Special Issue "Urban Crime Mapping and Analysis Using GIS"

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

Deadline for manuscript submissions: 30 September 2019

Special Issue Editors

Guest Editor
Prof. Michael Leitner

Department of Geography and Anthropology, Louisiana State University, USA
Website | E-Mail
Interests: geography of crime; medical geography; computer cartography
Guest Editor
Ms. Alina Ristea

Doctoral College GIScience, Interfaculty Department of Geoinformatics – Z_GIS University of Salzburg, Austria
Website | E-Mail
Interests: spatiotemporal crime analysis; crime prediction; social media integration into crime analysis

Special Issue Information

Dear Colleagues,

Spatial crime analysis and mapping started mostly by geographers in the early 1970s. The concurrent rise of Geographic Information Systems (GIS) coupled with the development of spatial crime analysis software programs led to a powerful suite of spatial analysis and visualization tools that allowed the rapid analysis of large amounts of crime incident data. As a result, spatial crime analysis became increasingly popular as a practical tool for law enforcement and as a research and teaching tool in geography, criminal justice, and other related programs.

This Special Issue is a follow-up publication of an edited book (Leitner 2013) and two previously published Special Issues (Leitner and Helbich 2015, Helbich and Leitner 2017) on crime analysis, modeling, and mapping. We believe that this new collection of papers will contribute to the contemporary research agenda on spatial and temporal crime-related issues. 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:

  • Big Data
  • Crime and Place
  • Crime Forecasting
  • Crime Perception
  • Criminogenic Factors
  • Exceptional Events and Crime
  • Geographic Profiling
  • Hot Spot Analysis
  • Human Trafficking
  • Micro-Spatial Crime Analysis
  • Modeling and Mapping Large Volume Crime
  • Near Repeat Pattern Analysis
  • Predictive Policing
  • Relationship between Alcohol-Serving Establishments and Disorder
  • Relationship between Foreclosure and Crime
  • Risk Terrain Modeling
  • Sex Offender Residency Restriction Laws
  • Simulation Modeling
  • Social Media
  • Social Network Analysis
  • Spatial Analysis of Gang Activities
  • Spatial and Temporal Crime Analysis
  • Temporal Approximation
  • Terrorism
  • Traffic Accidents Analysis
  • University Campus Crime
  • 3-D Crime Modeling
  • Etc.

Prof. Michael Leitner
Ms. Alina Ristea
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 (30 September 2019). Papers will be published continuously (as soon as final acceptance) 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 ([email protected] and [email protected]) until 30 April 2019. Authors will be notified by 12 May 2019 as to whether the research described in the abstract fits the topic of the Special Issue. In that case, authors will be invited to submit a full manuscript and the Editorial Office will post all accepted abstracts to the ISPRS International Journal of 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 the 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 1000 CHF (Swiss Francs).

References

Helbich, M. and M. Leitner (eds.) (2017) Frontiers in Spatial and Spatiotemporal Crime Analytics. Special Issue of ISPRS International Journal of Geo-Information, 6 (3), 73 (https://doi.org/10.3390/ijgi6030073).

Leitner, M. & M. Helbich (eds.) (2015) Innovative Crime Modeling and Mapping. Special Issue of Cartography and Geographic Information Science, 42 (2), 95-209 (https://doi.org/10.1080/15230406.2015.1010308).

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

Published Papers (4 papers)

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Research

Open AccessArticle
Comparative Analysis of Firearm Discharge Recorded by Gunshot Detection Technology and Calls for Service in Louisville, Kentucky
ISPRS Int. J. Geo-Inf. 2019, 8(6), 275; https://doi.org/10.3390/ijgi8060275
Received: 15 May 2019 / Revised: 5 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
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Abstract
Gunshot detection technology (GDT) has been increasingly adopted by law enforcement agencies to tackle the problem of underreporting of crime via 911 calls for service, which undoubtedly affects the quality of crime mapping and spatial analysis. This article investigates the spatial and temporal [...] Read more.
Gunshot detection technology (GDT) has been increasingly adopted by law enforcement agencies to tackle the problem of underreporting of crime via 911 calls for service, which undoubtedly affects the quality of crime mapping and spatial analysis. This article investigates the spatial and temporal patterns of gun violence by comparing data collected from GDT and 911 calls in Louisville, Kentucky. We applied hot spot mapping, near repeat diagnosis, and spatial regression approaches to the analysis of gunshot incidents and their associated neighborhood characteristics. We observed significant discrepancies between GDT data and 911 calls for service, which indicate possible underreporting of firearm discharge in 911 call data. The near repeat analysis suggests an increased risk of gunshots in nearby locations following an initial event. Results of spatial regression models validate the hypothesis of spatial dependence in frequencies of gunshot incidents and crime underreporting across neighborhoods in the study area, both of which are positively associated with proportions of African American residents, who are less likely to report a gunshot. This article adds to a growing body of research on GDT and its benefits for law enforcement activity. Findings from this research not only provide new insights into the spatiotemporal aspects of gun violence in urban areas but also shed light on the issue of underreporting of gun violence. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Graphical abstract

