Detecting spatial patterns of incidents and crime trends is a topic of interest to any state or local government.
CAST is the name of a free software, initials of Crime Analytics for Space - Time, which was released in 2013 as an open source solution for actuarial analysis, with spatial patterns and algorithms of trends in the handling of crime statistics.
CAST is a client application developed on Python and C ++ that works on Windos, Mac and Linux, developed by the GeoDA Center, which has developed various applications of computational and spatial analysis. This Center has a laboratory that was founded by the Director of the School of Geography and Urban Planning at the University of Illinois.
In the case of CAST, it was promoted through an award from the National Institute of Justice and the Office of Justice Programs of the United States Department of Justice. The methodology for the development of algorithms was worked with Arizona State University.
The application supports SHP files, the incidents usually at the point level and through spatial analysis generates trends from dates, for which polygon maps such as neighborhoods, blocks or neighborhoods are required.
As results can be apart from the charts, thematic maps from statistical deviations, also heat maps and calendar maps.
Perhaps the most attractive of the application is that it comes with specialized functions already defined for purposes of trend analysis and reports based on the subject. For example, a trend can be normalized by crossing population data to represent the number of violent incidents in segments, for example, the number of deaths per 100,000 inhabitants. Then it allows to make temporary analysis, to determine by means of graphs the growth, decrease and punctual cases of study at both the tabular and spatial level. Similarly, personalization of the calendar can make analysis between specific days, such as incidents on holidays or weekends.
It is to play with the tool, since even animated maps can be generated on a temporary scale, with which it is possible to determine where a crime spot will spread if trends are maintained. Of course, it should be interesting to apply new data based on safety measures taken, to see the impact that has. Something of great utility in urban areas with the current context of influence of organized crime and gangs that will surely be detected as interconnected clusters. And because the system is made for this purpose, it adapts to models of security management and prevention of violence, such as the management of quadrants, sectors or districts.
In conclusion, a valuable application. One lower the free code model, to which we wish dissemination sponsors, without considering what governments invest in security because of features not so specialized.