Analyzing the Factors of Deforestation using

Association Rule Mining

Mrs. K.R.Manjula Associate Professor,

Dept. of MCA, SIET, Puttur

manju_sakvarma@yahoo.co.in Prof. S. Jyothi

Head (i/c) CSE & IT BOS Chairperson

Sri Padmavati Women’s University Tirupati, India

jyothi.spmvv@gmail.com

Mr.S.Anand Kumar Varma Associate Professor,

Dept. of Civil Engineering, SIET, Puttur

manju_sakv@yahoo.co.in

• The socio-demographic factors and the land use patterns are – identified and – derived

from the remote Sensing images using GIS. • Given these elements, the main objective

of this paper is to analyze – the role of different driving factors for

deforestation and – the relationship among these factors in the

study area.

• For that

– an association analysis on deforestation factors

is done.

Abstract

• The widespread use of – spatial database and – spatial data mining technique

can be used to understand – inter–relational nature of spatial data.

• The spatial association extraction

algorithm discussed by –Koperski and Han [15]

is used in our study.

• The algorithm searches for associations

between – spatial objects or –non-spatial objects and attributes.

• In this study, rules are expressed by – spatial and

– non-spatial predicates.

• Typically, the analysis reveals – the positive association of each of the above

specified factors

for deforestation.

• The final outcome of this paper can be used as the suggestions for – policy issues and

– technological developments to the decision makers.

Introduction • Factors of the study are

–demographic,

– topography,

– road infra­structure and

–mining units.

• the study area is 26 mandals of

−Chittoor and

−Kadapa districts.

• The strong association rules can derive that

−population growth,

−urban development,

−road infrastructure,

−change in land use patterns,

−industrial development

• To control and decrease the forest degradation the government should know

–where, when, why and how such deforestation occurs and

–what measures can be taken to address the problem.

• The science and technologies of

GIS,

Remote Sensing and

Data mining technique

could be a perfect tool for answering the above questions.

Data Pre Processing • By classifying the forest and non-

forest areas

−taking 1991, 2001 and 2011 satellite images

−overlaying them and then

−the changes are identified.

• Information of these maps and other ancillary data are entered in the database [18][23].

Spatial data mining

• Mining knowledge from large amounts of spatial data, collected in various applications, ranging from

−Remote sensing to

−Geographical information systems (GIS),

−Computer cartography,

−Environmental assessment and

−Planning.

Spatial Association Rule Mining

• A spatial association rule is

–a rule which describes the implication of one or a set of features by another set of features in spatial databases.

• A spatial association rule is

–a rule of the form “AB”, where A and

B are set of spatial predicates.

Mining spatial association rules can be decomposed in three main steps:

• Extract spatial predicates:

−This is usually performed as a data preprocessing method.

• Find all frequent patterns/predicates:

– A set of predicates is a frequent pattern if its support is at least equal to a certain threshold, called minsup.

• Generate strong rules:

– A rule is strong if it frequent and the confidence is at least equal to a certain threshold, called minconf.

• Examples of spatial association rules.

– Non-spatial consequent with spatial antecedent(s).

is_a( x, house)^ close_to( x, beach) => is_expensive( x) (90%)

– Spatial consequent with non­spatial/spatial antecedent(s).

is_a( x, gas_station)=>close_to( x, highway) (75%).

METHODOLOGY • Data on limited and unlimited access

highways are acquired from

– the Environmental Systems Research Institute (ESRI) Streets Data-base.

• Land cover data for the 1991, 2001 are acquired from

– the U.S. Geological Survey’s (USGS) and

– 2011 from National Remote Sensing Centre (NRSC) and

– Topographic maps from Survey of India with 1,25,000 scale during the year 1970s.

• From these, vector polygon data are generated from historic aerial photography [11][12][13].

• The following variables are then calculated for each polygon

– Land Cover Change

– Urban Development

– Distance to Highway

– Population Density

The database relations for organizing and representing spatial objects are:

• Mandal (Name, Area, Type, District, Geo, …)

• Road (Name, Length, Type, Geo, ….)

• Mine (Name, Area, Type, Geo, …)

• Urban/Builtup (Name, Area, Type, Geo, …)

• Population (MandalName, PopulationSize, density…)

• Mining spatial association rules involve data mining query which extracts the above related data set from the database covering

–mandals,

– roads,

–builtup land,

–degraded areas and

– forest areas.

• Then the

– “adjacent_to” and

– “with_in” relationships

are computed along with support count.

Relational Table with Population information

Table showing classified data with Feature class

Spatial data with Spatial Predicates Name and Count

Spatial data with Spatial Predicates and Count

Map showing the change in builtup(Urban) land

Map showing the Buffering of Road

Map showing extent of Mining area

Algorithm for Mining Spatial Association Rules

• Algorithm:

– Mining the spatial association rules in a large spatial database.

• Input:

– The input consists of a spatial database,

– a mining query,

– a set of thresholds minimum support and

– minimum confidence

• Output:

– Strong spatial association rules for the relevant sets of objects and relations.

• Method:

– Mining spatial association rules proceeds as follows:

• Step 1:

– Taskrelevant DB := extract task relevant objects (Spatial and Non spatial)

• Step 2:

– Predicate_DB := spatial computation (Task relevant DB)

• Step 3:

– LargePredicate_DB :=filtering with minimum support (predicate DB)

• Step 4:

– Fine predicate DB: = refined spatial computation (Large predicate DB)

• Step 5:

– Find large predicates and mine rules (Fine predicate DB)

A few of the Interesting Rules

• Is_a(X, “Mine”) =>Adjacent_to(X, “Road”) : (73%)

• Is_a(X, “Builtup”)=>Adjacent_to(X, “Road”) : (80%)

• Is_a(X, “Mine”)=>Adjacent_to(X, “Degraded”) :(52%)

• Is_a(X, “Urban”) => Population(X, “High”) : (80%)

• Is_a(X, “Rural”) => Population(X, “Low”) : (72%)

• Is_a(X, “Mine”) ^ Within(X, “Forest”)=>Adjacent_to(X,

“Degraded”) : (80%)

• Is_a(X, Builtup”) ^ Adjacent_to(X, “Degraded”) =>

Adjacent_to(X, “Forest”) : (78%)

RESULT ANALYSIS AND DISCUSSION

• Demographic factors (population size, density and growth)

–Population growth and high density of population in large or urban areas which results in land scarcity and leads to deforestation from the rules if it is adjacent to forest.

• Is_a(X, “Urban”) => Population(X, “High”): 80%)

• Is_a(X, “Rural”) => Population(X, “Low”): (72%)

• Population Density and Urban growth

–High density of population and other developments converts and merges village to town and town to urban areas which results in deforestation from the rules if it is adjacent to forest.

• Is_a(X, “Urban”) =>Population(X, “High”): (80%)

• Is_a(X, “Rural”) => Population(X, “Low”): (72%)

• Is_a(X, “Urban”) ^ Adjacent_to(x, “Road”) ^

Adjacent_to(X, “Degraded”) => Population(X,

“High”): (80%)

• Road infrastructure and access to urban towns

– Logging and mining concessions that entail road building which to deforestation from the rules if it is within to forest.

• Is_a(X, “Mine”) ^ Within(X, “Forest”) ^ Adjacent_to(X, ”Road”) =>Aadjacent_to(X, “Degraded”) : (87%)