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Geoinformation for Disaster Risk Management
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Geoinformation for Disaster Risk Management

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

Deadline for manuscript submissions: closed (30 April 2015) | Viewed by 137238

Special Issue Editor

Dr. Christoph Aubrecht

E-Mail Website
Guest Editor
1. European Space Agency (ESA-ESRIN), Directorate of Earth Observation Programmes, Frascati, Italy
2. World Bank, Earth Observation for Sustainable Development Partnership, Washington, DC, USA
Interests: spatio-temporal analytics; integration of Earth Observation and GIScience; sustainable development; urban mapping; population distribution modeling; disaster risk management; exposure analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in the field of geoinformation/geospatial technologies (GIT) which includes GIS, mobile mapping, volunteered geographic information (VGI), remote sensing and spatial analysis in line with increased global awareness of the topic (see UN International Strategy for Disaster Reduction), have resulted in a strong promotion of an integrated and applied perspective on GIScience in disaster risk research. Locational aspects have increasingly been considered essential in the aim of building disaster resilient communities, through coordinated international action, by promoting increased situational risk awareness as an integral component of sustainable development.

With disasters and disaster management being an “inherently spatial” problem, geographic information and related tools and technologies, applied for data interpretation and information dissemination, can provide insight and decision support in all aspects of integrated disaster risk and crisis management and offer the basis for estimating and mapping risk, for determining damage potentials and impacted areas, for evacuation planning, for resource distribution during recovery, and for risk communication to involved stakeholders. Applications and challenges that GIScience and GIT are able to tackle in that regard include the representation, analysis, and cognition of geographic information, as well as associated spatio-temporal dynamics and uncertainties. Recent improvements in information and model interoperability, as well as inter-accessibility through new data sharing, crowdsourcing, and integration initiatives, add to this agenda.

Dr. Christoph Aubrecht
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 submissions that pass pre-check are 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 1700 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

  • exposure, vulnerability, and risk modeling for decision support
  • spatial disaster event databases
  • crowdsourcing and volunteered geographic information (vgi) in a disaster and crisis context and related geospatial modeling aspects
  • risk communication supported by geospatial mapping techniques
  • promotion of situational awareness in terms of communicating the actual spatial aspects and associated implications in a crisis context
  • near-real time mapping for response
  • crisis mapping and geovisualization
  • location technologies
  • data sharing initiatives for crisis and disaster management
  • interoperability aspects regarding disaster-related geodata
  • disaster and crisis related issues in spatial data infrastructures
  • webmapping for disaster and crisis support
  • spatio-temporal modeling
  • future challenges for disaster risk related geoinformation management

Published Papers (17 papers)

