Special Issue "Frontiers in Spatial and Spatiotemporal Crime Analytics"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2016).

Special Issue Editors

Guest Editor
Assoc. Prof. Marco Helbich Website E-Mail
Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands
Interests: spatial and spatiotemporal analyses; computational urban geography; GIS modeling; real estate economics; active transportation; built and natural environment; health geography
Guest Editor
Prof. Michael Leitner Website E-Mail
Department of Geography and Anthropology, Louisiana State University, USA
Interests: geography of crime; medical geography; computer cartography

Special Issue Information

Dear Colleagues,

Criminological theory is well-developed but analytical techniques to explore and model crime are lagging behind. Due to the emergence and accumulation of a wide range of environmental data, volunteered geographic information, and statistical data, among others, all being highly relevant for crime analytics, it is of particular relevance to keep pace with these developments.

While geographic information system-based methods have nowadays gained momentum to map crime patterns, advanced data-driven computational methods (e.g., machine learning, Bayesian spatiotemporal models) are still in its infancy and are far from being mainstream. However, other disciplines provide evidence that these approaches are highly capable for solving classification problems, forecasting, and to extract patterns hidden in the data otherwise overseen by basic methods. Therefore, the amalgamation of criminology with computational methods seems to be a rational next step on the research agenda. We anticipate that this methodological progress will yield more reliable risk assessments and more accurate predictions of crime as demanded by criminal justice agencies and needed for evidence-based criminal justice decision-making. 

Therefore, the prime aim of this Special Issue is to publish original research or review papers in order to stimulate further discussion on the development and application of latest data-driven scientific advances to understand crime patterns and criminal behavior, their dynamics over time and across space, and the underlying key mechanisms.

This Special Issue is a follow-up publication on an edited book (Leitner 2013) and a Special Issue (Leitner and Helbich 2015) on crime mapping principles and we believe that this new collection of papers will contribute to the contemporary research agenda on crime modeling from a computational and data-driven perspective. We encourage both theoretical, as well as application-oriented papers, dealing with these emerging issues. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics, including, but not limited to, the following:

  • Forecasting
  • Classification and detection of hotspots
  • Text mining
  • Predictive modeling
  • Model competitions
  • Risk assessments
  • Criminogenic exposure assessments
  • Criminal Geographic Profiling
  • (Bayesian) spatial and spatiotemporal modeling
  • Terrorism
  • Cyber crime
  • Human trafficking
  • Drug trafficking
  • Exceptional events and crime
  • Privacy issues and masking for privacy prevention
  • Etc.

Dr. Marco Helbich
Prof. Dr. Michael Leitner
Guest Editors

Submission

Manuscripts should be submitted to the ISPRS International Journal of Geo-Information online at www.mdpi.com by registering and logging into this website. Once you are registered, go to the submission form. Manuscripts can be submitted until the deadline (31.12.2016). Papers will be published continuously (as soon as finally accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words, including the authors’ names and affiliations) must be sent to the editors (, ) until 30.6.2016. Authors will be notified by 8.7.2016 whether the research described in the abstract fits the topic of the special issue. In that case authors are invited to submit a full manuscript and the Editorial Office will post all accepted abstracts to the ISPRS Geo-Information website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except shorter versions in the form of conference proceedings papers, which must be indicated explicitly on the submitted manuscript.). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 900 CHF (Swiss Francs).

References

Leitner, M. (ed.) (2013) Crime Modeling and Mapping Using Geospatial Technologies. Springer: Heidelberg, 446 pages.

Leitner, M. & M. Helbich (eds.) (2015) Innovative Crime Modeling and Mapping. Special Issue of Cartography and Geographic Information Science, vol. 42, no. 2, pp. 95–209.

