1. It’s the Geography, Cupid!

2. GTECH 201 Lecture 04 Introduction to Spatial Data

3. Today’s Content Types of spatial data World models Spatial data models Spatial data structures The geo-relational principle

4. Types of Spatial Data Locations or regions Relative positions Points, lines, or areas Regular vs. irregular Continuous vs. discrete

5. Geostatistical Data – aka random field data Measurements taken at fixed locations Spatially continuous Small-scale variation Tobler’s Law of Geography

6. Lattice Data Regular lattice Satellite image Irregular lattice Polygon map

7. Spatial Point Patterns Distribution of locations e.g., bald eagles or earth quakes

8. Why do we Need Models? It wont fit!

10. Vector View

11. Raster / Image View

12. What is where? versus Where is what? “What is where?” – Vector space is occupied by objects that are described by their attributes “Where is what?” – Raster variation of an attribute as a continuous field

13. Raster Vector Each world view presents different aspects of the “real” world Thus we can: ask different questions (e.g. apply different operations) get different answers (e.g. apply different analytical tools) …….. so choose carefully

14. Raster Vector continued Converting between the raster and vector data models results in error

15. Chrisman’s Spheres

16. ANSI-SPARC Model for Software Development GIS are systems to model the world User Model Conceptual Model Operational Model

17. GIS are Systems to Model the World User Model – how we intuitively think Conceptual Model Operational Model ANSI-SPARC Model for software development

18. User Model Conceptual Model Operational Model ANSI-SPARC Model for software development how we systematically define ideas GIS are Systems to Model the World

19. User Model Conceptual Model Operational Model how we fuse systematic thinking into a technologically defined context GIS are Systems to Model the World

20. The ANSI/SPARC Model and Chrisman’s Spheres computer science geoinformation theory application disciplines context discipline spatial modeling conceptual modeling logical data modeling physical data modeling OPERATIONAL

21. Digital Maps as Models Representing a complex reality Continuous variation Spatial Data: spatial, temporal and thematic Data Models

22. What sort of Models are These? Raster Model - The world as regular tessellations defined by areal property Vector Model - The world as points, lines, areas and attributes….. making objects Object Model - The world as interacting entities with spatial dimensions

23. Vector Data Models Spaghetti model Topological models A file of spatial data that is a just a collection of co- ordinate strings. Each entity (or piece of spaghetti) is represented by one data entry. There is no topology. Topology refers to the spatial relationships between objects. The topological model represents spatial relationships such as: - length - area - connectivity - contiguity

24. Raster Models Pros : Simple, computer friendly, scanner friendly, field- friendly, compressible Cons : Large, unstructured, inflexible

25. Vector Models Pros : Structure, cognitive consonance(!), compactness(?), accuracy Cons : Inflexibility, complexity, spuriously precise(?), atemporal

26. Object-centered Models Pros : Structure, power, potential process links, consistency(?) Cons : Extreme complexity, power hungry

27. Data Structure

28. Attributes unique stand number dominant cover group avg. tree height stand site index stand age 001deciduous3G 100 002dec/con4M 80 003dec/con4M 60 004coniferous4G 120 Forest Inventory

29. Geo-Relational Principle

30. Database Relations

31. Further Reading ANSI/SPARC model Laurini & Thompson. Fundamentals of GIS, p.357-362 Chrisman’s Spheres Chrisman, N. 1997. Exploring Geographic Information Systems Key Text for Concepts De Mers, M. 2004. Fundamentals of Geographic Information Systems. NY: John Wiley & Sons