Criminology Of Place Research Articles

Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text Analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.

Hotspots vs. harmspots: Shifting the focus from counts to harm in the criminology of place

The prevailing pattern in much of the social sciences, including geography and criminology, relies on count data. “Hotspots” — geospatial areas with disproportionally more crime than the rest of the city — are usually identified by the number of events in these areas. Yet no attention is given to their severity, or any other weighting system of harm, despite the common-sense view that not all crimes are created equal. To illustrate the value of focusing on harm in addition to count data, we turn to a spatial analysis of crime by observing crime concentrations (hotspots) against harm concentrations (harmspots), across fifteen councils in the United Kingdom. The definition of “harm” is based on the Sentencing Guidelines for England and Wales, as each crime category (n = 415) attracts a different severity weight. Both “hotspots” and “harmspots” are defined as being at least 2 standard deviations from the mean distribution within each city: This procedure creates comparable datasets. The data suggest that half of all crime events are concentrated within 3% of all street segments in the selected councils, yet harm is even more heavily concentrated, with half of all harm located in just 1% of each council [OR = 3.49; 95% CI 3.268–3.728]. The intra-unit variance was also reduced by approximately half — from 0.75% to 0.45%. We discuss the implications of using harm, in addition to counts, for research and policy by arguing that a shift in focus is required both for the development of theories and for cost-effective prevention strategies.

The Next Step: A Spatiotemporal Statistical Model of the Birth and Death of Crime Hot Spots

The Criminology of Place is an important book. It brings together more than two decades of research by Weisburd and colleagues on the question of why it is that one street corner or block face can be in the 99th percentile of calls for service while an adjacent location is virtually crime free. The concentration of crime and disorder at discrete locations is one of the most striking empirical regularities in criminology not only because of its important implications for the strategic use of police to prevent crime but also for its implications for understanding the causes of crime. On its face the highly disproportionate concentration of crime at discrete locations is at odds with, or at least, is inexplicable with sociologically-based theories of the causes of crime that emphasize the role of community-level factors such social disorganization or collective efficacy or even higher level macro-structural forces such as inequality of opportunity. Similarly, economics-based theories of crime with their emphasis on the role of incentives such as sanction risk and legal alternatives to crime are silent on the phenomenon of crime hot spots. Weisburd, Groff, and Yang offer the most thorough currently available exploration of the capacity of extant theory to explain the hot spots phenomenon.

Social Disorganization, Crime Opportunities, and The Criminology of Place

Social Disorganization, Crime Opportunities, and The Criminology of Place

The Criminology of Place: Key Contributions and Commentary

The Criminology of Place grew out of research program funded by the National Institute of Justice (USA) supporting a retrospective longitudinal data collection at the micro-geographic level.1 This work in turn was a continuation of a research program, also supported by the National Institute of Justice that was the first longitudinal study of crime at micro-geographic places.2 While study of crime at place had begun to develop in the late 1980s,3 and its policy implications began to be examined in the 1990s,4 the 2004 Criminology article published by Weisburd and colleagues, staked out new ground and raised key new questions for this emerging area of study.5 Looking at crime over 14 years in Seattle, Washington, Weisburd, Bushway, Lum and Yang, showed that there were specific developmental patterns of crime at street segments in a city. One pattern, including just 1 percent of the streets, had persistently high rates of crime, and included some 23 per cent of crime during the study period. These were termed chronic crime hot spots, and they showed that crime was “coupled” to places in ways that challenged the traditional view that crime moved simply from place to place in the city. They also found that most streets in the city had little or no crime, and the crime drop in Seattle (following crime declines in other cities during the study period) was created by a small proportion of the city streets. Perhaps as important they found that some streets had “crime waves” during the study period, suggesting that speaking about city crime rates overall, masks deeply divergent trends across the city.6

Crime Specifics, Offender Residences, and Social Change: Developing The Criminology of Place

Crime Specifics, Offender Residences, and Social Change: Developing The Criminology of Place

The Gains of Greater Granularity: The Presence and Persistence of Problem Properties in Urban Neighborhoods

