Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Quantifying the Effect of Socio-Economic Predictors and Built Environment on Mental Health Events in Little Rock, AR

Proper allocation of law enforcement resources remains a critical issue in crime prediction and prevention that operates by characterizing spatially aggregated crime activities and a multitude of predictor variables of interest. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework while extending the popular risk terrain modeling (Caplan et al., 2011, 2015; Drawve, 2016) approach. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear models, spatial regression models and a tree based method, viz., Poisson regression, spatial Durbin error model, Manski model and Random Forest. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources

Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends

Proper allocation of law enforcement agencies falls under the umbrella of risk terrainmodeling (Caplan et al., 2011, 2015; Drawve, 2016) that primarily focuses on crime prediction and prevention by spatially aggregating response and predictor variables of interest. Although mental health incidents demand resource allocation from law enforcement agencies and the city, relatively less emphasis has been placed on building spatial models for mental health incidents events. Analyzing spatial mental health events in Little Rock, AR over 2015 to 2018, we found evidence of spatial heterogeneity via Moran’s I statistic. A spatial modeling framework is then built using generalized linear models, spatial regression models and a tree based method, in particular, Poisson regression, spatial Durbin error model, Manski model and Random Forest. The insights obtained from these different models are presented here along with their relative predictive performances. These inferential tools have the potential to aid both law enforcement agencies and the city in properly allocating resources required for such events

Crime Mapping through Geo-Spatial Social Media Activity

The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction

Spatio-Temporal Analysis of Crime Incidents for Forensic Investigation

Crime analysis and mapping has been routinely employed to gather intelligence which informs security efforts and forensic investigations. Traditionally, geographic information systems in the form of third-party mapping applications are used for analysis of crime data but are often expensive and lack flexibility, transparency, or efficiency in uncovering associations and relationships in crime. Each crime incident and article of evidence within that incident has an associated spatial and temporal component which may yield significant and relevant information to the case. Wide variations exist in the techniques that departments use and commonly spatial and temporal components of crime are evaluated independently, if at all. Thus, there is a critical need to develop and implement spatio-temporal investigative strategies so police agencies can gain a foundational understanding of crime occurrence within their jurisdiction, develop strategic action for disruption and resolution of crime, conduct more informed investigations, better utilize resources, and provide an overall more effective service. The purpose of this project was to provide foundational knowledge to the investigative and security communities and demonstrate the utility of empirical spatio-temporal methods for the assessment and interpretation of crime incidents. Two software packages were developed as an open source (R) solution to expand current techniques and provide an implementable spatio-temporal methodology for crime analysis. Additionally, an actionable method for near repeat analysis was developed. Firstly, the premise of the near repeat phenomenon was evaluated across crime types and cities to discern optimal parameters for spatial and temporal bandwidths. Using these parameters, a method for identifying near repeat series was developed which draws inter-incident linkages given the spatio-temporal clustering of the incidents. Resultant crime networks and maps provide insight regarding near repeat crime incidents within the landscape of their jurisdiction for targeted investigation. Finally, a new approach to the geographic profiling problem was developed which assesses and integrates the travel environment of road networks, beliefs and assumptions formed through the course of the investigation process about the perpetrator, and information derived from the analysis of evidence. Each piece of information is evaluated in conjunction with spatio-temporal routing functions and then used to update prior beliefs about the anchor point of the perpetrator. Adopting spatio-temporal methodologies for the investigation of crime offers a new framework for forensic operations in the investigation of crime. Systematic consideration about the value and implications of the relationship between space, time, and crime was shown to provide insight regarding crime. In a forward-looking sense this work shows that the interpretation of crime within a spatio-temporal context can provide insight into crime occurrence, linkage of crime incidents, and investigations of those incidents

Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks

Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: ‘‘street robbery’’ and ‘‘larceny’’. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR