1. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 GIS BOOTCAMP Topic 4-8 Todd Bacastow

2. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Topic 3: Representing Spatial Entities

3. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 VECTOR Feature Abstraction (Data Models) RASTER = = = 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 Cell based Better for surface analysis Can represent discrete and continuous data Generalizes features more than vector

9. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Feature Abstraction Consider how a road might be represented: As a network model focusing on cartographic products As a polygon model accurately representing curbs and shoulders

10. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Feature Abstraction IMAGE AREA FEATURE POINT FEATURE Is is necessary to store attribute data about the feature? Is it necessary to tabulate area perimeter, or length from feature geometry?

11. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Feature Abstraction Some spatial representation choices are easier than others: A building is a point, a polygon, or both A road is a line or a polygon Some spatial representation choices difficult

12. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Topic 4: Coordinates, Datums, and Projections

13. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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 Meridians converge at the poles Latitude range: 0 to 90 degrees north and south Longitude range: 0 to 180 degrees east and west 0º Latitude Prime Meridian 0º Longitude Equator 90º N Latitude 90º S Latitude Southern Hemisphere Northern Hemisphere Eastern Hemisphere Western Hemisphere 90º W Longitude 0º Longitude 180º Longitude 90º E Longitude

14. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Spherical Coordinates A spherical coordinate measure is expressed in degrees (º), minutes (‘) and seconds (“) 1º = 60’ = 3,600” ; 1’ = 60” Expressed as: ddd mm ss N/S, ddd mm sss E/W Note the convention is to express latitude (y) before longitude (x), but computer environments use x,y In most digital environments, degrees, minutes and seconds are converted to decimal degrees: degrees + (min/60) + (sec/3600) Harrisburg International Airport is: 40º12’N, 76 º45’W, or 40.20N, 76.75W

15. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Spherical Coordinates Eastern and Northern Hemisphere: +x, +y Eastern and Southern Hemisphere:+x, -y Western and Northern Hemisphere: -x, +y Western and Southern Hemisphere:-x, -y

16. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Cartesian Coordinates X axis Y axis 0,0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 (2.0,3.0) (4.5, 4.5) (7.0,2.0)

17. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Horizontal 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 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 Meades Ranch Kansas Earth Center Clarke 1866 Center Clarke 1866 Spheroid GRS80 Spheroid Meades Ranch Kansas Earth Center Clarke 1866 Center Clarke 1866 Spheroid GRS80 Spheroid NAD 1927 DATUM NAD 1983 DATUM

18. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vertical Datum 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 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 Vertical Datum (mean sea level) Land Mass Sea Floor Sea Level

19. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Shape of the Earth Z = rotational axis Y X o a b a Spheroid: a three-dimensional geometric surface generated by rotating an ellipse about one of its axes. It provides an approximate model of the earth’s shape, the first step in constructing a projection

20. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Projections To represent a spherical model of the earth on a flat plane requires a map projection! Projection

21. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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.

22. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Projections CYLINDRICALPLANAR CONIC

23. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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)

24. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Projections There are many map projections Each one is good at representing one or more spatial properties No projection can preserve all four properties The goal is to select a projection that best matches the intended use of the map. Projection distortion significantly affects the properties of a small-scale map Large scale maps are less effected by projection distortion

25. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Projection Distortion Conformal projections Preserve relative angle and shape for small areas, but area is very distorted Used for navigation, meteorological charts Equivalent projections Preserve area but shape and angles are very distorted. A coin placed at any location on the map covers the same amount of area

26. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Projection Distortion MERCATOR (Conformal) ROBINSON PETERS (Equivalent)

27. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Universal Transverse Mercator (UTM) D A EMB C 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) 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 320,000 mE EMB DCA 680,000 mE 500,000 mE 0º 00’ 00” 80º 30’ 84º 30’

28. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Universal Transverse Mercator (UTM)

29. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Universal Transverse Mercator (UTM) Central Meridian Standard Meridian v v UTM ZONE 17UTM ZONE 18 81º W 75º W 72º W 78º W 84º W Pennsylvania falls between two UTM Zones: Zone 17 and 18 Using either zone for a statewide projection causes excessive scale distortion Defining a custom UTM zone with a Central Meridian at 78º W and Standard Meridians at 81º W and 75 º W would be a better customized use of the UTM projection for PA.

30. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

31. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Pennsylvania State Plane North Zone Scale: 1.000000 Scale:.9999568 Scale: 1.000000 Standard Parallel Central Parallel Central Meridian 77º 45’W Projection Origin 40º 10’N, 77º 45’W 40º 53’N 41º 57’N 41º 25’N

32. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Pennsylvania State Plane South Zone Scale: 1.000000 Scale:.9999595 Scale: 1.000000 Standard Parallel Central Parallel Central Meridian 77º 45’W Projection Origin 39º 20’N, 77º 45’W 39º 56’N 40º 58’N 40º 27’N

33. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Pennsylvania State Plane Origin Offsets For North and South Zones X offset: 2,000,000’ y offset: 0’ projection origin for both Zones: 2,000,000’, 0’ 2,000,000’ x min  1,188,150’ y min  153,500’ x max  2,813,400’ y max  677,900’ 2,000,000’ x min  1,204,600’ y min  162,000’ x max  2,805,600’ y max  771,700’

34. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Pennsylvania Statewide Projection Projection: Lambert Conformal Conic Spheroid: GRS80 Central Meridian: 77º 45’ 00.0” W (-77.75) Standard parallels: 40º 36’ 10.8” N (40.603) 41º 16’ 33.6” N (41.276) Reference latitude: 39º 19’ 59.9’ N (39.333) Considerations for selecting a statewide projection for Pennsylvania: Pennsylvania’s east/west extent is best suited for a conic projection If you need to preserve area, use Alber’s Equal Area Conic If you need to shape and angle, use Lambert Conformal Conic Select two standard parallels that divide the state into approximately even thirds north to south Select a central meridian that divides the state approximately into equal halves

35. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

36. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

37. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Scale Considerations for selection of source scale cost required accuracy desired output map detail desired feature representation density of features to be displayed

38. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

39. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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 Map Scale woods or

40. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Scale Feature resolution is defined as : Minimum mapping unit: the smallest area feature that can be effectively discerned at plot scale Generally around.15” as measured on map  1:24,000 (300 ft. on ground =.15” on map)  1:4,800 (60 ft. on ground =.15” on map)  1:2,400 (30 ft. on ground =.15” on map)  1:1,200 (15 ft. on ground =.15” on map) 60’ 30’ 15’ f(scale) =

41. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Map Scale Area features smaller than the minimum mapping unit are: Merged into surrounding data Converted from area to line (drainage) Converted from area to point (cities) Deleted/omitted LARGER SCALE 1:60,000 SMALLER SCALE 1:8,000,000

42. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Topic 5: Spatial Data Models

43. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

44. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation Point and multi-point 0 dimension feature Derived geographic properties  Proximity (distance to nearest point, vertex on line, or vertex on polygon edge)  Coincidence (point is coincident with another point)  Containment (point is within a polygon)  Connectivity (point occurs on a line)

45. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation Line and multi-line 1 dimension feature (length) Derived geographic properties  Proximity (point, line or polygon within a specified distance of)  Intersection (line intersects another line)  Connectivity (line is connected to a line or point)  Adjacency (polygon adjacent to line)  Containment (line is within a polygon)

46. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation Polygon and multi-polygon 2 dimension feature (area and perimeter) Derived geographic properties  Proximity (point, line or polygon within a specified distance of)  Intersection (polygons intersects another polygon)  Coincidence (point, line, or polygon edge coincides with polygon edge)  Adjacency (polygon adjacent to polygon)  Containment (point, line, poly within polygon)

47. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation X axis Y axis 0,0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 (2.0,3.0) (4.5, 4.5) (7.0,2.0) (1.25,5.0) Point: no dimension Line: length Polygon: area and perimeter (2.1,0.4) (5.4,1.3) Origin Implied directionality

48. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

49. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation Roads as area features: Area and perimeter available Used for: - pavement management -material estimation -large scale map production Intersection represented Intersection not represented Roads as line features: Length available Used for: - address matching - routing

50. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

51. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

52. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation (Networks) Key considerations: Unique segment identifiers  Unique sequential number Connectivity  “hidden segments” must be represented Directionality of segment orientation  downstream gravity flow  road name and increasing address range  route and increasing mile marker

53. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation (Networks) 5.6 0 2.5 3.1 RTE 322 Route system network: Orient by route number and measurement system Continuous under bridges Address system network: Orient by road name and address range Continuous under bridges 199 Main St Oak Ave Main St First Ave 101 100 198 9998 2 1

54. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Vector Representation (Networks) Cartographic Model Network Model Alternative intersection configurations

55. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Raster Representation Grid data (map algebra) Continuous value Discrete values Image data (viewing) Geo-referenced images Digital pictures

56. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

57. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Raster Representation # of Columns # of Rows 0,0 1 2 3 4 5 6 7 8 9 7 6 5 4 3 2 1 Grid Cell or Pixel 1.2 Origin 10.8 Cell size = Cell width Cell height 8.1 9.3 7.6 16.9 4.5 6.2 12.1 2.3 12.6 9.9 6.8 13.6 14.2 12.8 11.7 10.1 Measured or Coded value 10.1 8.7 7.1 5.5 3.1 6.9 7.4 1.1 2.6 1.2 10.8 8.1 6.8 13.6 14.2 2.3 12.6 9.9 12.8 11.7 10.1 12.8 11.7 10.1 9.3 7.6 16.9 9.3 7.6 16.9 12.6 9.9 6.8 7.4 1.1 2.6 7.4 1.1 2.6 8.7 7.1 5.5 3.1 6.9 7.4

58. Cell size and feature resolution True polygon area = 679,707 m² smaller cells = higher resolution = larger file size Raster Representation

59. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Raster Representation 32..332.131.432.230.6 34.8 33.8 43.0 48.2 32.633.632.733.1 34.233.5 36.1 42.840.238.540.5 35.135.031.9 34.631.2 A GRID CAN REPRESENT CONTINUOUS DATA Elevation Data are stored as floating point an reflect measurements

60. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Raster Representation A GRID CAN REPRESENT DISCRETE DATA Land cover Data are stored as integer and represent a code for classification 1 2 1 1 2 2 2 22 3 3 3 3 1 1 2 2 2 1 2

61. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Topic 6: Attribute Data Management

62. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Attribute Data Management Update History 1 02/06/1991 Brian Miller 1 11/10/1990 Dennis Ellsworth 4 01/21/1979 Linda Casey 4 07/19/1990 Brian Miller Object Instance 1 Object Instance 2 Object Instance 3 Object Instance 4 Last updated by x,y 1 25 1953 A x,y 2 30 1961 B x,y 3 40 1978 C x,y 4 35 1958 A 02/06/1991 Brian Miller 06/15/1989 Dennis Ellsworth 01/21/1979 Linda Casey 07/19/1990 Brian Miller Primary Key Foreign Key Geometry Attribute Geometry is Joined to Attribute Tables

63. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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)

64. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Attribute Management Avoid temptation to store all data for a feature in the same data table: Performance and maintenance will be an issue Separate data into different tables based on  Logical entity  Maintenance responsibility/ownership  Functional requirements  Data normalization rules

65. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 1. Minimize the amount of attribute data stored directly with the geometry Ownership of the geometry is typically with someone’s “GIS people” 2. Store and update attributes in a related database Ownership of the attribute data is typically the data creator (which many not be “GIS people’) 3. Make provisions (i.e., keys) to join geometry to the attributes tables Common standards are essential 4. There must be a person in charge! Attribute Database Management Rubrics

66. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Topic 8: Address Data

67. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 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

68. Some of this material was presented by Bruce Stauffer, Advanced Technology Solutions, Inc., and Todd Bacastow, Penn State, at a PA GIS Conference Seminar, June 2001 Derived Spatial Data (Address Data) ADDRESS CENTERLINE TABLE Seg_ID From To From To Left Left Right Right Street Name 111 99 2 98 Oak Ave 10101 199 100 198 Oak Ave 9 201 299 200 298 Oak Ave 199 Main St Oak Ave Main St First Ave 101 100 9998 2 1 1 2 3 4 5 6 78 9 11 12 Trimble Rd 10 299 201 298 198 200