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: closed (15 February 2020).

Special Issue Editors

Prof. Michael Leitner
Website
Guest Editor
Department of Geography and Anthropology, Louisiana State University, USA
Interests: geography of crime; medical geography; computer cartography
Special Issues and Collections in MDPI journals
Dr. Alina Ristea
Website
Guest Editor
School of Public Policy and Urban Affairs, Northeastern University, 310 Renaissance Park, 1135 Tremont St, Boston 02115, MA, USA
Interests: GIScience; spatial crime analysis; safety perception; social media mining; predictive analytics

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 (14 papers)

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Research

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Open AccessArticle
The Impact of “Strike Hard” on Repeat and Near-Repeat Residential Burglary in Beijing
ISPRS Int. J. Geo-Inf. 2020, 9(3), 150; https://doi.org/10.3390/ijgi9030150 - 06 Mar 2020
Abstract
“Strike Hard” is an enhanced law-enforcement strategy in China that aims to suppress crime, but measurement of the crime-reducing effect and potential changes in the spatiotemporal concentration of crime associated with “Strike Hard” remain unknown. This paper seeks to examine the impact, if [...] Read more.
“Strike Hard” is an enhanced law-enforcement strategy in China that aims to suppress crime, but measurement of the crime-reducing effect and potential changes in the spatiotemporal concentration of crime associated with “Strike Hard” remain unknown. This paper seeks to examine the impact, if any, of “Strike Hard” on the spatiotemporal clustering of burglary incidents. Two and half years of residential burglary incidents from Chaoyang, Beijing are used to examine repeat and near-repeat burglary incidents before, during, and after the “Strike Hard” intervention and a new technique that enables the comparison of repeat and near repeat patterns across different temporal periods is introduced to achieve this. The results demonstrate the intervention disrupted the repeat pattern during the “Strike Hard” period reducing the observed ratio of single-day repeat burglaries by 155%; however, these same single-day repeat burglary events increased by 41% after the cessation of the intervention. Findings with respect to near repeats are less remarkable with nominal evidence to support that the intervention produced a significant decrease, but coupled with other results, suggest that spatiotemporal displacement may have been an undesired by-product of “Strike Hard”. This study from a non-Western setting provides further evidence of the generalizability of findings related to repeat and near repeat patterns of burglary and further highlights the limited preventative effect that the “Strike Hard” enhanced law enforcement campaign had on burglary. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
The Effects of Physical, Social, and Housing Disorder on Neighborhood Crime: A Contemporary Test of Broken Windows Theory
ISPRS Int. J. Geo-Inf. 2019, 8(12), 583; https://doi.org/10.3390/ijgi8120583 - 12 Dec 2019
Abstract
The current study tests neighborhood (i.e., block group) effects reflective of broken windows theory (i.e., neighborhood, public space, social, housing disorder) on crime. Furthermore, these effects are tested independently on serious (i.e., Part I), and less serious (i.e., Part II) crime rates. Disorder [...] Read more.
The current study tests neighborhood (i.e., block group) effects reflective of broken windows theory (i.e., neighborhood, public space, social, housing disorder) on crime. Furthermore, these effects are tested independently on serious (i.e., Part I), and less serious (i.e., Part II) crime rates. Disorder data on a racially/ethnically stratified sample of block groups (N = 60) within Milwaukee, Wisconsin, U.S.A. were collected through systematic observations. Using these data, along with census and crime data, linear regression modeling was employed to test the effect of disorder measures on each crime outcome measure. Consistent with broken windows theory, disorder was associated with crime rates; however, the effect of disorder on crime was limited to the public space disorder measure. Furthermore, the effects of disorder on Part I crime rates were mediated by Part II offenses. Partial support was found for broken windows theory, in which neighborhood context had a greater effect on less serious offenses. Neighborhoods with increasing frequencies of disorder may benefit from bolstering partnerships between law enforcement officers, community members, and other local stakeholders with the aim of deterring offending at all levels, and consequently, decreasing indices of disorder and crime. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Types of Crime, Poverty, Population Density and Presence of Police in the Metropolitan District of Quito
ISPRS Int. J. Geo-Inf. 2019, 8(12), 558; https://doi.org/10.3390/ijgi8120558 - 04 Dec 2019
Abstract
This exploratory study identifies spatial patterns of crimes and their associations with the index of Unsatisfied Basic Needs (UBN), with Communitarian Policy Units (CPU) density, as well as with population density. The case study is the Metropolitan District of Quito. Correlation analyses were [...] Read more.
