Research Website
http://www.purdue.edu/discoverypark/vaccine/
Keywords
Visual Analytics, Predictive Analytics, Dynamic Covariance Kernel Density Estimation, Seasonal-Trend Decomposition
Presentation Type
Event
Research Abstract
There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well as design an interface to present these results to the user in a timely fashion. In our approach, we make use of the Dynamic Covariance Kernel Density Estimation (DCKDE) method to visualize the data in a geospatial context. The results are represented as a heat map showing the areas with a higher probability of crime. In the temporal context, a modified Seasonal Trend decomposition based on Loess (STL) is used to decompose time series signals in order to isolate trends that are used to predict the number of crime occurrences in pre-defined areas for a given time interval. These techniques were applied to Tippecanoe County to make predictions for the next time step. We evaluated the results of our prediction technique against observed data. We note that our methods are applicable to any situation where incidents may have a local spatial correlation.
Session Track
Data Analytics
Recommended Citation
James Q. Tay, Abish Malik, Sherry Towers, and David Ebert,
"Spatiotemporal Crime Analysis"
(August 7, 2014).
The Summer Undergraduate Research Fellowship (SURF) Symposium.
Paper 97.
https://docs.lib.purdue.edu/surf/2014/presentations/97
Included in
Analysis Commons, Applied Statistics Commons, Graphics and Human Computer Interfaces Commons, Longitudinal Data Analysis and Time Series Commons, Statistical Models Commons
Spatiotemporal Crime Analysis
There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well as design an interface to present these results to the user in a timely fashion. In our approach, we make use of the Dynamic Covariance Kernel Density Estimation (DCKDE) method to visualize the data in a geospatial context. The results are represented as a heat map showing the areas with a higher probability of crime. In the temporal context, a modified Seasonal Trend decomposition based on Loess (STL) is used to decompose time series signals in order to isolate trends that are used to predict the number of crime occurrences in pre-defined areas for a given time interval. These techniques were applied to Tippecanoe County to make predictions for the next time step. We evaluated the results of our prediction technique against observed data. We note that our methods are applicable to any situation where incidents may have a local spatial correlation.
https://docs.lib.purdue.edu/surf/2014/presentations/97