outline: overview of models data and levels of measurements raster and vector models conversion...

OUTLINE: Overview of models Data and levels of

measurements Raster and vector models Conversion between models Databases

DDATA ATA MMODELSODELS ININ GISGIS

DIGITAL INFORMATION

GIS requires that both data and maps be represented as numbers

GIS places data into the computer’s memory in a physical data structure (i.e. files and directories).

files can be written in binary or as ASCII text.

binary is faster to read and smaller, ASCII can be read by humans and edited but uses more space.

sent through a “pipe” consisting of 0s and 1s

stored on devices that can store only 0s and 1s

processed as 0s and 1s

locational and attribute data in a GIS

attribute type: discrete vs continuous

discrete: presumed to occur at distinct locations with empty locations having a value of zero for the attribute in question

continuous: feature occurs throughout geographical region; no locations are empty

DATADATA

Levels of Measurement:

four levels are commonly recognized – nominal, ordinal, interval and ratio

each subsequent level includes all characteristics of preceding levels

data available at higher levels can be reduced to lower levels; opposite is not true

DATADATA

LEVEL OF MEASUREMENTSLEVEL OF MEASUREMENTS

Nominal Scale

objects are classed into groups; groups possess arbitrary labels (numbers/names)

i.e. religion, land use/cover

discrete variable

Ordinal Scale

categorization plus an ordering/ranking of data

i.e. country road, street, highway

can identify larger/smaller but can not comment on degree between variables

K=5, L=3, M=1 equivalent to K=500, L=300, M=10

discrete variables


Interval Scale

measurements arranged in rank and distance between measurements is known

no “true” zero point

i.e. elevation/topographic lines, temperature in oC

discrete or continuous


Ratio Scale

like interval scaling: both rank and separation are known, but there is also a known, fixed starting point

i.e. temperature on Kelvin scale; speed

continuous and discrete


1. Reality – total phenomena as they actually exist

2. Conceptual Data Model – describes and defines included entities (how they will be represented)

3. Logical Data Model – logical organization of the database elements

4. Physical Data Model or File Structure – how information will be structured for access

DATA MODELS – REPRESENTING DATA

DATA MODELS – REPRESENTING DATA

DATA MODELS DATA MODELS

logical data model is how data are organized for use by the GIS.

GISs have traditionally used either raster or vector for maps.

raster – based on pixels

vector – based on points, lines and polygons

while most GIS systems can handle raster and vector, only one is used for the internal organization of spatial data.

rasters and vectors can be flat files … if they are simple

Vector-based line

4753456 6234124753436 6234244753462 6234784753432 6234824753405 6234294753401 6235084753462 6235554753398 623634

Flat File

Raster-based line

00000000000000000001100000100000101010000101000011001000010100000000100010001000000010001000010000010001000000100010000100000001011100100000000100001110000000000000000000000000

Flat File

DATA MODELS DATA MODELS

RASTER DATA MODELS RASTER DATA MODELS basic unit is cells or pixels which are uniformly

spaced

each cell/pixel has spatial and spectral information.

i.e. digital elevation data and digital images

spatially exhaustive sampling of the area of interest

every cell has a value, even if it is “missing.”

cell has a resolution, given as the cell size in ground units.

higher resolution, smaller cell dimensions

Generic structure for a grid.

Row

s

Columns

Gridcell

Grid extent

Resolution

RASTER DATA MODELS RASTER DATA MODELS

Fining of ResolutionFining of Resolution


CREATING RASTER DATA MODELS


creating raster is like laying a grid over a map

code each cell with a value representing attribute

every cell has a value, even if null or zero (integers, ratios, etc.)

values for each cell are written into a file

spreadsheet, data base, word processor

imported into GIS so it can be reformatted

each pixel presumably has one value – in reality is this correct? mixed pixel issue

RASTER AND MISSING DATA RASTER AND MISSING DATA

GIS data layer as a grid with a large section of “missing data,” in thiscase, the zeros in the ocean off of New York and New Jersey.

