1. Geography 413/613 Lecturer: John Masich jamasich@gmail.com
Vector Data
Geography 413/613
Lecturer: John Masich

2. Agenda Questions from last week/article review Labs? Spatial Data
Data Modelling
Vector Data
Vector Data Management
Next Weeks Lecture

3. Questions
Coordinates and Projection systems

4. Labs
How are they going?

5. What makes Data Spatial
Needs to be referenced to some location on the earth
Latitude and Longitude
Grid coordinates
Place name
Description
Postal Code
Distance and Bearing

6. What makes Data Spatial
The world in infinitely complex
Our objective with spatial data is to model the real and fictitious world.
The GIS is our medium for doing so

7. What makes Data Spatial Characteristics
Geometry – Shape
Location - Place
Topology - Relationship

8. Geometry Shapes – Boundaries/Regions/buildings Path – Streams/Roads
Topography – Landscape

9. Location Latitude and Longitude (54°58'34“, 124°38'47“)
Grid Coordinate (645345E, N)
Postal Code (V2L-4P8)
Lots ( )

10. Topology
Connectivity
Containment
Adjacency
North-east of . .

11. Modelling Spatial Data Identify the Feature
Roads
Buildings
Parking Lots
Streams
Lamp posts
Manhole covers
Ponds

12. Modelling Spatial Data Classify the feature
Lamp Posts
Manhole Covers
Point
Roads
Streams
Line
Buildings
Parking Lots
Ponds
Poly

13. Modelling Spatial Data Attribute the feature
Gives intelligence/meaning to your data
Allows complex queries and analysis
“Validates” the data

14. Spatial Data Storage
Vector data

15. Vector Data (ESRI – GIS Dictionary) A coordinate-based data model
Geographic features represented as points, lines, and polygons.
Point feature is represented as a single coordinate pair
Line and polygon features are represented as ordered lists of vertices.
Attributes are associated with each vector feature.
(ESRI – GIS Dictionary)

16. Vector Data – Point, Line, Poly
Points (0 Dimension)
Display data as a single location (x/y).
Has neither length or area
Lines (1 Dimension)
Sequence of xy coordinate pairs
Displays length at any given scale
Polygons (2 dimensions)
Connected sequence of xy coordinate pairs
Displays area at any given scale

17. Vector Data Coordinate Pairs
Stored explicitly
Cartesian coordinates

18. TINS (3d Vector)
A vector data structure that partitions geographic space into contiguous, non-overlapping triangles. The vertices of each triangle are sample data points with x-, y-, and z-values. These sample points are connected by lines to form Delaunay triangles. TINs are used to store and display surface models. (ESRI-GIS Dictionary)

19. Advantages of Vector Data Structures
Small amount of data
Easy to update
Logical data structure
Attributes are combined with objects
Preserves quality after interactivity (e.g. scaling)
More sophisticated in spatial analysis

20. Disadvantages of Vector Data Structures
Continuous data is not represented effectively
Spatial analysis and filtering within polygons is impossible
Needs a lot of manual editing to get good quality
It always introduces hard boundaries
Unable to model uncertainty

21. Vector Data Analysis
Overlay/Buffer

22. Vector overlay much more complex compared to raster overlay
Creates spurious data results
Multiple features created during the process.

23. Basic overlay processes

24. Map overlay concepts
Map overlay addresses the relationship of the intersection and overlap between spatial features.
Map overlay combines the spatial and attribute data of two input themes.
Three input feature types, overlay cover is always polygon:
point-in-polygon, points are output
line-in-polygon, lines are output
polygon-in-polygon polygons are output

25. Point in Poly

26. Line-on Poly

27. Poly in Poly overlay

28. Examples of Vector overlay

29. Vector analysis Patterns - Distance
Cost surface
Distance to features
Buffering
How far is something from something else?
What zone is it in?
What data is within the buffer area?
How long does it take to get from point a to point b?

30. GIS Project Management
Vector Data

31. Remember this ...
“Failing to plan is planning to fail” (Sir Winston Churchill)

32. Why GIS Projects fail Poor Scope Schedule No quality Standards
No systems integration
No executive sponsorship
No staff training
Failure to mange risk
Unrealistic cost estimates
No Internal marketing
Planned obsolescence
Not all of these will apply to you

33. $ The excuses No time Costs too much Not important Too busy
I need to start now
Costs for Planning
Costs for Correction $ $
Before Project Starts
After Project Starts

34. How do you manage your data?
What is your plan for managing your vector data?

35. Managing Vector data
The geodatabase

36. Data formats
So far in your GIS courses you have used different data types (Shapes, .kml, coverages …)
These are ultimately containers for your information
Not always and effective way to store your data …

37. Why? Topological relationships not maintained in shapefiles
Data is not contained … scattered
Projection inconsistencies occur
Attribute data formats not enforced
Lets look at the geo-database

38. What is a Geodatabase
A geodatabase is a database designed to store, query, and manipulate geographic information and spatial data.

39. Managing your data using a geo-database
Benefits
See handouts
The user will ultimately decide how the data should be stored and managed.

40. Why ask the question
Before starting your project you must have some idea of how to deal with
Spatial/attribute Data integrity
Error resolution
Tolerances
Methodology

41. Let take a quick look at some of the features and how
Yes – the Live Demo