2013/4/3

1

www.yorku.ca/gisweb/course

AS/SC/GEOG4340Geographic Information Systems

Course website

Outline

1. What is a GIS?2. How does GIS work?3. What are the main components of a GIS?4. Who needs to use GIS?5. How GIS is different from other

computers systems?

Introduction to GISLecture 1

Reading materials: Chapter 1, Intro GIS by J. R. Jensen and R.R. JensenGIS.com website

What are the main components of a GIS?

HardwareSoftwarePersonnel (humanware)DataProcesses (application ideas)?

What areData?Information?Knowledge?Spatial Data or Geographic Data?System?

Spatial Data

Information

KnowledgeNon Spatial

Data Spatial Data

Geography Matters

TextTableChart

MapImageTextTableChart

2013/4/3

2

Geometry and Location Matters

Distance

Inside and outside

Area, length, number

Neighbourhood

Connectivity

Topological Information

Spatial Analysis

Ask spatial questions?

Spatial query - based on spatial features and locations

Nonspatial query –SQL structured query language

Analysis and modeling

Analysis - Processes of turning data into information

Modeling – Processes of analysis

Non SpatialData Spatial Data

Table Map

Chart

GIS Statistics

Spatial Statistics

2013/4/3

3

GIS Database

Spatial Database

GIS Remote Sensing

VectorRaster

Modeling with GIS – Processes of analysis

2013/4/3

4

Spatial Decision Support System (SDSS)GIS Data Integration for Prediction

Remote Sensing

Geological

Geochemical

Geophysical

.

.

.Integration Potential

Evidential Layers (X)

Modeling (F) Output Data

S

Processing

DBMS

GIS Database

Data PreprocessingInterpreting

DBMS

DBMS

DBMS

Lecture Two: Explore ArcGISThis lecture explores the systems of ArcGIS including

ArcMap, ArcInfo and ArcView etc. The materials for this lecture can be found at ESRI website and from ArcGIS

system on-line documentation.

1. ESRI web site PDF2. ArcGIS documentation?

ESRI PRODUCTS

Company supported Extensions

Public Domain Extensions http://www.esri.com/software/arcgis/arcgisserv

er/features#power

2013/4/3

5

MapObjectsArcObjectsArcEngine

2010.1 29

Lecture Three

Vector Spatial Data Model

Geographic Information Systems

Cheng. Q. , Earth and Space Science and Engineering, qiuming@yorku.ca

Reading materials: Chapter 5 of Intro GIS by J. R. Jensen and R.R. Jensen

Spatial Data

Data Model

Data Structure

File Format

Spatial Analysis GIS DatabaseSpatial Analysis

2013/4/3

6

Topological Data Model

Feature attribute tables

•Point Attribute Table – PAT•Arc Attribute Table – AAT•Node Attribute Table – NAT•Polygon Attribute Table – PAT

Polygons

Files Coordinate filesLanduse.PATLanduse.AATLanduse.NAT

Arcs

Files Coordinate filesStreet.AATStreet.NAT

Points Files Coordinate files

Well.PAT

2010.1 33

Topological relationships2010.1 34

Reference locationpoint locations Node attribute tab

Arc attribute tab

Label file

Coveragedata model

1 2

3 4

56

n Landuse.shp Map of same features

n Landuse.shx Exchange file

n Landuse.dbf DBS file for attr. table

Shapefile data format

No shape id1 12 23 34 45 56 6

Landuse.shp Landuse.dbf

2010.1 36

Geodatabase of ArcGIS is object-based georelational spatial database. It works for storing vector data, attribute data and raster data. It generally represents multiple sets of geographic objects such as roads, parcels, soil units or forest stands in a given area and support the georelations among these sets.

Geodatabase

2013/4/3

7

2010.1 37

Geodatabase：object-based geo-relational spatial database

Lecture Four

Vector-Based Spatial Analysis:Tools

Processes

Geographic Information Systems GEOG4340 2013winter

Cheng. Q. , Earth and Space Science and Engineering, qiuming@yorku.ca

Reading materials: Chapter 6 of Intro GIS by J. R. Jensen and R.R. Jensen

GIS Database

Processes

Information

Interpretation

Spatial Analysis KnowledgeDecision Support Combine Vector Layers (Overlay Operation)

Layer

Operator

Operator

Layer A

Layer B

Layer C

Node Line Polygon

Node

Line

Polygon

Combine Vector Layers (Overlay Operation)

relationships

Site for Wastewater Treatment Plant

Application and Vector-based Analysis

2013/4/3

8

Model of Processes

BufferRiver

Areas

Land

R_BufferOverlay Intersect

Lecture Five

Modelling Processes and ArcGIS Model Builder

Geographic Information Systems GEOG4340 2013 winter

Cheng. Q. , Earth and Space Science and Engineering, qiuming@yorku.ca

Link to the An overview of Model builderhttp://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=An_overview_of_ModelBuilder

A General Spatial Modeling Processes

•Stating the problem

•Breaking the problem down

•Exploring input datasets

•Determining analysis processes

•Verifying the model’s result

•Implementing the result and reporting

Model of Processes for finding Distance from rec. facilities

BufferRecreational Site

Distance toRec. Site

Rec. SiteBuffer

Reclassify

2013/4/3

9

Modeling Process:tool, parameters (variables)

A Conceptual overview of a Model

Raster Data Model Cell-based Tools

Modeling ProcessesApplication

Geographic Information SystemsGEOG4340.03 2013 winter

Cheng. Q. , Earth and Space Science and Engineering, qiuming@yorku.ca

Lecture Six

2013/4/3

10

Header information – grid properties

cell size; #row, #column; value type; minX, minY

Window extentMask

Cell size (resolution)Weighting factor (fuzzy mask)

Cell-based Functions

Local - individual cellFocal - specified neighborhood cellsZonal - cells within each zone

of the second gridGlobal – on all cells within the gridApplication – application specific functions

2013/4/3

11

GEOG4340 Geographic Information System

Lecture NineSpatial Decision Support System

(SDSS)Multicriteria Analysis (MCA)

Analytic Hierarchy Process (AHP)

Spatial Decision Support System Multiple Map Modeling

Decision Theory is concerned with the logic by which one arrives at a choice between alternatives.

Alternative ActionsAlternative hypothesesAlternative objects

so on

Potential Applications

Site Selection Suitability Assessment Favorability AssessmentProbability Assessment

2013/4/3

12

Spatial Decision Support System (SDSS)GIS Data Integration for Prediction

Remote Sensing

Geological

Geochemical

Geophysical

.

.Potential

Evidential Layers (X)Factors

Modeling (F)

Output Data (S)

Processing

GIS Data Sources

Data PreprocessingInterpretingInformation Extraction

Integration

DBMS

DBMS

DBMS

DBMS

Geographical

General Model

n

nn xwxwxwS +++= ...2211

S – Index map showing SuitabilityProbability

xi - maps or evidenceswi - weights

Model Constraints Normalization:

1. Convert maps into comparable unit

=noyes

xi ,0,1

2. Weights showing relative importance of maps

101...21

≤≤=+++

i

n

wwww

=

nounknown

yesxi

,0,5.0

,110...,,2,1=ix

Model Types

1. Probabilistic

2. Deterministic

S – random variable showing probability 0 ≤ S ≤ 1 with uncertainty

S – Score 0 ≤ S ≤ 1

Methods for Calculating Weights for Data Integration

Data Driven Methods:Weights of evidenceLogistic regression

Artificial Neural network

Knowledge driven Methods:Fuzzy logic

AHP

Hybrid Methods:Fuzzy weights of evidence

Relationships Between Different Models

Simple Overlay Model (Union, Intersect, Identity)

Linear Model (adding weights)

Logistic Model (Weights of Evidence, Logistic Regression)

Fuzzy Logic model (various operators)Analytic Hierarchy Process (AHP)

Saaty Method

2013/4/3

13

(From Wikipedia, 2010)

A simple AHP hierarchy, with final priorities. The decision goal is to select the most suitable leader from a field of three candidates. Factors to be considered are age, experience, education, and charisma. According to the judgments of the decision makers, Dick is the most suitable candidate, followed by Tom and Harry.

How to Assign Weights

Saaty’s Method or Characteristic Analysis

(Saaty, 1973; McCommon, 1983)

Relative Comparison

Extremely less

important

Relatively less

important

Relatively important

Extremely important

1/10 1/8 1/6 ¼ ½ 1 2 4 6 8 10

Equallyimportant

Analytic Hierarchy Process (AHP)

Decision Matrix X1 X2 X3 X4 R_Sum weight Rank

X1 1 6 1 5 13 0.40 AX2 1/6 1 3 2 6.17 0.19 CX3 1 1/3 1 10 12.33 0.37 BX4 1/5 1/2 1/10 1 1.80 0.05 D

Sum 33.3

Consider the size of areas in the map

Percentage of points = #of points/total # of points

Density of points = #of points/area

Density of points = 10/100 = 0.10prior probability

Area = 100 cellsPoints = 10

Assume each point occupies one cell

2013/4/3

14

5.0)|( =DABP

area point density

B 52 7 0.13

not B

48 3 0.06

0.06

0.13

density of points

B

not B

0.30

0.05

A

not A

density of points

area point density

A 20 6 0.30

not A

80 4 0.05

ID Area PolyA PolyB Points points/area1 7 A B 4 0.572 13 A notB 2 0.153 35 notA not B 1 0.024 45 notA B 3 0.06

A B

A

not A

not B

B

not A not B

A not B

not A B

A Bnot A not B

A not B

not A B

0.57

0.15

0.02

0.06

Prior probability total number of point / total area10/100 = 0.10 (10%)

Posterior probability: number of point /pattern area

Prior probability: total number of point / total area10/100 = 0.10 (10%)

Posterior probability: number of point /pattern area (density of point/area) - P(D|AB)

Concept of Prior probability and Posterior probability

not A not B

A not B

not A B

0.57

0.15

0.02

0.06

Prior probability total number of point / total area10/100 = 0.10 (10%)

Posterior probability: number of point /pattern area

2013/4/3

15