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1742 KiB  
Article
Weather Conditions, Weather Information and Car Crashes
by Adriaan Perrels, Athanasios Votsis, Väinö Nurmi and Karoliina Pilli-Sihvola
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2681-2703; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042681 - 27 Nov 2015
Cited by 18 | Viewed by 7547
Abstract
Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. [...] Read more.
Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. This is done by illustrating trends and spatiotemporal variation in the crash rates, by showing how a GIS application can evidence the association between temporary rises in regional crash rates and the occurrence of bad weather, and with a regression model on crash rate sensitivity to adverse weather conditions. The analysis indicates that a base rate of crashes depending on non-weather factors exists, and some combinations of extreme weather conditions are able to substantially push up crash rates on days with bad weather. Some spatial causation factors, such as variation of geophysical characteristics causing systematic differences in the distributions of weather variables, exist. Yet, even in winter, non-spatial factors are normally more significant. GIS data can support optimal deployment of rescue services and enhance in-depth quantitative analysis by helping to identify the most appropriate spatial and temporal resolutions. However, the supportive role of GIS should not be inferred as existence of highly significant spatial causation. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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1599 KiB  
Article
Data Integration for Climate Vulnerability Mapping in West Africa
by Alex De Sherbinin, Tricia Chai-Onn, Malanding Jaiteh, Valentina Mara, Linda Pistolesi, Emilie Schnarr and Sylwia Trzaska
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2561-2582; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042561 - 19 Nov 2015
Cited by 17 | Viewed by 9303
Abstract
Vulnerability mapping reveals areas that are likely to be at greater risk of climate-related disasters in the future. Through integration of climate, biophysical, and socioeconomic data in an overall vulnerability framework, so-called “hotspots” of vulnerability can be identified. These maps can be used [...] Read more.
Vulnerability mapping reveals areas that are likely to be at greater risk of climate-related disasters in the future. Through integration of climate, biophysical, and socioeconomic data in an overall vulnerability framework, so-called “hotspots” of vulnerability can be identified. These maps can be used as an aid to targeting adaptation and disaster risk management interventions. This paper reviews vulnerability mapping efforts in West Africa conducted under the USAID-funded African and Latin American Resilience to Climate Change (ARCC) project. The focus is on the integration of remotely sensed and socioeconomic data. Data inputs included a range of sensor data (e.g., MODIS NDVI, Landsat, SRTM elevation, DMSP-OLS night-time lights) as well as high-resolution poverty, conflict, and infrastructure data. Two basic methods were used, one in which each layer was transformed into standardized indicators in an additive approach, and another in which remote sensing data were used to contextualize the results of composite indicators. We assess the benefits and challenges of data integration, and the lessons learned from these mapping exercises. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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2195 KiB  
Article
Improving Post-Earthquake Insurance Claim Management: A Novel Approach to Prioritize Geospatial Data Collection
by Massimiliano Pittore, Marc Wieland, Mustafa Errize, Cagatay Kariptas and Ismet Güngör
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2401-2427; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042401 - 30 Oct 2015
Cited by 6 | Viewed by 5837
Abstract
With a population exceeding 14 million and a GDP of more than 300 billion USD, Istanbul dominates the Turkish economy. Unfortunately, this concentration of social and economic assets is permanently threatened by potentially devastating earthquakes, given the city’s close proximity to several well-known [...] Read more.
With a population exceeding 14 million and a GDP of more than 300 billion USD, Istanbul dominates the Turkish economy. Unfortunately, this concentration of social and economic assets is permanently threatened by potentially devastating earthquakes, given the city’s close proximity to several well-known fault systems. As a measure to mitigate the consequences of such events, and to increase the resilience of the exposed communities, the Turkish Catastrophe Insurance Pool (TCIP) has been set up to provide affordable and reliable earthquake insurance to households all over the country. In the aftermath of a damaging event, especially in Istanbul, the operational capacity of TCIP will be seriously challenged by the high number of claims whose settlement would have to be swift and fair in order to kick-start the recovery process. In this paper we explore an integrated approach based on mobile mapping and ad hoc prioritization techniques to streamline the data collection and analysis process, with application to both the pre-event and post-event phases. Preliminary results obtained in Besiktas, a populous district of Istanbul, are presented and discussed. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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1331 KiB  
Article
Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals
by Brenden Jongman, Jurjen Wagemaker, Beatriz Revilla Romero and Erin Coughlan De Perez
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2246-2266; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042246 - 23 Oct 2015