Published Papers (8 papers)

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Editorial

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Open AccessEditorial
Frontiers in Spatial and Spatiotemporal Crime Analytics—An Editorial
ISPRS Int. J. Geo-Inf. 2017, 6(3), 73; https://doi.org/10.3390/ijgi6030073 - 06 Mar 2017
Cited by 1
Abstract
Environmental criminological theory is well-developed [1,2] but analytical techniques to explore and model crime incidents are lagging behind. Due to the emergence and accumulation of a wide range of environmental data [...] Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)

Research

Jump to: Editorial

Open AccessArticle
Land Use Influencing the Spatial Distribution of Urban Crime: A Case Study of Szczecin, Poland
ISPRS Int. J. Geo-Inf. 2017, 6(3), 74; https://doi.org/10.3390/ijgi6030074 - 08 Mar 2017
Cited by 11
Abstract
This paper falls into a common field of scientific research and its practical applications at the interface of urban geography, environmental criminology, and Geographic Information Systems (GIS). The purpose of this study is to identify types of different land use which influence the [...] Read more.
This paper falls into a common field of scientific research and its practical applications at the interface of urban geography, environmental criminology, and Geographic Information Systems (GIS). The purpose of this study is to identify types of different land use which influence the spatial distribution of a set of crime types at the intra-urban scale. The originality of the adopted approach lies in its consideration of a large number of different land use types considered as hypothetically influencing the spatial distribution of nine types of common crimes, geocoded at the address-level: car crimes, theft of property—other, residential crimes, property damage, commercial crimes, drug crimes, burglary in other commercial buildings, robbery, and fights and battery. The empirical study covers 31,319 crime events registered by the Police in the years 2006–2010 in the Polish city of Szczecin with a population ca. 405,000. Main research methods used are the GIS tool “multiple ring buffer” and the “crime location quotient (LQC)”. The main conclusion from this research is that a strong influence of land use types analyzed is limited to their immediate surroundings (i.e., within a distance of 50 m), with the highest concentration shown by commercial crimes and by the theft of property—other crime type. Land use types strongly attracting crime in this zone are alcohol outlets, clubs and discos, cultural facilities, municipal housing, and commercial buildings. In contrast, grandstands, cemeteries, green areas, allotment gardens, and depots and transport base are land use types strongly detracting crime in this zone. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis
ISPRS Int. J. Geo-Inf. 2017, 6(3), 65; https://doi.org/10.3390/ijgi6030065 - 25 Feb 2017
Cited by 10
Abstract
This paper aimed to analyze the spatial distribution of drug-related police interventions and the neighborhood characteristics influencing these spatial patterns. To this end, police officers ranked each census block group in Valencia, Spain (N = 552), providing an index of drug-related police interventions. [...] Read more.
This paper aimed to analyze the spatial distribution of drug-related police interventions and the neighborhood characteristics influencing these spatial patterns. To this end, police officers ranked each census block group in Valencia, Spain (N = 552), providing an index of drug-related police interventions. Data from the City Statistics Office and observational variables were used to analyze neighborhood characteristics. Distance to the police station was used as the control variable. A Bayesian ecological analysis was performed with a spatial beta regression model. Results indicated that high physical decay, low socioeconomic status, and high immigrant concentration were associated with high levels of drug-related police interventions after adjustment for distance to the police station. Results illustrate the importance of a spatial approach to understanding crime. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria
ISPRS Int. J. Geo-Inf. 2017, 6(1), 3; https://doi.org/10.3390/ijgi6010003 - 30 Dec 2016
Cited by 8
Abstract
The near-repeat phenomenon refers to the increased risk of repeat victimization not only at the same location but at nearby locations up to a certain distance and for a certain time period. In recent research, near-repeat victimization has been repeatedly confirmed for different [...] Read more.
The near-repeat phenomenon refers to the increased risk of repeat victimization not only at the same location but at nearby locations up to a certain distance and for a certain time period. In recent research, near-repeat victimization has been repeatedly confirmed for different crime types such as burglaries or shootings. In this article the near-repeat phenomenon is analyzed for each day of the week separately. That is, the near-repeat pattern is evaluated for all consecutive Mondays, Tuesdays, Wednesdays, etc. included in the dataset. These consecutive weekdays represent the fictive set of consecutive dates to allow for spatial and temporal analysis of crime patterns. Using these principles, it is hypothesized that street robberies cluster in space and time and by the same day of the week. This research analyzes street robberies from 2009 to 2013 in Vienna, Austria. The overall research goal investigates whether near-repeat patterns of robberies exist by weekdays and in an additional step by time of day, and whether these near-repeat patterns differ from each other and from purely spatial patterns. The results of this research confirm the existence of near-repeat patterns by weekday and especially by time of day. Distinctive locations have been identified that differ greatly per weekday and time of day. Based on this information, law enforcement agencies in Austria can optimize strategic planning of police resources in combating robberies. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation
ISPRS Int. J. Geo-Inf. 2016, 5(9), 162; https://doi.org/10.3390/ijgi5090162 - 09 Sep 2016
Cited by 5
Abstract
This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, [...] Read more.
This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00–03:59) and highest in the evening (20:00–21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
Evaluating Temporal Analysis Methods Using Residential Burglary Data
ISPRS Int. J. Geo-Inf. 2016, 5(9), 148; https://doi.org/10.3390/ijgi5090148 - 25 Aug 2016
Cited by 5
Abstract
Law enforcement agencies, as well as researchers rely on temporal analysis methods in many crime analyses, e.g., spatio-temporal analyses. A number of temporal analysis methods are being used, but a structured comparison in different configurations is yet to be done. This study aims [...] Read more.
Law enforcement agencies, as well as researchers rely on temporal analysis methods in many crime analyses, e.g., spatio-temporal analyses. A number of temporal analysis methods are being used, but a structured comparison in different configurations is yet to be done. This study aims to fill this research gap by comparing the accuracy of five existing, and one novel, temporal analysis methods in approximating offense times for residential burglaries that often lack precise time information. The temporal analysis methods are evaluated in eight different configurations with varying temporal resolution, as well as the amount of data (number of crimes) available during analysis. A dataset of all Swedish residential burglaries reported between 2010 and 2014 is used (N = 103,029). From that dataset, a subset of burglaries with known precise offense times is used for evaluation. The accuracy of the temporal analysis methods in approximating the distribution of burglaries with known precise offense times is investigated. The aoristic and the novel aoristic e x t method perform significantly better than three of the traditional methods. Experiments show that the novel aoristic e x t method was most suitable for estimating crime frequencies in the day-of-the-year temporal resolution when reduced numbers of crimes were available during analysis. In the other configurations investigated, the aoristic method showed the best results. The results also show the potential from temporal analysis methods in approximating the temporal distributions of residential burglaries in situations when limited data are available. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
How Do Vegetation Density and Transportation Network Density Affect Crime across an Urban Central-Peripheral Gradient? A Case Study in Kitchener—Waterloo, Ontario
ISPRS Int. J. Geo-Inf. 2016, 5(7), 118; https://doi.org/10.3390/ijgi5070118 - 15 Jul 2016
Cited by 6
Abstract
The relationship between vegetation, transportation networks, and crime has been under debate. Vegetation has been positively correlated with fear of crime; however, the actual correlation between vegetation and occurrences of crime is uncertain. Transportation networks have also been connected with crime occurrence but [...] Read more.
The relationship between vegetation, transportation networks, and crime has been under debate. Vegetation has been positively correlated with fear of crime; however, the actual correlation between vegetation and occurrences of crime is uncertain. Transportation networks have also been connected with crime occurrence but their impact on crime tends to vary over different circumstances. By conducting spatial analyses, this study explores the associations between crime and vegetation as well as transportation networks in Kitchener-Waterloo. Further, geographically weighted regression modeling and a dummy urban variable representing the urban center/other urban areas were employed to explore the associations across an urban central-peripheral gradient. Associations were analyzed for crimes against persons and crimes against property for four specific crime types (assaults, vehicle theft, sex offences, and drugs). Results suggest that vegetation has a reverse association with crimes against persons and crimes against property while transportation networks have a positive relationship with these two types of crime. Additionally, vegetation can be a deterrent to vehicle theft crime and drugs, while transportation networks can be a facilitator of drug-related crimes. Besides, these two associations appear stronger in the urban center compared to the urban periphery. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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Open AccessArticle
Modeling Spatial Interactions between Areas to Assess the Burglary Risk
ISPRS Int. J. Geo-Inf. 2016, 5(4), 47; https://doi.org/10.3390/ijgi5040047 - 01 Apr 2016
Cited by 3
Abstract
It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors [...] Read more.
It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011–2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas. Full article
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
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