15, 5YM 48C .9

50 W..ITE(1.,1.QUI 501009 14r(,tLLT EACH SYNSOL QUOTATION iASt/ . St

15EPA.A1E tA1 Y16J ITN A GO4llA. 52REAO(1,,1 (5!M(,LZ1,MS14l 53

55C

00 1'.') It,.YM00 133 Jt,t2Ob 56

13' (Y(L; .EU.ISY3L(JLi GO TO 14+0 57140 £S1l(IJ 58C 59

60 153 E43(tt [0101 jCQL2 ICW2, (,AP2(I;,I22,37P 601010 F0iAT(i./6A,18I4

GO TO 101F:co_2.GT.0 o TO lao 63

C a'.65 IT(1,t,jiO'+2JU 05

661t(2,t,jOM2FU 67NC(2,t,I0W2)0 68

C70 160 MAP2(IIT(1,CCUL2 £O2) 70

K2(1FNG(1,C)L2,L4OM2) 71

C 72GO 180 I2,37 73

7'.75 IF(H2L).E.0I GO TO 180

IF(14P2(I EQ.-9081 GO TO 1807576

IF(MAPZ(A).EO.999) GO TO 180 77

C 76F(IOw2.EA.1J GO TO 170 79

139

$0 C.O TO 1743KtUJKt(i)+1 8182

C 8.3170 Ic(1P2() .F1. P2(1-i)) GO TO 1Q 8'.

85 K2:)x2(i).1K2tL-t)(2(L-iI+1180 CQNtZ4UC190 GO TO 2O

90 iF(iC1.Ei.i) G(, TO 200LT (1. ,.. _1. , Lp,ji 1. 1.Pt (1)NC(i,iC._1 ,tui)4.i(l.NC (2.iC._1 , kOi1 ,:K1(2)

95 C00 iIOLf1

97C(t,..3,i1)K1LJ7)C

IQO TE(1,1Q11I IOL.,IW1,((1(L),L2,3?)1011 FlAt.21..,36Z2I 101icLI2.E.) 0 TO 23 12C 13210 00 220 1 .31 1UL

105 MA1(11?2(II 10522Q Ki(I)K2(LI

COL1IJ...2 1371.08

(.0 t iJ 1091.10

230 EiiO 1) tl.1,.W1N3 1.1. 112.EW&) L3 11.3

11'.115 2.0 E.)(it,i01 11.5

Q T 250 Li6RE4(i.Q11 COL3.LGW3,(3(II,L:2,7) 117

C MAP3 (1):jT (1. i3.IOd3I iH1211 (3(1) 11, L3,iUW4 t2U

C 121C:tCO.3fi 12237(i,C.IOi43) 23P33oIIT(2. Zt.iR3.43 12'.

125 125c t2b

tF(C3_3.EQ.1.Au.d3.EQ.i) Q TO 36 127C t2825U 00 3, :2,37

130 iF1.2(jI.E,.UI o ro o 130F(l'2() 0 TO 3'.0 131iF(1.'2(jI .Et.-99) TO .J 132TCTC+t. 33

C t.3L135 IF(cZ(i) GO TO 3J 135

16iF(M.E.1rIN c.. TO 270 13700 2b 1,'i1M 138

á0 IF(12(I.E..i$Y(JI) GO TO 3'3 13911.0 270 ECECf1.

00 J1,. 1.1280 L(JI1O 1'.2

143IF(LOis3.EQ.2) GO TO 290 1'4+

1.5 IF((i(1I.E.) C,) T 2O t'.5(MP2(I) M4P1 (1)1.(iIi43SM(1)l1() 1'.?

c290 tFIK2IifI.EQ.0) G3 TO 30

1'.8

150 L(2II3$(MAP2U)l4P2U'tI) 150M(2)M4P2(I1I 11

c 152300 iF(tC3.E.9I TO JjQ 153

£F(i(U.3.Et.t) Q TO 3t 1,4155 U(3(iI .EQ.QI Go TO itO 15

L3)3S(lP2(IFM4P3U)) t57C 15$

140

3t3 IF(K2(I-L).i.QJ GJ TO 3231643 3i(I)MAP2(LL) 160

M(I i.2(-LJ 1o1C

1ó2.3za 4Ml(1i b.

