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

CellTradeMap: Delineating trade areas for urban commercial districts with cellular networks

Understanding customer mobility patterns to com-mercial districts is crucial for urban planning, facility manage-ment, and business strategies. Trade areas are a widely appliedmeasure to quantify where the visitors are from. Traditionaltrade area analysis is limited to small-scale or store-level studiesbecause information such as visits to competitor commercialentities and place of residence is collected by labour-intensivequestionnaires or heavily biased location-based social media data.In this paper, we propose CellTradeMap, a novel district-leveltrade area analysis framework using mobile flow records (MFRs),a type of fine-grained cellular network data. CellTradeMap ex-tracts robust location information from the irregularly sampled,noisy MFRs, adapts the generic trade area analysis frameworkto incorporate cellular data, and enhances the original trade areamodel with cellular-based features. We evaluate CellTradeMap ona large-scale cellular network dataset covering 3.5 million mobilephone users in a metropolis in China. Experimental results showthat the trade areas extracted by CellTradeMap are aligned withdomain knowledge and CellTradeMap can model trade areaswith a high predictive accuracy

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI