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

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

Abstract 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 R2 $R^{2}$ 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

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From research to purchase: an empirical analysis of research-shopping behaviour in the insurance sector

Though almost every insurer provides an integrated solution for online product research and purchase for existing and potentially new customers, there is still a significant percentage of customers turning into research-shoppers, a practice of using one channel for products search and another for purchase. This trend is visible from the channel usage statistics: according to various studies, while more than half of the customers worldwide use the insurers own website for product research, only a minor percentage of them stays there for purchase purposes. The preferred purchase channel often remains the one that enables personal contact to the sales person. This situation is mostly due to the high complexity of the insurance products. In addition, insurance products belong to the category of experience goods, where the evaluation of the product price and characteristics is difficult and can be based only upon previous experience, e.g. after experiencing a claim. While channel switch might lead to higher profit since multichannel customers were found to spend more, the change of the insurer is a serious threat. In this paper we address this issue and analyse the research-shopper phenomenon in the insurance industry. We investigate which customer and policy characteristics influence the research-shopping behaviour in terms of duration from research conducted via an online channel to purchase conducted using offline channels. Our empirical study is based on a sample of approximately 10 000 research-shopper customers of a large Swiss insurance company across the three insurance products: motor, household/liability and travel insurance. The obtained results show that there are several customer characteristics that have an effect over the duration to purchase and that these characteristics differ across different products. Our findings are relevant to academics and practitioners alike and are important for multichannel management and better understanding of the customer journey

HUMAN VERSUS TECHNOLOGY: COMPARING THE EFFECT OF PRIVATE SECURITY PATROL AND CRIME PREVENTION INFORMATION SYSTEM OVER THE CRIME LEVEL AND SAFETY PERCEPTION

Crime reduction became one of the major issues of the modern society. In order to achieve public re-assurance, police forces all over the world are undertaking actions to involve citizens in crime preven-tion through community policing. In parallel, technological platforms were deployed in order to share crime-related information with the public and to support the development of problem-solving strate-gies. However, the impact of these initiatives in terms of crime reduction and perception has not been sufficiently investigated yet. Furthermore, up to now, no previous studies compared the effectiveness between the traditional approach of preventive patrolling and technology-based crime prevention so-lutions. Therefore, we present a study design which aims at assessing the effectiveness of the two aforementioned crime prevention measures. The goal is to evaluate and compare their effects over the local criminal activity and citizen’s safety perception measured by Fear of Crime (FOC) and Per-ceived Risk of Victimization (PRV) constructs. Preliminary results show a rather low level of FOC across the whole sample, paired by a high level of PRV. Furthermore, potential explanatory back-ground factors for the previous constructs have been identified and will be explored in future work

Prevention or Panic: Design and Evaluation of a Crime Prevention IS

Crime prevention is one of the major challenges of modern society which can be addressed by IS. Yet, research in the direction of design principles, perception and implications of crime prevention IS is limited. In this paper we design and evaluate a system to support individuals in increasing their safety. We build upon concepts shown to be effective for crime prevention from several literature streams, deriving six main design principles: (1) information provision, (2) community involvement, (3) preventive tips provision, (4) targeted notifications, (5) gamification and (6) social media integration. We evaluate the proposed system from three perspectives: (1) effect of dissemination of crime information over fear of crime, (2) technology acceptance and its relation to fear of crime, and (3) usage motivations. Our results indicate that the proposed system does not increase the fear of crime. Instead, it holds a potential to motivate its users to undertake preventive measures

DESIGN OF A HEALTH INFORMATION SYSTEM ENHANCING THE PERFORMANCE OF OBESITY EXPERT AND CHILDREN TEAMS

The globally increasing prevalence of childhood obesity is one of the most serious public health challenges of the twenty-first century. Du to the need for multi-professional therapies that require a high amount of personnel and financial resources, IT-supported interventions promise help. So far, meta-studies show their limited impact on health outcomes. This work presents therefore a design theory that helps constructing health information systems (HIS) that positively impact the performance of obesity expert and children teams. Team performance is measured through self-reports, patients´ adherence to therapy and positive health outcomes. In order to assess the utility of the proposed design theory, its underlying design process was adopted by an interdisciplinary team of therapists, patients, their parents, IS researcher and computer scientists. This team developed and evaluated several HIS services collaboratively over the course of two years. Results of this design process show first evidence of the utility of the HIS design theory. However, challenges with regard to the design process still exist and are discussed