1b5 00 3.30 .42,. 1o5IF(L(JI...M) O 10 3.30

M tJ 1671MI(J) 168

330 CONTX4UE170 C

170IF(LM.E. LQU CO TO 3.0 171MPMP( ii 172MA2(Ii1M 173

L7tF5 tCELL(1XE,.I_LMM)+L 17S

176.3.0 CvNTU 177C

178R.jTE(L3,1QtUJ £C,IO2,LMAP2(LJ,L2,37) 179

160 IF ( O TQ 30 101LOG 350 Z1,38 L2

1(a:1.'(L) L3350 K1(I):K2(I 18'.

t85 iLLI3..2 L85

.360 30 37 IL,3 137M.P(Z)1A?3(LJ L8

370 2(Z)K3(j)t0 iO.2IL3LKOW2:iUWJ L1GO TO 2. 1-

CL3

3a0 iITE(t3,1Ut2) LCOL3,IW3 1''.L95 1Q12 F1T(I l5

C O 3O It.N t71F(ME.(i).E2.0) .J TO 39 18NOU1

200 30 CcNTLUE 2i1EO(tU, t013) iT , ,oS, (TT .E (U ,t,6) 22

WcITE(1, LJ131 NT.L .NJ. (TLTLE(I.It.S 203101.3 FT(i.I,,oALUI

CO 430 1,tiT 2SE1O,LO11. (TN(I ,I1,e)

O0 WRITE(12. LOt'.) (T.NLL)1Ot FMr(3.1

ZtO DU 4O 210RE.3(LO. L'1 ,K1(t,tOOCt1,N.1.. 11

1015 s0 AT(i,,.A,. 212.20O.tt1

2t5 .tO REJ(1OtO1â)1016 FoM.r(_A,...ALu) 2tS

217'.20 F(CE._tJ).E4.0) G) TO .

219

Z20 100. 20iTClL. ttIII) J,N lJ., ,PC(,K1(1), (OC(1,M),M1,.) 21

10t7 22

F(MCC..0 t.( TO o

00 3O22'.

225 .,30 WKiTE(12.11o) K1(N,OCtNN,,N1,) 225COPTLNJ

C27

EA3(LO 1UL+ ITANj(I 11 )

iTE41,10t. (TRAlSlL),it,8 29

230 C30

UCSC-C 31

P1$C/ttO0.PCEC/10.PC3IJC/..tO. 3I,

25 PCSCfT1Q.PC5EC/it0O. 236PC6uCfiLUU. 237

141

CPCOrC/E.'tUO.

tulol

232.0

1.018 FCMr(,gtO. LH3E. F CELS 2.2ST3T. Js1E CF iNLE £,F7.1/ 2.321E F £.;E CEi..S E..1GLE FQ. :3ui 3Ek JF ES GMT £ ,F7.t/ 2.3

43E .F ASjEJ CELLS 2.6tE..S Cp E IGE = ,Fô.2/ 2.76EkcETGE OF E..IG3L FJ ss;oMEr ,F5.2/ 2.37CENN.CE CF CES iElOEC FCM C'1ENT ,F.2/250 CF CE.. s'1.jCl ESGEO ,F,.2/ 250

CF NCL CEL.3 ELI8LE FJ ,EjSjGNMENT S,F6.2/ 251.gPEcE,-r.CE CF SINCLE CEL.S xEMCEC F.OM E4SjGNMENT ,F.2/ 252

3ZFIENTCE OF SLNG wIc wE.E ESSIGNE3 z,F6.2/ 253czpERENrG F ECI3L CE.$ MtlICd wEiE REASGMEO ,F6.2) 25'.255 C 235

iTOP 256ENQ 25?

APPENDIX D

142

143

L

5

C,rM J(TPE1,TAPE21,TA?E'.5Q1MENSi4 1 £N( 01, Pl(7),1P2(37),TITLE 1,

1ICJ(12a,10J,J40JI1296,13J,iQUTL1Q) JOLJT(l3)1JEAC(i2,1Q).A(123ôPOATA JA)J/12o00/,MUVER/0/,I/1296G/

cEW1NQ 1

123

5a

.EW14O 1 911) Ew1NO .5 10

C 11WRITE(.iQOi t21001 F 1T( CEL..S)

15 1002EA3(1,1QQ) T,NC,NS,(TT..E(fl,j1,FAT(Z,1.,QM1Q) 1'.

