Raster Data Analysis Chapter 11

Introduction  Regular grid  Value in each cell corresponds to characteristic  Operations on individual, group, or grid  ArcView with Spatial Analyst Extension  ARC/INFO GRID

Data Analysis Environment  Extent and cell size Extent x-y min/max or combination (union or intersect of two grids)  Mask grid limits analysis to cells that do not carry “No Data” value remember zero is different from “no data”  Accuracy to least

Local Operations  Cell by cell operations creating a new grid from either a single input grid or multiple input grids manipulated by a function.  Single grid output as mathematical function such as  arithmetic (+, -, *, /, abs, int, floating)  logarithmic (exponentials, logarithms)  trigonometric (sin, cos, tan, arcsin, arccos, arctan)  power (square, square root, power) radians

Local Operations  Multiple grids (compositing, overlaying, or superimposing)  More frequent to have multiple  Summary statistics (max, min, range, sum, mean, median, standard deviation)  Categorical statistics (majority, minority, unique values)

Local Operations  In ArcView Cell Statistics or Map Calculator  Map Calculator uses Avenue [object].request(paramter) format large assortment of arithmetic, logical, Boolean, logarithmic, trigonometric and power functions

Applications of Local Operations  Universal Soil Loss  A = R K L S C P A = average soil loss in tons R = rainfall intensity K = erodibility of the soil L = slope length S = slope gradient C = cultivation factor P = supporting practice factor

Neighborhood Operations  Focus cell and surrounding cells  F+4 and F+8 most common  Circule, annulus, wedge  Focus Cell  computation  back to Focus Cell  Similar to what happens with grid1+grid2=grid3  Summary statistics available

Neighborhood Operations  Neighborhood operations in ArcView Neighborhood Statistics using grid or point Statistic and neighborhood Statistic (min, max, mean, median, sum, range, standard deviation, majority, minority, and variety) Neighborhood (rectangle, circle, doughnut, and wedge) Block Stats (not cell to cell but block to block)

Neighborhood Operations  Applications of Neighborhood Operating Data simplification using moving average with average of neighborhood being assigned to the focus cell Variation by using variety in the focus cell Filtering, convolution, moving window operations Edge enhancement (range to focus) Smoothing (majority to focus)

Zonal Operations  Zonal operation works with groups of cells of same values or like features called zones  Describe the geometry of zones such as area, perimeter thickness and centroid  Can be one grid or input + zonal = output

Zonal Operations  Zonal operations in ArcView Map Calculator can calculate geometry of zones in an input grid Summarize Zones, Histogram by Zone and Tabulate Areas for two grids

Applications of Zonal Operations  Landscape ecology (ie shape index)  Comparison grids

Distance Measure  Distance measure operations calculate distances away from cells designated as the source cells.  1 cell lateral, cells for diagonal link  Physical distance vs cost distance  Can be coded in direction units

Physical Distance Measure Operations  Cell units for measurements  Buffer source cells

Cost Distance Measure Operations  Cell + cost cell in second grid  Impedance as defined by the application  Average of cells in link used for calculation  Least cost path is objective

Distance Measure Operations in ArcView  Assign proximity  CostDistance  CostPath

Applications of Distance Measure Operations  Modeling  Buffering not as accurate as vector

Spatial Autocorrelation  Spatial autocorrelation measures the relationship among values of a variable according to the spatial arrangement of the values.  High correlation if like values are closely packed  Moran’s I  Geary’s C