GIS211

1. GIS211 GIS Data Week 3 HW Britz

2. The Vector Data Model To prepare spatial data for computer processing, the vector data model first uses points and their x-, y-coordinates to represent spatial features as points, lines, and areas. Then it organizes geometric objects and their spatial relationships into digital data files that the computer can access, interpret, and process.

3. ESRI Vector data models • 1990 – Arc/Info: Coverage • 1994 – ArcView: Shapefile • 2003 – ArcGIS: Geodatabase

4. Georelational Data Model The georelational data model, such as ESRI’s coverage model, stores spatial and attribute data separately in a split system: spatial data (“geo”) in graphic files and attribute data (“relational”) in a relational database.

5. Based on the georelational data model, an ArcInfo coverage has two components: graphic files for spatial data and INFO files for attribute data. The label connects the two components.

6. Simple Features The vector data model uses the geometric objects of point, line, and area to represent simple spatial features Point = 0 dimensions - property of location Line = 1 dimension – property of length Area = 2 dimensions – properties of area (size) and perimeter

7. Point, line, and area features.

8. Topology • Topology is the study of those properties of geometric objects that remain invariant under certain transformations such as bending or stretching. • Diagrams or graphs are used in topology for studying the arrangements of geometric objects and the relationships between objects.

9. Draw a stream network and show how topological relationships of connectivity and contiguity can be applied to the network • Show the stream network • Insert nodes at junctions for connectivity • Mark direction of flow for contiguity

10. How any arcs connect at node 12? The adjacency matrix and incidence matrix for a digraph, to illustrate Topology.

11. Importance of Topology • It ensures data quality and integrity • Lines that do not meet correctly • Gabs on continuous roads (network analysis) • Shared boundaries. • Enhance GIS analysis • Traffic flow, stream flow

12. The Coverage • The coverage supports 3 basic topological relationships • Connectivity (arcs connect at nodes) • Area definition (area = series of connected arcs) • Contiguity (Arcs have directions and left and right polygons)

13. Geo-relational data structure of a point coverage (Arc/Info)

14. Geo-Relational The data structure of a line coverage.

15. Geo-Relational The data structure of a polygon coverage.

16. Figure 3.10 A polygon coverage, shown in a, has topological errors. Each small square symbol represents an error caused by lines that do not meet correctly. The shapefile, shown in b, is converted from the polygon coverage.

17. The Shapefile • The shapefileis a standard, nontopological data format used in ESRI products. • Although the shapefile treats a point as a pair of x-, y-coordinates, a line as a series of points, and a polygon as a series of line segments, no files describe the spatial relationships between these geometric objects.

18. Advantages of Nontopological Vector Datae.g. shapefiles • Nontopological data such as shapefiles have two main advantages: • They can display more rapidly on the computer monitor than topology-based data. • They are nonproprietary and interoperable, meaning that they can be used across different software packages (e.g., MapInfo can use shapefiles and ArcGIS can use MapInfo Interchange Format files).

19. Object-Based Data Model(Geodatabase) • The object-based data modeltreats spatial data as objects. An object can represent a spatial feature such as a road, a timber stand, or a hydrologic unit. • The object-based data model stores both the spatial and attribute data of spatial features in a single system. • The object-based data model allows a spatial feature (object) to be associated with a set of properties and methods.

20. The object-based data model stores each land use polygon in a record. The Shape field stores the spatial data of land use polygons. Other fields store attribute data such as Landuse_ID and Category.

21. The Geometry property of the Feature class can differentiate the object types of point, line, and polygon.

22. Classes and Relationships between Classes • Objects are organized into a hierarchical structure. A class is a set of objects with similar attributes. • Relationships between classes include association, composition, aggregation, type inheritance, and instantiation.

23. An example of type inheritance.

24. The Geodatabase Data Model • The geodatabase data model, an object-based data model, is the third major data model offered by ESRI, Inc., following the coverage model in the 1980s and the shapefile in the 1990s. • The geodatabase is part of ArcObjects, a collection of thousands of objects, properties, and methods that provides the foundation for ArcGIS Desktop.

