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Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR)

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

Deadline for manuscript submissions: closed (30 April 2017) | Viewed by 83614

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Special Issue Editor

Prof. Dr. Milan Konecny

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Guest Editor

Former President of ICA, Chairman of the ICA Commission Cartography for Early Warning and Crises Management, Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic
Interests: early warning and disaster/crises management; disaster risk reduction; big data; space and geospatial solutions; GI Science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Overall Motivation

The United Nations work to reduce disaster risk is built on the knowledge and experience of countries and other stakeholders over the past several decades. Milestones include the 1990s being declared the international decade for natural disaster reduction, the adoption of the International Framework for Action for the International Decade for Natural Disaster Reduction, the Yokohama Strategy and Plan of Action for a Safer World adopted by the 1^st World Conference on Natural Disaster Reduction, the endorsement of the International Strategy for Disaster Reduction, and the adoption of the Hyogo Framework for Action (HFA) at the 2^nd World Conference on Disaster Reduction.

The last U.N. Disaster Risk Reduction (DRR) conference in Sendai, Japan (March 18, 2015) decided to continue in best practices for the present and also to enhance the sustainability of all efforts through the deeper, wider, and complex approaches to Disaster Risk Reduction, formulated in four most important priorities: 1. Understanding disaster risk; 2. Strengthening disaster risk governance to manage disaster risk; 3. Investing in disaster risk reduction for resilience; and 4. Enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation, and reconstruction.

As never before, such U.N. efforts are formulated expectations from research and science, which would make all efforts more progressive, effective, and efficient. In realizations of priorities, it is expected that space and (geo)spatial methods and technologies and various parts of mapping (e.g., risk one) will be fully included. Moreover, our research efforts can fill up ISPRS intentions about “the central role of imagery and derived products in disaster management and homeland establishment, complementing its traditional central place in defence”. GI Science can also offer new and strong efforts, such as VGI (Volunteer Geographic Information), VGE (Virtual Geographic Environments), new ways of fully use Big Data, personalization of maps, by understanding context and adaptive cartography, etc. These, all together, can progressively improve the quality of the disaster risk management cycle (preparedness, early warning, alert, assessment, etc.) and also, and it is the main topic of this Special Issue, to improve complex smart solutions for the realization of Disaster Risk Reduction tasks.

Aims of this Special Issue

This Special Issue aims to promote innovative concepts, methods and tools that help in solving current and future problems in DRR, assisted by space and (geo)spatial technologies, approaches in GI Science, and mapping.

Global, national, and local level development, which periodically updates and disseminates, as appropriate, location-based disaster risk information, is expected from our research communities. There are requests to design and improve risk maps for decision makers, the general public, and communities at risk of exposure to disaster, in an appropriate format by using, as applicable, geospatial information technology; e.g., to promote real-time access to reliable data, make use of space and in situ information, including geographic information systems (GIS), and use information and communications innovations, and, last but not least, to enhance measurement tools and the collection, analysis, and dissemination of data.

Very important is to formulate the role of Big Data technologies and methods and potentials of newly appearing activities, such as citizen science, which should also effectively enhance approaches used in DRR.

Topics

In line with the specific DRR context, as outlined above, we would like to invite original research contributions on the following topics (which might be extended):

Space Solutions:

Remote Sensing Usability for DRR
Matching RS and PE to 2D maps and 3D models
Remote sensing in different disaster management phases (warning, monitoring, relief, assessment and recovery).
UAV application in DRR
Open RS clouds for DRR
New sensor data for disaster monitoring and response
Sensor cloud for disaster data acquisition
Multi-source RS data fusion for disaster management
Automated disaster information extraction from RS data
Automated 3D scene reconstruction for disaster management

Spatial solutions:

Big Data and VGI for disaster risk reduction
Smart Maps for DRR
Geoinformatics for DRR
Real-time mapping
Dynamic geovisualization
Geo-process model bases
Efficiency of disaster maps
Standardization of maps of hazards and disasters
Geo Map Web Services
Disaster Early warning systems
Disaster Assessment and aftermath monitoring
Prediction of combined hazards, risks and disaster effects.
Evaluation of hazard, risk and disaster scenarios
3D modeling and mapping of hazards, risks and disasters
Space-Time Data Mining and Knowledge Discovery
Disaster Risk management and sustainable development in DRR
Sharing and use of non-sensitive data and information
Geospatial and space-based technologies and related services for decision-makers, inhabitants and customers

Prof. Dr. Milan Konecny
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.