MIXED PIXEL ISSUE MIXED PIXEL ISSUE

W GW

W W G

W W G

W GG

W W G

W G G

W GE

W E G

E E G

Water dominates Winner takes all Edges separate

““Largest share”Largest share”

““Central point”Central point”

35%

100%80%

70%

Land

Water

““Presence/Absence”Presence/Absence”

““Percent occurrence”Percent occurrence”

MIXED PIXEL ISSUE MIXED PIXEL ISSUE

raster data visualized as map layers

map layer: data describing a single characteristic for a location

multiple items of information require multiple layers

creates problems – raster databases can become enormous

each map layer has thousands of cells



Advantages

simple data structures

each cell can be owned by only one feature.

overlay and combination of maps and remote sensed images easy

simulation easy, because cells have the same size and shape

technology is cheap

RASTER DATA MODELS

RASTER DATA MODELS

Advantages

some spatial analysis methods simple to perform

local: cell by cell calculations

focal: models cell value based on neighbours

zonal: models cell value based on geographical areas

global: models cell value based on all cells

RASTER DATA MODELS

RASTER DATA MODELS

Disadvantages

volumes of graphic data

use of large cells to reduce data volumes

poor at representing points, lines and areas; good at surfaces

must often include redundant or missing data

network linkages are difficult to establish

projection transformations are time consuming

RASTER DATA MODELS

RASTER DATA MODELS

COMPRESSION TECHNIQUES


raster compression techniques used in GIS are run-length encoding and quad trees

Run-length Encoding – more efficient

values often occur in runs across several cells

form of spatial autocorrelation

e.g. array 0 0 0 1 1 0 0 1 1 1 0 0 1 1 1 would be entered as 3 0 2 1 2 0 3 1 2 0 3 1

Row-by-row coding:Row-by-row coding:

CCCCCBBDCCCCBBDCCCBBBDDCBBAADDDDBAADDBBBAADDDAAAADDDAAAA

Run-length coding:Run-length coding:

5C 2B 1D 4C 2B 1D 3C 3B 2D 1C 2B 2A 4D 1B 2A 2D 3B 2A 3D 4A 3D 4A

56 entries for 7x8 array, or56 entries for 7x8 array, or

22 pairs (44 entries) for 7x8 array22 pairs (44 entries) for 7x8 array

A. Mixed Conifer

B. Douglas Fir

C. Oak Savannah

D. Grassland

RUN-LENGTH CODINGRUN-LENGTH CODING

Quadtree Compression

hierarchical data model using a variable-sized grid cell

finer subdivisions are used in areas requiring finer detail (higher resolution)

pixel in each higher layer is derived from average or majority of 4 pixels from the lower layer

not as efficient for more variable or complex data

used primarily as a way to store data for rapid retrieval on display devices



QUAD TREE STRUCTURE QUAD TREE STRUCTURE

RASTER DATA FORMAT

RASTER DATA FORMAT

most raster formats are digital image formats.

most GISs accept TIF, GIF, JPEG or encapsulated PostScript, which are not georeferenced.

DEMs are true raster data formats.

RASTER DATA FORMAT

RASTER DATA FORMAT

think of world as a space populated by discrete features of various shapes and kinds – points, lines, areas.

any location in space may be empty or occupied by one or more point, line or area.

VECTOR DATA MODELS VECTOR DATA MODELS

point zero-dimensional abstraction of an object represented by a

single X,Y co-ordinate.

normally represents a geographic feature too small to be displayed as a line or area

stored by their real (earth) coordinates


line set of ordered co-ordinates that represent the shape of

geographic features too narrow to be displayed as an area at the given scale or linear features with no area

lines and areas are built from sequences of points in order.

lines have a direction to the ordering of the points.


polygon feature used to represent areas.

defined by the lines that make up its boundary and a point inside its boundary for identification.

have attributes that describe the geographic feature they represent.


vector data evolved the arc/node model in the 1960s.

an area consist of lines and a line consists of points.

points, lines, and areas can each be stored in their own files, with links between them.

endpoint of a line (arc) is called a node; arc junctions are only at nodes.

stored with the arc is the topology (i.e. the connecting arcs and left and right polygons).


TOPOLOGYTOPOLOGY

topological data structures dominate GIS software.

stored explicitly

allows automated error detection and elimination.

rarely are maps topologically clean when digitized or imported.

GIS has to be able to build topology from unconnected arcs.

Arc/Node Map Data Structure with Files.