Cited by 107 | Viewed by 14638
Abstract
Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite [...] Read more.
Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite observations of water coverage and flood-related social media activity from Twitter, can be used to support rapid disaster response, using case-studies in the Philippines and Pakistan. For these countries we analyze information from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity analysis. The results demonstrate that these sources of near-real-time information can be used to gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods. In terms of location, we produce daily impact maps based on both satellite information and social media, which can dynamically and rapidly outline the affected area during a disaster. In terms of timing, the results show that GFDS and/or Twitter signals flagging ongoing or upcoming flooding are regularly available one to several days before the event was reported to humanitarian organizations. In terms of event understanding, we show that both GFDS and social media can be used to detect and understand unexpected or controversial flood events, for example due to the sudden opening of hydropower dams or the breaching of flood protection. The performance of the GFDS and Twitter data for early detection and location mapping is mixed, depending on specific hydrological circumstances (GFDS) and social media penetration (Twitter). Further research is needed to improve the interpretation of the GFDS signal in different situations, and to improve the pre-processing of social media data for operational use. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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5329 KiB  
Article
Developing a Relative Ranking of Social Vulnerability of Governorates of Yemen to Humanitarian Crisis
by Joseph Kandeh and Lalit Kumar
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1913-1935; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4041913 - 29 Sep 2015
Cited by 8 | Viewed by 7734
Abstract
The social vulnerability of the Yemeni population to humanitarian emergencies is not evenly distributed between the governorates. Some governorates may be more susceptible to the impacts than others, based on the circumstances of the people residing within them. In this paper, we present [...] Read more.
The social vulnerability of the Yemeni population to humanitarian emergencies is not evenly distributed between the governorates. Some governorates may be more susceptible to the impacts than others, based on the circumstances of the people residing within them. In this paper, we present a methodology for assessing social vulnerability of governorates of Yemen to humanitarian emergencies using a Geographic Information Systems approach. We develop a spatial index of social vulnerability from an initial list of 80 variables that were reduced to 12 factors through Principal Component Analysis. Our findings show that the differences in social vulnerability between governorates are primarily driven by 12 factors, of which education, lack of basic services in health, water and sanitation, electricity, housing quality, poverty, limited livelihood opportunities, and internal and external displacement are the major determinants. The results show that the factors that contribute to social vulnerability are different for each governorate, underpinning the need for context-specific vulnerability reduction approaches. The most vulnerable governorates are characterized by conflicts, armed clashes and violence. The geographic variability in social vulnerability further underpins the need for different mitigation, humanitarian response and recovery actions. The use of Geographic Information Systems approach has contributed to our understanding of the geographies of vulnerability to humanitarian emergencies in Yemen. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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715 KiB  
Article
Critical Data Source; Tool or Even Infrastructure? Challenges of Geographic Information Systems and Remote Sensing for Disaster Risk Governance
by Alexander Fekete, Katerina Tzavella, Iuliana Armas, Jane Binner, Matthias Garschagen, Carlo Giupponi, Vahid Mojtahed, Marcello Pettita, Stefan Schneiderbauer and Damien Serre
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1848-1869; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4041848 - 24 Sep 2015
Cited by 35 | Viewed by 10319
Abstract
Disaster risk information is spatial in nature and Geographic Information Systems (GIS) and Remote Sensing (RS) play an important key role by the services they provide to society. In this context, to risk management and governance, in general, and to civil protection, specifically [...] Read more.
Disaster risk information is spatial in nature and Geographic Information Systems (GIS) and Remote Sensing (RS) play an important key role by the services they provide to society. In this context, to risk management and governance, in general, and to civil protection, specifically (termed differently in many countries, and includes, for instance: civil contingencies in the UK, homeland security in the USA, disaster risk reduction at the UN level). The main impetus of this article is to summarize key contributions and challenges in utilizing and accepting GIS and RS methods and data for disaster risk governance, which includes public bodies, but also risk managers in industry and practitioners in search and rescue organizations. The article analyzes certain method developments, such as vulnerability indicators, crowdsourcing, and emerging concepts, such as Volunteered Geographic Information, but also investigates the potential of the topic Critical Infrastructure as it could be applied on spatial assets and GIS and RS itself. Intended to stimulate research on new and emerging fields, this article’s main contribution is to move spatial research toward a more reflective stance where opportunities and challenges are equally and transparently addressed in order to gain more scientific quality. As a conclusion, GIS and RS can play a pivotal role not just in delivering data but also in connecting and analyzing data in a more integrative, holistic way. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
