1 / 52

Intro to Raster GIS

Intro to Raster GIS. GTECH361 Lecture 11. CELL. COLUMN. ROW. CELL with VALUE. NO DATA. Raster Attribute Table. NODATA. Coordinate Space and the Raster Dataset. Discrete and Continuous. Resolution. Realm of Analysis.

juana
Download Presentation

Intro to Raster GIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Intro to Raster GIS GTECH361 Lecture 11

  2. CELL

  3. COLUMN ROW

  4. CELL with VALUE

  5. NO DATA

  6. Raster Attribute Table

  7. NODATA

  8. Coordinate Space and the Raster Dataset

  9. Discrete and Continuous

  10. Resolution

  11. Realm of Analysis • Windowidentifies spatial limits for future analysis and processing • Maskfurther defines the cells for processing • Cell sizedetermines the size of the cells on the output grids; default is the coarsest input grid

  12. Window

  13. The Effect of a Mask

  14. Map Algebra aka Cartographic Modeling

  15. Dana Tomlin 1990Geographic Information Systems and Cartographic ModelingPrentice-Hall

  16. Map Algebra • Mathematical combinations of layers • Several types of functions: • Local • Focal • Zonal • Global • Functions can be applied to one or more layers

  17. Local Functions • Sometimes called layer functions • Work on every single cell in a raster layer • Cells are processed without reference to surrounding cells • Operations can be arithmetic, trigonometric, exponential, logical or logarithmic functions • As we are dealing with numbers, we can use a plethora of mathematical computations.

  18. Local Functions • new layer is a functionof two or more inputlayers • output value for eachcell is a function ofthe values of the corresponding cells in the input layers • neighboring or distant cells have no effect

  19. Local Function Examples • Multiply cells by a constant valueUse a multiplier grid We can use a range of arithmetic functions

  20. Local Operations • Creating grids from nothing (0-ary operations) RandomPopulate the cell values with independent identically distributed (iid) floating point values between 0 and 1 NormalPopulate the cell values with iid values from the standard Normal distribution (mean 0 and variance 1).

  21. Local Operations- unary operations - • Rescale: multiply all values by a constant • Compare a grid to a constant value.  The result of a comparison is 1 for the cells where the comparison is true, 0 where the comparison is false • Apply a mathematical (or logical) function to a grid, cell by cell

  22. Local Operations- binary operations - • Mathematical operators • MOD * / + - ()exp • Logical operators • AND OR XOR NOT

  23. Neighborhood Operations(focal) • output cell value isa function of a groupof neighboring cellsin the input raster • operations could be- average (focalmean)- sum (focalsum)- variance (focalvar)- …..

  24. Focal Functions • Focal functions process cell data depending on the values of neighboring cells • We define a ‘kernel’ to use as the neighborhood • for example, 2x2, 3x3, 4x4 cells • Sometimes in spatial analysis we use shapes to define the focal neighborhood • Around edges a reduced kernel size is used • Types of focal functions might be: • focal sum, focal mean, focal max, focal min, focal range

  25. Focal Function Examples • Focal Sum (sums the value of a neighborhood) • Focal Mean (computes the moving average of a neighborhood)

  26. Another Focal Example • slope- steepness of slope in elevation layer- computed by comparing cell elevation with neighboring values- measured as the angle from horizontal

  27. Zonal Functions • Process and analyze cells on the basis of ‘zones’ • Zones define cells that share a commoncharacteristic • Cells in the same zonedon’t have to be contiguous

  28. Zonal Functions • A typical zonal function requires two grids • a zone grid which defines the size, shape and location of each zone • a value grid which is to be processed • Typical zonal functions include zonal mean, zonal max, zonal sum, zonal variety • also: sum of the values in different raster that fall into the same zone (e.g., mean district elevation) • results could be assigned to each cell in that zone, or written to a summary table

  29. Zonal Function Example • Zonal maximum - identify the maximum in each zone • Useful when we have some regions to classify with • for example, different forest types

  30. Buffer as a Zonal Function • Can be thought of as spreading a feature by a given distance Feature Buffer

  31. Global Functions • The output value of each cell is a function of the entire grid • Typical global functions are distance measures, flow directions, or weighting measures. • Useful when we want to work out how cells ‘relate’ to each other

  32. Global Function Examples • Distance measures • Euclidean distance computes distance based on cell size • Use a ‘cost’ grid to weight functions

  33. Map Algebraon Multiple Layers • outgrid = zonalsum(zonegrid, valuegrid) • outgrid = focalsum(ingrid1, rectangle, 3, 3) • outgrid = (ingrid1 div ingrid2) * ingrid3 • Map algebra can also be used for multivariate and regression analysis

  34. Application Functions • Surface Analysis • Hydrologic Analysis • Geometric Transformation • Generalization

  35. Surface Analysis • Slope • Aspect • Hill shade • View shed • Curvature • Contour

  36. Hydrologic Analysis • Stream network • Watershed • Discharge • .. more under modeling

  37. Generalization Original satellite “Nibble” Majority filter image

  38. GIS-based Spatial Analysis • Mapping distance • Mapping density • Interpolating to raster • Surface analysis • Neighborhood statistics • Cell statistics • Zonal statistics • Reclassifying data • Raster calculator

  39. Mapping Distance • Straight line distance (Thiessen/Voronoi)

  40. What Direction?

  41. Allocation • Identifying the customers served by a series of stores • Finding out which hospital is the closest • Finding areas with a shortage of fire hydrants • Locating areas that are not served by a chain of supermarkets

  42. Cost-weighted Distance • Cost can be money, time, or preference • Two input grids • One regular distance grid • One friction surface • Reclassifying your datasets to a common scale • Weighting datasets according to percent influence

  43. Cost Raster

  44. Shortest Path • Combination of • (weighted) distance • and direction

  45. Density Mapping • In a simple density calculation, points or lines that fall within the search area are summed and then divided by the search area size to get each cell’s density value.

  46. Interpolation • e.g. precipitation

  47. Surface Analysis • Contours • Slope, aspect • Viewshed

  48. Illumination at different times of the day 45° 315° Viewshed analysis

  49. Cell Statistics • Majority • Maximum • Mean • Median • Minimum • Minority • Range • Standard deviation • Sum • Variety

More Related