WZTE(.,,lU33 1,T,C,NS,(TITLE(i),1,6.) to1003 F TL1i,j.Z,lc, 12,l,.,lAo1Qt 1700 100 jt,T (TITLE(Z),1,cj

20 1i30 WflE(.5. 100..1 (TTLE(fl ,L,O i 20100k FU1T(,t1Ui 21C - 22110 EA)(21.1Q,) (P2Ii,I2,37 231005 F0k..1AT(ZQI/*,1* 2'.

25 CiF(14O_.EQ.399i u TO 230

25

FjC)L.1.1j GItIOW)=U 2728

C 2930 OQ 210 1:2,37 30Mt4gP2Ii 31

32GO TO 210 33£TET1 3'.

35 £F(iQ.Q.1i O TO 200 35M21P1(a.1 36

37C10 GO TO 200 3IF(11.E..12i 0 TO 2O .0jF(11.E.0) TO 1.3 '.1zF(zi..r.10i 11lU .200 130 J:1,Il130 £F(1A0J(l1,Jj.Q.MZ 0 13 50

.5 LFUI..T.1U) tJ TO 1.0 .5C 'loCA.. SP1.(.1t,M .7

Gu TO 13OC .9

51) 140 iAi1Eiijs1 5JI14(M1i 5152

J11 53C130 JOJ(lL,JiJJ(M1,J)+1C 56160 £F(12.E.0i GO TO 160iF(I2.L.LOi j2L0 58

00 17 It,I2 5960 170 IF(1A3J(12,J).Q.,11j GO TO 1O 60

IF(12._1.1Oi C, TO L0 óLC

CAL. SP..L(42,Mt) 63GO TO 2Q'l

65iao iA(M2lj12.t

67

JI 697U C 70

L30 J4OJ(M2,JJA)Jl2,J)+1 71C 72200 IF(1TET.E.2) GO IQ 210 73l21AP2(i-1) 7'.

15 75ITEST: 7aGO TO L 77

21.0 CONTINuEC 19

144

80

220

MAGINU)W):MMP2(J?)00 220 Lt,37MAP1(I)'P2(fl808182

Go ro ito 33c 8'.

85 230 00 290 :1,SNZACI)

8586

jF(N.L. toi Gu TO 270 8783

C 8990 00 250 J t ,;'UJEjF(u1E.(J).NE.I) GO TO 250

909102

2,0 Kt,NEA 93LL'i

95 jOU1(L):A(J,K) 95JouTL.)Ji.C(J,i()

2.0 IFL .EQ.i).L)0 GO TO 250

tOO

23CZOO270

240

COtI4UPt300 S0 Jt,NIOUT(JLAUJ(i,J)J0Ut(J:JU.J(i,.J)

9899

100131102103

105C

2001006

NIM(I)i1E(,5 tOQo) £,N.(ZOUT(J),J0Ut(J,Ji,N)F0'iAT(3I'.)

to'.105106107

Csro

1381139

1113 ENO 110

145

t Uk0UTZE i..U11.'12)

