Project

​ Geographic Profiling Cloud App

Goal: Our goal is to develop the cloud application for Geographic Profiling (GP). As features of the application, We are also investigating the effectiveness of environmental information such as the past crime data to GP accuracy and exploring the standard coefficients of distance decay functions based on a simulation study.

Date: 1 July 2014

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Project log

Shumpei Haginoya
added a research item
Existing geographic profiling software that performs the widely tested probability dis- tance strategies has issues when implemented in criminal investigation in Japan. Therefore, we developed the Spatial Analysis Methods of Offender’s Nodes (SAMON) software based on a free software environment, R. Given the issues involving existing software, SAMON includes the fol- lowing three features: (1) prediction of an offender’s home base using different distance decay func- tions constructed from Japanese burglars’ Journey-to-Crime distances; (2) validation of prediction accuracy in the solved case; and (3) calibration of the distance decay functions using a sample of solved cases in a type and region that the user is interested in. We expect that SAMON will improve the availability of probability distance strategies and its accuracy in the Japanese context.
Shumpei Haginoya
added an update
SAMON added a function to show the cumulative relative frequency of Journey-to-Crime distances in response to requests from professionals. This can be applied for estimating the probability of including the offender's home base in each radius from the offense location when analyzing one-off crime.
You can try this function in the trial versions in each language:
Should you have any question, please contact Shumpei Haginoya.
 
Shumpei Haginoya
added an update
Spanish and Japanese versions are added to SAMON. You can try them from the following links:
We welcome your feedback and request to utilize these on your practice/research.
 
Shumpei Haginoya
added an update
The demonstration version of the Geographic Profiling cloud application named "SAMON: Spatial Analysis Methods of Offender's Nodes" is released for public use:
We are happy to have your feedback on this app. Please, see the following list for the current/upcoming features:
Currently you can try to use:
- Prediction of offender's home base using distance decay methods
- Validation of prediction accuracy using arrested offender's home base location
- Creation and implementation of the original functions based on the solved crime data
Upcoming features:
- Japanese and Spanish (only English for now)
- Commuter warning (automated detection of offenders who are traveling from the outside of offending area)
- Cross-validated calculation of prediction accuracy measures (search area and error distance) for solved crime data
 
Shumpei Haginoya
added 2 research items
The accuracy of geographic profiling for predicting a serial offender's home/base location was compared by using three different distance measures—the Euclidean distance, the Manhattan distance, and the Shortest route distance—using the data of 1,856 crimes committed by 124 residential burglars in Northern Tohoku area of Japan from 2004 to 2015. Logarithmic and the negative exponential coefficients were estimated as the distance decay function for each distance measure by using leave-one-out cross-validation. Also, search areas were calculated to compare the accuracy of geographic profiling. Results of the Friedman's test indicated significant differences in search areas of the three distance measures for the wide area group which consisted of offenders having a long distance between crime locations. The search area when utilizing the Shortest route distance was the smallest for the logarithmic function, whereas the search areas using the Euclidean distance and the Shortest route distance were smaller than the Manhattan distance for the negative exponential function. Results of the narrow area group did not indicate significant differences in search areas for the three distance measures. Therefore, it was concluded that geographic profiling might be improved by using the Shortest route distance when calculating the probability distribution for offenders committing crimes in a wide area that includes many edges, such as rivers, railroads, and mountains, as well as paths such as bridges and railroad crossings.
Human behavior is characterized by inherent regularity, and previous studies have confirmed that criminal behavior is no exception. In particular, three distinguishable aspects of geographic offending behavior may be characterized by consistency: destination, distance, and direction. In this study, which focused on the aspect of direction, we carried out Van Daele and Bernasco's (2012) additional tests concerning residential burglary cases in Japan. Similar to Van Daele and Bernasco, we proposed the directional consistency could be verified by mean angulation calculated with the average angle between all possible pairs of offenses in a series and compared these to the thresholds that were simulated using the angles chosen at random by the previous authors. Our results confirmed directional consistency in the cases of residential burglary in Japan. We expect this information will improve the accuracy of estimating the location of the offender's residence in geographic profiling. However, the offenders did not show as much consistency as those observed in the previous study. Furthermore, these findings do not take into account preferences in crime patterns or the environmental characteristics. Therefore, it is necessary to verify the impact of these factors on directional consistency.
Shumpei Haginoya
added a research item
This study examines the effects of neighbourhood attractiveness on the residential burglar's crime location choice process using a discrete choice model. We show that past crime data are an important index of a neighbourhood's attractiveness and can be combined with other attractiveness indices adapted from previous studies. We used data from 369 solved cases committed by 70 offenders and related these data to 1,134 areas (500 m grid cells) in Sendai City, Japan. The results showed that residential burglars were attracted to the following potential locations for crimes: (a) areas in proximity to his or her own residence; (b) areas having many or at least a higher proportion of residential burglaries in the past; (c) areas having many residential units; and (d) areas having a higher proportion of single‐family dwellings. The results confirm the validity of past crime data as an index of a neighbourhood's attractiveness for residential burglary.
Shumpei Haginoya
added a project goal
Our goal is to develop the cloud application for Geographic Profiling (GP). As features of the application, We are also investigating the effectiveness of environmental information such as the past crime data to GP accuracy and exploring the standard coefficients of distance decay functions based on a simulation study.