1. Data Queries Raster & Vector Data Models
Ming-Chun Lee

2. Types of GIS Data Describes Objects In Terms Of: Spatial Data
Absolute X,Y Coordinates – Locations
Size, and Shape
Attributes
Characteristics associated with Geography
We can categorize GIS data into three different types:
First type is Spatial Data, include location, size, and shape,
Absolute X,Y Coordinates – Locations
Size, and Shape
For example, like land parcels, cone with locations, different sizes and shapes
Second one is non-spatial attributes:
Non-spatial Attributes
Characteristics Unrelated to Geography
For example, the owners’ names of those parcels, an owner can own multiple properties, the name is one of the attribute associate with parcels, but no really location specific
Topology
Spatial Relationships Between Features
How each feature relate with each other, like which one is connected to which one, which one is on the left of which one, those kinds of relations

3. GIS Associates Spatial and Attribute Data in a Geo-Referenced Database
ARC
INFO 1 3 5
ID AREA PERIM CLASS LANDUSE
Single Family
Multi Family
Commercial
Forest
Water 2 4
GIS Associates Spatial and Attribute Data in a Geo-Referenced Database

4. Data models
Data models: Formats in which geographic data is stored and managed.
Vector Data Models (Features)
Points
Lines
Polygons
Raster Data Models (Surfaces)

5. Raster and Vector Data Models
Raster Representation
Vector Representation
line
polygon
point
Let get started
Quick comparison between two different data models
Raster is represented by a grid of cells,
Vector is represented by point and line and polygon features
Point,

6. Raster Data Models Raster Data Model
Location is referenced by a grid cell in a rectangular array or matrix
Attribute is represented as a single value for that grid cell
Let’s take a closer look at the two data models:
First look at Raster:
the map on the bottom shows the reality
In raster data the entire area of the map is subdivided into a grid of tiny cells. A value is stored in each of these cells to represent the nature of whatever is present at the corresponding location on the ground.

7. Raster Data Models

8. Raster Data Models Raster Data Model Typical data sources:
Images from remote sensing (LANDSAT, SPOT)
Elevation data from USGS
Best for continuous data:
Elevation
Temperature

9. Raster Data Model Uses Elevation Temperature Noise Levels Air Quality
Distance or Accessibility Surfaces
Probabilities (e.g. flooding, liquefaction)
Photographic Images

10. Vector Data Models Vector Data Model
Location is referenced by x,y coordinates, which can be linked to form lines and polygons
Attributes referenced through unique ID number to tables
In vector data the features are recorded one by one, with shape being defined by the numerical values of the pairs of xy coordinates.
A point is defined by a single pair of coordinate values.
A line is defined by a sequence of coordinate pairs defining the points through which the line is drawn.
An area is defined in a similar way, only with the first and last points joined to make a complete enclosure.

11. Features
Geographic objects that have different shapes are represented as features
Geographic objects that have different shapes are represented as features

12. Features Points are a pair of x,y coordinates

13. Features Lines are sets of coordinates that define a shape

14. Features
Polygons are sets of coordinates defining boundaries that enclose areas.
Polygons are sets of coordinates defining boundaries that enclose areas.

15. Vector Data Models Vector Data Model Typical data sources:
DIME (Dual Independent Map Encoding) and TIGER (Topologically Integrated Geographic Encoding and Referencing file) files from US Census
DLG (Digital Line Graph) from USGS for streams, roads, etc.
Best for features with discrete boundaries:
Soil Type
Land Use
DIME file (Dual Independent Map Encoding)
introduced for the 1970 US Census and used again in 1980; replaced by TIGER in 1990
TIGER File (Topologically Integrated Geographic Encoding and Referencing file)
introduced for 1990 Census to eliminate inconsistencies between census products
USGS DLG (Digital Line Graph) file showing roads, streets, rivers,

16. Vector Data Model Uses Land Use or Zoning Classifications Polygons
Transportation Networks
Points and Polylines
Hydrology
Polylines for Rivers, Polygons for Water Bodies
Utilities
Points for Facilities, Polylines for Pipelines & Wires

17. Raster Advantages & Disadvantages
Easy to Understand the Data Model
Easy to Analyze
Low Computing Requirements
Compatible with Remote Sensing Sources
Efficient for Continuous Data
Easy to Use in Modeling
Raster Disadvantages
Spatial Inaccuracies Common
Low Resolution, Relative to Vector Data
Imprecise Locational Data
Requires All Cells to be Coded
Not As Efficient for Discrete Data or Features

18. Vector Advantages & Disadvantages
More Readable as a Map
Higher Resolution than Raster Data
Can Have High Spatial Accuracy
Can Have Storage Advantages
Can Be Topological
Efficient for Discrete Data or Features
Vector Disadvantages
Difficult to Manage Data Storage
High Computing Requirements
Complex to Perform Overlay and Modeling Operations
Inefficient for Continuous Data

19. Basic Spatial Analysis Operations
Data Review Tasks:
Viewing Spatial Data
Viewing Attribute Data
Selecting Data Interactively
Selecting Data by Data Query
Measurement
Data Manipulation Tasks:
Extract a Subset
Dissolves
Reclassification

20. Viewing Spatial Data

21. Viewing Attribute Data: The Identify Tool

22. Viewing Attribute Data: The Attribute Table

23. Selecting Data Interactively

24. Identifying specific features
Query
Identifying specific features
Where is parcel No. 2945?
Identifying features based on conditions
Find all trees with DBH > 35 cm

25. Selecting Data by Data Query

26. Measurement: Distance

27. Measurement: Area & Perimeter

28. Extract a Subset
For Any Current Selection from a Data Layer, Creates a New Data Layer with Only the Selected Features

29. Extract a Subset

30. Dissolve
Combine Features that Share Values of a Particular Attribute

31. Dissolve

32. Reclassification