1JEAL(12,.10,(t 2901111112113

1; 1 1'.5 LF(NOvE.EO.3l GO 10 120 115

15C

03 110 ZthEAI'.EAIiF(I(L).t.ilti G 1011000 100 Jt.NEA

116117118I 13120

100 jIEA.L,Jt.E(.Il2l GO TO t'.0F(1Ex._1.t0l O 10 130

121122

15

110C120

CW41iUEN0EPE..+t

12312.125126

4EA (NOd EUtIEAI (G1, 1(:clZ127128

JEX (NudE,tl1 129211 IA (MU 1 (Ill + 1 1 33

131I;1311 EANEXt

132133

NEX (I) '4EA 13'.25 135

JEAG (.4EX)1IA (?It)A (1111+1

E I U R.N

1361 37133

30C

i'.0 AC(I,J)JEAC(I.Jlt1.301.0

E I U i.N 1 '+1ENC t'.2

APPENDIX E

146

147

t poi 1EU,IST(INpUT,OUTpUT,TpEl,TPE2.TPE3,

j

C

T?Et0,2Tlt,TEt2,TApEt3,TMpEt..jUT,TAPEt5:0UTPUT)MElI

0ETE(L2,,8QA1 O.I.u5I IWTIII,JWTItOi',KWTI 1011/, IDELETE/l0.368'O/,lIASI,N/l2ó0/,IAE)J/l2963/

23

5678

10 I6JF710It

KS5 12ICiKO 13

15C EAJU.tf10t) T,NP4R

1415

1001 ForIz,2).,.00 100 j1,T 17100 EAt,tUQ2 LOUM 181002 FOM4T(t)

20 UC 110 I1,t4PA 20110 REA3(t,100J ITYPEIII 21100.3 EOcMAT(1A,F11.l,5A,I+ 22C

EAC2,) NTYPE,MLU23V.

25 00 120 j1,TYPE.EA3(2, (AL.UCtI,J),J1,1LU)

2526

00 120 Jt,NLU 27120 T0LE,1I,JJQL4LL0cI,J) 28C

2030 iEA(3,') t4LU

U0 160 j1,NLU3031

REAJ(3,') Ii..ELTtI.JI,J*1,l.U) 320 160 Jt!.0FICJLr(a.,J' 130,t4.0,15

333'.

35 IJO iCNF.T(I,fl1Go ro to3536

11,0 ICFLT(I,J)1 37

150loU

0 TO 1ICtFLr(L,J:1CCNTINu

3639

170 cEwIIiQ 4 '+21,3

IcNr:otCRIJ.IPA0C

i0J'.A(+,t0Q.I rT,MP4 '.8

5) uO t i1 (1 ,EA3(.,1Uh £àMC

çEJ(<S, 10Q5) t4.U,NPRECET5253

1I05 FORMAT(j4. 5'.55 TE(t,i0O5) 4LU,t4P4R

5556

C19J tQAkiP+1

57

60 ILUOIFLEA0TCIF.E:ZTYPE1IPMI) 63

65C

10J6kE)(.,10o) £,Ia,x(J),JN(J),J=t,IaIFT(3'.) ô5

6600 20 J:1,13KNI4tJ)

67â6

2IOC

IAJJ1KNI:JN(J)IFIPA.T.NEET) GO TO 21

707t

REA3IKS,) IG,IS 72IF(tAS.E.0) GO TO 21 7.

75 IFICHK.GT.Q) GO TO 35O TO J2

757a

C210 00 310 L1,i4LU

P1.3:0

777879

148

60 JFLEA0IF(ZfK.LE.1) GO TO 220iF(E.1rE(IPi,I;.LT.0) GO TO 310

220 .FtC1K.L,E.3i G T) 230IFULLO(M,Ii.L.T.) GO To 310

336.

d5 230 IFF=MLC,+TOE(1,I1AGE(PAR)86

jF(IC.E. G T) 2.O 87

IF(OIFF.._T.0) GO TO 31021.G G. TO 290G

IF(ICHK.T.Qi JPARl 91

00 20 JJPAr,MPAM1:3 fl

M2G35 00 27(3 <:j,NU

F(LJETE(J,Kj.LT.0) O 1) 270IF(UOJ(J).E.0) G TO 250IF(IcNF_ru,K)) 27Q,250,260

too250,

260270

M1=111GO r 2T112M2+1cuNri4u:IF(M1.Gr.o.op.M2.L.T.3) 13l31

10010112103

20 JFLJFLEA+CWTM1JWTM2105 C

JFLEX:J.EA.(dTM3 1)6IF(JFLE(iFLA) 31o,2go,3o 17

290 IF(iLu.E.U) i,Q TO 3UiF(1OLF.T.JLF) 1,Q To 310

1a109

Lb 300 LUjjFLJ:EA11.tu.11.2

1t5

310C

CONTINUE

IFtILU.E.0) GO TO 36fl

11.3114115

320 OC 3 JJP.4PA 116IFtIDJtJi.E.0) GO To 3'+O

33(30 33C 11.

113t20 3.0 coNriluE 12

121.