25. Geometric Representation of Spatial Features in Geodatabase • Similar to the coverage model, the geodatabase data model also uses the geometries of point, polyline, and polygon to represent vector-based spatial features.

26. Data Structure in the Geodatabase • The geodatabase data model distinguishes between feature classes and feature datasets in data structure. • A feature class stores spatial data of the same geometry type. A feature dataset stores feature classes that share the same coordinate system and area extent. • A feature class does not have to be included in a feature dataset. In that case, the feature class is called a standalone feature class.

27. In a geodatabase, feature classes can be standalone feature classes or members of a feature dataset.

28. Topology Rules in the Geodatabase • The geodatabase defines topology as relationship rules and lets the user choose the rules, if any, to be implemented in a feature dataset. • The geodatabase offers 25 topology rules by feature type.

29. Topology rules in the geodatabase data model

30. The Raster Data Model • A raster represents a continuous surface, but for data storage and analysis, a raster is divided into rows, columns, and cells. • Raster data represent points by single cells, lines by sequences of neighboring cells, and areas by collections of contiguous cells.

31. Draw a stream network and show how the topological relationships of connectivity and contiguity can be applied. • Draw a small TIN to illustrate that it is a composite of simple features • Explain the relationship between geodatabase, feature dataset and feature class

32. A continuous elevation raster with darker shades for higher elevations.

33. Figure 5.2 Representation of point, line, and area features: raster format on the left and vector format on the right.

34. Elements of the Raster Data Model • Cell value. Each cell in a raster carries a value, which represents the characteristic of a spatial phenomenon at the location denoted by its row and column. The cell value can be integeror floating-point. • Cell size. The cell size determines the resolution of the raster data model. • Raster bands. A raster may have a single band or multiple bands. • Spatial reference. Raster data must have the spatial reference information so that they can align spatially with other data sets in a GIS.

35. UTM coordinates for the extent and the center of a 30-meter cell.

36. DEMs at three resolutions: 30 meters, 10 meters, and 3 meters. The 30-m and 10-m DEMs are USGS DEMs. The 3-m DEM is a derived product from LIDAR data.

37. Types of Raster Data • Satellite Imagery • Digital Elevation Models (DEMs) • Digital Orthophotos (DOQ) • Bi-Level Scanned Files • Digital Raster Graphics (DRGs) • Graphic Files • GIS Software-Specific Raster Data

38. 1-meter black-and-white Digital orthophoto

39. A bi-level scanned file showing soil lines. (containing values of 1 or 0

40. Raster Data Structure • Cell-by-Cell Encoding • Run Length Encoding • Quad Tree

41. The cell-by-cell data structure records each cell value by row and column.

42. 1:5 6 2: 4 6 ….. The run length encoding method records the cell values in runs. Row 1, for example, has two adjacent cells in columns 5 and 6 that are gray or have the value of 1. Row 1 is therefore encoded with one run, beginning in column 5 and ending in column 6. The same method is used to record other rows.

43. The regional quad tree method divides a raster into a hierarchy of quadrants. The division stops when a quadrant is made of cells of the same value (gray or white). A quadrant that cannot be subdivided is called a leaf node. In the diagram, the quadrants are indexed spatially: 0 for NW, 1 for SW, 2 for SE, and 3 for NE. Using the spatial indexing method and the hierarchical quad tree structure, the gray cells can be coded as 02, 032, and so on. See text for more explanation.

44. NW NE SW SE 3 0 1 2 0123

45. Data Conversion The conversion of vector data to raster data is called rasterization, and the conversion of raster data to vector data is called vectorization.

46. Figure 5.12 On the left is an example of conversion from vector to raster data, or rasterization. On the right is an example of conversion from raster to vector data, or vectorization.

48. 14. Use a diagram to explain how the run length encoding method works.

49. 15. Refer to the figure below, draw a quad tree, and code the spatial index of the shaded (spatial) feature. 0 1 3 2 0 1 3 2

50. Figure 10. Run-length encoding