Spatial Data Modeling: Abstraction and Representation

1. GIS BOOT CAMPTopic 4-8 Todd Bacastow

2. Topic 3: Representing Spatial Entities

3. Feature Abstraction • GIS data sets are models of the real world: • They emphasize or represent some aspects of reality • They ignore or greatly simplify other aspects of reality.

4. Feature Abstraction • Abstraction is the process of defining: • what features are going to be represented • how they are going to be represented • Data modeling is the formal process of feature abstraction

5. Feature Abstraction • The geographic features that are represented in a GIS and the manner in which they are represented depends on: • Data source and level of its abstraction • The intended use • Software environment limitations and capabilities

6. Feature Abstraction(Data Models) RASTER VECTOR • Cell based • Better for surface analysis • Can represent discrete and continuous data • Generalizes features more than vector • Object (feature) based • Non-object space is not stored • Less storage space • More accurate object representation better for maps • x,y coordinates store feature representation • Required for network and dynamic segmentation = = =

7. Topic 4: Coordinates, Datums, and Projections

8. 90º N Latitude Northern Hemisphere Eastern Hemisphere Equator 0º Latitude Prime Meridian 0º Longitude Western Hemisphere Southern Hemisphere 90º S Latitude 90º W Longitude 90º E Longitude 0º Longitude 180º Longitude Spherical Coordinates Spherical “grid” is called a graticule Latitude references north and south Longitude references east/west Line of constant latitude is a parallel Line of constant longitude is a meridian Meridiansconverge at the poles Latitude range: 0 to 90 degrees north and south Longitude range: 0 to 180 degrees east and west

9. (4.5, 4.5) 1 2 3 4 5 6 Y axis (2.0,3.0) (7.0,2.0) 0,0 1 2 3 4 5 6 7 8 9 X axis Cartesian Coordinates

10. NAD 1927 DATUM GRS80 Spheroid Meades Ranch Kansas Earth Center Clarke 1866 Center Clarke 1866 Spheroid Horizontal Datum • North American Datum of 1927 • A local datum centered on the Meades Ranch in Kansas. Surface of ellipsoid was tangent to the Meades Ranch • 300,000 permanent control network • Clarke 1866 spheroid used to define the shape and size of the earth NAD 1983 DATUM • North American Datum of 1983 • an earth centered datum where the center of the spheroid is the center of the earth • based on the Geodetic Reference System of 1980 (GRS80): a better approximation of earth’s true size and shape. • twice as accurate as the NAD27: resulted in controls shifted up to 100 meters Meades Ranch Kansas GRS80 Spheroid Earth Center Clarke 1866 Center Clarke 1866 Spheroid

11. Land Mass Sea Level Sea Floor Vertical Datum (mean sea level) Vertical Datum • National Geodetic Vertical Datum of 1929 • vertical datum based mean sea level as determined by years of observations at tidal gauging stations • 585,000 permanently monumented vertical benchmarks interconnected by leveling • North American Vertical Datum of 1988 • 1929 datum adjusted based on more precise measurements of geoid shape and mean sea levels. • some bench mark heights changed up to 2 meters, but heights between adjacent benchmarks changed < a few millimeters • provides better geoid height definitions in order to convert earth centered GPS derived heights

12. Map Projections • Transform spherical geographic space to a 2-D planar surface. • Eliminates need to carry a globe around in the pocket! • 2-D Cartesian coordinate space is better suited than spherical coordinates when conducting traditional surveys, mapping, and ground measurements.