C122

350 ICTI4r+1(IPA) 12.

125

130

C360

370C

C

IS3G(iP..JiLU00 37) .J1,NPu.IJtJ)UhIrE(,1OO, iPgik,SSIGM(IPA..)iFIP..T.Pki GO 10 1JwjrtJ,,13J5 LU,MTVPE00 38J I1,N(YPE

1.251?612712312

13113213313k

135 301007

drE(R,1uu7) (4LOCU,J),Jl,MLWF1TU12.1)1313613713

;rE(1i,1oi I,ICT,J4T,T.&E 13

thu bOb FiT(,tNLh13 OF (E44ED TrI44TSI tNtJi F £t NED i4GtlET ui i

2NtJiBE UF u;4E AAES1Er UNiTSt,I1/t, I'./

1s3

1+23*TTML E4GE ASSGNEO t,F1i.1) 143

1.5C

KSIRI2 145lkó1k7

150C

1009

F(LCr4K.EQ.3) GO TO 39J

w.TE(1, 1009)FtMAT (3U YO WISH TO RELAX 501E OF

1CUMSTiNT*/AO INAT GE SGMEMT(1,1Q02) V

ICi1i(IC1<+1

INE ASS IG$MENT t,LLL 3E AOE'*)

1+61.9101511215315.

C390

IF(MY.E.1HY) G TO 170

STOPEQ

1â157t5a

APPENDIX F

149

150

-

L

5

PROGRAM )ETECT(INPUT65,OU1PU15,TAPE9INPUT,ITAPEIO:3JTPUT TAPElS13 TAPE2513 TAPE3=5t32TAPE'.5t3 TAP5=513,TAP6513 TAP7=5t3,TAP3=5L33TAPE1151.'3,1APEi2=513,1APE1313,1APE1513,1ApEj513,'.TAPEl6s5t3,1APE17=513,1APE18513,TApE19513,1ApE20=5135TAPE2I=5 t3,1'APE22513,1'APE23=51J,1APE2513,TAPE255l3

123

56

INTEGER NSN U8 7DIMENSI3N LN(2),TRANS(10),IAOJ(200),JAOJ(200l,LUC(20,20J,

11'ITLE(613

IS COMlON/N/I5YMBL(12g6),AS(1296),tJ5$(20,'.j,IT(1296J,IACR( 1296) ,PCACR(1296J ,00C(1296 ,'.e

10II

C 12WRIIE(l0,1001I 131001 FORMAT(/ICOMFLICT DETECTION ROLTINE/1X,CREATEO BY R. )IAGESTEDT/ 1'.

15 18,OEC. 1978e 15C 16

WRIJE(l0, 1002) 171002 FORIAI(/IIHE FOLLOWING FILE NUMBERS ARE SET./ 13

tITHE USER MUST BE CONSISTENT WITI THEIR OESIGNATION./ 1920 23A,151M331_ LIST CIJ$13X,IACTIVITY ASSIGNMENTS t2)$/ 20

33,1AC1LwITY NAME LIST (3)I/3X,ACIIVIIY SPILLOVER MATRIX (kIll 21'.3X,IADJACENT MANAGEMENT UNITS (5)X/ 2253X,IMANAGEMENT UNIT MAP DOCUMENTATION (6)1/ 2363X,IMINIIUM MANAGEMENT UNIT ACREGES (7)1) 2'.

25 c 25REWIND I 26REwIND 2 27REWIND 3 23REWIND 29

30 REWIND 0 30REWIND 5 31REWIND 7 32

C 33WRITE(1IJ,117031 3'.

35 003 FORMAT(1IOU YOU WISH TO INPUT ACTIVITY NAMES, MINIMUM ACREAGES,$/ 35IIOR SPILLOVERS ONTO A FILEeY) 36READ(9,10IJ'.) IA

