Geographic Information Systems (GIS) in Environmental Studies ENVS 6189 3.0 Winter 2003 Session III John Sorrell York University sorrell@yorku.ca
Session Purpose: To discuss the various concepts of space, the dominant data types, themes and models used to represent and organize geographic information.
Cognitive Representation of Space: Humans have many conceptualization of space. A few include: Figural Space Vista Space Environmental Space Geographic Space Existing GIS is concerned largely with Geographic Space
The Nature of Geographic Data: For effective use in a computer environment, geographic data entities have the following characteristics: 1) Geographic Position: a location specified in a unique way (where is it). 2) Attributes: non-spatial descriptions of the feature (what is it). 3) Spatial Relationships: selected relationships among features explicitly defined (what s around it) 4) Time: referring to a point or period in time as defined in real world or database time-units (when is it).
Descriptions of Geographic Data Organization: Spatial Organization: Disaggregated Data Individuals or Single Entities; Aggregated Data Mass observations defined by criterion. Temporal Organization: Cross-Sectional data in a single time period; Longitudinal one discrete are over a series of observation periods. Spatiotemporal Relationship: Geometric Transformation spatial change of location, and/or properties of object (TSA). Very complex in a database when recorded changes effect location, attributes and relation to other objects.
Geographic Data Themes: Social-Economic Data: Information about humans, their activities and the spaces/structures used; Examples: Land Registry Data, Municipal By-law maps, Demographics, Retail Activity, Transportation Networks. Sources: Government Private Database - Field Surveys; Considerations/Constraints: Cost, Quality, Format, Time.
Data Themes (cont d): Natural/Environmental Data: Information about the natural or built environment. Characteristics: - comparatively more static than human data; - mapping unit and scale may smaller scaled; - most effective when merged with human data. Sources: Government Private Database - Field & Lab Work; Considerations/Constraints: Cost, Quality, Format, Time.
Modeling the Real World: Data Model: A logical construct for organizing data in an information system. Real World User view level Exact Object Database level Graphic Mode level
Spatial Data Models: Data Structure: A logical and physical mean for digitally encoding geospatial data. A. study area B. raster representation C. vector representation
Characteristics of the Raster Data Model: Coordinate Reference of start of point Column no. Row no. Pixel Cell value Spatial resolution Spatial unit Coordinate Reference of end of point Different attributes stored in different layers
Raster Data Model: Data Layer in Map Form Representation of Data in Raster Format Overlay Analysis Forest Types Forest Types Soil Types Soil Types Topography Topography Study Area Analysis Results
Vector Data Model: (Arc-Node Topological data model) Topology: a branch of math that defines spatial relationships between features and their properties in elastic space. Triangulated Irregular Network (TIN)
The Vector Data Model Spatial Representations: forest drainage area drainage lines drainage point Vector Layers highway layer Vector map Actual terrain
Geographic Data Types: GI Data uses wide variety of data types and Forms: Vector (Point, Line Polygon) Raster (Images) Raster (Surface) CAD Tabular 3D Geo-referenced data (features): Geo-referenced data forms the locations and shapes of map features such as buildings, streets, or cities. This data intrinsically includes location as a key variable (x,y).
Combined Data Models and Thematic Layers: Clients Central Offices Statistical Data Administrative Boundaries Zoning Combined Spatial Layers Road Network and Addresses interrelated data powerful analysis tools easily understood and presentable co-relations link data through common geography Topography
Comparison of Raster & Vector Model: RASTER MODEL Advantages: 1. It is a simple data structure. 2. Overlay operations are easily and efficiently implemented; 3. High spatial variability is efficiently more efficient represented in a raster format 4. The raster format is more or less required for efficient manipulation and enhancement of digital images. VECTOR MODEL Advantages: 1. It provides a more compact data structure than the raster model. 2. It provides efficient encoding of topology, and, as a result, more efficient implementation of operations that require topological information, such as network. 3. The vector model is better suited to supporting graphics that closely approximate hand-drawn maps. Disadvantages: Disadvantages: 1. The raster data structure is less compact. 2. Topological relationships are more difficult to implement. 3. The output of graphics is less aesthetically pleasing because boundaries tend to have a block appearance. 1. It is a more complex data structure than a simple raster format. 2. Overlay operations are more difficult to implement. 3. The representation of high spatial variability is inefficient. 4. Manipulation and enhancement of digital images cannot be effectively done in a vector domain.