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Group-Based Modeling of Development

Predicting when and where crimes are likely to occur is crucial for prioritizing police resources. Prior victimization is an excellent predictor of risk. Repeat victimization, when it occurs, tends to occur swiftly after an initial incident. The predictive power of prior victimization is greater than that of other analysed variables (see Budd 1999). Self-evidently, prior victimization yields no prediction about properties as yet unvictimized. This article, using data about the crime of domestic burglary, contends that research should seek to realize the predictive potential to be gained from both pre-and post-victimization factors. One of the advantages of crime reduction through the prevention of repeats is that it offers a constant (and, it is hoped, declining) rate of events that trigger preventive action, and hence a natural pace for preventive work. In that spirit, postvictimization prevention should, as well as targeting the victimized home, also protect other properties that are similar with respect to the dimensions used by burglars in target selection. The central purpose of the research here reported is to identify the ways in which it is prudent to allocate crime reduction resources in the wake of an offence and across time and location relative to the burgled home. We analysed police-recorded crime burglary data for the county of Merseyside. Using statistical techniques developed to study the transmission of disease, we first confirmed that burglaries do cluster in space and time. The operational payoff of this result is that a residential burglary flags the elevated risk of further residential burglaries in the near future (1-2 months) and in close proximity (up to 300-400 metres) to the victimized home. Put simply, the burglary event should trigger preventive action that is not restricted to the burgled home. The data enable prospective burglary hot-spotting.

The Burglary as Clue to the Future: The Beginnings of Prospective Hot-Spotting

Online mapping technologies such as Google Maps and Street View have become increasingly accessible. These technologies have many convenient uses in everyday life, but law enforcement agencies have expressed concern that they could be exploited by offenders and might alter existing offending patterns and habits. For environmental criminologists, they have the potential to open up new approaches to conducting research. This paper draws on the results of earlier studies in related fields and a handful of criminological studies to discuss how these online mapping applications can trigger new research questions, and how they could be considered a valuable methodological addition to criminological research.

https://biblio.ugent.be/publication/4344882/file/5763027.pdf

Should criminologists be feeling lucky!? Applying Google Maps and Google Street View in criminological research

How COVID-19 reshaped quality of life in cities: A synthesis and implications for urban planning.

බළන්ගොඩ ප්රදේශය ආශ්රිත ශාන්තිකර්ම පිළිබඳ තුලනාත්මක අධ්යයනයක් (Unpublished doctoral dissertation)

Open Data for Crime and Place Research: A Practical Guide in R

Now that you are familiar with creating data in different formats in R, we can start to discuss one of the most important steps of the data analysis process—transforming your data into what Hadley Wickham (Wickham, Journal of Statistical Software, 59(10), 1–23, 2014) calls tidy data. Fittingly, many useful functions for data tidying are in a set of packages called the tidyverse. Data tidying is a very important step that will ensure your data are in the format you need to conduct your analyses. It includes steps such as viewing data types; viewing, editing, and adding both variable labels and value labels; formatting classes; and recoding and creating new variables. Learning basic techniques to determine, for example, how different variables in your dataset are stored or whether a variable has too many missing cases can be extremely useful when you are planning what you can feasibly analyze and how to do it. In pretty much any research project involving data analysis, you can expect that your data will require some level of manipulation. We rarely receive data that are perfectly clean and set up for our purpose! Fortunately, R offers a great deal of flexibility in how to accomplish these tasks. In this section, we will walk through some examples of common data transformations you may need to perform in your own analysis while at the same time practicing the concept of levels of measurement using data from the National Crime Victimization Survey.

Getting to Know Your Data

Many people involved in criminology and criminal justice research spend time making predictions about populations in the real world. These predictions tend to be based on a theoretical framework and are formally stated as hypotheses in order to answer a specific research question. Using inferential statistics (see Chap. 6), we can test to what extent our data support these hypotheses and provide empirical evidence to support (or reject) our expectations in R. This chapter uses a simulated dataset of results from a crime reduction intervention for at-risk youth to explore how the binomial distribution allows us to generalize from a sample of 100 participants in a study to the wider population.

Samuel Langton

Papers

Open Data for Crime and Place Research: A Practical Guide in R

Getting to Know Your Data

Hypothesis Testing Using the Binomial Distribution