This exploratory study identifies spatial patterns of crimes and their associations with the index of Unsatisfied Basic Needs (UBN), with Communitarian Policy Units (CPU) density, as well as with population density. The case study is the Metropolitan District of Quito. Correlation analyses were applied between number of registers of each type of crime, and the UBN index, CPU density and population density measures. The spatial autocorrelation index of Getis-Ord Gi* was calculated to identify hotspots of the different types of crime. Ordinary least squares regressions and geographically weighted regressions considering types of crime as dependent variables, were calculated. Larceny and robbery were found to be the predominant crimes in the study area. An inverse relationship between the UBN index and number of crimes was identified for each type of crime, while positive relationships were found between crimes and CPU density, and between crimes and population density. Significant hotspots of fraud, homicide, larceny, murder, rape and robbery were found in all urban parishes. Additionally, crime hotspots were identified in eastern rural parishes adjacent to urban parishes. This study provides important implications for crime prevention in the Metropolitan District of Quito (MDQ), and the obtained results contribute to the ecology of crime research in the study area. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Analyzing the Relationship between Perception of Safety and Reported Crime in an Urban Neighborhood Using GIS and Sketch Maps
ISPRS Int. J. Geo-Inf. 2019, 8(12), 531; https://doi.org/10.3390/ijgi8120531 - 27 Nov 2019
Abstract
This study analyzes the perception of safety among residents of Main South neighborhood in Worcester, MA, USA and compares it to reported crimes. This neighborhood is the focus of a community-based crime reduction project funded by the Bureau of Justice Assistance, the policy [...] Read more.
This study analyzes the perception of safety among residents of Main South neighborhood in Worcester, MA, USA and compares it to reported crimes. This neighborhood is the focus of a community-based crime reduction project funded by the Bureau of Justice Assistance, the policy development arm of the U.S. Department of Justice. We collected social disorder and violent crime data from the Worcester Police Department and conducted 129 household surveys to understand residents’ perception of safety in the neighborhood and trust in community institutions. The surveys included a map on which residents indicated where they felt unsafe. The goal of this research was twofold: (1) to use geographic information systems (GIS) to analyze the differences in perception of neighborhood safety by gender and length of residency in the neighborhood and (2) to explore the relationship between reported crime and perception of safety in the community. Findings indicate that the strength of the correlation between perceived safety and reported crime varies and that gender and length of residency are significant factors that shape perceptions of safety. Implications of this research suggest the need for comprehensive community-based development initiatives to offer differentiated strategies that address a broad range of safety perceptions and crime experiences among a diverse group of residents. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy
ISPRS Int. J. Geo-Inf. 2019, 8(11), 488; https://doi.org/10.3390/ijgi8110488 - 30 Oct 2019
Abstract
A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are [...] Read more.
A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
The Spill Over of Crime from Urban Centers: An Account of the Changing Spatial Distribution of Violent Crime in Guyana
ISPRS Int. J. Geo-Inf. 2019, 8(11), 481; https://doi.org/10.3390/ijgi8110481 - 25 Oct 2019
Abstract
As the rate of crime decelerates in the developed world, the opposite phenomenon is being observed in the developing world, including Latin America and the Caribbean. Crime in Latin America and the Caribbean has been concentrated in urban settings, but the expertise for [...] Read more.
As the rate of crime decelerates in the developed world, the opposite phenomenon is being observed in the developing world, including Latin America and the Caribbean. Crime in Latin America and the Caribbean has been concentrated in urban settings, but the expertise for studying crime and providing guidance on policing remain heavily rooted in the developed world. A hindrance to studying crime in the developing world is the difficulty in obtaining official data, allowing for generalizations on where crime is concentrated to persist. This paper tackles two challenges facing crime analysis in the developing world: the availability of data and an examination of whether crime is concentrated in urban settings. We utilized newspaper archival data to study the spatial distribution of crime in Guyana, South America, across the landscape, and in relation to rural indigenous villages. Three spatial analysis tools, hotspot analysis, mean center, and standard deviation ellipse were used to examine the changing distribution of crime across 20 years. Based on 3900 reports of violent crime, our analyses suggest that the center of the gravity of crime changed over the years, spilling over to indigenous peoples’ landscapes. An examination of murder, where firearms and bladed weapons were the weapons of choice, suggests that these weapons moved beyond the coastal zone. The movement of weapons away from the coast raises concerns for the security of indigenous peoples and their associated wildlife. Our analysis suggests that policing measures should seek to extend towards Amerindian landscapes, and this is perhaps indicative of Latin American states with demographics similar to Guyana’s. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Does Income Inequality Explain the Geography of Residential Burglaries? The Case of Belo Horizonte, Brazil