1918 KiB  
Article
Simulating and Communicating Outcomes in Disaster Management Situations
by Michal Lichter, Asher Yair Grinberger and Daniel Felsenstein
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1827-1847; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4041827 - 24 Sep 2015
Cited by 8 | Viewed by 6247
Abstract
An important, but overlooked component of disaster managment is raising the awareness and preparedness of potential stakeholders. We show how recent advances in agent-based modeling and geo-information analytics can be combined to this effect. Using a dynamic simulation model, we estimate the long [...] Read more.
An important, but overlooked component of disaster managment is raising the awareness and preparedness of potential stakeholders. We show how recent advances in agent-based modeling and geo-information analytics can be combined to this effect. Using a dynamic simulation model, we estimate the long run outcomes of two very different urban disasters with severe consequences: an earthquake and a missile attack. These differ in terms of duration, intensity, permanence, and focal points. These hypothetical shocks are simulated for the downtown area of Jerusalem. Outcomes are compared in terms of their potential for disaster mitigation. The spatial and temporal dynamics of the simulation yield rich outputs. Web-based mapping is used to visualize these results and communicate risk to policy makers, planners, and the informed public. The components and design of this application are described. Implications for participatory disaster management and planning are discussed. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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6386 KiB  
Article
Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index
by Ryan Hile and Thomas J. Cova
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1774-1790; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4041774 - 24 Sep 2015
Cited by 8 | Viewed by 4759
Abstract
The Social Vulnerability Index (SoVI) has served the hazards community well for more than a decade. Using Utah as a test case, a state with a population exposed to a variety of hazards, this study sought to build upon the SoVI approach by [...] Read more.
The Social Vulnerability Index (SoVI) has served the hazards community well for more than a decade. Using Utah as a test case, a state with a population exposed to a variety of hazards, this study sought to build upon the SoVI approach by augmenting it with a non-linear Artificial Neural Network (ANN). A SoVI was created for the state of Utah at the census block group level using five-year data (2008–2012) from the American Community Survey. The SoVI provided a dataset from which to train a neural network. The ANN was then used to classify a subset of the state to determine if it could provide a comparable classification of vulnerability. The ANN produced a vulnerability classification that was approximately 26% consistent with the SoVI created using the traditional approach. The differences in classifications were assessed using radar plots of block group variable averages to explore how the variables were handled in each classification. The results of this study warrant further investigation of the capabilities of an ANN-enhanced SoVI. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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1693 KiB  
Communication
Rethinking Engagement: Innovations in How Humanitarians Explore Geoinformation
by Pablo Suarez
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1729-1749; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031729 - 11 Sep 2015
Cited by 9 | Viewed by 6523
Abstract
When humanitarian workers embark on learning and dialogue for linking geoinformation to disaster management, the activities they confront are usually more difficult than interesting. How to accelerate the acquisition and deployment of skills and tools for spatial data collection and analysis, given the [...] Read more.
When humanitarian workers embark on learning and dialogue for linking geoinformation to disaster management, the activities they confront are usually more difficult than interesting. How to accelerate the acquisition and deployment of skills and tools for spatial data collection and analysis, given the increasingly unmanageable workload of humanitarians? How to engage practitioners in experiencing the value and limitations of newly available tools? This paper offers an innovative approach to immerse disaster managers in geoinformation: participatory games that enable stakeholders to experience playable system dynamic models linking geoinformation, decisions and consequences in a way that is both serious and fun. A conceptual framework outlines the foundations of experiential learning through gameplay, with clear connections to a well-established risk management framework. Two case studies illustrate this approach: one involving flood management in the Zambezi river in southern Africa through the game UpRiver (in both physical and digital versions), and another pertaining to World Bank training on open data for resilience that combines applied improvisation activities with the need to understand and deploy software tools like Open Street Map and InaSAFE to manage school investments and schoolchildren evacuation in a simulated flood scenario for the city of La Plata, Argentina. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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879 KiB  