References

Bandrova, T.; Zlatanova S.; Konecny, M. Three-dimensional maps for disaster management. ISPRS Ann. Photogramm. Remote Sens. 2012, doi:10.5194/isprsannals-I-4-245-2012.

Bandrova, T.; Konecny, M.; Zlatanova, S. Thematic Cartography for the Society; Springer: Berlin, Germany, 2014.

Bandrova, T.; Zlatanova, S.; Konecny, M. Geoinformation for Disaster and Risk Management, Examples and Best Practices; Joint Board of Geospatial Information Societies (JB GIS) and United Nations Office for Outer Space Affairs (UNOOSA): Copenhagen, Denmark, 2010.

ISDR, 2007, Hyogo Framework for Action, 2005-2015, Building the Resilience of Nations and Communities to Disasters. Avalible online: https://www.unisdr.org/we/coordinate/hfa (accessed on 14 April 2016).

Konecny, M.; Zlatanova, S.; Bandrova T. Geographic Information and Cartography for Risk and Crisis Management; Towards Better Solutions; Springer: Berlin Heidelberg, 2010.

Konecny, M.; Reinhardt W. Early warning and disaster management: the importance of geographic information (Part A). Int. J. Digit. Earth 2010, 3, 217–220.

Konecny, M.; Reinhardt W. Early warning and disaster management: the importance of geographic information (PartB). Int. J. Digit. Earth 2010, 3, 313–315.

Lin, H.; Batty, M.; Jørgensen, S.E.; Fu, B.; Konecny, M.; Voinov, A.; Torrens, P.; Lu, G.; Zhu, A-X.; Wilson, J.P.; Gong, J.; Kolditz, O.; Bandrova T.; Chen. M. Virtual environments begin to embrace process-based geographic analysis. Transact. GIS 2015, doi:10.1111/tgis.12167.

Nayak S.; Zlatanova S. Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters; Springer Science & Business Media: Berlin, Germany, 2008.

Altan, O.; Backhause, R.; Boccardo, P.; van Manen, N.; Trinder J.; Zlatanova. S. The Value of Geoinformation for Disaster and Risk Management (VALID): Benefit Analysis and Stakeholder Assessment; Joint Board of Geospatial Information Societies (JB GIS): Copenhagen, Denmark, 2013.

UNISDR. Sendai Framework for Disaster Risk Reduction 2015–2030; United Nations: New York, NY, USA, 2015.

Published Papers (14 papers)

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Research

14750 KiB

Open AccessArticle

Geo-Hazard Detection and Monitoring Using SAR and Optical Images in a Snow-Covered Area: The Menyuan (China) Test Site

by Qihuan Huang, Yian Wang, Jia Xu, Angelique Nishyirimbere and Zhimin Li

ISPRS Int. J. Geo-Inf. 2017, 6(10), 293; https://doi.org/10.3390/ijgi6100293 - 21 Sep 2017

Cited by 8 | Viewed by 5557

Abstract

In this work, we combine SAR and optical images for geo-hazard detection and monitoring in Western China. An extremely small baseline of C-band SAR image pairs acquired from Sentinel-1A at Menyuan, China, is analyzed. Apart from the large area of coseismal deformation, we proposed an earthquake-derived landslide detecting method by removing the coseismal deformation with polynomial fitting, then the detected moving areas were confirmed with Chinese Gaofen-1 optical satellite images. Sentinel-1A C-band interferograms show about a 7-cm line of sight movement caused by the M_S 6.4 Menyuan earthquake; meanwhile, several features indicative of ground movement were detected by the proposed method and demonstrated by the Gaofen-1 optical images; the interpretation of high-resolution optical data complemented the goal of better understanding the behavior of geo-hazard disasters. InSAR time series analysis provides an opportunity for continuous monitoring of geo-hazards in remote areas, while the optical image method is easily affected by decorrelation due to snowfall. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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1799 KiB

Open AccessArticle

Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

by Tomáš Řezník, Vojtěch Lukas, Karel Charvát, Karel Charvát, Zbyněk Křivánek, Michal Kepka, Lukáš Herman and Helena Řezníková

ISPRS Int. J. Geo-Inf. 2017, 6(8), 238; https://doi.org/10.3390/ijgi6080238 - 06 Aug 2017

Cited by 24 | Viewed by 7365

Abstract

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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1759 KiB

Open AccessArticle

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

by Otakar Čerba, Karel Jedlička, Václav Čada and Karel Charvát

ISPRS Int. J. Geo-Inf. 2017, 6(8), 237; https://doi.org/10.3390/ijgi6080237 - 06 Aug 2017

Cited by 8 | Viewed by 4120

Abstract

Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by—or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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Open AccessArticle

