DOC cannot independently confirm the accuracy of all information. The Wisconsin Department of Corrections is not responsible for any errors or omissions produced by secondary dissemination of this information. If you believe that any of the information contained in the registry is inaccurate, please send us your comments. It is not the intent of the Legislature that this information be used to injure, harass, or commit a criminal act against persons named in the registry, their families, or employers. Anyone who takes any criminal action against these registrants, including vandalism of property, verbal or written threats of harm or physical assault against these registrants, their families or employers is subject to criminal prosecution.
W isconsin D epartment of C orrections. Conditions of Use: Purpose This data is being provided on the Internet to make the information more easily available and accessible, not to warn about any specific individual. Figure 3. Although St. Louis was chosen as the study area because of data availability, it is also an interesting city in terms of crime analysis. Louis is the second largest city in Missouri, and has a total population of about , people.
The city is well studied in terms of its economic and racial segregation see Farley Farley, J. Louis Case. Louis, Louis Metropolitan Area, Crimes patterns of various types are described in a variety of manuscripts e. Kubrin and Weitzer Kubrin, C. Louis, MO Final TGDE intensity surfaces were generated from the paired home and work locations for the 87 sex offenders in the study sample.
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Three surfaces were created for comparison using different values for the parameter measuring the additional discretionary travel distance: , , and meters. These values were chosen to represent a range of travel patterns ranging from very restricted movement near the shortest path between home and work to more flexible activity away from this route. The results were joined into a larger attribute table containing all of the distances organised by network node and individual.
Second, the degree of the shortest path tree was calculated for each individual. This was accomplished by counting the number of unique shortest paths in each potential path tree. Unique paths were identified as having different total lengths. The calculated potential path tree degrees were also joined to the larger attribute table containing relevant distances.
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Once these intensity values were calculated for each node in the network, they were interpolated to continuous space using a Voronoi diagram. A Voronoi diagram is a spatial tessellation widely used in GIS that assigns each location in space to the nearest input point Okabe et al. Boots , K. Sugihara , and S. Series in Probability and Statistics. Chichester: Wiley. In this case, all locations in the city are assigned to the nearest network node, which also gives them the intensity value of that node. This process also facilities geovisualisation of the intensity values. Intensities were mapped using a quintile classification scheme.
The intensity surfaces quantifying the areas most accessible to sex offenders were compared to observed rape incident data to evaluate how well they predict crime. First, the rape incidents were spatially joined with the TGDE surfaces to identify the intensity value at each location. The calculated intensities were multiplied by a scaling factor of 1 x 10 3 to reduce excessive decimals, as is common for density estimation techniques.
Summary statistics were reported for each of the three surfaces. Second, those values were compared to random points 1, sets of random incidents. Random points were not generated inside Census tracts that had zero population if rapes were not observed there to avoid artificially biasing the results towards areas with little expected crime. The northernmost Census tracts of low population density were also excluded for the same reason. The results were compared using a Kolmorogov—Smirnov test.
This nonparametric test was used since the intensity values were not normally distributed in either sample. The purpose of the comparison was to determine whether the rape incidents were at locations with higher intensity values than recorded for random locations. If this was the case, then it was concluded that TGDE intensity surfaces derived from sex offender activity spaces effectively predicted the locations of rapes. Final TGDE surfaces representing estimated sex offender activity spaces with different discretionary travel distances in St.
Louis are shown in Figure 4. The highest intensity areas were found mostly in the eastern and central parts of the city, while those outside this area mostly occurred in residential areas in the southwest and north. The high-intensity areas occurred mostly along the shortest paths between offender home and work, since the estimated travel budget is quite restrictive. The incidents had a median scaled intensity of 0.
The Kolmorogov—Smirnov test indicated the differences in medians was significant , meaning that intensities were greater at rape incident locations than expected by random. TGDE surfaces representing estimated sex offender activity spaces in St. Figure 4. Intensity maps for the larger discretionary travel distances produce much the same general pattern but encompass greater proportions of the city.
The top quintiles of intensity occur within the same areas of eastern and central St. As the travel budget increases, the high-intensity areas are less linearly shaped compared to the smaller distances. The median intensity for the incident sites was 0. This compares to a median intensity at random points of 0. The rape sites not captured by the potential path trees were located in the northwest part of the city.
