1. Spatial (coordinate) data model Relational (tabular) data model
Introduction to GIS
Overview
Spatial (coordinate) data model
Relational (tabular) data model
Scale issues
Sample data _
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2. GIS are driven by spatial data
Spatial Data Model
GIS are driven by spatial data
2 basic spatial data models exist
vector
points
lines
polygons
raster
grid cells (images, bitmaps, DEMs) _
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3. Characteristics of the vector data model:
Features positioned accurately
Shape of features represented correctly
Features represented discretely (no fuzzy boundaries)
Complex data structure (especially for polygons) _
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4. Points: represent discrete point features
Vector Data Model
Points: represent discrete point features
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5. Lines: represent linear features
Vector Data Model
Lines: represent linear features
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6. Lines: represent linear features
Vector Data Model
Lines: represent linear features
Lines start and end at nodes
line #1 goes from node #2 to node #1
Vertices determine shape of line _
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7. Polygons: represent bounded areas
Vector Data Model
Polygons: represent bounded areas
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8. Polygons: represent bounded areas
Vector Data Model
Polygons: represent bounded areas
Polygon #2 is bounded by lines 1 & 2
Line 2 has polygon 1 on left and polygon 2 on right _
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9. Polygons: represent bounded areas
Vector Data Model
Polygons: represent bounded areas
complex data model
“arc/node topology” _
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10. Types (formats) of vector data available in ArcView
Vector Data Model
Types (formats) of vector data available in ArcView
ArcView shapefiles
ArcInfo coverages and libraries
CAD files (AutoCAD DWG, DXF; Microstation DGN)
StreetMap files
Spatial Database Engine (SDE) data
ASCII point coordinate data _
© Phil Hurvitz,

11. A relatively new vector data format Preferred in ArcView Draws fast
Vector Data Model
ArcView shapefiles
A relatively new vector data format
Preferred in ArcView
Draws fast
Fully editable in ArcView
Simple in structure
Does not use arc-node topology
“Connected” lines do not necessarily share a common node
Adjacent polygons do not share common bounding arcs
Data sets are either point or line or polygon _
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12. A commonly found format Data model more complex
Vector Data Model
ArcInfo coverages
A commonly found format
Data model more complex
Draws more slowly in ArcView
Coordinate data not editable in ArcView
Can be used in both ArcView and ArcInfo
Polymorphic
Problematic file structure (more on this later in the term) _
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13. AutoCAD & Microstation CAD drawing data
Vector Data Model
AutoCAD & Microstation CAD drawing data
CAD data are very common (industry standard)
DXF, DWG, and DGN formats supported in ArcView
Coordinate data not editable in ArcView
Frequently contain “sloppy” data
No enforced topology rules
Gaps in data
Frequently contain little or no useful attribute data _
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14. Easy to obtain from a variety of sources GPS Traverse
Vector Data Model
ASCII coordinate data
Easy to obtain from a variety of sources
GPS
Traverse
Direct reading from maps
OS, architecture, and application independent
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15. Characteristics of the raster data model:
Rectangular grid of square cells
Shape of features generalized by cells
Continuous (surface) data represented easily
Simple data structure _
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16. Raster data are good at representing continuous phenomena
Raster Data Model
Raster data are good at representing continuous phenomena
Wind speed
Elevation, slope, aspect
Chemical concentration
Likelihood of existence of a certain species
Electromagnetic reflectance (photographic or satellite imagery) _
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17. Raster spatial data model
Raster Data Model
Raster spatial data model
origin is set explicitly
cell size is known
cell references (row/column locations) are known
cell values are referenced to row/column location
values represent numerical phenomena or index codes for non-numerical phenomena _
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18. A few different types of raster data
Raster Data Model
A few different types of raster data
digital orthophoto
digital elevation model (DEM) _
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19. Relational Database Model & Attribute Data Structures
The “where” of GIS is determined by coordinate (map) data structures, but …
The “what” of GIS is determined by tabular (relational database) data structures
Thus, tabular data are just as important as coordinate data _
© Phil Hurvitz,

20. Relational Database Model & Attribute Data Structures
Attribute data are stored in database tables.
Tables are composed of:
fields
and
records _
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21. Relational Database Model & Attribute Data Structures
You may already be familiar with relational databases
dBase
rBase
Access
Excel (database functionality)
Oracle, INFORMIX, INGRES, SQL Server
INFO (in ArcInfo) _
© Phil Hurvitz,

22. Relational Database Model & Attribute Data Structures
ArcView uses tabular data formats from dBase, ASCII text, and INFO files
tables are stored on the disk as
.dbf,
.txt, or
in INFO directories _
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23. Relational Database Model & Attribute Data Structures
Tables can be linked and joined (“related”) by use of common values in fields
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24. Relational Database Model & Attribute Data Structures
Different types of attribute tables in ArcView
Vector
point attribute tables
polygon attribute
line attribute
node attribute*
text attribute*
route & event tables*
Raster
value attribute _
* in ArcInfo coverage data only
© Phil Hurvitz,

25. Relational Database Model & Attribute Data Structures
Relationship between tabular and map data
one-to-one between features and records _
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26. Scale of data plays an important role, and frequently causes problems
Scale Issues
Scale of data plays an important role, and frequently causes problems
Be aware of:
Data’s source scale
Mixing data from different source scales
Appropriateness of output scale _
© Phil Hurvitz,

27. Map measurement and true ground measurement
Scale Issues
Map measurement and true ground measurement
A 1/40th in line on a 1:24,000 scale map is 50 ft on the ground
A .30 mm line on a 1:200,000 scale map is almost 2,000 ft on the ground _
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28. Data from different sources and scales can vary widely
Scale Issues
Data from different sources and scales can vary widely
1:100,000 scale data from USGS DLG
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29. Data from different sources and scales can vary widely
Scale Issues
Data from different sources and scales can vary widely
1:1,000,000 scale data from DCW (DMA)
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30. Data from different sources and scales can vary widely
Scale Issues
Data from different sources and scales can vary widely
1:2,000,000 scale data from USGS DLG
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31. Beware of scale statements
Scale Issues
Beware of scale statements
“one to two-hundred”: does this mean
one inch on the map equals 200 inches on the ground? or
one inch on the map equals 200 feet on the ground? _
© Phil Hurvitz,

32. Pack Forest GIS database
Course Sample Data
Pack Forest GIS database
Original data sources
Legacy maps
USGS digital line graphs
DNR data
GPS surveys
Digital orthophoto interpretation _
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33. Pack Forest GIS database
Course Sample Data
Pack Forest GIS database
CD directory pfdata
Forest stands
Streams
Roads, trails
Soils
Elevation contours
Culverts
Forest inventory data
Digital orthophotos
Digital elevation model
… _
© Phil Hurvitz,

34. Course Sample Data ESRI Sample Data CD directory esridata
Worldwide data sets
countries
major rivers
United States data
states
counties
cities
rivers
roads
Canada
Mexico _
© Phil Hurvitz,

35. Projection STATEPLANE
Sample Data
Pack Forest GIS database projection & coordinate definition
Unless otherwise specified,
Projection STATEPLANE
Washington South Zone (State Plane 5626 or FIPSZone 4602)
Datum HPGN (a.k.a. NAD83/91)
Units FEET
Spheroid GRS1980 _
© Phil Hurvitz,