100'. FORIAT(A1I 38IF(LA.E.iNY) CALL IN 39

'.0 C '.0REAOU,1005) (ISYMBL(I),It,1296) '.11005 FORIAT(IOAZI '.2C '.3

REAO('.,1006) NLU '.4'.5 1006 FORP4AT(I,) '.5

REAO('.,I ((LUC(I,J),J*t,NLU),I1,NLUIC '.7

READ (6 1007) NT,NO,NP '.31007 FOR?1AT1212 I'.) '.9

50 IF(MO.GE.2) GO TO 270 50DO 100 J21,NJ 51

100 REAO(6,100.I IOUM 52C 5300 110 Z'I,NP 5'.

55 110 REAO(6,100d) ITC(I) ACR(I),PCACR(I),ASN(I),(OOC(I,J),J.t,k) 551008 FORMAT ('.( ,18,F11. 1,F5. 1, Ik,kA.U) 56C 57

WRITE(1O,1009) 531009 FORMAT(/IWHICN SPILLOVER STRATEGY IS BEING USED. (1 OR 2)1) 59

60 REAO(9 ) IZ 60IF(LZ.E.2I GO TO 125 61

CREAO(2,1010) NLU,NPAR 63

1010 FORMAT(2L'.) 6'.

65 DO 120 I:t,NPAR 65120 REAQ(2,10i0) IPAR,ASN(IPAR) 66

125 READ(3,') NLU 6800 130 NLU 69

70 130 REAO(3 totli I (USES(I,L),L1,'.) 701)011 FORIAT(I'.,'.AtO) 71

IF(IZ.E.1) GO TO 1'.O 72C 73

WRITE(t0, 1012) 7'.75 1012 FORMAT(/IOO YOU WISH TO DETECT MINIMUM MANAGEMENT UNIT 1,

IISIZE IIOLATIONSeI7576

READ(5,100..) IA 77IF(XA.E.1HY) CALL MINIMUM 78

C 79

151

88 14.0 READ(5,11313) NTITLE,NOOC,NPAR,(TITLE(I),Is1,6) 801813 FQRMA1(1UtX,2,1h,I2,1x,I4.,jX,6..jOJ 51

NFt5IM..J 8200 150 LF13,NF 83

150 WRXIE(LF 1014.) (TITLEiI) X1 6) 84.

85 181k FORMAT(IOETECTION OF 0ERIMNTAL SPXLLOVERS*IX-*,3X,6A18) 8500 160 J:j,NTIT).E 86REAO(5,1I31,) (TITLE(I),I1,6) 8700 160 LFt3 NF 88

160 WQXTE(LF,j01) (TITL(I),Ij,J 8990 1015 FORNAT($(,6A10) 90

C 9100 170 E:1,3 92LF1241 93

110 WRITE(LF,1016) I95 1016 FOMAT( OE1INENTAI. SPILLO*ERS MITI SEYERITY t,11 95

00 180 I1,NLULF=t541

180 wRITE(LF,10t7 I,(USES(I,J),J=t,4.) 98

10$1617 FORMAT(-DETLNENTAL SPILLO*ES ASSOCIATED NITH ACTI*ITY :,

112/5X,4.A10)99

1(00C tot

00 230 K1,NPAR 102READ(5,IOt8) IPAR,NAOJ,(IADJ(J) JA0J(J),,J1,NA0J) 103

1818 FORMAT(3214.) 104.185 C 105

00 230 J:j,NAOJ 136IF(EPAR.T.IAO,j(J)) GO TO 238 107ISN(1):tSN(K) 108XSN(2ASN(IADJ(J)) 109

110 C 110ISVLUC(ISN(1),ISN(2)) 111IF(ISVR.GE.0) GO TO 230 112ISVRXASS(ISVR) 11300 220 4.1,3 114.

115 IF(M.E9.3) GO TO 198 115LFt54IS'l(N) 116GO TO 200 117

190 LF:t24I*R 118C 119

129 200 WRITE(LF,1019) XSVR,JAOJ(J) 1201019 FQRMAT(-SEVERITY = :,I1,5X,:OQUNOARY LENCTI's x *,15) 121C 122

NIPAR 12300 220 t.1,2 124.

125 IF(I.EQ.2) NIAOJ(J) 125IF(IZ.E.2) GO TO 210 126

C 127NN.ISN(E) 128WRITE(L,1020) ISYM3L(M) N,(USES1NN,L),L1,4.) 129

13$ 1020 FOIlAT(:0t 5X *MANAGEIIENT UNIT ,A2,* (*,I4.,*U,5X,A10) 130WRITE(LF,t021) ITC(M),ACR(1l),PCACR(1) 131

1821 FORP4AT(ttX,I3,t CELLSX,5X,F1l.1,t ACRESX,5X,F5.1,t PERCENT 132WRITE(LF,1322) (DOC(M,L),L1,4.I 133

1822 F0IMAT122X,4.A10) 134.135 GO TO 22) 135

C 136210 WRITE(LF,t020) ISYNOL(M) M,(OOC(M L) L=1,4.) 137

WRIIE(L,1021) 1TC(M),ACR(ll),PC.CR(M) 138220 CONTIMU 139

1'.O 230 CONIINUE 1.0C 141

WRIIE(t0,t023) 14.21023 FORMAT(ItW4IICH SPILLOVER LIST: YOU WANT ON OUTPUT FILE 84*1 14.3

1X(0)NOME*I*11)SEVEITY LISTS ONLV*I*(2)ACTIVITY LISTS ONLY/ 14.4.

