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    Albuquerque, New Mexico - Burglary Hot Spots (2015 - 2016)

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    Updated Feb 7, 2017
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    Larry Spear's GIS Research Projects (2017). Albuquerque, New Mexico - Burglary Hot Spots (2015 - 2016) [Dataset]. https://hub.arcgis.com/maps/0d3db036147b4b7fbe7a2691ed723722
    Explore at:
    Dataset updated
    Feb 7, 2017
    Dataset authored and provided by
    Larry Spear's GIS Research Projects
    Area covered
    Description

    Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

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Click to copy link
Link copied
Close
Cite
Larry Spear's GIS Research Projects (2017). Albuquerque, New Mexico - Burglary Hot Spots (2015 - 2016) [Dataset]. https://hub.arcgis.com/maps/0d3db036147b4b7fbe7a2691ed723722

Albuquerque, New Mexico - Burglary Hot Spots (2015 - 2016)

Explore at:
Dataset updated
Feb 7, 2017
Dataset authored and provided by
Larry Spear's GIS Research Projects
Area covered
Description

Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

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