Visual analytics law enforcement tools
A system and method for visually displaying and analyzing criminal and/or public health and safety data for geospatial and/or time variations, including the collection of incident data coupled with geographic and time data, filtering the symptom data based upon a selected time period and geographic range, and creating a visual result based upon statistical modeling including power transform and/or data normalization. According to at least one embodiment, the system for visually displaying and analyzing includes selecting and performing at least one aberration detection method and displaying the result to a user via a visual analytics arrangement.
1. A method comprising:
obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations;
sorting the first data based upon incident categories;
applying seasonal trend decomposition with loess smoothing for the first data;
plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit;
displaying the plotted incidents to a user via a graphic user interface;
obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations;
filtering the first data based upon the correlation of the first data with the second data; and
predicting the occurrence of additional incidents based upon the filtered data.
2. A method comprising:
obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations;
sorting the first data based upon incident categories;
applying seasonal trend decomposition with loess smoothing for the first data;
plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit; and
displaying the plotted incidents to a user via a graphic user interface,
wherein plotting the incidents within the first data is performed as a density estimated heatmap, wherein the density estimated heatmap is performed by employing a variable kernel method which scales the parameter of an estimation by allowing a kernel width to vary according to the algorithm
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3. The method of claim 2 , further comprising generating display information based on the density estimated heatmap.
4. The method of claim 1 , further comprising plotting spatial statistics of the first data utilizing a multidirectional optimum ectope-based algorithm to create a spatial weights matrix to identify hot and cold spots in mapped data.
5. The method of claim 1 , further comprising:
generating quantile measures based on a select epicenter and time window;
displaying information representative of the quantile measures.
6. The method of claim 2 , further comprising obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations.
7. The method of claim 6 , further comprising:
filtering the first data based upon the correlation of the first data with the second data.
8. The method of claim 7 , further comprising predicting the occurrence of additional incidents based upon the filtered data.
9. A method comprising:
obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations;
sorting the first data based upon incident categories;
applying seasonal trend decomposition with loess smoothing only for the first data;
plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit;
obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations; and
displaying the plotted incidents and second data to a user via a graphic user interface.
10. The method of claim 9 , further comprising:
generating quantile measures based on a select epicenter and time window;
displaying information representative of the quantile measures.
11. A method comprising:
obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations;
sorting the first data based upon incident categories;
applying seasonal trend decomposition with loess smoothing for the first data; plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit;
obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations; and
displaying the plotted incidents and second data to a user via a graphic user interface,
wherein plotting the incidents within the first data is performed as a density estimated heatmap, wherein the density estimated heatmap is performed by employing a variable kernel method which scales the parameter of an estimation by allowing a kernel width to vary according to the algorithm
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12. The method of claim 11 , wherein generating the heatmap includes displaying a color of the glyph based on the value for the site.
13. The method of claim 9 , further comprising plotting spatial statistics of the first data utilizing a multidirectional optimum ectope-based algorithm to create a spatial weights matrix to identify hot and cold spots in mapped data.
14. The method of claim 13 , wherein the spatial weights matrix is determined according to the value of G* i , where G* i is defined as:
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15. The method of claim 14 , further comprising: displaying a color of a group based on the G* i value.