Open AccessArticle
An Assessment of Police Officers’ Perception of Hotspots: What Can Be Done to Improve Officer’s Situational Awareness?
ISPRS Int. J. Geo-Inf. 2019, 8(6), 260; https://doi.org/10.3390/ijgi8060260
Received: 17 April 2019 / Revised: 21 May 2019 / Accepted: 28 May 2019 / Published: 1 June 2019
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Abstract
The idea behind patrol activity is that police officers should be the persons best acquainted with the events and people in their patrol area. This implies that they should have access to relevant data and information (e.g., where and how to pay attention, [...] Read more.
The idea behind patrol activity is that police officers should be the persons best acquainted with the events and people in their patrol area. This implies that they should have access to relevant data and information (e.g., where and how to pay attention, when and how crimes are committed) in order to effectively perform their police duties. To what extent their perceptions of the places prone to crime (hotspots) are accurate and what the implications are for police efficiency if they are incorrect is an important question for law enforcement officials. This paper presents the results of a study on police practice in Serbia. The study was conducted on a sample of 54 police officers and aimed to determine the accuracy of the perception of residential burglary hotspots and to evaluate the ways police officers are informed about crimes. The results of the study have shown that the situational awareness of police officers is not at a desired level, with ineffective dissemination of relevant data and information as one of the possible reasons. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Anisotropic Diffusion for Improved Crime Prediction in Urban China
ISPRS Int. J. Geo-Inf. 2019, 8(5), 234; https://doi.org/10.3390/ijgi8050234
Received: 20 March 2019 / Revised: 5 May 2019 / Accepted: 14 May 2019 / Published: 20 May 2019
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Abstract
As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, [...] Read more.
As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, it is difficult to enhance the spatial accuracy of crime prediction. We propose the use of anisotropic diffusion to include environmental factors of the evaluated geographic area in the traditional crime prediction model, thereby aiming to predict crime occurrence at a finer scale regarding spatiotemporal aspects and environmental similarity. Under different evaluation criteria, the average prediction accuracy of the proposed method is 28.8%, improving prediction accuracy by 77.5%, as compared to the traditional methods. The proposed method can provide strong policing support in terms of conducting targeted hotspot policing and fostering sustainable community development. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Could Crime Risk Be Propagated across Crime Types?
ISPRS Int. J. Geo-Inf. 2019, 8(5), 203; https://doi.org/10.3390/ijgi8050203
Received: 23 March 2019 / Revised: 26 April 2019 / Accepted: 2 May 2019 / Published: 4 May 2019
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Abstract
It has long been acknowledged that crimes of the same type tend to be committed at the same location or proximity in a short period. However, the investigation of whether this phenomenon exists across crime types remains limited. The spatial-temporal clustered patterns for [...] Read more.
It has long been acknowledged that crimes of the same type tend to be committed at the same location or proximity in a short period. However, the investigation of whether this phenomenon exists across crime types remains limited. The spatial-temporal clustered patterns for two types of crimes in public areas (pocket-picking and vehicle/motor vehicle theft) are separately examined. Compared with existing research, this study contributes to current research from three aspects: (1) The repeat and near-repeat phenomenon exists in two types of crimes in a large Chinese city. (2) A significant spatial-temporal interaction between pocket-picking and vehicle/motor vehicle theft exists within a range of 100 m. Some cross-crime type interactions seem to have a stronger ability of prediction than does single-crime type interaction. (3) A risk-avoiding activity is identified after spatial-temporal hotspots of another crime type. The spatial extent with increased risk is limited to a certain distance from the previous hotspots. The experimental results are analyzed and interpreted with current criminology theories. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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