Disaster Hashtags in Social Media

by Nikita Murzintcev and Changxiu Cheng

ISPRS Int. J. Geo-Inf. 2017, 6(7), 204; https://doi.org/10.3390/ijgi6070204 - 05 Jul 2017

Cited by 33 | Viewed by 8032

Abstract

Social media is a rich data source for analyzing the social impact of hazard processes and human behavior in disaster situations; it is used by rescue agencies for coordination and by local governments for the distribution of official information. In this paper, we propose a method for data mining in Twitter to retrieve messages related to an event. We describe an automated process for the collection of hashtags highly related to the event and specific only to it. We compare our method with existing keyword-based methods and prove that hashtags are good markers for the separation of similar, simultaneous incidents; therefore, the retrieved messages have higher relevancy. The method uses disaster databases to find the location of an event and to estimate the impact area. The proposed method can also be adapted to retrieve messages about other types of events with a known location, such as riots, festivals and exhibitions. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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Open AccessArticle

Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data

by Young-joo Kwak

ISPRS Int. J. Geo-Inf. 2017, 6(7), 203; https://doi.org/10.3390/ijgi6070203 - 05 Jul 2017

Cited by 37 | Viewed by 6361

Abstract

As part of the contribution to flood disaster risk reduction, it is important to identify and characterize flood areas, locations, and durations. Multiple satellite-based flood mapping and monitoring are an imperative process and the fundamental part of risk assessment in disaster risk management. In this paper, the MODIS-derived synchronized floodwater index (SfWi) was used to detect the maximum extent of a nationwide flood based on annual time-series data of 2015 in order to maximize the application of optical satellite data. The selected three major rivers—i.e., Ganges, Brahmaputra, and Meghna (GBM), transboundary rivers running through the great floodplain delta lying between Bangladesh and eastern India—show that a propensity of flood risk was revealed by the temporal and spatial dynamics of the maximum flood extent during the 2015 monsoon season. Resultant flood maps showed that SfWi-indicated flood areas were small but more accurate than those derived from the single use of the MODIS-derived water index. The return period of SfWi-indicated maximum flood extent was confirmed to be about 20 years based on historical flood records. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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8527 KiB

Open AccessArticle

Geospatial Big Data-Based Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area

by Han-Saem Kim, Chang-Guk Sun and Hyung-Ik Cho

ISPRS Int. J. Geo-Inf. 2017, 6(6), 174; https://doi.org/10.3390/ijgi6060174 - 15 Jun 2017

Cited by 19 | Viewed by 5112

Abstract

Seismic site effects are influenced mainly by geospatial uncertainties corresponding to geological or geotechnical spatial variance. Therefore, the development of a geospatial database is essential to characterize site-specific geotechnical information in multiscale areas and to optimize geospatial zonation methods with potentially high degrees of spatial variability based on trial-and-error geostatistical assessments. In this study, a multi-source geospatial information framework, which included the construction of a big data platform, estimation of geostatistical density, optimization of the geostatistical interpolation method, assessment of seismic site effects, and determination of geospatial zonation for decision making, was established. Then, this framework was applied to the Seoul metropolitan area, South Korea. The GIS-based framework was established to develop the geospatial zonation of site-specific seismic site effects before considering the local characteristics of site effects dependent on topographic or geological conditions, based on a geospatial big-data platform in Seoul. The zonal conditions were composed of geo-layers, site effect parameters, and other multi-source geospatial maps for each administrative area, and infrastructure was determined based on the integration of the optimized geoprocessing framework. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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5140 KiB

Open AccessArticle

Geospatial Analysis of Earthquake Damage Probability of Water Pipelines Due to Multi-Hazard Failure

by Mohammad Eskandari, Babak Omidvar, Mahdi Modiri, Mohammad Ali Nekooie and Ali Asghar Alesheikh

ISPRS Int. J. Geo-Inf. 2017, 6(6), 169; https://doi.org/10.3390/ijgi6060169 - 09 Jun 2017

Cited by 18 | Viewed by 6044

Abstract

The main purpose of this study is to develop a Geospatial Information System (GIS) model with the ability to assess the seismic damage to pipelines for two well-known hazards, including ground shaking and ground failure simultaneously. The model that is developed and used in this study includes four main parts of database implementation, seismic hazard analysis, vulnerability assessment and seismic damage assessment to determine the pipeline’s damage probability. This model was implemented for main water distribution pipelines of Iran and tested for two different earthquake scenarios. The final damage probability of pipelines was estimated to be about 74% for water distribution pipelines of Mashhad including 40% and 34% for leak and break, respectively. In the next step, the impact of each earthquake input parameter on this model was extracted, and each of the three parameters had a huge impact on changing the results of pipelines’ damage probability. Finally, the dependency of the model in liquefaction susceptibility, landslide susceptibility, vulnerability functions and segment length was checked out and specified that the model is sensitive just to liquefaction susceptibility and vulnerability functions. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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8289 KiB

