CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope

Data Mining and Predictive Policing

This paper focuses on the operation and utilization of predictive policing software that generates spatial and temporal hotspots. There is a literature review that evaluates previous work surrounding the topics branched from predictive policing. It dissects two different crime datasets for San Francisco, California and Chicago, Illinois. Provided, is an in depth comparison between the datasets using both statistical analysis and graphing tools. Then, it shows the application of the Apriori algorithm to re-enforce the formation of possible hotspots pointed out in a actual predictive policing software. To further the analysis, targeted demographics of the study were evaluated to create a snapshot of the factors that have attributed to the safety of the neighborhoods. The results of this study can be used to create solutions for long term crime reduction by adding green spaces and community planning in areas with high crime rates and heavy environmental neglect

Reconciling Big Data and Thick Data to Advance the New Urban Science and Smart City Governance

Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation

Gang culture and their territorial space: Graffiti analysis using geographical information systems (GIS)

The purpose of this study is to understand gang culture and how gangs come to define their territorial space. This thesis will focus on identifying gang spaces by utilizing geographic techniques to aid in determining where high gang activity and/or crime is taking place. This will be done by point pattern, data analysis, visualization analysis, and heat mapping on complaints, arrest, shooting, and graffiti data. This research has been conducted deductively, as it will use the theories mentioned in the literature review to define hypotheses. Gangs are known for their violent and disruptive behavior. They ravage community resources and introduce all kind of crimes. Additionally, gangs have implemented their recruiting in school settings. This creates unsafe learning environments and is negatively impacting young lives as they are lured by gang culture, which leads to violence and crime. The significance of this research is the potential application(s) of doing crime analysis using Geographical Information Systems/Science. There are numerous agencies who are involved in the study, understanding, and mitigation of gangs. Inspecting gang culture and excavating crime data in a study area can help us understand the crime dynamics in these gang spaces. The study of the geography of gang space can help to further understand how to help the community and how law enforcement can tackle the issues at hand

Associations between Greenspace and street Crimes in Toronto: Evidence from a spatial analysis study at dissemination area level

Introduction: Earlier criminologists have explored various factors generating or attracting crime in urban cities coupled with crime studies focusing on the influence of social, built and natural environments in urban centres. According to Statistics Canada (2019), the Crime severity index of Canada and Toronto has been on the rise since 2014, which found the violent crime severity index showing higher trends than non-violent crime severity. This study, first, examined the crime trends and seasonality in Toronto. Next, the association between greenspace variables and street crime rates across the city at the dissemination level using the spatial statistical methods were explored. Previous crime studies have also investigated the relationship between the crime rate (property and violent) and greenspace, albeit this study only focused on analyzing crime that usually occurs outsides, namely “street crimes.” There are two schools of thought concerning the association between crime rates and greenspace. The first belief suggests greenspace facilitates criminal activities because it conceals the offender from the victims/bystanders, while the second belief insists that greenspace deter criminal activities. Methods: Street crime considered for this research included assault, auto-theft and robbery crime. This study explored the association between greenspace variables and street crime rates across the City of Toronto. Crime data were extracted from the Toronto Police Service public safety data portal; the greenspace data were obtained from Toronto Open Data, and sociodemographic data were exported from the 2016 Census Data collected from Statistic Canada. Street crime trends and seasonality were first, and they were carried out using crime data from the year 2014 to 2018 to generate a line graph depicting yearly, monthly and seasonal crime trends in the study area. Greenspace variables (stem density; basal area density and tree density) were estimated from tree inventory data. The sociodemographic variables considered were median household income, lone parent, unemployment rate, high school degree holders, owner-occupied housing and renter-occupied housing. Spatial distribution maps for the dependent and independent variables were generated to show the geographical variation of the data. The Global Moran’s I and Local Indicator of Spatial Association (LISA) statistics were carried out on the street crime data to detect the spatial autocorrelation and clustering in the dependent variables. The spatial regression analyses were then carried out using the spatial lag model and the spatial error model on street crime rates, greenspace and sociodemographic variables. Results: There were changing crime trends and seasonal variation of the three-street crime occurrences. Consequently, the street crime rates indicated spatial clustering with the locations of hot and cold spots for assault and robbery crime rates similar. In contrast, auto-theft crime rates emerge in different locations across the City of Toronto. Results from the spatial regression analyses show that the stem density and tree density are negatively associated with street crime rates after controlling for specific sociodemographic factors. Also, the basal area density was not significant in the spatial regression analyses on street crime rates. The six sociodemographic indicators (median household income, unemployment rate, lone parent, high school degree holders, housing units occupied by owners and renters) were significantly associated with the three street crime rates in this current study. Conclusion: This thesis contributes to the existing literature by using a spatial-statistical approach to estimate greenspace variables and explored their relationship with street crime rates. This study draws attention to the use of specific sociodemographic factors with street crime types, and the influence parts of a tree (greenspace) could have on street crime rates across the City of Toronto. Limitations of the data were discussed, future studies concerning the recommendation of different tree species and the influence of weather on greenspace were discussed

