Repeat and Near Repeat Burglary Victimization in Taiwan

Extensive evidence shows that repeat victimization is common and widespread, but studies on the prevalence of repeat victimization in Asia are limited. This study examines the extent and patterns of repeat and near-repeat burglary victimization in Taiwan using both 2015 Taiwan Area Victimization Survey data and police recorded burglary data. Results indicated that: (1) burglaries against the same household in Taiwan are highly concentrated (with the top 10% most burgled households making up around 30% of reported victimizations), more so than is often found in many Western countries; (2) the risk of (repeat) burglary is not consistently spread over space and time, particularly within the 100-m range of an initial burglary incident; and (3) the levels of near repeat burglaries identified in this study are notably lower than was observed in prior studies both in China and in many western countries. The findings highlight the value of developing prevention strategies specifically targeting repeat burglary victimization

Measuring the Temporal Stability of Near-Repeat Crime Patterns: A Longitudinal Analysis

This study investigates the temporal stability of identified near-repeat robbery patterns in Newark, New Jersey. With one noteworthy exception, scholars have yet to explore the temporal stability of identified spatiotemporal crime clusters. Furthermore, researchers have yet to measure the near-repeat phenomenon longitudinally. To fill this gap, this study employs a longitudinal design to measure variation in effect size and significance of identified near- repeat crime patterns across 13 “rolling” one-year time periods within a 2-year study period (January 2015–December 2016). Temporal instability was found within two out of six spatiotemporal crime clusters. Results are reported in the form of formalized descriptive statistics and visualizations of temporal trends

Analysis of Burglary Hot Spots and Near-Repeat Victimization in a Large Chinese City

A hot spot refers to numerous crime incidents clustered in a limited space-time range. The near-repeat phenomenon suggests that every victimization might form a contagion-like pattern nearby in terms of both space and time. In this article, the near-repeat phenomenon is used to analyze the risk levels around hot spots. Utilizing a recent burglary dataset in N (a large city located in southeastern China), we examine the near-repeat phenomenon, the results of which we then use to test the contributions of hot spots. More importantly, we propose a temporal expanded near-repeat matrix to quantify the undulation of risk both before and after hot spots. The experimental results demonstrate that hot spots always form. Space-time areas of high risk are always variable in space and time. Regions in the vicinity of hot spots simultaneously share this higher risk. In general, crime risks around hot spots present as a wave diffusion process. The conclusions herein provide a detailed analysis of criminal patterns, which not only advances previous results but also provides valuable research results for crime prediction and prevention

Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

Patterns and Predictors of Crime and Fear of Crime during the Crime Drop: A Multilevel Analysis of Repeated Cross-Sectional Data in Japan, 2007-2018

Many attempts have been made to examine the determinants of victimisation and the fear of crime, often guided by social disorganisation theory and environmental criminology. However, there have been only a handful of studies that have been carried out in East Asia so far. Consequently, it is unclear whether those factors which are reliably associated with higher or lower levels of crime and fear of crime in Western societies are generalisable to the dissimilar context of Japan. Against this backdrop, this thesis is concerned with the patterns and predictors of victimisation, repeat victimisation, fear of crime and perceived risk of victimisation in Japan, drawing on repeated cross-sectional data collected as part of a nationally representative household survey “the Japanese Public Safety Survey” (JPSS) and the census. Study 1 is concerned with the patterns and predictors of household property crime. Exploratory factor analysis was first performed to reveal the factor structure of eleven perceived neighbourhood disorder variables used in the JPSS. A series of multilevel logistic regression models demonstrated that the year variables were found to be negatively associated with household property crime risk. Detached house, homeownership, social support, and the presence of community policing were found to be associated with household property crime risk. Study 2 examined the patterns and predictors of repeat victimisation of residential burglary and vandalism. In contrast with what was found in Study 1, the survey year variables were not correlated with the risk of repeat residential burglary and vandalism victimisation. Social support and university degree were found to be the factors which distinguish repeat residential burglary victims from other groups. Social support and social disorder were found to be the factors that distinguish repeat vandalism victims from other groups. Social support and high ratio of manufacturing industry were found to be the factors that distinguish repeat residential burglary victims from single victims. Social disorder was found to be the factor that distinguishes repeat vandalism victims from single victims. Study 3 examined the patterns and predictors of fear and perceived risk of household property crime. The results of multilevel regression models revealed that, at the individual/householdlevel, experiencing previous victimisation, being older, living in a detached house and having higher annual household income were associated with increased fear of household property crime. At the neighbourhood-level, the presence of social disorder and community policing were statistically related to the levels of fear of household property crime. There was a statistical association between prior victimisation and perceived risk of victimisation, and different predictors were found to be associated with fear of crime and perceived risk of victimisation. The survey year variables were not found to be associated with fear of and the perceived risk of household property victimisation. The findings from the analysis furthered support the three models of fear of crime. In summary, the findings of three empirical studies yielded both consistencies and inconsistencies with the relevant literature derived mainly from studies conducted in Western industrialised countries, showing some applicability of the criminological theories to Japan. The thesis demonstrated the usefulness of multilevel modelling and multiple secondary data sources, and the importance of introducing measures dealing with neighbourhood social disorder, and crime prevention measures which reflect the crime trends or related problems of each municipality