Open AccessArticle

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

by Xuejia Sang, Linfu Xue, Xiaopeng Leng, Jiwen Liu and Liang Zhan

ISPRS Int. J. Geo-Inf. 2017, 6(6), 167; https://doi.org/10.3390/ijgi6060167 - 05 Jun 2017

Cited by 1 | Viewed by 4020

Abstract

Decision-making departments need more detailed and timely data in order to meet the needs of emergency response. Sichuan province is an area that frequently suffers natural disasters, and many disasters are caused by rainfall. This study establishes a decision support system (DSS) based on geoprocessing (GP) services, which can locate the region that overran the rainfall threshold and provide the population or property analysis, query, map plot, and path analysis functions. Most of the functions of the system are developed on the basis of geoprocessing services. This paper uses the warning–judgment module as an example to introduce the structure and function of the DSS system. The system satisfies the demands of real-time data acquisition, calculation, analysis, and presentation. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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Open AccessArticle

Design of a Model Base Framework for Model Environment Construction in a Virtual Geographic Environment (VGE)

by Chunxiao Zhang, Gang Ai, Xinqi Zheng, Kun Fang and Jiayang Li

ISPRS Int. J. Geo-Inf. 2017, 6(5), 145; https://doi.org/10.3390/ijgi6050145 - 04 May 2017

Cited by 2 | Viewed by 5317

Abstract

The model environment is a key component that enables a virtual geographic environment (VGE) to meet the scientific requirements for simulating dynamic phenomena and performing analyses. Considering the comprehensiveness of geographic processes and the requirements for the replication of model-based research, this paper proposes a model base framework for a model environment of a VGE that supports both model construction and modelling management, resulting in improved reproducibility. In this framework, model management includes model metadata, creation, deposition, encapsulation, integration, and adaptation; while modelling management focuses on invoking the model, model computation, and runtime control of the model. Based on this framework, to consider the problem of ever-worsening air quality, we applied the Linux-Apache-MySQL-Perl stack plus Supervisor to implement the model base to support a VGE prototype using professional meteorological and air quality models. Using this VGE prototype, we simulated a typical air pollution case for January 2010. The prototype not only illustrates how a VGE application can be built on the proposed model base, but also facilitates air quality simulations and emergency management. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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20072 KiB

Open AccessArticle

Multi-Objective Emergency Material Vehicle Dispatching and Routing under Dynamic Constraints in an Earthquake Disaster Environment

by Jincheng Jiang, Qingquan Li, Lixin Wu and Wei Tu

ISPRS Int. J. Geo-Inf. 2017, 6(5), 142; https://doi.org/10.3390/ijgi6050142 - 02 May 2017

Cited by 13 | Viewed by 5845

Abstract

Emergency material vehicle dispatching and routing (EMVDR) is an important task in emergency relief after large-scale earthquake disasters. However, EMVDR is subject to dynamic disaster environment, with uncertainty surrounding elements such as the transportation network and relief materials. Accurate and dynamic emergency material dispatching and routing is difficult. This paper proposes an effective and efficient multi-objective multi-dynamic-constraint emergency material vehicle dispatching and routing model. Considering travel time, road capacity, and material supply and demand, the proposed EMVDR model is to deliver emergency materials from multiple emergency material depositories to multiple disaster points while satisfying the objectives of maximizing transport efficiency and minimizing the difference of material urgency degrees among multiple disaster points at any one time. Furthermore, a continuous-time dynamic network flow method is developed to solve this complicated model. The collected data from Ludian earthquake were used to conduct our experiments in the post-quake and the results demonstrate that: (1) the EMVDR model adapts to the dynamic disaster environment very well; (2) considering the difference of material urgency degree, the material loss ratio is −10.7%, but the variance of urgency degree decreases from 2.39 to 0.37; (3) the EMVDR model shows good performance in time and space, which allows for decisions to be made nearly in real time. This paper can provide spatial decision-making support for emergency material relief in large-scale earthquake disasters. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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8620 KiB

Open AccessArticle

Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images

by Jihui Tu, Deren Li, Wenqing Feng, Qinhu Han and Haigang Sui

ISPRS Int. J. Geo-Inf. 2017, 6(5), 131; https://doi.org/10.3390/ijgi6050131 - 27 Apr 2017