Article
Space for Climate
by Pierre-Philippe Mathieu
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1569-1583; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031569 - 01 Sep 2015
Viewed by 4471
Abstract
This paper describes how Earth Observation (EO) data—in particular from satellites—can support climate science, monitoring, and services by delivering global, repetitive, consistent, and timely information on the state of the environment and its evolution. Some examples are presented of EO demonstration pilot projects [...] Read more.
This paper describes how Earth Observation (EO) data—in particular from satellites—can support climate science, monitoring, and services by delivering global, repetitive, consistent, and timely information on the state of the environment and its evolution. Some examples are presented of EO demonstration pilot projects performed in partnership with scientists, industry, and development practitioners to support climate science, adaptation, mitigation, and disaster risk management. In particular, the paper highlights the challenge of gathering observations and generating long-term climate data records, which provide the foundation of risk management. The paper calls for a science-based integrated approach to climate risk management supported by data and knowledge, providing decision-makers with a unique analytical lens to develop a safety net to risk and maximize opportunities related to climate change and variability. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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654 KiB  
Article
Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery
by Qunying Huang and Yu Xiao
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1549-1568; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031549 - 24 Aug 2015
Cited by 205 | Viewed by 15833
Abstract
Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort [...] Read more.
Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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527 KiB  
Article
Enhancing Disaster Management: Development of a Spatial Database of Day Care Centers in the USA
by Nagendra Singh, Mark Tuttle and Budhendra Bhaduri
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1290-1300; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031290 - 30 Jul 2015
Cited by 3 | Viewed by 5933
Abstract
Children under the age of five constitute around 7% of the total U.S. population, and represent a segment of the population that is totally dependent on others for day-to-day activities. A significant proportion of this population spends time in some form of day [...] Read more.
Children under the age of five constitute around 7% of the total U.S. population, and represent a segment of the population that is totally dependent on others for day-to-day activities. A significant proportion of this population spends time in some form of day care arrangement while their parents are away from home. Accounting for those children during emergencies is of high priority, which requires a broad understanding of the locations of such day care centers. As concentrations of at risk population, the spatial location of day care centers is critical for any type of emergency preparedness and response (EPR). However, until recently, the U.S. emergency preparedness and response community did not have access to a comprehensive spatial database of day care centers at the national scale. This paper describes an approach for the development of the first comprehensive spatial database of day care center locations throughout the U.S. utilizing a variety of data harvesting techniques to integrate information from widely disparate data sources followed by geolocating for spatial precision. In the context of disaster management, such spatially refined demographic databases hold tremendous potential for improving high-resolution population distribution and dynamics models and databases. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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2640 KiB  
Article
Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management
by Alan Smith, Andy Newing, Niall Quinn, David Martin, Samantha Cockings and Jeffrey Neal
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1118-1141; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031118 - 09 Jul 2015
Cited by 10 | Viewed by 7150
Abstract
Human populations are not static or uniformly distributed across space and time. This consideration has a notable impact on natural hazard analyses which seek to determine population exposure and risk. This paper focuses on the coupling of population and environmental models to address [...] Read more.
Human populations are not static or uniformly distributed across space and time. This consideration has a notable impact on natural hazard analyses which seek to determine population exposure and risk. This paper focuses on the coupling of population and environmental models to address the effect of seasonally varying populations on exposure to flood risk. A spatiotemporal population modelling tool, SurfaceBuilder247, has been combined with LISFLOOD-FP flood inundation model outputs for a study area centred on the coastal resort town of St Austell, Cornwall, United Kingdom (UK). Results indicate strong seasonal cycles in populations and their exposure to flood hazard which are not accounted for in traditional population datasets and flood hazard assessments. Therefore, this paper identifies and demonstrates considerable enhancements to the current handling of spatiotemporal population variation within hazard exposure assessment and disaster risk management. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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2362 KiB  
