WSU - ESRI ARCMAP CHECKLIST
Spatial Analytics / Statistics
Creating and understanding spatial relationships and patterns is why we use GIS software and how we can better understand the world around us. This is the heart of the world of Geographic Information Systems. Spatial analytics provide understanding to the world around us via mapping where things are, how they relate to one another, what is all means, and what actions we should take. Per Esri: From computational analysis of geographic patterns to finding optimum routes, site selection, and advanced predictive modeling, spatial analysis is at the very heart of geographic information system (GIS) technology.
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Interpolation: IDW, Empirical Bayesian Kriging
Density: Line Density, Point Density
Mapping Clusters: Cluster and Outlier, Grouping, Hot Spot, Optimized Hot Spot
Extract by Mask
Interpolation - IDW
Inverse Distance Method: Estimates cell values of sample data points in the neighborhood of each processing cell. The closer a point is to the center of the cell being estimated, the more influence or weight it has in the averaging process.
Created IDW raster based on SEStores by total number of employees, then clipped the raster to the states it was based on.
Interpolation - Empirical Bayesian Kriging
Empirical Bayesian kriging (EBK) is a geostatistical interpolation method that automates the most difficult aspects of building a valid kriging model. Unlike other kriging methods, EBK automatically calculates these parameters through a process of subsetting and simulations and generates an estimated surface from a scattered set of points with z-values.
Used Modelbuilder to krig SEStores by total number of employees to then compare to IDW raster.
Density - Line Density
Calculates a magnitude-per-unit from polyline features that fall within a radius around each cell. Requires input raster --> output raster. [Length of portion of each line that falls within circle is multiplied by its population field value. These figures are summed, and total is divided by circle's area.]
Calculated magnitude of tornado tracks in Kansas.
Density - Point Density
Calculates a magnitude-per-unit area from point features that fall within a neighborhood around each cell. [A neighborhood is defined around each raster cell center, and the number of points that fall within the neighborhood is totalled and divided by the area of the neighborhood.]
COStores by incremental battery sales.
Mapping Clusters - Cluster and Outlier
Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
COStores based on incremental battery sales.
Mapping Clusters - Grouping
Groups features based on feature attributes and optional spatial or temporal constraints.
Photos A + B: 5 groups, non-spatial constraint. Photos C + D: 5 groups, k-nearest neighbor.
Mapping Clusters - Hot Spot
Given a set of weighted features, identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) using the Getis-Ord Gi* statistic. It creates a new Output Feature Class with a z-score, p-value, and confidence level bin (Gi_Bin) for each feature in the Input Feature Class.
Photos A + B: COhotspot - battery sales. Photos C + D: COtotalhotspot - totalsales.
Mapping Clusters - Optimized Hot Spot
Interrogates your data to automatically select parameter settings that will optimize your hot spot results. It will aggregate incident data, select an appropriate scale of analysis, and adjust results for multiple testing and spatial dependencies. The parameter options it selects are written as messages, and these may help you refine your parameter choices in the regular hot spot tool.
Photo A + B: COtotalhotspotOPT (optimized) - totalsales. Photo C + D: USEmplhotspotOPT (optimized) - number of employees.
Create and execute Map Algebra expressions.
Average of IDW and EBK rasters from earlier by adding the two together and dividing by 2 in the Raster Calculator tool.
Extract by Mask
Extracts the cell of a raster that correspond to the area defined by a mask. [Input = raster / Output = raster / Mask = raster OR feature dataset]
Clip point density raster by outline of US states shapefile, then apply symbology based on a different layer (in different section of the "checklist").
Mask by Environment
Applies to Spatial Analyst and Geostatistical Analyst extension tools that output a raster. Also applies to tools in the Raster Interpolation, Raster Math, Raster Reclass, and Raster Surface toolsets of 3D Analyst extension that output a raster.
Converts each cell value of a raster to an integer by truncation
Snow coverage maps - example