Special Issue "Using GIS to Improve (Public) Safety and Security"

Special Issue Editor

Assoc. Prof. Igor Ivan
Website
Guest Editor
Department of Geoinformatics, VSB-Technical University of Ostrava, 17. listopadu 15, Ostrava, 708 00,Czech Republic
Interests: quantitative geography; spatial statistics; visualisation; crime and fear mapping; transport accessibility
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The right of citizens to safety and security is a key aspect of modern society. Modern geospatial technologies, together with spatial big data and geographic data science, are fighting against old and new safety and security threats, endangering contemporary society. We often live in smart countries, smart regions, smart cities or smart districts, and there is a vast array of new technologies using a magical word “smart”, but this encroaching smart environment is often causing smart safety and security threats. Additionally, we are dealing with a crucial decision between personal freedom and collective safety and security.

This Special Issue is a follow-up to a previous successful Special Issue (Ivan, Burian, Caha, 2018) on safety and security management, collecting mainly papers from the Symposium GIS Ostrava 2018. Accepted papers in this Special Issue will contribute to contemporary research on safety- and security-related issues. Authors are encouraged to submit both theoretical as well as application-oriented papers focusing on safety and security issues covering topics including but not limited to the following:

  • Spatial big data (incl. sensor networks, social networks, mobile phones, UAVs and CCTV);
  • Geographic data science (incl. methods of Artificial Intelligence);
  • Natural safety and security issues and GIS;
  • Human-made safety and security issues and GIS;
  • GI-software and GI-hardware tools.

Assoc. Prof. Igor Ivan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind 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 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial big data
  • geographic data science
  • natural safety and security issues
  • human-made safety and security issues
  • GI-sotfware and GI-hardware tools

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia
ISPRS Int. J. Geo-Inf. 2020, 9(9), 540; https://doi.org/10.3390/ijgi9090540 - 08 Sep 2020
Cited by 1
Abstract
Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the [...] Read more.
Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the association between crash outcomes and available explanatory variables. However, they are unable to capture the latent spatial heterogeneity accurately. Further, the vast majority of previous studies have focused on detailed spatial analysis of crashes from an aggregated viewpoint without considering the attributes of the built environment and land use. This study first uses geographic information systems (GIS) to examine crash hotspots based on two severity groups, seven prevailing crash causes, and three predominant crash types in the City of Dammam, Kingdom of Saudi Arabia (KSA). GIS-based geographically weighted regression (GWR) analysis technique was then utilized to uncover the spatial relationships of traffic collisions with population densities and relate it to the land use of each neighborhood. Results showed that Fatal and Injury (FI) crashes were mostly located in residential neighborhoods and near public facilities having low to medium population densities on highways with relatively higher speed limits. Distribution of hotspots and GWR-based analysis for crash causes showed that crashes due to “sudden lane deviation” accounted for the highest proportion of crashes that were concentrated mainly in the Central Business District (CBD) of the study area. Similarly, hotspots and GWR analysis for crash types revealed that “collisions between motor vehicles” constitute a significant proportion of the total crashes, with epicenters mostly stationed in high-density residential neighborhoods. The outcomes of this study could provide analysts and practitioners with crucial insights to understand the complex inter-relationships between traffic safety and land use. It can provide useful guidance to policymakers for better planning and effective management strategies to enhance safety at zonal levels. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Open AccessArticle
Geointelligence against Illegal Deforestation and Timber Laundering in the Brazilian Amazon
ISPRS Int. J. Geo-Inf. 2020, 9(6), 398; https://doi.org/10.3390/ijgi9060398 - 17 Jun 2020
Abstract
Due to the characteristics of the Southern Amazonas Mesoregion (Mesorregião Sul do Amazonas, MSA), conducting on-site surveys in all licensed forestry areas (Plano de Manejo Florestal, PMFS) is an impossible task. Therefore, the present investigation aimed to: (i) analyze the use of geointelligence [...] Read more.
Due to the characteristics of the Southern Amazonas Mesoregion (Mesorregião Sul do Amazonas, MSA), conducting on-site surveys in all licensed forestry areas (Plano de Manejo Florestal, PMFS) is an impossible task. Therefore, the present investigation aimed to: (i) analyze the use of geointelligence (GEOINT) techniques to support the evaluation of PMFS; and (ii) verify if the PMFS located in the MSA are being executed in accordance with Brazilian legislation. A set of twenty-two evaluation criteria were established. These were initially applied to a “standard” PMFS and subsequently replicated to a larger area of 83 PMFS, located in the MSA. GEOINT allowed for a better understanding of each PMFS, identifying illegal forestry activities and evidence of timber laundering. Among these results, we highlight the following evidences: (i) inconsistencies related to total transport time and prices declared to the authorities (48% of PMFS); (ii) volumetric information incompatible with official forest inventories and/or not conforming with Benford’s law (37% of PMFS); (iii) signs of exploitation outside the authorized polygon limits (35% PMFS) and signs of clear-cutting (29% of PMFS); (iv) no signs of infrastructure compatible with licensed forestry (17% of PMFS); and (v) signs of exploitation prior to the licensing (13% of PMFS) and after the expiration of licensing (3%). Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Open AccessArticle
Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors
ISPRS Int. J. Geo-Inf. 2020, 9(6), 383; https://doi.org/10.3390/ijgi9060383 - 10 Jun 2020
Abstract
In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government [...] Read more.
In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle
Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data
ISPRS Int. J. Geo-Inf. 2020, 9(6), 342; https://doi.org/10.3390/ijgi9060342 - 26 May 2020
Abstract
In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns [...] Read more.
In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Open AccessArticle
How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?
ISPRS Int. J. Geo-Inf. 2019, 8(12), 544; https://doi.org/10.3390/ijgi8120544 - 29 Nov 2019
Cited by 2
Abstract
Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE [...] Read more.
Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Open AccessArticle
Comparing Residents’ Fear of Crime with Recorded Crime Data—Case Study of Ostrava, Czech Republic
ISPRS Int. J. Geo-Inf. 2019, 8(9), 401; https://doi.org/10.3390/ijgi8090401 - 08 Sep 2019
Cited by 3
Abstract
The fear of crime is an established research topic, not only in sociology, environmental psychology and criminology, but also in GIScience. Using spatial analysis to analyse patterns, explore hotspots and determine the significance of respective surveys is one reason for the increase in [...] Read more.
The fear of crime is an established research topic, not only in sociology, environmental psychology and criminology, but also in GIScience. Using spatial analysis to analyse patterns, explore hotspots and determine the significance of respective surveys is one reason for the increase in popularity of such research topics for geographers, cartographers and spatial data scientists. This paper presents the results of an intensive online map-based questionnaire with 1551 respondents from the city of Ostrava, Czech Republic. The respondents marked 3792 points associated with the fear of crime over a ten week period. The perception data were compared with recorded crime data acquired from police department records for the years 2015–2018. This paper explores the spatial autocorrelation from perceived hotspots and from recorded crime hotspots. Our findings fit into the literature confirming results about the locations that most frequently attract fear, but there is still room for more investigations regarding the links between recorded crime and the fear of crime. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Show Figures

Figure 1

Back to TopTop