ISPRS Int. J. Geo-Inf. 2019, 8(10), 439; https://doi.org/10.3390/ijgi8100439 - 07 Oct 2019
Abstract
The relationship between crime and income inequality is a complex and controversial issue. While there is some consensus that a relationship exists, the nature of it is still the subject of much debate. In this paper, this relationship is investigated in the context [...] Read more.
The relationship between crime and income inequality is a complex and controversial issue. While there is some consensus that a relationship exists, the nature of it is still the subject of much debate. In this paper, this relationship is investigated in the context of urban geography and whether income inequality can explain the geography of crime within cities. This question is examined for the specific case of residential burglaries in the city of Belo Horizonte, Brazil, where I tested how much burglary rates are affected by local average household income and by local exposure to poverty, while I controlled for other variables relevant to criminological theory, such as land-use type, density and accessibility. Different scales were considered for testing the effect of exposure to poverty. This study reveals that, in Belo Horizonte, the rate of burglaries per single family house is significantly and positively related to income level, but a higher exposure to poverty has no significant independent effect on these rates at any scale tested. The rate of burglaries per apartment, on the other hand, is not significantly affected by either average household income or exposure to poverty. These results seem consistent with a description where burglaries follow a geographical distribution based on opportunity, rather than being a product of localized income disparity and higher exposure between different economic groups. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction
ISPRS Int. J. Geo-Inf. 2019, 8(9), 383; https://doi.org/10.3390/ijgi8090383 - 02 Sep 2019
Cited by 1
Abstract
Crime geographical displacement has been examined in many Western countries. However, little is known about its existence, distribution, and potential predictive ability in large cities in China. Compared to the existing research, this study contributes to the current research in three ways. (1) [...] Read more.
Crime geographical displacement has been examined in many Western countries. However, little is known about its existence, distribution, and potential predictive ability in large cities in China. Compared to the existing research, this study contributes to the current research in three ways. (1) It provides confirmation that crime geographical displacement exists in relation to burglaries that occur in a large Chinese city. (2) A crime geographical displacement detector is proposed, where significant displacements are statistically detected and geographically displayed. Interestingly, most of the displacements are not very far from one another. These findings confirm the inferences in the existing literature. (3) Based on the quantitative results detected by the crime geographical displacement detector, a crime prediction method involving crime geographical displacement patterns could improve the accuracy of the empirical crime prediction method by 7.25% and 3.1 in the capture rate and prediction accuracy index (PAI), respectively. Our current study verifies the feasibility of crime displacement for crime prediction. The feasibility of the crime geographical displacement detector and results should be verified in additional areas. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services
ISPRS Int. J. Geo-Inf. 2019, 8(9), 371; https://doi.org/10.3390/ijgi8090371 - 25 Aug 2019
Cited by 1
Abstract
The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an [...] Read more.
The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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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 - 13 Jun 2019
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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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 - 01 Jun 2019
Cited by 3
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 - 20 May 2019
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 - 04 May 2019
Cited by 5
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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Review

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Open AccessReview
Analysing the Police Patrol Routing Problem: A Review
ISPRS Int. J. Geo-Inf. 2020, 9(3), 157; https://doi.org/10.3390/ijgi9030157 - 09 Mar 2020
Abstract
Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage [...] Read more.
Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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