1'.S 2*(3)SEVILTY ANO ACTIVITY LISSt) 14.5P,EAD(9,') IA 14.6IF(tA.E.0) GO TO 288 14.7

C 14.8L313 14.9

150 U815+NLJ 1501F(IA.E.1) uazls 151IF(IA.E.2) L616 152

C 15300 260 I'L8,08 15'.

155 REWIND 1 15521.0 REAO(I,1)2'.) (TRANS(J),J=1,10) 156102'. FORMATII)AiO) 157

IF(EOF(t)) 260,230 156

15Z

160250 WRZTELS IOV.) tTRAt4SIJ),J1,1O)

GO TO 2.

19160

165

260C

1025

CONUNUE

WRITE(10,t025)FORIAT(SPI.L.0VERS HAVE BEEN w1flEM ON FI.E 6./1RENEMB TO RGUTE THAT FILE.)

16216316165

170

C2701)26

C2$)

GO TO 230

WRITEIIU, 1026) NCORATtATHERE AE ,I2 LINES F L.EGENO IN THIS NAP.tl

1$THt$ ROUTINE CAN HANOL ON).Y I LI4E OF LEGEI4O.$)

STOPEMO

16168169170171172173

153

SUBROUTINE IN t7l.OIMENS!39 USE(kJ,LUCFLT(20,20 t75OATA LUGLT/1.000/ 176

C177

5 WRITE(tO,I000)11100 FORNAT(IYENTER THE NUN8ER OF ACTIVITIES 179

REAO(9,') NLU 180

c tatWRITE(t0.tOOt) 182

to tOOt FORMAT(YOO YOU WISH TO INPUT LANDUSE NAMES4t) 183REO(9.I002) IA 18'.

1002 FORMAT(At) t85IFLIA.E2.IHN GO TO 110 186

C187

15 WRITE(.3 1003) NLU 1881003 FORMAT(i.,3X,tt) 189C

190

WRITE(1O,100k) 1.91.

100'. FOpUIAT(ø'tENTER THE MANE OF EACH ACTIVITY AFTER THE NUMBER $ 192

211 IflS LISTEO./tLIMIT 15 1,0 CHACTERS.) 19300 100 £1,NLU 19'.WRITE(tO,t005) I 195

1005 FORWAT(!.) 195REAO(9,1008) (USE(J,J1,1. 197

25 1006 FORMAT('.AlO) 198100 WRLTE(.3, 1007) I, (USE(J),J21.l.) 199

1007 FORNAT(C,,kAtOl 200

REWIND 3 201

C 202

30 110 WRITE(tO,1008) 2031008 FO.NAT(YOO YOU WISH TO INPUT MINIMUM MANAGEMENT UNIT A, 20'.

IACREAGESe) 205REAO(9 1002) IA 206IF(IA.U.tHN) GO TO 130 207

35 C208

WRITE(7 1005) NLU 209WR1TE(ti. 1.009) 210

10119 FORMATI/AENTER THE IINIMUI ACREAGE OF MANAGEMENT UNITS AFTER Al 211IAEACN ATIVITY NUMBER IS LISTEO.A) 212

1,0 00 120 L:1,NLU 213WRITEIIO iooi x 21'.

REuGlO,') MIi4ACR 215ACR$INFLOAT(M1NACR) 216

120 WRITE(7 1010) ACRHIN 217

1.5 1010 FORMAT(Fti.t) 218

REWIND 7 219C

220

130 WRITE(10,t011) 221

1011 FORMAT(AO0 YOU WISH TO INPUT LANDUSE SPILLOVERSeA) 222

50 REb.O(9 1002) IA 223IF(LA.h.IHM) GO TO 170 22'.

WRITE('.,1005) NLU 225

C225

WRITE(10,1012) 227

55 1012 FORNAT(tENTER EACH SPILLOVER BY LISTING THE TWO ACTIVITY A, 2281ANUMBERA/AFOLLOWEO BY THE TYPE (.,) AND LEVEL OF THE SPILLOEEi.A 2292,'ASEPARATE EACH OF THE NUMBERS WITH A COMMA.A/TERMINATE THE A, 2303:ENTRY EOUENCE WITH A SERIES OF ZEROS.A) 231

11.0 REA3(O 3) J,l( 232

60 IFLL.EILO) &O TO 150 233C

23L.