1 1,2,3,4,5,6,7

Arcs File

POLYGON “A”

A: 1,2, Area, Attributes

File of Arcs by Polygon

1

23

4

5

6

7

8

9

10

1112

13 1 x y2 x y3 x y4 x y5 x y6 x y7 x y8 x y9 x y10 x y11 x y12 x y13 x y

Po

ints

Fil

e

1

2

2 1,8,9,10,11,12,13,7

TOPOLOGYTOPOLOGY

relationship between nodes, arcs and polygons.

topologically structured database for ease of retrieval and implementation of spatial-relational operations.

advantages:

simple, elegant and efficient

relational database construction and analysis

complete topology makes map overlay feasible.

topology allows many GIS operations to be done without accessing the point files.

TOPOLOGYTOPOLOGY

VECTOR DATABASE CREATIONVECTOR DATABASE CREATION

database creation involves several stages: input of the spatial data

input of the attribute data

linking spatial and attribute data

spatial data is entered via digitized points and lines, scanned and vectorized lines or directly from other digital sources

once the spatial data has been entered, much work is still needed before it can be used

Building Topology

once points are entered and geometric lines are created, topology must be "built"

this involves calculating and encoding relationships between the points, lines and areas

this information may be automatically coded into tables of information in the database


Editing

during topology generation process, problems such as overshoots, undershoots and spikes are either flagged for editing by the user or corrected automatically

automatic editing involves the use of a tolerance value which defines the width of a buffer zone around objects within which adjacent objects should be joined


Advantages

good representation of structures (points, lines, polygons)

compact and more efficient

topology can be completely described

accurate graphics

retrieval, updating and generalization of graphics and attributes possible

work well with pen and light-plotting devices and tablet digitizers.

VECTOR DATA MODELS

VECTOR DATA MODELS

Disadvantages

complex data structures

combination of several vector polygon maps or polygon and raster maps through overlay creates difficulties

simulation is difficult

display and plotting can be expensive

technology is expensive

not good at continuous coverage or plotters that fill areas.

TIN must be used to represent volumes.

VECTOR DATA MODELS

VECTOR DATA MODELS

vector formats are either page definition languages or preserve ground coordinates.

page languages are HPGL, PostScript, and Autocad DXF.

true vector GIS data formats include ArcView Shapefiles and ArcGIS Interchange Files (E00) which has topology.

VECTOR DATA FORMATS

VECTOR DATA FORMATS

List of coordinates “spaghetti”

simple

easy to manage

no topology

lots of duplication, hence need for large storage space

very often used in CAC (computer assisted cartography)

VECTOR DATA MODELS

VECTOR DATA MODELS

Vertex Dictionary

no duplication, but still this model does not use topology

VECTOR DATA MODELS

VECTOR DATA MODELS

Dual Independent Map Encoding (DIME)

developed by US Bureau of the Census

nodes (intersections of lines) are identified with codes

assigns a directional code in the form of a "from node" and a "to node"

both street addresses and UTM coordinates are explicitly defined for each link

VECTOR DATA MODELS

VECTOR DATA MODELS

VECTOR TO RASTER EXCHANGE


data exchange by translation (export and import) can lead to significant errors in attributes and in geometry.

efficient data exchange is important for the future of GIS.

triangulated irregular network is a set of elevation points which have been connected to form a network of triangles.

developed in early 1970s as a simple way to build a surface

the sample points are connected by lines to form triangles; within each triangle the surface is usually represented by a plane

triangles fit together in a manner which simulates the face of the land.

ADVANCED DATA MODELS - TIN


irregularly spaced sample points can be adapted to the terrain

rough terrain - more points

smooth terrain - less points

an irregularly spaced sample is more efficient



TINs can be seen as polygons having attributes of

slope, aspect and area,

three vertices having elevation attributes

TIN model work best in areas with sharp breaks in slope



Advantages ability to describe the surface at different level

of resolution efficiency in storing data allows simple calculation of basin areas,

slopes, channels, and many other geometric parameters

Disadvantages in many cases require visual inspection and

manual control of the network



a spatial database is a collection of spatially referenced data that acts as a model of reality

these selected phenomena are deemed important enough to represent in digital form

the digital representation might be for some past, present or future time period

DATABASESDATABASES

scaleless- data can be stored at the level of detail found in the environment

cartographer is responsible for choosing the content and resolution

scale critical factor:

level of resolution set by field instruments

digitizing - resolution of instrument and abstraction and production factors

DIGITIAL DATABASES

DIGITIAL DATABASES

problems when using data sets of different resolutions

i.e. roads may not line up

resolved using ancillary source materials

additional problems when using data sets of different themes

i.e. combing elevation and drainage data – water running uphill or non-level lakes