Cited by 31 | Viewed by 5100

Abstract

The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine (SVM) classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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2079 KiB

Open AccessArticle

A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution

by Marc Wieland and Massimiliano Pittore

ISPRS Int. J. Geo-Inf. 2017, 6(4), 114; https://doi.org/10.3390/ijgi6040114 - 08 Apr 2017

Cited by 17 | Viewed by 6690

Abstract

With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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15497 KiB

Open AccessArticle

A Procedural Construction Method for Interactive Map Symbols Used for Disasters and Emergency Response

by Guoqiang Peng, Songshan Yue, Yuting Li, Zhiyao Song and Yongning Wen

ISPRS Int. J. Geo-Inf. 2017, 6(4), 95; https://doi.org/10.3390/ijgi6040095 - 24 Mar 2017

Cited by 9 | Viewed by 5765

Abstract

The timely and accurate mapping of dynamic disasters and emergencies is an important task that is necessary for supporting the decision-making that can improve the efficiency of rescue and response efforts. The existing emergency symbol libraries are primarily composed of point symbols and simple line symbols, focusing on the representation of disasters, related facilities, and operations. However, various existing response factors (e.g., the distribution and types of emergency forces) are also important for further decision-making and emergency responses; there is a need to design complex and diverse symbols to represent this rich information. Moreover, traditional mapping systems only provide static map symbols that cannot be easily edited after creation, making it difficult to support interactive editing after the symbols are mapped, thus hindering the representation of dynamic disasters and response factors. This article targets a solution of the above issues by proposing a procedural construction method of interactive map symbols for dynamic disasters and emergency responses. There are two primary research points. First, an emergency response and decision symbol library was classified and integrated into the existing attachments to form a richer symbol library for comprehensively representing disasters and emergencies. Second, an interactive map symbol procedural construction method was designed based on (1) primitive geometric compositions and geometric graphics algorithms to construct the map symbol graphics; (2) an interactive graphics control and drawing attributes configuration method to support user interactive editing of the visual variables of the mapped symbols; (3) and a dynamic updating and drawing strategy to support the real-time refreshing of the changing visual variables. The experiment was conducted using the Wenchuan earthquake as a case study, and the results demonstrate a powerful capacity of the produced interactive map symbols, which will contribute to the improvement of the mapping efficiency and representation capability of disasters and emergency response. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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Open AccessArticle

A Spatial Lattice Model Applied for Meteorological Visualization and Analysis

by Mingyue Lu, Min Chen, Xuan Wang, Jinzhong Min and Aili Liu

ISPRS Int. J. Geo-Inf. 2017, 6(3), 77; https://doi.org/10.3390/ijgi6030077 - 09 Mar 2017

Cited by 6 | Viewed by 5985

Abstract

Meteorological information has obvious spatial-temporal characteristics. Although it is meaningful to employ a geographic information system (GIS) to visualize and analyze the meteorological information for better identification and forecasting of meteorological weather so as to reduce the meteorological disaster loss, modeling meteorological information based on a GIS is still difficult because meteorological elements generally have no stable shape or clear boundary. To date, there are still few GIS models that can satisfy the requirements of both meteorological visualization and analysis. In this article, a spatial lattice model based on sampling particles is proposed to support both the representation and analysis of meteorological information. In this model, a spatial sampling particle is regarded as the basic element that contains the meteorological information, and the location where the particle is placed with the time mark. The location information is generally represented using a point. As these points can be extended to a surface in two dimensions and a voxel in three dimensions, if these surfaces and voxels can occupy a certain space, then this space can be represented using these spatial sampling particles with their point locations and meteorological information. In this case, the full meteorological space can then be represented by arranging numerous particles with their point locations in a certain structure and resolution, i.e., the spatial lattice model, and extended at a higher resolution when necessary. For practical use, the meteorological space is logically classified into three types of spaces, namely the projection surface space, curved surface space, and stereoscopic space, and application-oriented spatial lattice models with different organization forms of spatial sampling particles are designed to support the representation, inquiry, and analysis of meteorological information within the three types of surfaces. Cases studies are conducted by (1) performing a visualization of radar data that is used to describe the reflectivity factor of a raindrop and the pressure field information acquired from the National Centers for Environmental Prediction (NCEP), and (2) taking cutting analysis as another example where advanced meteorological analysis is performed. The results show that the proposed spatial lattice model can contribute to the feasible and effective analysis of meteorological information. Full article

(This article belongs to the Special Issue Smart Solutions for Disaster Risk Reduction: Big Data Concepts for Disaster Risk Reduction (DRR))

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