Chapter 3

1. Chapter 3 Digital Representation of Geographic Data

2. Digital geographic data • are numerical representations that describe real-world features and phenomena • must be in digitial form and organized as a geographic database for use in a GIS • are dynamic, in contrast to the static data displayed on a conventional map (i.e., paper)

3. Conceptual model for organizing geographic data for analysis ?

4. Geographic matrix* • geographic data described according to location (columns) and attributes (rows) • it facilitates areal differentiation, the study of differences among various locations see figure 3.1 *(Berry, 1964)

5. Real world data exist as: Objects - buildings, highways, cities Phenomena - terrain, temperature, ethnicity

6. Data models for GIS • Object-based (vector) • Field-based (raster)

7. Object-based model (vector) geographic space is populated by discrete and identifiable objects

8. An object: • Has identifiable boundaries or spatial extent • Is relevant to some intended application • Is describable by one of more attributes (characteristics)

9. Exact objects - are generally man-made features with precise boundaries • Inexact objects - are generally natural features with transitional, or “fuzzy” boundaries

10. objects are represented as: • Points • Lines • Polygons

11. Field-based model (raster) geographic space is populated by one or more spatial phenomena

12. Spatial phenomena are real-world features that vary continuously over space with no obvious or specific extent and are represented as surfaces

13. the surfaces in a field-based model can be conceptualized as being composed of: • Grid cells or pixels • regular tessellations • Polygons (i.e., triangles) • irregular tessellations

14. Representation of spatial relationships ? • Geometric - when adjacent features share common boundary • Proximal - when one feature is “close” to another one see Figures 3.5 and 3.6

15. Representation of temporal relationships ? Temporal scaling 1 : 7200

16. To be usable, digital data must: • Be properly encoded • Be properly organized

17. Logical organization focuses upon data classification and geocoding Physical organization focused upon the way in which the data are stored in the computer’s memory

18. Levels of data measurement • Nominal grouped by category • Ordinal rank-order • Interval numerical values • Ratio numerical values with a true origin (absolute zero)

19. Data classification schemes Descriptive names • identifying classes and subclasses • may be based upon form or function (“high-rise” vs commercial”) Definitions • descriptions of classes and subclasses

20. Data classification schemes • example see Figure 3.8 • Criteria see page 70 (Rhind and Hudson, 1980)

21. Geographic data precision • Computer numbers are discrete, whereas real world values are continuous • When the original data contain more precise measurements than those supported by the computer, rounding occurs and precision is reduced

22. GIS coordinates are normally stored as floating-point numbers (real numbers) in double-precision mode to minimize the impact of rounding during data processing.

23. Database organization attribute (stored field) = one data item record (tuple) = group of related items data file = collection related records ASCII files (alphanumeric) Binary files (0 and 1)

24. Digitial data files are commonly referred to as: • Layers • Themes • Coverage

25. Raster geographic data representation • Is best employed to represent geographic phenomena that are continuous over a large area • use tessellations to model a surface

26. tessellations are geometric arrangements (triangular, square, or hexagonal) of figures that completely cover a flat surface note the need for map projection!

27. reasons for the popularity of raster data format: • compatibility with different types of hardware devices for data capture and output • compatibility with bit-mapped images • compatibility with grid-oriented coordinate systems (i.e., plane rectangular )

28. Nature and characterisitics of Raster data • Geographic data is subdivided into grid cells • Linear dimension of each pixel defines the spatial resolution • Grid size should be one-half the minimum mapping unit (smallest object to be represented)

29. One value (character, integer, or floating-point number) assigned to each grid cell • These values can be used for computations (like interpolation of contours) or as codes linked to a look-up table or color palette

30. Map layers • In a raster database, each individual attribute (characteristic) is stored in a separate file • thus data processing requires the use of multiple map layers

31. downside • Identities of individual spatial objects are lost in a raster data model upside • Since the data is stored in a linear array and the dimensions of database (rows and columns) is know, there is no need to store the coordinates of the cells in the data file

32. WARNING!!! You must know the raster data format and data compression algroithm used to construct the files that you are using for a particular project.

33. Principles of raster data compression • Raster data files tend to be quite large, requiring large amounts of storage space and making data transmission problematic

34. file size is a function of: • Resolution number of pixels • Bit depth 8 bit (28) 0-255

35. Run length encoding adjacent cells in one row are treated as group See figure 3.18

36. Quadtree data model is a hierarchical tessellation model that used grid cells of variable sizes