Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

Spatial spillover effects of crime on private investment at nearby micro-places

Criminal activity may influence the decisions of existing property owners or prospective investors to invest in a property, given the potential elevated expenses and increased uncertainty that often accompany crime. This study investigates the relationship between crime and private investment at nearby micro-places using location-specific crime incident and building permit data from 2008 to 2018 in the cities of Chicago, Los Angeles, New York, Philadelphia, San Antonio and Seattle. Data were aggregated to the blockface-level to examine how changes in investment on a blockface are influenced by changes in crime on that blockface and adjacent blockfaces in the subsequent year. Results indicate that an increase in total crime on a blockface was associated with a significant decrease in building permit activity the following year in all six cities, but the relationship is less strong when aggregating crime from adjacent blockfaces. When looking at spillover effects, higher crime on adjacent blockfaces was significantly associated with lower levels of investment beyond the effect of the crime trend on that blockface in Los Angeles and San Antonio. Though the relationship between adjacent blockface crime and investment was negative across the other four cities, it was not statistically significant. Taken together, these findings suggest that effective targeted crime prevention policies may have the added benefit of spurring local economic investment and that the impact of crime is very localised.

Criminal Records Information Retrieval System: A Conceptual Model

This model helps to increase communication between Police and public. It will reduce time & increase the problem solving efficiency in time period it will be more helpful. In this admin is key person, user(police) is also have secure registration & public can communicate with all other users through mail. Here we are going to use clustering technique because it more powerful to forming accurate cluster, speed of creating cluster, identifying crime trend & crime zone ,crime density of state.

THE IMPACT OF PHYSICAL FEATURES AND ENVIRONMENT ON CRIME IN URBAN NEIGHBOURHOOD AREAS

The physical design of features and environment will stimulate a safe and sustainable development of the neighbourhood. Despite possessing a proper form, a private space which does not control and keep could even raise fear and crime incident. Therefore, this study planned to analyse the fear, perception and potential feature that caused the crime event in neighbourhoods. Three years of burglary data got from the Royal Malaysia Police helped to explain the crime trend and pattern. Besides, a statistical analysis conducted to examine the fear level and perception towards crime incident using a set of questionnaires. Next, the potential features and visibility level examined in the unit of a house that has experienced the burglary event. The purpose is to find any element that can affect crime event. Results prove that actual crime data not associated with fear and perception of the crime. Besides, accessibility, visibility and crime pattern were the key elements to consider for crime prevention.

An enhanced rule mining algorithm to detect suspects of crime against women in the state of Tamil Nadu

Crime against women in India has become a prominent topic of argument in the recent years and the issue has been brought to the foreground for concern due to the increasing trend in crimes performed against women. It is the major challenge to the investigators to detect and prevent crimes, particularly crime against women. Most of the crimes get reported and a massive dataset is being generated every year. Analyzing the crime reports can help the law enforcement officers to take preventive measures for reducing the crime, but processing this voluminous data is backbreaking and error prone. So, the application of various data mining techniques can help in visualizing the crime trend. Crime is one of the interesting applications where data mining plays an important role in terms of prediction and analysis in the interest of society. This paper covers in detail analysis of modus operandi of committing crimes and effective use of data mining techniques and algorithms in narrow down to identify the criminals at a short span of time.