Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

Urban Crime Trends Analysis and Occurrence Possibility Prediction based on Light Gradient Boosting Machine

Big Data and Machine learning have been increasingly used to fight against Urban crimes. Our goal is to discover the connection between crime-related factors and the underlying complex crime pattern. Therefore, to predict the possibility of crime occurrence. Light Gradient Boosting Machine (LightGBM) Model is adopted in our study to predict the crime occurrence possibility based on actual crime information. We found that the prediction results are approximately consistent with an actual variation. We hope this work could help with crime prevention and policing

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

Communications and Methodologies in Crime Geography: Contemporary Approaches to Disseminating Criminal Incidence and Research

Many tools exist to assist law enforcement agencies in mitigating criminal activity. For centuries, academics used statistics in the study of crime and criminals, and more recently, police departments make use of spatial statistics and geographic information systems in that pursuit. Clustering and hot spot methods of analysis are popular in this application for their relative simplicity of interpretation and ease of process. With recent advancements in geospatial technology, it is easier than ever to publicly share data through visual communication tools like web applications and dashboards. Sharing data and results of analyses boosts transparency and the public image of police agencies, an image important to maintaining public trust in law enforcement and active participation in community safety

Sustainable urban form:Socio-demographic and permeability factors as determinants of crime spots in cities, case of Akure, Nigeria

ABSTRACTUrbanization comes with the price of the negative complexity of crime. It is a reflection of the socio-demographic and permeability factors. This paper aims to integrate socio-demographic crime factors with street permeability to find the association with residential burglary spots. We first find the residential burglary hotspots area related to socio-demographic factors of the study area, then identify potential residential burglary risk areas based on the factors of street permeability and find the association of residential burglary with socio-demographics and street permeability factors in Akure. The methodology employed includes the Inverse Distance Weight factor analysis, space syntax, and the Poisson regression analysis. The findings showed hotspots of burglary within neighbourhoods, confirming the relationship between the factors. Issues identified herein denote some logical starting points for criminological engagement with the Sustainable Development Goals. In the conclusion, we discussed the implications for the statistical output

Scalable model selection for spatial additive mixed modeling: application to crime analysis

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects the model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran

Unraveling urban form and collision risk: The spatial distribution of traffic accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fa-talities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotem-poral association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotem-poral association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions