Geospatial Data Model

1. Geospatial Data Model Ihab Hijazi. MSc in GIS

2. Data Modeling Process • Maps and GIS are models of reality (geospatial phenomena). • Maps emphasize some aspects of reality in a cartographic representation. • GIS emphasize some aspects of reality in a database representation.

3. Geographic Data

4. Types of attributes • Nominal: is one that serves only to identify or distinguish one entity from another. • Ordinal: if their values have a natural order, for example Palestine classify its soil type by classes, with class 1 is the best class 2 not so good … etc. • Ratio: if the ratio between vales make sense weight is ratio • Interval

5. GIS Data Modeling

6. Real World to Conceptual model • Perception is subjective • Conceptual model: a formalized model to represent the real world with some simplification

7. GIS Conceptual Data Models Object-view vs. Field-view of reality: • Object-view: collection of discrete objects with spatial reference (discrete entities) e.g., buildings, trees, rivers, roads, bridges, towers… • Field-view: geographic phenomena that vary continuously throughout space e.g., elevation, temperature, air pressure, gravity, soil

8. Object View: Points

9. Object View: Lines

10. Object View: Polygon

11. The object-view uses points, lines, or polygons to represent objects with discrete boundaries. However, object representations are generalizations or approximations of variation, sometimes poorly defined. • Example: forested area

12. Models of fields • Models of fields: piecewise, contours, sampling Piecewise: an exhaustive tessellation of space into regions (no ‘islands’): • Constant • linear • higher order functions

13. Tessellation of space

14. Models of fields • Piecewise – Constant The value of the variable is constant within each region ,e.g. soils, land cover ,etc.

15. Piecewise – Linear The value of the variable varies as a linear function within each region, e.g. TIN representation of elevation TIN: Triangulated Irregular Network

16. Piecewise – Higher Order Functions: The value of the variable varies as a non-linear function within each region, e.g. a TIN in which triangles do not represent planes, but curved surfaces

17. Models of fields • Contours A set of lines indicating a constant attribute value along each line

18. Models of fields • Sampling: Capturing the value of a variable at a finite set of points Regular lattice – samples are regularly or irregularly spaced

19. Vector vs Raster • Vector: Implementation of Objects • Point • Line • Polygon • Raster: Implementation of Fields • Piecewise tessellation • (Grids, TIN, coverage map) • Contour • Sampling

20. Raster vs Vector Does vector take a field or object view?

21. Raster vs Vector Does vector take a field or object view? If this is a point Feature class representing building locations (elevation sampling)?

22. Raster vs Vector A note on networks: Object-view of a network: nodes and links Field-view of a network: an attribute that varies along the length of the network Networks are typically represented using the object-view, i.e. lines, yet often the important information associated with the network is fieldlike, e.g. the variation of surface conditions along a road network.

23. Please read from page 66-74/75

24. Quiz • How many layers are presented in the Table of Contents • The homeshp layer represents building as ------- features • Select any building by selection icon • Select all the buildings that are within 100 m from the selected building • Zoom to selected features and create a bookmark with name Quiz • Add to the selection all the buildings with total area (shape_area) less than 100 m2 (select by attribute) • Label the building layer with its owner names • Classify the building into four classes based on their areas • Change homeshp layer name to building • Create a layout view for your data • Add a title for your map • Add a scale bar and north arrow to your map