1. 1 Spatial Data Models and Structure

2. 2 Part 1: Basic Geographic Concepts Real world -> Digital Environment –GIS data represent a simplified view of physical phenomena These data contain: –Locational Information –Non-spatial attributes

3. 3 Symbolization In a GIS, we represent real world phenomena in a digital format

4. 4 Vocabulary Real-World Entities or Phenomena Data Objects Cartographic Objects

5. 5 Terminology Entities or Phenomenon -- real world features to be represented in a database

6. 6 Terminology Data Objects -- digital representations of entities or phenomena Pasture Road House

7. 7 Terminology Cartographic Objects -- real-world entities as depicted on maps

8. 8 Real World  Data Objects Attributes –Information about object (e.g., characteristics) Location/Spatial information –Coordinates –May contain elevation information Time –When collected/created –Why? Objects may have different attributes over time

9. 9 Real World  Cartographic Objects Real world objects differ in: –Size –Shape –Color –Pattern These differences affect how these objects are represented on maps Where possible the cartographic objects (i.e., map symbols) can relate to the entities they are representing (e.g., water = blue)

10. 10 Topology The spatial relationships between data objects

11. 11 Conceptualizing Topology Primary –Adjacency –Connectivity –Containment Secondary –Direction –Proximity (distance)

12. 12 Adjacency Springfield Shelbyville

13. 13 Connectivity These roads are connected at the black points.

14. 14 Containment Moe’s Kwik-E-Mart NuclearPlant Springfield

15. 15 Direction Moe’s is Northeast of the Kwik-E-Mart The nuclear plant is Southeast of the Kwik-E- Mart Moe’s Kwik-E-Mart NuclearPlant

16. 16 Proximity Homer lives near Ned Homer lives far from Grampa

17. 17 Complex Case: Overlap Springfield Blue Lake

18. 18 Entities in the real world are represented as one of the following in a GIS: –Raster data Pixels in an array –Vector data Points Lines Areas (or polygons) Part 2: GIS Data Models Key concept!

19. 19 “The continuous field view represents the real world as a finite number of variables, each one defined at every possible position. “ “The discrete object view represents the geographic world as objects with well- defined boundaries in otherwise empty space. “ Continuous & Discrete

20. 20 Continuous & Discrete Some data types may be presented as either discrete or continuous –Example Population at a point (discrete) Population density surface for an area (continuous)

21. 21 Continuous & Discrete Continuous –Data values distributed across a surface w/out interruption –Key words: What varies and how smooth? –Examples: elevation, temperature Discrete –with well-defined boundaries in otherwise empty space –Examples Points: Town, power pole Lines: Highway, stream Areas: U.S. Counties, national parks

22. 22 Continuous or Discrete? www.regional.org.au/au/asa/2003/i/6/walcott.htm

23. 23 Continuous & Discrete In computer databases –Raster data models represent continuous data –Vector data model are used for discrete objects

24. 24 Cell (x,y) The raster data model represents the Earth’s surface as a two-dimensional array of grid cells, with each cell having an associated value: 12358 46839 35331 75439 22452 Cell value Cell size = resolution columns rows Raster Data Model

25. 25 Raster data example Elevation data: each cell contains a number representing the elevation of that cell.

26. 26 Part 3: The vector data model

27. 27 Vector Data Objects Geographic building blocks Points –0 dimensional Lines –1 dimensional Polygons –2 dimensional

28. 28 Spatial Objects Data objects in the vector data model can be: –A point can represent: Tree, airport, a city, street intersection, a movie theater, a benchmark –A line is a data object, made up of a connected sequence of points. It can represent: Roads, rivers, regional boundaries, fences, hedgerows, power lines –A polygon is an enclosed area. Examples: A census tract, Saunders building, boundary of Chapel Hill, a lake, a watershed, a city