Sketching mental maps of urban spaces for the visual analysis of spatial data

Quantitative spatial data can be used to substantially augment our understanding of topics of interest to civic planning, including human behavior. However, it does not necessarily capture the qualitative values or understanding of an environment from the people that live within it, which is important for promoting public participation in GIS. Interactive mental maps may provide an external cognition aid that allows people to confront and interpret their qualitative understanding of an urban environment during quantitative analysis of spatial data. Mental maps encode the aspects of an area that a person considers core to the nature of that place, and the creation of a digital mental map can both incorporate the significant elements of the urban environment and reflect the beliefs of the person who created the map. Creating digital expressions of these elements and beliefs within software systems may facilitate cooperative discussion of spatial data between everyday people and experts.Ph.D

Economic Welfare Of Firefighting Service In Detroit

Chapter 1 is concerned with the effect of public fire service quality on individual utility. I develop a theoretical model to account for fire risk as a function of socio-economic, housing, and spatial factors. I review relevant literature on certain inherent public fire service issues regarding technology and cost structure before I briefly discuss the importance of public fire service with regard to overall social welfare. Finally, I employ equity mapping in a case study to assess the effect of a budget cut on equity of fire service allocation in Detroit. Chapter 2 examines whether socio-economic factors, various aspects of housing, and spatial features can explain differences in building fire risk across Detroit. Using a complete Detroit fire incidents data set for the years 2008-2012, matched by census tract to American Community Survey (ACS) data for the same period, I employ kernel density mapping and spatial regression techniques to address the research question. Estimations suggest a positive correlation between poverty and fire risk, especially with regard to intentional building fires. In the case of unintentional building fires, no such conclusion can be drawn easily. I find evidence for fire clustering and spillover effects. Chapter 3 approaches the question of optimal fire station siting in Detroit from a welfare economics viewpoint. Therefore, I assess the effects of a decrease in public budget in 2012 on distributional equity. First, regression analysis is used to determine the effect on response time as an indicator of fire service quality. Second, I use various statistical measures to evaluate intra-city service distribution with respect to equality. Third, I develop a fire risk index and link it to service quality to determine need satisfaction. I find ambiguous effects on distributional equality, while there is strong evidence of the change in budget having a negative effect on equity interpreted as need

Associations of weather variables, violent crimes and urbanism in Brasília, Distrito Federal

