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In this paper, we introduce a new approach to the analysis and prediction of the spatial pattern of terrorist attacks. Our analysis makes use of a new methodology that is based upon a topological rather than geometric approach. It exploits the space grammar of the observed events to derive complex inferences which relate to the deep structural layers of the data generating process, and to the organizational architecture of its spatial distribution. We test our methodology on two of the currently most important and virulent theaters of terrorist activity, Libya and Syria, and we find that our methodology provides a rich array of insights which cannot be expected to be generated by more traditional geographical profiling methods. [ABSTRACT FROM AUTHOR]

We investigate an underexplored externality of crime: the impact of violent crime on individuals' participation in walking. For many adults walking is the only regular physical activity. We use a sample of nearly 1 million people in 323 small areas in England between 2005 and 2011 matched to quarterly crime data at the small area level. Within area variation identifies the causal effect of local violent crime on walking and a difference-in-difference analysis of two high-profile crimes corroborates our results. We find a significant deterrent effect of violent crime on walking that translates into a drop in overall physical activity. [ABSTRACT FROM AUTHOR]

Geographical masking is the conventional solution to protect the privacy of individuals involved in confidential spatial point datasets. The masking process displaces confidential locations to protect individual privacy while maintaining a fine level of spatial resolution. The adaptive form of this process aims to further minimize the displacement error by taking into account the underlying population density. We describe an alternative adaptive geomasking method, referred to as Adaptive Areal Elimination (AAE). AAE creates areas of a minimum K-anonymity and then original points are either randomly perturbed within the areas or aggregated to the median centers of the areas. In addition to the masked points, K-anonymized areas can be safely disclosed as well without increasing the risk of re-identification. Using a burglary dataset from Vienna, AAE is compared with an existing adaptive geographical mask, the donut mask. The masking methods are evaluated for preserving a predefined K-anonymity and the spatial characteristics of the original points. The spatial characteristics are assessed with four measures of spatial error: displaced distance, correlation coefficient of density surfaces, hotspots' divergence, and clusters' specificity. Masked points from point aggregation of AAE have the highest spatial error in all the measures but the displaced distance. In contrast, masked points from the donut mask are displaced the least, preserve the original spatial clusters better, have the highest clusters' specificity and correlation coefficient of density surfaces. However, when the donut mask is adapted to achieve an actual K-anonymity, the random perturbation of AAE introduces less spatial error than the donut mask for all the measures of spatial error. [ABSTRACT FROM AUTHOR]

Big data is the voluminous and complex collection of data that comes from different sources such as sensors, content posted on social media website, sale purchase transaction etc. Such voluminous data becomes tough to process using ancient processing application. There are various tools and techniques in the market for big data analytics. With continually increasing population, crimes and crime rate analyzing related data is a huge issue for governments to make strategic decisions so as to maintain law and order. This is really necessary to keep the citizens of the country safe from crimes. The best place to look up to find room for improvement is the voluminous raw data that is generated on a regular basis from various sources by applying Big Data Analytics (BDA) which helps to analyze certain trends that must be discovered, so that law and order can be maintained properly and there is a sense of safety and well-being among the citizens of the country. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose an agent-based model to simulate crime in public places. [•] The proposed model has been applied to Santiago (Chile) and Vancouver (Canada) and provided reliable results. [•] The proposed model can be used to test patrolling strategies as well as predictive crime models. [ABSTRACT FROM AUTHOR]

Abstract: Whilst analysis of crime for tactical and strategic reasons within the criminal justice arena has now become an established need, predictive analysis of crime remains, and probably always will be, a goal to be desired. Opening a window on this over the last 2 decades, prominent research from academia has focused on the phenomenon of repeat victimisation and more recently ‘near repeat’ victimisation, both firmly grounded in the geography of crime. Somewhat limited to the establishment of near repeat behavioural patterns in whole area data, these can be utilised for crime prevention responses on a local scale. Research reported here however, explores the phenomenon through the examination of serial offending by individual offenders to establish if such spatio-temporal patterns are apparent in the spatial behavioural patterns of the individual burglar, and if so how they may be defined and therefore utilised on a micro rather than macro scale. It is hypothesised that offenders' responsible for more than one series of offences will display consistency across their crime series within time and distance parameters for their closest offences in space. Results improve upon current knowledge concerning near repeat offending being the actions of common offenders. Testing of the extracted data indicates that offenders maintain personal boundaries of ‘closeness’ in time and space even when actions are separated by significant time spans, creating stylised behavioural signatures appertaining to their use of and movement through space when offending. [Copyright &y& Elsevier]

A significant amount of research and practice in the law enforcement arena focuses on spatial and temporal event analysis. And although some efforts have been made to integrate spatial and temporal analysis, the majority of the previous work focuses on either a spatial or temporal analysis. This research adds temporal and neighborhood indicator functions to a feature-space based Generalized Linear Model (GLM) to identify patterns both spatially and temporally within an actor's site selection criteria. The development of this hybrid GLM methodology improves event forecasting accuracy and offers better insight for law enforcement resource allocation. We use a surveillance plot to compare model performance across a space-time surface using both simulated and real-world crime data. The enhanced GLM is compared against both simulated data and criminal incidents collected from police reporting. Initial results show that considering a GLM with temporal indicators provides a computationally efficient and sufficiently accurate alternative to similar methods, such as kernel density and hierarchical models. [ABSTRACT FROM AUTHOR]