Abstract
In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.
| Original language | English (US) |
|---|---|
| Article number | 6875970 |
| Pages (from-to) | 1863-1872 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 20 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 31 2014 |
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Keywords
- Law enforcement
- Natural scales
- Seasonal Trend decomposition based on Loess (STL)
- Visual analytics
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement. / Malik, Abish; Maciejewski, Ross; Towers, Sherry; McCullough, Sean; Ebert, David S.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 20, No. 12, 6875970, 31.12.2014, p. 1863-1872.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement
AU - Malik, Abish
AU - Maciejewski, Ross
AU - Towers, Sherry
AU - McCullough, Sean
AU - Ebert, David S.
PY - 2014/12/31
Y1 - 2014/12/31
N2 - In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.
AB - In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.
KW - Law enforcement
KW - Natural scales
KW - Seasonal Trend decomposition based on Loess (STL)
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=84909606231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84909606231&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2014.2346926
DO - 10.1109/TVCG.2014.2346926
M3 - Article
C2 - 26356900
AN - SCOPUS:84909606231
VL - 20
SP - 1863
EP - 1872
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 12
M1 - 6875970
ER -