Data Science For Dummies
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You can use GIS technologies, data modeling, and advanced spatial statistics to build information products for the prediction and monitoring of criminal activity. Spatial data is tabular data that's earmarked with spatial coordinate information for each record in the dataset.

Many times, spatial datasets also have a field that indicates a date/time attribute for each of the records in the set — making it spatio-temporal data. If you want to create crime maps or uncover location-based trends in crime data, use spatial data analysis.

You can also use spatial analysis methods to make location-based inferences that help you monitor and predict what crimes will occur where, when, and why.

Crime mapping with GIS technology

One of the most common forms of data insight that's used in law enforcement is the crime map. A crime map is a spatial map that visualizes where crimes have been committed during any given time interval. In olden days, you might have drawn this type of map out with pencil and paper, but nowadays you do the job using a GIS software, such as ArcGIS Desktop or QGIS.

Although crime mapping has become increasingly sophisticated while advances have been made in spatial technologies, the purpose has remained the same — to provide law enforcement decision makers and personnel with location information that describes on-the-ground criminal activities so that they can use that information to optimize their efforts in protecting public safety. GIS software can help you make crime maps that can be used as a descriptive analytic or as a source for simple inference-based predictions.

Going one step further with location-allocation analysis

Location allocation is a form of predictive spatial analytics that you can use for location optimization from complex spatial data models. For example, in law enforcement, location optimization can predict optimal locations for police stations so that dispatched officers can travel to an emergency in any part of the city within a 5-minute response-time window. To help your agency predict the best locations to position officers so that they can arrive immediately at any emergency in any part of town, use location-allocation analysis.

You can most easily do a location-allocation analysis by using the ArcGIS for Desktop Network Analyst add-on to carry out a maximum coverage analysis. (Check out ArcGIS for Desktop.) In this form of analysis, you input data about existing facilities, demand points — points that represent places in the study area that exhibit a demand for law enforcement resources — and any spatial barriers that would block or severely impede law enforcement response times. The model outputs information about the optimal locations to place officers for the fastest, most well-distributed response times. Packages such as the Network Analyst add-on are easy to use, which is one of the feature benefits that might have you choose ArcGIS over open-source QGIS. The figure shows map results that are derived from a location-allocation analysis.

A map product derived from location-allocation analysis.

Analyzing complex spatial statistics to better understand crime

You can use your skills in GIS, mathematics, data modeling, and spatial statistics in many ways to build descriptive and predictive information products that support the decision making of law enforcement officials. Proprietary spatial-analysis software applications have greatly simplified this work by providing special add-on tools specifically designed for spatial analysis of crime data. In addition, free open-source applications such as the CrimeStat III program are available to help you carry out more advanced forms of statistical analysis. In the following sections, I introduce how you can use your data science skills to derive descriptive and predictive spatial data insights that help law enforcement agencies optimize their tactical response planning.

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Lillian Pierson is the CEO of Data-Mania, where she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over one million individuals on the topics of AI and data science. Lillian has assisted global leaders in IT, government, media organizations, and nonprofits.

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