Anti-Raider ATM System Using Mobilenetv2

Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.

INVESTIGATING LIGHT AND CRIME USING AMBIENT LIGHT LEVEL

A recent study investigated the influence of lighting on crime by considering the effect of change in ambient light level on crimes recorded in three US cities for the ten-year period 2010 to 2019. The results suggested a significant increase in robbery after dark, but did not suggest significant change in for any other type of crime. The current study was conducted to validate this by considering crimes recorded in three different US cities. This analysis confirmed the statistically significant increase in robbery after dark. These data do not suggest that change in ambient light level has a practically relevant effect on overall crime counts: in other words, the potential benefit of lighting for crime reduction is limited.

Uncovering dynamic textual topics that explain crime

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.

The Effect of Lighting on Crime Counts

The influence of lighting on crime was investigated by considering the effect of ambient light level on crimes recorded in three US cities for the ten-year period 2010 to 2019. Crime counts were compared for similar times of day, before and after the biannual clock change, therefore employing an abrupt change of light level but without an obvious intervention such as improving road lighting in an area. The results suggest a significant increase in robbery during darkness, confirming previous studies. The results also suggest darkness leads to an increase in arson and curfew loitering offenses, and to a decrease in disorderly conduct, family offences (non-violent) and prostitution. Future research investigating the effectiveness of improved street lighting should consider that this may not be beneficial for all types of crime.

Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach

Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.

Is It Dangerous to Live in Neighborhoods with More Immigrants? Assessing the Effects of Immigrant Concentration on Crime Patterns

The immigration-crime nexus has been the subject of much empirical attention and research findings consistently indicate that neighborhoods with large immigrant populations exhibit comparatively lower crime rates. However, it is still imperative to explain how these effects take place in different contexts of structural circumstances of communities. This study aims to examine the effects of immigrant concentration as well as its conditioning effects for racial/ethnic segregation and concentrated disadvantage in Dallas, Texas. Results show that immigrant concentration is negatively associated with crime counts and, most importantly, that immigrant concentration moderates the effect of structural conditions on crime. Generally, immigration has crime-reducing effects and helps ameliorate the negative effects of structural conditions on crime.

Does the Association Between Flows of People and Crime Differ Across Crime Types in Sweden?

Places with persistently high levels of crime, hot spots, are an important object of study. To some extent, the high levels of crime at such hot spots are likely to be related to flows of people. City center locations with large flows of people are quite often also hot spots, e.g., hot spots for pick pocketing at a central train station, or hot spots for assault in the nightlife district. This can be related to crime pattern theory, or to the routine activity perspective, which both suggest that flows of people can affect crime. The present study attempts to explore and quantify whether there are differences in the association between flows of people and crime for different crime types. The analysis considers locations with high crime counts for six crime types in the city of Malmö, Sweden. For each crime type, hot spots are identified and mapped, and in order to explore whether, or how, these are related to flows of people, the crime levels are then analyzed in relation to the number of people who boarded a local bus (N = 33,134,198) nearby. The paper shows that all six crime types are associated with flows of people, although less so for arson and vandalism. This is hypothesized to be due to the relatively constant target availability for these crimes as opposed to the other crime types studied.

Does the association between flows of people and crime differ across crime types in Sweden?

Places with persistently high levels of crime, hot spots, are an important object of study. To some extent the high levels of crime at such hot spots are likely to be related to flows of people. City center locations with large flows of people are quite often also hot spots, e.g. hot spots for pick pocketing at a central train station, or hot spots for assault in the nightlife district. This can be related to crime pattern theory, or to the routine activity perspective, which both suggest that flows of people can affect crime. The present study attempts to explore and quantify whether there are differences in the association between flows of people and crime for different crime types. The analysis considers locations with high crime counts for six crime types in the city of Malmö, Sweden. For each crime type, hot spots are identified and mapped, and in order to explore whether, or how, these are related to flows of people, the crime levels are then analyzed in relation to the number of people who boarded a local bus (N=33,134,198) nearby. The paper shows that all six crime types are associated with flows of people, although less so for arson and vandalism. This is hypothesized to be due to the relatively constant target availability for these crimes as opposed to the other crime types studied.

Environmental Design and Neighborhood Context: A Multi-level Analysis of Crime at Apartments in Cincinnati Neighborhoods

The present study examines the role of opportunity on crime counts within the multicontextual opportunity theoretical framework. We used weighted multilevel regression modeling of site observation data from a Cincinnati-based sample of 1003 apartments nested within 228 census block groups. Results indicate that only a couple of environmental design features are associated with crime in the expected direction, and some of these associations are neighborhood-context-dependent. We conclude that the results support the propositions of multicontextual opportunity theory suggesting that neighborhood level factors condition the relationship between micro level opportunity factors and crime. Since there is a scant literature on this topic, more research is needed to see if the findings hold true in other places.