29. 29 Object example: oak tree

30. 30 Thought question: How are you going to represent the California OAK tree in digital format? A point? A polygon? Or a pixel? It will depend on: -Scale of observation -Purpose of your research -The type of data you have access to in the GIS

31. 31 – When do you want to represent Chapel Hill as a polygon object instead of a point object? – When do you want to represent a river as a polygon instead of a line? Thought questions:

32. 32 point linepolygon(area) (x,y) The vector data objects The vector data model represents geographic features similar to the way maps do –A point: recorded by a pair of (x,y) coordinates. –A line: recorded by joining more than one point, –A polygon: recorded by a joining multiple points that enclose an area (x,y)

33. 33 Vector Data Storage in Computers: Points Points Data Storage +1 +2+3 +4 Point ID Coordinates 1 1, 1 2 4, 2 3 6, 2 4 2, 4 0

34. 34 (x1,y1) (x2,y2) (x3,y3) ② ① Line # Coordinates ① (x1, y1) (x2,y2) ② (x2,y2) (x3,y3) Note: In GIS, this is considered a line (a connected set of individual lines). Vector Data Storage in Computers: Lines (Sometimes called arcs)

35. 35 (x1,y1) (x2,y2) (x3,y3) (x4,y4) (x6,y6) (x5,y5) ① ② Polygon # Coordinates ① (x1,y1) (x2,y2) (x3,y3) (x4,y4) ② (x3,y3) (x4,y4) (x5,y5) (x6,y6) Vector Data Storage in Computers: Polygons

36. 36 The Arc-Node Data Structure The arc-node structure allows efficient data storage for vector data Benefit: How does it work? It stores data so that nodes construct arcs, and arcs construct polygons Nodes define the two endpoints of an arc. They may or may not connect two or more arcs. An arc is the line segment between two nodes. The points between two nodes defining the shape of an arc are called vertices. Nodes and vertices are represented as x, y coordinates.

37. 37 1 6 5 2 4 3 ① ② ③ Arc: ①, ②, ③ Nodes: 2, 5 Vertices: 1, 6 for arc ① 3, 4 for arc ② Arc # Start Node Vertices End Node 1 2 1,6 5 2 2 3,4 5 3 2 5 Polygon arc list A ①, ③ B ②, ③ A B Points 1 x1,y1 2 x2,y2 3 x3,y3 4x4,y4 5 x5,y5 6 x6,y6 The Arc-Node Data Structure

38. 38 Topology defines spatial relationships. The arc-node data structure supports three major topological concepts: Connectivity: Arcs connect to each other at nodes Area definition: Arcs that connect to surround an area define a polygon Contiguity: Arcs have direction and left and right sides enables topology definition Arc-Node Data Structure: enables topology definition

39. 39 Topology: Connectivity 101112 13 14 15 ⑤ ① ② ③ ④ Arc From-Node To-Node 1 10 11 2 11 12 3 11 13 4 13 15 5 13 14 Arc-node list Connected arcs are determined by searching through the list for common node numbers. Because of the common node 11, arcs 1, 2, and 3 all intersect. The computer can determine that it is possible to travel along arc 1 and turn onto arc 3. But it is not possible to turn directly from arc 1 to arc 5.

40. 40 Topology: Area Definition B C D E 1 2 3 4 5 6 7 8 9 Polygon Arc List B 1,5,8,4 C 2,6,9,5 D 6,3,4,7 E 9,7,8 Polygon-Arc Topology Polygons are simply the list of arcs defining its boundary, arc coordinates are stored only once, therefore, reducing the amount of data and ensuring that the boundaries of adjacent polygons don’t overlap

41. 41 Topology: Contiguity Two geographic features which share a boundary are called adjacent. Contiguity is the topological concept which allows the vector data model to determine adjacency. An Arc From-Node To-Node Direction left right B C D E 1 2 3 4 5 6 7 8 9 Arc Left Right Polygon Polygon 5 C B 9 E C 6 ? ? 1 ? ?