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Geospatial Data Types

Geospatial Data Types. 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

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Geospatial Data Types

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  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. Raster and Vector Data Models Real World 600 1 2 3 4 5 6 7 8 9 10 G 1 B Trees 500 2 G B G 3 B 400 G G 4 B Trees Y-AXIS 5 B G G 300 B BK 6 G G House 7 B 200 B B 8 River 100 9 B 200 500 100 300 600 400 10 B X-AXIS Raster Representation Vector Representation adapted from Lembo, 2003

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

  6. Raster – Advantages and Disadvantages • 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

  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. 5 6 7 8 10 13 5 7 8 10 12 13 4 5 12 8 15 (16-20) 15 3 4 (11-15) 5 13 15 16 3 5 (6-10) 17 15 11 14 (1-5) 4 5 2 17 16 16 Landcover Raster Grid Legend Mixed conifer Douglas fir Oak savannah Grassland

  10. 5 6 7 8 10 13 5 7 8 10 12 13 4 5 12 8 15 15 3 4 5 13 15 16 3 5 17 15 11 14 4 5 2 17 16 16 Raster = Grid Matrix of Equal-Area Cells Pixel columns 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. rows The bounding box defines the geographic extent of the grid in terms of its coordinates [min_x, max_x, min_y, max_y]

  11. 5 6 7 8 10 13 5 7 8 10 12 13 4 5 12 8 15 15 3 4 5 13 15 16 3 5 17 15 11 14 4 5 2 17 16 16 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

  12. Table Format

  13. 5 6 7 8 10 13 5 7 8 10 12 13 4 5 12 8 15 15 3 4 5 13 15 16 3 5 17 15 11 14 4 5 2 17 16 16 Contoured Plots 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 meter 5 meter Same number of pixels (rows and columns) 1 meter

  18. Resolution 1 meter 5 meter 25 meter Same geographic area (m X m)

  19. Spatial Dimensionality Another way to classify spatial object types is by their dimensionality 0-dimensional, L0 points and nodes 1-dimensional, L1 lines 2-dimensional, L2 (x,y) areas, polygons 3-dimensional, L3 (x, y, z) volumes 4-dimensional, L4 (x, y, z, t) 3-D plus time

  20. 2.5 Dimensions

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

  22. Types of Attributes A common approach to classifying attributes is based on their level of measurement • 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

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

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

  25. Defining Reclassification Categories

  26. 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.

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

  28. Graphic Visualization Components

  29. 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

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

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