This study applies the growing emphasis on micro-places to the analysis of addresses, assessing the presence and persistence of “problem properties” with elevated levels of crime and disorder. It evaluates what insights this additional detail offers beyond the analysis of neighborhoods and street segments. We used over 2,000,000 geocoded emergency and non-emergency requests received by the City of Boston’s 911 and 311 systems from 2011–2013 to calculate six indices of violent crime, physical disorder, and social disorder for all addresses (n = 123,265). We linked addresses to their street segment (n = 13,767) and census tract (n = 178), creating a three-level hierarchy that enabled a series of multilevel Poisson hierarchical models. Less than 1% of addresses generated 25% of reports of crime and disorder. Across indices, 95–99% of variance was at the address level, though there was significant clustering at the street segment and neighborhood levels. Models with lag predictors found that levels of crime and disorder persisted across years for all outcomes at all three geographic levels, with stronger effects at higher geographic levels. Distinctively, ~15% of addresses generated crime or disorder in one year and not in the other. The analysis suggests new opportunities for both the criminology of place and the management of public safety in considering addresses in conjunction with higher-order geographies. We explore directions for empirical work including the further experimentation with and evaluation of law enforcement policies targeting problem properties.

Community Criminology: Fundamentals of Spatial and Temporal Scaling, Ecological Indicators, and Selectivity Bias. By Ralph Taylor. New York: New York University Press, 2015. Pp. xii+329. $49.00.

<i>Community Criminology: Fundamentals of Spatial and Temporal Scaling, Ecological Indicators, and Selectivity Bias</i>. By Ralph Taylor. New York: New York University Press, 2015. Pp. xii+329. $49.00.

Security Concerns at Hot-Spot Bus Stop Locations

Adequately addressing security concerns within public transportation will become a pressing issue over the next decade. These safety concerns frequently relate to terrorism, crime, and general disorder within public transportation with little systematic analysis directed toward transit locations. The given study geocoded 2 years of outdoor crime data within a midsized city and found the main bus terminal and three bus stop locations generated the highest levels of concentrated crime. Nonparticipatory windshield survey site observations were conducted at each location to qualitatively identify attributes that were possibly generating general disorder and crime incidents within these hot-spots. Implications for the criminology of place are discussed along with policing/security strategies for specific locations that facilitate an unusually high amount of official crime.

The Criminology of Place: Street Segments and Our Understanding of the Crime Problem

The Criminology of Place: Street Segments and Our Understanding of the Crime Problem

The criminology of place: Street segments and our understanding of the crime problem

The criminology of place: Street segments and our understanding of the crime problem

Weisburd D., Groff E. and Yang, S. The Criminology of Place: Street Segments and Our Understanding of the Crime Problem

Weisburd D., Groff E. and Yang, S. The Criminology of Place: Street Segments and Our Understanding of the Crime Problem

Book Review: The criminology of place: Street segments and our understanding of the crime problem

Book Review: The criminology of place: Street segments and our understanding of the crime problem

Reinventing Probable Cause: Target Selection in Proactive Investigations

Journal of Social IssuesVolume 43, Issue 3 p. 87-94 Reinventing Probable Cause: Target Selection in Proactive Investigations Lawrence W. Sherman, Corresponding Author Lawrence W. Sherman University of Maryland and Crime Control Institute, Washington, DC. LAWRENCE W. SHERMAN is Professor of Criminology at the University of Maryland and President of the Crime Control Institute in Washington, D.C. His current research interests include controlled experimentation in the effectiveness of criminal sanctions, the criminology of place and time, and reinventing the police.Crime Control Institute, 2125 Bancroft Place, N.W., Washington, D.C. 20008-4019.Search for more papers by this author Lawrence W. Sherman, Corresponding Author Lawrence W. Sherman University of Maryland and Crime Control Institute, Washington, DC. LAWRENCE W. SHERMAN is Professor of Criminology at the University of Maryland and President of the Crime Control Institute in Washington, D.C. His current research interests include controlled experimentation in the effectiveness of criminal sanctions, the criminology of place and time, and reinventing the police.Crime Control Institute, 2125 Bancroft Place, N.W., Washington, D.C. 20008-4019.Search for more papers by this author First published: Fall 1987 https://doi.org/10.1111/j.1540-4560.1987.tb02350.xCitations: 1 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume43, Issue3Fall 1987Pages 87-94 RelatedInformation