1. Geospatial Data Types

2. Data Types Two general views to organizing spatial data: –Objects Monitoring measurement points, rivers, structures Have attributes or features attached to them Point, line or area format Values exist at entity locations Commonly stored and rendered in raster format (grids) –Fields Continuous data such as temperature gradient fields and satellite imagery Values exist over an area Every location has a value Commonly stored and rendered in raster format (grids)

3. Haining, 2003

4. Vector Representation X-AXIS 500 400 300 200 100 600 500 400 300 200 100 Y-AXIS River House 600 Trees B B BB B B B B G G BK B B B G G G G G Raster Representation 12345678910 1 2 3 4 5 6 7 8 9 Real World G Raster and Vector Data Models adapted from Lembo, 2003

5. Vector – Advantages and Disadvantages Advantages –Good representation of reality –Relatively compact data structure –Accurate graphics Disadvantages –Complex data structures –Some spatial analysis is difficult or impossible to perform

6. Advantages –Simple data structure –Uniform size and shape –Computationally cheaper to process Disadvantages –Large amount of data –Less visually pleasing (“blocky”) –May lose information due to generalization –Projection transformation is difficult –Different scales between grids can make comparison difficult Raster – Advantages and Disadvantages

7. Objects and Fields Objects and fields can be transformed to the other type Objects Vectors Fields Raster adapted from Bolstad, 2002

8. Vector vs. Raster Burroughs, 1996

9. Landcover Raster Grid Legend Mixed conifer Douglas fir Oak savannah Grassland (1-5) (6-10) (11-15) (16-20) 2 17 16 15 1411 1315 13 12 16 10 8 8 8 7 7 65 5 5 5 5 5 4 4 3 3 4

10. Raster = Grid columns rows The bounding box defines the geographic extent of the grid in terms of its coordinates [min_x, max_x, min_y, max_y] Abbreviation for PICTURE ELEMENT, which is the smallest unit in an image. In raster based GIS systems, attribute information can be assigned to each pixel. Pixel Matrix of Equal-Area Cells 2 17 16 15 1411 1315 13 12 16 10 8 8 8 7 7 65 5 5 5 5 5 4 4 3 3 4

11. Grid File Format (ASCII) ncols 6 nrows 6 xllcorner 210 yllcorner 370 cellsize 20 nodata_value 0 5, 6, 7, 8, 10, 13 5, 7, 8, 10, 12, 13 4, 5, 8, 12, 15, 15 3, 4, 5, 13, 15, 16 3, 5, 11, 14, 15, 17 2, 4, 5, 16, 16, 17 2 17 16 15 1411 1315 13 12 16 10 8 8 8 7 7 65 5 5 5 5 5 4 4 3 3 4

12. Table Format XYValue 2203802 2204003 2204203 2204404 2204605 2204805 2403804 2404005 2404204 2404405 2404607 2404806

13. Contoured Plots 2 17 16 15 1411 1315 13 12 16 10 8 8 8 7 7 65 5 5 5 5 5 4 4 3 3 4 Also known as an Isopleth Plot

14. Map Scale Map scale is based on the representative fraction, the ratio of a distance on the map to the same distance on the ground. Most maps used in GIS fall between 1:1 million and 1:1000. A GIS is scaleless because maps can be enlarged and reduced and plotted at many scales other than that of the original data. To meaningfully compare maps in a GIS, both maps MUST be at the same scale

15. Scale of a baseball earth Baseball circumference = 226 mm Earth circumference approx 40 million meters Scale is 1:177 million

16. Scale Dependent Measurements How long is Maine’s coastline? length=340 km length=355 km length=415 km From Longley et al., 2001

17. Resolution 25 meter5 meter 1 meter Same number of pixels (rows and columns)

18. Resolution 1 meter5 meter 25 meter Same geographic area (m X m)

21. Spatial Dimensionality 0-dimensional, L 0 points and nodes 1-dimensional, L 1 lines 2-dimensional, L 2 (x,y) areas, polygons 3-dimensional, L 3 (x, y, z) volumes 4-dimensional, L 4 (x, y, z, t) 3-D plus time Another way to classify spatial object types is by their dimensionality

22. 2.5 Dimensions

23. Attributes Attributes are the values and properties of an object or entity

24. Types of Attributes Nominal – Simply identifies or classifies an entity so that it can be distinguished from another. e.g. sensor ID, building name –Cannot be manipulated using mathematical operations. However, frequency distributions are meaningful. Ordinal – Values based on an order or ranking, e.g. agricultural potential classes –Cannot be manipulated using mathematical operations. However, frequency distributions are meaningful. Interval – Differences between entities are defined using fixed equal units, e.g. Celsius temperature –Can be manipulated using addition and subtraction Ratio - Differences between entities can be defined using ratios, e.g. distance –Can be manipulated using multiplication and division Cyclic - differences between entities depending on repeating sequence, e.g. wind direction A common approach to classifying attributes is based on their level of measurement

25. Structured Query Language (SQL) SELECT column name SQL is a formal search language that allows you to work with, access and filter data stored in a relational database format FROM data table name WHERE data condition The most common use for SQL is to retrieve subsets of data based on specified conditions

26. ArcGIS Select by Attribute SELECT * FROM MO_STN WHERE O3 > 80 AND PM25 > 15

27. Defining Reclassification Categories

28. Classification Schemas Natural breaks: classes are defined according to apparently natural groupings of data values. (GIS programs that automatically determine classes usually base them on relatively large jumps in data values.) Quantile breaks: classes are defined by having an equal number of observations Equal interval breaks: classes are defined by uniform intervals Standard deviation breaks: classes are defined by differences from the mean value.

29. Color Brewer http://www.personal.psu.edu/faculty/c/a/cab38/ColorBrewerBeta.html

30. Graphic Visualization Components

31. Summary  Two general data types: object & field  Generally, “handled” as either vector or raster  Data can have multiple attributes (properties) associated with features or grid cells  Levels of measurement helps formalize the arithmetic and statistics that are appropriate for a particular dataset

32. DateTopicReading Problem SetTutorial 31-AugGIS OverviewBolstad Chp 1 Gorr, Chp1 7-SepGeospatial DataLongley Chp 3 Gorr Chp2-3 14-Sep Projections and Coordinate Systems Bolstad Chp 3 Problem Set 1 distributed Gorr Chp4, Chp 5 (p. 172-180) 21-SepFeature AnalysisBolstad Chp 9 Gorr Chp 8 (p. 272- 290), Chp 9 28-SepSurface AnalysisBolstad Chp 10/11 PS1 due; PS2 distr. Handout: Suitability Analysis 5-OctSpatial Data AnalysisBolstad Chp 12 Handout: California Air Pollution 12-OctSpatial Modeling / Web GISBolstad Chp 13 PS2 dueGorr Chp 8 (p. 291- 299), Handout: Groundwater Modeling 19-OctExam / Project Presentations

33. Gistutorial\UnitedStates States Counties Cities Capitals Utah Nevada Pennsylvania Gistutorial\Layers Tutorial3-1.mxd Tutorial3-NativeAmericans.mxd