DIGITIAL DATABASES

DIGITIAL DATABASES

Value of databases:

Cost of creationCost of creation – cheaper to get data from an existing database

Appropriateness of useAppropriateness of use

Lack of alternative data sourcesLack of alternative data sources

Graphic outputGraphic output

DIGITIAL DATABASES

DIGITIAL DATABASES

“data about the data”

could include data elements that: identify the data, identify the custodians and access conditions to the data, describe projection, content, quality of data

describes the action taken when handling databases of varying scale

METADATA

METADATA

Dataset information

Title Ortofotos'95

Abstract Ortofotos'95 is a collection of ortho-rectified aerial photographs. These aerial photographs cover Portugal and were obtained in August 1995 in false color infra red film at scale 1:40 000. CNIG, The Directorate General of Forests and The Paper Mill industry are the owners of the aerial photographs (in paper format).

Type of dataset Airborne data>Aerial photos

Locations Portugal

Temporal Range 1995-

Dataset scales 1:25 000-1:50 000

Dataset resolution 1 - 3 meters

Dataset quality remarks

Aquisition of data: aerial photographs, the film is scanned at very high resolution and ortho-rectified using DTM derived from topographic cartography at scale 1:25 000

Information creation date

1999-10-29

pre-1970s, command line based with read and write to hard disk, tapes, diskettes

database approach – all reading and writing through simple interface (no need to care about tapes, etc.)

small GIS projects sufficient to store geographic information as simple files.

with large data volumes and number of data users best to use a database management system (DBMS)

relational design has been the most useful (since 1980s)

DATABASES

DATABASES

DATABASE MANAGEMENT SYSTEMS

DATABASE MANAGEMENT SYSTEMS

contain tables or feature classes in which:

rows: entities, records, observations, features

all information about one occurrence of a feature

columns: attributes, fields, data elements, varaibles

one type of information for all features

key field is an attribute whose values uniquely identify each row Parcel Table

Parcel # Address Block $ Value8 501 N Hi 1 105,4509 590 N Hi 2 89,78036 1001 W. Main 4 101,50075 1175 W. 1st 12 98,000

entity

AttributeKey field

tables are related or joined using a common record identifier (column variable) present in both tables

Example:

goal: produce map of values by distinct/neighbourhood

problem: no distance code available in parcel table

DATABASES - RDBM

DATABASES - RDBM

Parcel TableParcel # Address Block $ Value

8 501 N Hi 1 105,4509 590 N Hi 2 89,78036 1001 W. Main 4 101,50075 1175 W. 1st 12 98,000

solution: join parcel table containing values with geography table containing location codings, using Block as key field

Geography TableBlock District Tract City

1 A 101 Dallas2 B 101 Dallas4 B 105 Dallas12 E 202 Garland

Secondary or foreign key

Parcel TableParcel # Address Block $ Value

8 501 N Hi 1 105,4509 590 N Hi 2 89,78036 1001 W. Main 4 101,50075 1175 W. 1st 12 98,000

DATABASES - RDBM

DATABASES - RDBM

Water RightLocations

Relational LinkagesSpatial Attributes

Descriptive Attributes

DATABASES - RDBM

DATABASES - RDBM

Advantage

very flexible

export data to another system easily

enables simple operations

i.e. search for records satisfying some condition

DATABASESDATABASES

Description Thickness Code

New Ice <10 cm 1

Nilas, Ice Rind 0-10 cm 2

Young Ice 10-30 cm 3

Grey Ice 10-15 cm 4

Grey-White Ice 15-30 cm 5

First-Year Ice 30-200 cm 6

Thin First-Year Ice 30-70 cm 7

Thin First-Year Ice, first stage 30-50 cm 8

Thin First-Year Ice, second stage 50-70 cm 9

Medium First-Year Ice 70-120 cm 1.

Thick First-Year Ice 120-200 cm 4.

Old Ice 7.

Second-Year Ice 8.

Multi-Year Ice 9.

outline: overview of models data and levels of measurements raster and vector models conversion...

Documents

data slide

gis slide

odels slide

attribute data

easurements slide

orderingranking of data

true d ata slide

physical data model