13. Map Projections CONIC CYLINDRICAL PLANAR

14. Map Projections • Any representation of the Earth’s 3-D surface on a 2-D plane involves distortion of one or more of the following: • shape • area • distance (scale) • direction (angle)

15. PETERS (Equivalent) MERCATOR (Conformal) ROBINSON Map Projection Distortion

16. A C B M E A C D 84º 30’ 500,000 mE 320,000 mE 680,000 mE 0º 00’ 00” 80º 30’ B M E Universal Transverse Mercator (UTM) D • The cylinder is made secant to the sphere, cutting into the sphere along the lines AB and DE • Lines AB and DE are standard meridians 360,000 meters apart. The scale is exact (1) along these lines. • The scale for the area between the standard meridians is < 1 (scale too small). Outside these meridians, the scale is too large (> 1) • Line CM is the Central Meridian, which starts and stops at the poles • The UTM projection is applied every 6º, resulting in 60 UTM zones for the earth (360 / 6 = 60) • Good projection if map extent falls within a zone. Should not be used if map extent spans multiple zones • Used as State Plane projection system for states that are predominately N-S orientation (e.g. Vermont, Maine, Idaho) 0 mN 10,000,0000 mS

17. Universal Transverse Mercator (UTM)

18. Pennsylvania State Plane Coordinate System • Based on two different applications of the Lambert Conformal Conic Projection • results in two different zones: a North and South Zone • Minimizes scale and angle distortions for use by surveyors • Local governments are required by State Law to use the PA State Plane Coordinate System

19. Scale: 1.000000 Standard Parallel 77º 45’W 41º 57’N Scale: .9999568 Central Parallel 41º 25’N Scale: 1.000000 Standard Parallel 40º 53’N Projection Origin 40º 10’N, 77º 45’W Central Meridian Pennsylvania State Plane North Zone

20. 77º 45’W Central Meridian Scale: 1.000000 Standard Parallel 40º 58’N Scale: .9999595 Central Parallel 40º 27’N Scale: 1.000000 Standard Parallel 39º 56’N Projection Origin 39º 20’N, 77º 45’W Pennsylvania State Plane South Zone

21. Map Scale • Map scale: the relationship between map distance (or display distance) and actual ground distance • Scale Calculations: • Scale = map distance / (ground distance x conversion factor) • To determine map scale when map and ground distances are known: • 2.5” on map = 500 feet on ground 2.5/500*12 = 2.5/6,000 = 1:2,400

22. Map Scale • Small Scale Maps • Large denominator in RF (1:14,000,000) • Maps of continents and world maps • Medium Scale Maps • Medium denominator in RF (1:24,000) • USGS Topographic Quadrangles • Large Scale Maps • Small denominator in RF (1:2,400) • Tax maps, utility maps • The smaller the number in the denominator, the larger the map scale • ½ is “larger” than ¼ and ¼ is “smaller” than ½

23. Map Scale • Considerations for selection of source scale • cost • required accuracy • desired output map detail • desired feature representation • density of features to be displayed

24. Map Scale • In a GIS, scale is a function of: • source map scale (compiled scale) • desired plot scale(s) • Digital data can be plotted at any scale • accuracy is only as good as the original source scale • resolution of the data will become apparent if plot scale greatly exceeds source scale

25. woods or or or Map Scale • Map scale sets boundary for feature resolution • Feature resolution is defined as : • The density of features that can be shown at a given scale • The amount of detail (density of vertices) that can be used to represent a feature at a given scale

26. Topic 5: Spatial Data Models

27. Vector Representation • Use vector model when • accurate shape of a feature is needed for map production • The feature needs to have attributes associated with it • accurate representation of the length, perimeter, or area is desired from the geometry • Analysis can benefit from topology

28. Vector Representation Point: no dimension (1.25,5.0) (4.5, 4.5) Polygon: area and perimeter 1 2 3 4 5 6 Y axis (2.0,3.0) (7.0,2.0) Implied directionality (5.4,1.3) Line: length (2.1,0.4) 0,0 1 2 3 4 5 6 7 8 9 Origin X axis

29. Vector Representation • Ideally, one feature is represented as one type of geometry • Easier to query and maintain • Some features will require more than one representation • To fulfill functional requirements • Roads typically require more than one representation

30. Vector Representation(CAD Data) • CAD data may look like a map, but may require more work to be a GIS model: • It might not be geo-referenced • Polygons might not be closed • Linear networks might not be connected • Lines are typically omitted when feature is hidden from view • CAD to GIS translation tips • Avoid fonted lines during translation • Features may be on wrong layers

31. Vector Representation (Networks) • Uses of network models • utilities • stream drainage • transportation networks • Functionality • Address match (centerlines) • Routing (shortest distance, shortest time) • Linear referencing: map location of events • Trace upstream and downstream • Allocation of demand to supply

32. Raster Representation • Grid data (map algebra) • Continuous value • Discrete values • Image data (viewing) • Geo-referenced images • Digital pictures

33. Raster Representation • Use when • Need to model a surface characteristics as opposed to discrete objects on the surface • When the phenomena of interest represents sampled measurements and is continuous across a surface • Need to analyze surface characteristics • Watershed delineation from elevation data • Optimal path across a weighted surface • Storm water run-off • Forest fire simulations

34. 670,000 sq. m area 100 m cell 12 x 12 grid 640,000 sq. m area 400 m cell 3 x 3 grid 720,000 sq. m area 200 m cell 6 x 6 grid Raster Representation Cell size and feature resolution True polygon area = 679,707 m² smaller cells = higher resolution = larger file size

35. 48.2 42.8 40.2 38.5 40.5 43.0 36.1 35.1 35.0 31.9 33.8 34.2 33.5 34.6 31.2 34.8 32.6 33.6 32.7 33.1 32..3 32.1 31.4 32.2 30.6 Raster Representation A GRID CAN REPRESENT CONTINUOUS DATA Data are stored as floating point an reflect measurements • Elevation

36. 3 3 2 1 1 2 3 3 2 2 1 1 1 2 2 2 1 2 2 2 Raster Representation A GRID CAN REPRESENT DISCRETE DATA Data are stored as integer and represent a code for classification • Land cover

37. Topic 6: Attribute Data Management

38. Primary Key Last updated by Geometry Attribute x,y 1 25 1953 A Object Instance 1 02/06/1991 Brian Miller x,y 2 30 1961 B Object Instance 2 06/15/1989 Dennis Ellsworth x,y 3 40 1978 C Object Instance 3 01/21/1979 Linda Casey x,y 4 35 1958 A Object Instance 4 07/19/1990 Brian Miller Foreign Key 1 02/06/1991 Brian Miller 1 11/10/1990 Dennis Ellsworth Update History 4 01/21/1979 Linda Casey 4 07/19/1990 Brian Miller Attribute Data Management Geometry is Joined to Attribute Tables

39. Good Management Begins with Good Design • Data table design considerations • Compile lists of attribute data from reports • Focus on the underlying data • To what geographic feature does the attribute associate? • Identify common attributes for features • Identify the “real” owner of the geographic feature and the attributes (they may not be the same)

40. Attribute Database Management Rubrics • Minimize the amount of attribute data stored directly with the geometry • Ownership of the geometry is typically with someone’s “GIS people” • Store and update attributes in a related database • Ownership of the attribute data is typically the data creator (which many not be “GIS people’) • Make provisions (i.e., keys) to join geometry to the attributes tables • Common standards are essential • There must be a person in charge!

41. Topic 8: Address Data

42. Derived Spatial Data (Address Data) • Address data can be geo-coded using: • Tax parcel or building polygons • Most accurate spatial representation • Road centerlines • Address interpolated as a point to approximate location along a road segment • Zip code boundaries • Address’s zip code matched to the center of zip code boundary area • Multiple addresses will be assigned to the same coordinate

43. 1 Main St 2 299 298 ADDRESS CENTERLINE TABLE Seg_ID From To From To Left Left Right Right Street Name 9 First Ave 200 201 • 1 99 2 98 Oak Ave • 101 199 100 198 Oak Ave • 9 201 299 200 298 Oak Ave 198 7 8 199 6 12 10 100 101 5 99 98 3 Trimble Rd 4 Main St Oak Ave 2 1 11 Derived Spatial Data (Address Data)