Tese (doutorado)—Universidade de Brasília, Centro de Desenvolvimento Sustentável, Programa de Pós-Graduação em Desenvolvimento Sustentável, 2020.Há muito se supõe que o clima desempenha um papel importante no comportamento violento. No entanto, demonstrar empiricamente essa associação é uma tarefa complexa de superar. Assim, adotamos abordagens inovadoras como o processamento de big data em nuvem combinado com análises temporais e espaciais revelando que o calor acima da temperatura média foi responsável por uma variação de 9% dos homicídios mensais em Brasília de 2009 a 2017. Além disso, pontos críticos mapeados de crimes violentos e altas temperaturas da superfície da terra revelaram semelhanças entre paisagens urbanas criminogênicas, sendo estes identificados como tecidos urbanos extremamente densos e espaços verdes que não são necessariamente tão verdes do ponto de vista do urbanismo sustentável. As temperaturas se mostraram como sendo indicadores de vulnerabilidade espacial à violência, conforme demonstrado neste estudo. Além de encontrar padrões sazonais para a ocorrência de crimes violentos, esta tese correlacionou séries temporais de temperatura do ar, temperatura da superfície da terra (LST) e pontos geocodificados de crime. Os resultados mapearam morfologias urbanas criminogênicas semelhantes como vazios urbanos abandonados, por exemplo. Avançar com a 11a Meta de Desenvolvimento Sustentável, que prevê cidades mais inclusivas, seguras, justas e sustentáveis, significa enfrentar mudanças abruptas no microclima devido não apenas ao conforto térmico e estético, mas também para melhorar a segurança pública.It has been long assumed that weather plays a role in violent behavior. Nevertheless, empirically demonstrating this association is a complex task to overcome. Thus, we adopted innovative approaches such as processing big data in-cloud combined with temporal and spatial analyses revealed that warmth beyond average temperature accounted for a variation of 9% of homicides monthly in Brasília (2009 - 2017). Also, mapped hotspots of violent crimes and high land surface temperatures revealed similarities between criminogenic cityscapes such as extremely dense urban tissues and green spaces that are not so green from the perspective of sustainable urbanism. Temperatures are insightful indicators of spatial vulnerability to violence as demonstrated in this study. Besides finding seasonal patterns for violent crime occurrence, this thesis correlated a time-series of air temperature and land surface temperature (LST) to geocodes of crime. Results mapped similar urban morphologies prone to heat and crime. Advancing with the 11th Sustainable Development Goal which foresees more inclusive, safer, just and sustainable cities means tackling abrupt microclimate changes due not only to thermal comfort and aesthetics but also for enhancing public security

Ubiquitous intelligence for smart cities: a public safety approach

Citizen-centered safety enhancement is an integral component of public safety and a top priority for decision makers in a smart city development. However, public safety agencies are constantly faced with the challenge of deterring crime. While most smart city initiatives have placed emphasis on the use of modern technology for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in a resource constrained environment, such as in Africa. In particular, crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations and has great potential in helping to fight against crime and promoting safety in smart cities. This research focuses on detecting the situation of crime through data mining approaches that can be used to promote citizens' safety, and assist security agencies in knowledge-driven decision support, such as crime series identification. While much research has been conducted on crime hotspots, not enough has been done in the area of identifying crime series. This thesis presents a novel crime clustering model, CriClust, for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset, drawing on sound scientific and mathematical principles, as well as assumptions from theories of environmental criminology. The analysis is augmented using a dual-threshold model, and pattern prevalence information is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger-Stein mincut algorithm. We introduce two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals underlying strong correlations and defining features for a series. Our findings on experimental quasi-real data set, generated based on expert knowledge recommendation, reveal that identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery in a smart city development. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to competing baseline algorithms and related research. It was found that Monte Carlo technique and adaptive graph size mechanism for crime similarity clustering yield substantial improvement. The study also found that proportion estimation (PDE) and PSE of series clusters can provide valuable insight into crime deterrence strategies. Furthermore, visual enhancement of clusters using graphical approaches to organising information and presenting a unified viable view promotes a prompt identification of important areas demanding attention. Our model particularly attempts to preserve desirable and robust statistical properties. This research presents considerable empirical evidence that the proposed crime cluster (CriClust) model is promising and can assist in deriving useful crime pattern knowledge, contributing knowledge services for public safety authorities and intelligence gathering organisations in developing nations, thereby promoting a sustainable "safe and smart" city