1
Introduction to GIS and ArcGIS
Lecture 1
This lecture reviews key components of GIS and 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. www.gis.com www.esri.com2. ArcGIS documentation?
referenced information
2
What areData?Information?Knowledge?Spatial Data or Geographic Data?System?
Data
Information
Knowledge
Information Technology
Spatial Data
Information
Knowledge
GIS or GeoIT
Non SpatialData Spatial Data
Geography Matters
TextTableChart
MapImageText
TableChart
3
Map Data
Scale
Legend
Presentation
Location
Pattern
Colors
Geometry
Classification
Structural/Texture
Orientation
Anisotropy
Property of Map Data
4
Non SpatialData Spatial Data
Table Map
Chart
Digital Map
Static Map
Scalable Flexible presentation Thematic mapping Visualization Boundary definition
5
Geometry and Location Matters
Distance
Inside and outside
Area, length, number
Neighbourhood
Connectivity
Topological Information
Spatial Analysis
Scale Matters?
Scale = ratio of map size and real size on the ground
Change Scale vs. visualization Change Scale vs. complexity
Change Scale vs. estimation error
Upscaling vs. Downscaling
Scaling Analysis
εεx
1
2
34
Legend
Hig h : 0 .68
Low : 0. 06
0 4,900 9,8002,450
Meters
494488 5293826/
540709 5246559/
Ag Deposits
494488 5293826/
540709 5246559/
494488 5293826/
540709 5246559/
Map Classification Matters
Classification vs Colors or patterns
Classification Scheme Selection
Map Enhancement
Thematic Map
6
Diverse Map Types and Formats
Geo-referencing
Geo-coding
Map integration and modeling
Visualization
7
GIS Statistics
Spatial Statistics
GIS Database
Spatial Database
8
GIS Remote Sensing
VectorRaster
9
GIS
RS
Stat
CAD
DBMS
…. 3D
GPSGeostat
ESRI PRODUCTS
Company supported Extensions
Public Domain Extensions
10
11
12
13
14
15
16
17
ArcHydro Data Model for Hydrological Modeling
[1] Acknowledgement: we thank Dr. David G. Tarboton, Utah State University and Dr. David Maidment and Oscar Robayo, University of Texas at Austin for using ArcHydro Extension.
Spatial Data Modeler Extension: Arc-SDM
Weights of Evidence
Logistic Regression
Fuzzy Logic
Neural Network
18
Gold Mineral Potential Map
Red areas showing high potential and Blue areas showing low potential to find
gold deposits Circles represent targets with high potential but no discovered deposits
Multivariate Logistic Regression
19
MapObjectsArcObjects
20