LUCFLT(L,J11( 235LUCFLT(J I)K 236GO TO 1.0 237

65 C233

150 00 160 Let,MLU 239160 WRITE('.,1013) (LUCFLT(I.J),J1,NLU) 21.0

1013 FORMAT (2013) 21.1

REWIND b 21.270 C

21.31711 RETURN

END 21.5

154

SU8ROUTINE ?Ui4INU?tINTEGER SN 247DIHENSI3M TITLE(6) ACRHIN(20),IA3J(2001,JAOJ1200I 2L.

COMMON,#ISYM8L(1Z6,4S1296),JSE(20,),ITCt1296), 2.95 IACR(129S),PCAC(1296),O0C(i295,A.) 25

C 25tRE4O(5,1001) MTITt..E N00C,MPAR,TITLE(I),1'I,6I 252

1001 F0RMT(1/lXsI2,1X 2,lX,e tx,6A101 25WRITE(tl,1tJ02) (TITt.E(I),It,6) ZSL.

10 1002 F044T(tLOETECTI0N OF IAAGEMENT UNLT SUE VILATION5/1-*,3X,6A1Q) 25b

C 2S700 100 J1 NTITLE 253REAO(5,1003) (TITLECI) 1x1,ô) 259

15 100 WRITE(11.1.033 (TITt.E(I),11,6) 2i31003 F0ijiAT(8L,6A10) 26tc 262

WRITE(10,100'.) 2631tI0 FORIT&*OO YOU WISH TO SCREEN T4E MANAGEMENT UNITS WITl , 2ô4

2Q 1I1MIMU4 ACREAES') 265REA3(9,t035) IA Z6Ô

1009 FORMATUII 267IF(IA.E.1HN) GO TO t30 208

C 2625 REAOI7,e) NLU 270

00 110 1:1 NLU 271.

tb REAO(7,') ACiflINiII 272

C273

WRjTE(11,106) 27.30 t006 F0RlAt(-MINIMUM ACREAGE,3X,ACTIVITY) 275

00 1.20 t1,NLU 271211 WRIrE(11.1Q07) AgflN(I),(USES(I,J),J1,A.) 27711107 FORPIAT(3,F11.1,6,A.A10)

35 130 00 t60 t1,MPAR 2ORE40 (5, t008) IPAR, NAJ , I IAOJ U) ,JAOJ Li) ,J=1,NAOJI 2t

t08 FORMAT(32I.I 282

C23

IF(tA.E.1HNl GO TO tA.0 2a.J=ASN(I 25IF(CU).GT.ACRflIN(JH G TO 160 2a6

C 27t0 WITE(11,t0I39 I5YNdL(I),,(O0C(Is),t,16) 281009 FOw.NAr(] X nAMAGEflET UMIT A2 £,X,A1O

WRITE(1i,i10 ITC(I),ACRII),PCA&Rd) 20lOtO FOMAT(t1X,I8, CELLS 5X.,F11.1, ACJES 5X F5.i, PERCEI4TtI 291

t8X,tADJENT NANAGEMEMf UNITS ANl THEIR ASSIGNEO ACTIVITIES 22C 293

tF(NAOJ.EQ.0 GO TO 16 2.50 00 150 1,NADJ

J:IAQJLJ 26150 NRtTE(it,10i1 ISYMSL(J),J,(OOC(J K),Ki, 297

tt1 F0RAT(b0X,A2. (,q,),2X,.A1a) 23160 CONIINUE

55 C 300WRITECIO, 1012) 3Gb

tal2 F0RMAT(IM1NIIIUM MANAGEMENT UNIT SIZE VIOLATIONS ARE £, 3021*ON FILE 11. 3113

REWIND 5 30L.RETURi4 3G5

ENO