The Kolmorogov—Smirnov test indicated the differences in medians for both scenarios were significant. The three TGDE surfaces representing combined sex offender activity spaces produced relatively similar results regarding predicting rape locations in St. Although large percentages of the city fell within the potential path trees of offenders, areas of high intensity were confined to much smaller regions. All three discretionary travel distance scenarios identified roughly the same highest intensity areas in eastern and central St.
Louis — particularly in the downtown area — although some smaller regions were more pronounced under the higher distance scenarios. Although all three maps produced significantly higher intensities at rape locations compared to random points, indicating predictive power, it is desirable to assess which scenario might be the most accurate or useful. There is a trade-off between capturing higher percentages of incident sites and more precisely delineating the at-risk activity spaces, which may be important for targeting policing efforts.
As larger discretionary travel distances are used, potential path trees encompass more of the city — potentially extending beyond the study area boundaries if large enough values are used — and the intensity surface becomes much smoother. Larger distance values allow more incident sites to be captured but at the expense of including more areas without observed rapes within the activity spaces; as distances increase, high-intensity areas visible at smaller distances merge together into a larger, more homogenous region.
One way of evaluating the predictive power of each map is to calculate the ratio of the median intensities between rape sites and random points, with larger ratios indicating better performance. In this study, the calculated ratios are 1. As discretionary distances are increased beyond those reported in this paper, the relationship between the activity spaces and rape incident weakens, meaning the smallest travel distance scenario best predicts rape incidents.
This is interesting, as it suggests rape incidents are concentrated on and very close to the anchor points and expected travel paths of sex offenders, which is consistent with the journey-to-crime literature Andresen, Frank, and Felson Andresen, M. In terms of policing, relationship is helpful to exploit, as the effectiveness of a crime prediction technique can be measured by comparing the sizes of search areas to the overall study area Rossmo Rossmo, D.
Accessed 18 January The main advantage of this particular mapping approach is that it allows one to predict crimes based on the estimated activity spaces of potential criminals. In this way it models the mechanism creating the pattern of crime, namely the movements of potential criminals seeking possible victims from available opportunities within a city. In the case study presented in this paper, activity spaces estimated for 87 convicted sex offenders both working and residing in St.
Louis were sufficient to predict the spatial patterns of rape. There are three main explanations for the effectiveness of the approach. First, one might infer that the individuals in the data set were responsible for committing the known forcible or attempted rapes including sodomy analysed in the study. However, this explanation is unlikely given the number of incidents and the fact that recidivism rates for convicted sex offenders are relatively low compared to other types of crime Langan and Levin Langan, P. Furthermore, some of the offenders were released after many of the crimes were actually committed, given that the offender database was accessed in , while the rape data is from A more reasonable second explanation is that the estimated activity spaces of the sex offenders are representative of people likely to commit rape or sodomy.
Other research has found that sex crimes are more likely to be committed by people with prior criminal records Langan, Schmitt, and Durose Langan, P.
Sex Offender Registry Websites — FBI
Schmitt , and M. NCJ And, in general, people with criminal records — whether for sex crimes or other offenses — have much more limited housing and employment options than the general public Berenson and Appelbaum Berenson, J.
enter Murray , and E. For these reasons, activity patterns of the population at risk for committing rape might be approximated by that of convicted sex offenders. Third, one could also argue that the potential path trees generated in this paper are representative of the general population in St. Louis and predict crime patterns for that reason. Furthermore, several highly populated neighbourhoods in the city occur outside the estimated activity spaces and show few to no rapes.
The second argument most likely explains the effectiveness of the time-geographic approach in this case study.
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While the final TGDE surfaces created from estimated activity spaces were able to predict rape locations, there are some limitations to the approach that should be noted. First, the intensity maps were based on sex offender activity spaces that were calculated solely from their home and work addresses and estimated discretionary travel distances. In reality, individuals may choose alternate routes than those estimated by the shortest path between home and work, for instance if they have routine intermediate stops at more distant locations.
Additionally, different individuals will have different travel budgets depending on their mobility or time flexibility.
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