Article
Modeling Obstruction and Restoration of Urban Commutation Networks in the Wake of a Devastating Earthquake in Tokyo
by Toshihiro Osaragi
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1097-1117; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031097 - 07 Jul 2015
Cited by 3 | Viewed by 4562
Abstract
In the aftermath of a devastating earthquake, public transportation is presumed paralyzed and thus unavailable; large numbers of people are expected to experience difficulty in commuting. In recent years, implementation of district continuity plans (DCPs) and business continuity plans (BCPs) has become a [...] Read more.
In the aftermath of a devastating earthquake, public transportation is presumed paralyzed and thus unavailable; large numbers of people are expected to experience difficulty in commuting. In recent years, implementation of district continuity plans (DCPs) and business continuity plans (BCPs) has become a major concern for local governments and private firms, respectively. In this paper, we propose a pair of simulation models seeking to examine business commutation networks in terms of their possible obstruction and eventual restoration. The first of these model commuting intentions by analyzing individual daily commutes. The second offers a mobility model of commuters’ physical endurance for travel alternatives on foot or by bicycle. Next, we proceed to gauge the number of commuters likely to experience difficulty and adjudge their spatial distribution while taking into account such attributes as gender and employment. Lastly, we attempt to assess rates and patterns in the reduction of commutation constraints based on simulations that assume a restoration of rail infrastructure or its equivalent. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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841 KiB  
Article
Hybrid 3D Rendering of Large Map Data for Crisis Management
by David Tully, Abdennour El Rhalibi, Christopher Carter and Sud Sudirman
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1033-1054; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031033 - 26 Jun 2015
Cited by 9 | Viewed by 7571
Abstract
In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt [...] Read more.
In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt with. Some existing map data include errors and are not complete, which makes the generation of realistic and accurate 3D environments problematic. The domain of application of our work is crisis management which requires very accurate GIS or map information. We believe the use of creating a 3D virtual environment using real map data whilst correcting and completing the missing data, improves the quality and performance of crisis management decision support system to provide a more natural and intuitive interface for crisis managers. Consequently, we present a case study into issues related to combining multiple large datasets to create an accurate representation of a novel, multi-layered, hybrid real-world maps. The hybrid map generation combines LiDAR, Ordnance Survey, and OpenStreetMap data to generate 3D cities spanning 1 km2. Evaluation of initial visualised scenes is presented. Initial tests consist of a 1 km2 landscape map containing up to 16 million vertices’ and run at an optimal 51.66 frames per-second. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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3668 KiB  
Article
Manifestation of an Analytic Hierarchy Process (AHP) Model on Fire Potential Zonation Mapping in Kathmandu Metropolitan City, Nepal
by Sachin Kumar Chhetri and Prabin Kayastha
ISPRS Int. J. Geo-Inf. 2015, 4(1), 400-417; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4010400 - 19 Mar 2015
Cited by 36 | Viewed by 8906
Abstract
Even though fewer people die as a result of fire than other natural disasters, such as earthquake, flood, landslide, etc., the average loss of property due to fire is high. Kathmandu Metropolitan City is becoming more vulnerable to fire due to haphazard [...] Read more.
Even though fewer people die as a result of fire than other natural disasters, such as earthquake, flood, landslide, etc., the average loss of property due to fire is high. Kathmandu Metropolitan City is becoming more vulnerable to fire due to haphazard urbanization and increase in population. To control problems due to fire, systematic studies are necessary, including fire potential mapping and risk assessment. This study applies an Analytic Hierarchy Process (AHP) method in Kathmandu Metropolitan City, Nepal for generation of fire potential zonation map. The fire potential zonation map is prepared on the basis of available data of land use, fuel stations, and population density. This map shows that 58.04% of the study area falls under low fire potential zone, 32.92% falls under moderate fire potential zone and 9.04% falls under high fire potential zone. The map is also validated through major past fire incidents. The results show that the predicted fire potential zones are found to be in good agreement with past fire incidents, and, hence, the map can be used for future land-use planning. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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Review

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Review
Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management
by Hiroyuki Miyazaki, Masahiko Nagai and Ryosuke Shibasaki
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1936-1964; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4041936 - 29 Sep 2015
Cited by 27 | Viewed by 8309
Abstract
Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management [...] Read more.
Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies. Full article
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
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