Real Time Crime Prediction Using Social Media

There is no doubt that crime is on the increase and has a detrimental influence on a nation's economy despite several attempts of studies on crime prediction to minimise crime rates. Historically, data mining techniques for crime prediction models often rely on historical information and its mostly country specific. In fact, only a few of the earlier studies on crime prediction follow standard data mining procedure. Hence, considering the current worldwide crime trend in which criminals routinely publish their criminal intent on social media and ask others to see and/or engage in different crimes, an alternative, and more dynamic strategy is needed. The goal of this research is to improve the performance of crime prediction models. Thus, this thesis explores the potential of using information on social media (Twitter) for crime prediction in combination with historical crime data. It also figures out, using data mining techniques, the most relevant feature engineering needed for United Kingdom dataset which could improve crime prediction model performance. Additionally, this study presents a function that could be used by every state in the United Kingdom for data cleansing, pre-processing and feature engineering. A shinny App was also use to display the tweets sentiment trends to prevent crime in near-real time.Exploratory analysis is essential for revealing the necessary data pre-processing and feature engineering needed prior to feeding the data into the machine learning model for efficient result. Based on earlier documented studies available, this is the first research to do a full exploratory analysis of historical British crime statistics using stop and search historical dataset. Also, based on the findings from the exploratory study, an algorithm was created to clean the data, and prepare it for further analysis and model creation. This is an enormous success because it provides a perfect dataset for future research, particularly for non-experts to utilise in constructing models to forecast crime or conducting investigations in around 32 police districts of the United Kingdom.Moreover, this study is the first study to present a complete collection of geo-spatial parameters for training a crime prediction model by combining demographic data from the same source in the United Kingdom with hourly sentiment polarity that was not restricted to Twitter keyword search. Six unique base models that were frequently mentioned in the previous literature was selected and used to train stop-and-search historical crime dataset and evaluated on test data and finally validated with dataset from London and Kent crime datasets.Two different datasets were created from twitter and historical data (historical crime data with twitter sentiment score and historical data without twitter sentiment score). Six of the most prevalent machine learning classifiers (Random Forest, Decision Tree, K-nearest model, support vector machine, neural network and naïve bayes) were trained and tested on these datasets. Additionally, hyperparameters of each of the six models developed were tweaked using random grid search. Voting classifiers and logistic regression stacked ensemble of different models were also trained and tested on the same datasets to enhance the individual model performance.In addition, two combinations of stack ensembles of multiple models were constructed to enhance and choose the most suitable models for crime prediction, and based on their performance, the appropriate prediction model for the UK dataset would be selected. In terms of how the research may be interpreted, it differs from most earlier studies that employed Twitter data in that several methodologies were used to show how each attribute contributed to the construction of the model, and the findings were discussed and interpreted in the context of the study. Further, a shiny app visualisation tool was designed to display the tweets’ sentiment score, the text, the users’ screen name, and the tweets’ vicinity which allows the investigation of any criminal actions in near-real time. The evaluation of the models revealed that Random Forest, Decision Tree, and K nearest neighbour outperformed other models. However, decision trees and Random Forests perform better consistently when evaluated on test data

Criminal Victimisation in Taiwan: an opportunity perspective

Environmental criminology concerns the role of opportunities (both people and objects) existing in the environment that make crimes more likely to occur. Research consistently shows that opportunity perspectives (particularly with regard to individuals’ lifestyles and routines) help in explaining the prevalence and concentration of crimes. However, there is a paucity of studies investigating crime patterns from an opportunity perspective both outside western countries and in relation to cybercrimes. Hence, it is not clear whether non-Western and online contexts exhibit similar patterns of crime as would be predicted by an opportunity perspective. This thesis is concerned with criminal victimisation in Taiwan – a less researched setting in the field of environmental criminology. It covers both offline victimisation (with a focus on burglary) and online victimisation from the aforementioned opportunity perspective. The goal of this thesis is to identify individual- and area-level characteristics that affect the patterns of victimisation in Taiwan. To achieve this, the thesis draws on a range of secondary datasets, including police recorded crime statistics, the Taiwan Area Victimisation Survey, and the Digital Opportunity Survey for Individuals and Households. With the application of quantitative modelling, the thesis suggests that the generalisability the lifestyle-routine activity approach in explaining crime patterns in Taiwan should be taken with caution. The findings provide partial support for its applicability in relation to burglary and cybercrime in Taiwan. Furthermore, the findings reported here in relation to patterns of repeat and near repeat victimisation depart from those observed in the western literature. The thesis concludes by discussing the implications of the findings for academic research and practice in crime prevention