TOWARDS A STRATEGY FOR SAFETYORIENTED URBAN STRUCTURE

The paper represents analysis and modelling of urban spaces through their topological properties in order to make them safer in terms of robberies, larceny and motor vehicle thefts, stolen properties, weapons and drugs. The research is performed on a macro-scale in the city of New Haven, Connecticut, USA. The topological properties of streets were calculated and analyzed by the application of space syntax method and DepthMap software, GIS and SPSS. The results are explained in the terms of people movement and presence on the main streets which are on the natural search paths of possible criminals, and which on the other hand have been assigned to a greater surveillance effect from movement

TOWARDS A STRATEGY FOR SAFETYORIENTED URBAN STRUCTURE

The paper represents analysis and modelling of urban spaces through their topological properties in order to make them safer in terms of robberies, larceny and motor vehicle thefts, stolen properties, weapons and drugs. The research is performed on a macro-scale in the city of New Haven, Connecticut, USA. The topological properties of streets were calculated and analyzed by the application of space syntax method and DepthMap software, GIS and SPSS. The results are explained in the terms of people movement and presence on the main streets which are on the natural search paths of possible criminals, and which on the other hand have been assigned to a greater surveillance effect from movement

POSSIBILITIES OF APPLICATION OF CRIME PREVENTION THROUGH ENVIRONMENTAL DESIGN (CPTED) IN LITHUANIAN COMMERCIAL OBJECTS

Five commercial objects (Soviet-time, fully reconstructed and newly built) located on one of the most crime-ridden and problematic streets of Kaunas city, Lithuania, are researched according to crime prevention through environmental design (CPTED) strategies: natural surveillance, access control, territoriality, maintenance, and activity support. Theft from motor vehicle, robbery and intentional damage of property are analyzed. Research results reveal that CPTED is poorly implemented in all analyzed objects. Though, in Soviet-time commercial objects it is even harder to implement activity support strategy. Correlation analysis demonstrates significant strong relations between the analyzed crimes and some criteria from surveillance, access control, territoriality and activity support strategies. Recommendations for safety improvement in commercial objects are proposed based on CPTED strategies

Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

Urban Planning and Design for Terrorism Resilient Cities

The actuality of this research is determined by a significant number of recent terror attacks and their disastrous impacts on urban forms. The target of terror attacks has moved from developing to developed countries. Existing urban polices in most countries do not meet counterterrorism standards. Consequently, implementation of counter-terrorism guidelines while planning safe places has turned into essential factor for the 21st century design. The research comprises identification of environmental design (CPTED) factors as well as spatial urban structures that influence the choice of places for terror attacks. 14 sites with terror attacks and 21 sites without terror attacks were assessed according to the developed CPTED questionnaire. For understanding spatial urban structure 6 cases have been analyzed with space syntax method. The research results reveal that the following CPTED factors are related to the choice of place of terror attacks: Strong separation of private and public activities; Site that has a direct access to the main street; Site that has a multiple entrances and exits; Minimization of vehicle access points to the building; Access to private and public space; Site that has a direct access to the city center; Site is well-used; Redistribution of same functional buildings on the site; Presence of a medical institution nearby the site. According to the results of the automatic regression analysis, the following CPTED factors do the biggest impact on the choice of places for terror attacks: 1) vehicle access points to the buildings are minimized, 2) public and private activities are separated, 3) there are many same functional buildings redistributed in the surrounding area. Descriptive statistics reveal the weakest points on the analyzed sites: 1) public and private activities are not separated, 2) many same functional buildings are not redistributed in the surrounding area, 3) access points to the building are not minimalized, 4) the object is surrounded by an open space, 5) there is no security police presence at the site, and 6) there no minimum required setback distance between the building and site boundaries. After the visual comparison of segment maps of integration, choice, mean depth and connectivity, we have discovered that almost all terror attacks happened on the most globally integrated (R=n) street segments, except Tel Aviv case study. Finally, the recommendations for the elements of site reorganization and the elements of street network reorganization are proposed

Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data

This research aims to identify spatial and time patterns of theft in Manhattan, NY, to reveal urban factors that contribute to thefts from motor vehicles and to build a prediction model for thefts. Methods include time series and hot spot analysis, linear regression, elastic-net, Support vector machines SVM with radial and linear kernels, decision tree, bagged CART, random forest, and stochastic gradient boosting. Machine learning methods reveal that linear models perform better on our data (linear regression, elastic-net), specifying that a higher number of subway entrances, graffiti, and restaurants on streets contribute to higher theft rates from motor vehicles. Although the prediction model for thefts meets almost all assumptions (five of six), its accuracy is 77%, suggesting that there are other undiscovered factors making a contribution to the generation of thefts. As an output demonstrating final results, the application prototype for searching safer parking in Manhattan, NY based on the prediction model, has been developed

Prediction of Hourly Effect of Land Use on Crime

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night