Leveraging Volunteered Geographic Information to Improve Disaster Resilience

Volunteered Geographic Information (VGI) has emerged as an important additional source of information for improving the resilience of cities and communities in the face of natural hazards and extreme weather events. This chapter summarizes the existing research in this area and offers an interdisciplinary perspective of the challenges to be overcome, by presenting AGORA: A Geospatial Open collaboRative Architecture for building resilience against disasters and extreme events. AGORA structures the challenges of using VGI for disaster management into three layers: acquisition, integration and application. The chapter describes the research challenges involved in each of these layers, as well as reporting on the results achieved so far and the lessons learned in the context of flood risk management in Brazil. Furthermore, the chapter concludes by setting out an interdisciplinary research agenda for leveraging VGI to improve disaster resilience.

[1]  Jo Ueyama,et al.  AGORA-GeoDash: A geosensor dashboard for real-time flood risk monitoring , 2014, ISCRAM.

[2]  C. Haruechaiyasak,et al.  The role of Twitter during a natural disaster: Case study of 2011 Thai Flood , 2012, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[3]  Wouter Joosen,et al.  A middleware platform to support river monitoring using wireless sensor networks , 2011, Journal of the Brazilian Computer Society.

[4]  Pascal Neis,et al.  A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis , 2014, Trans. GIS.

[5]  Muhammad Imran,et al.  Coordinating human and machine intelligence to classify microblog communications in crises , 2014, ISCRAM.

[6]  Bernd Hellingrath,et al.  oDMN: An Integrated Model to Connect Decision-Making Needs to Emerging Data Sources in Disaster Management , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[7]  Alexander Zipf,et al.  An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index , 2015, Trans. GIS.

[8]  Luke S. Smith,et al.  Assessing the utility of social media as a data source for flood risk management using a real‐time modelling framework , 2017 .

[9]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[10]  Sameer Kumar,et al.  Before and after disaster strikes: A relief supply chain decision support framework , 2013 .

[11]  Frank O. Ostermann,et al.  Digital Earth from vision to practice: making sense of citizen-generated content , 2012, Int. J. Digit. Earth.

[12]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[13]  Sven Schade,et al.  Architecture of a Service-Enabled Sensing Platform for the Environment , 2015, Sensors.

[14]  Alexander Zipf,et al.  Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned , 2016 .

[15]  Eduardo Mario Mendiondo,et al.  Histórico da Expansão Urbana e Incidência de Inundações: O Caso da Bacia do Gregório, São Carlos - SP , 2007 .

[16]  Flávio Eduardo Aoki Horita,et al.  Geographical prioritization of social network messages in near real-time using sensor data streams: an application to floods , 2016, GEOINFO.

[17]  Muhammad Imran,et al.  Integrating Social Media Communications into the Rapid Assessment of Sudden Onset Disasters , 2014, SocInfo.

[18]  Carlos Eduardo Pereira,et al.  Dynamic Sensor Management: Extending Sensor Web for Near Real-Time Mobile Sensor Integration in Dynamic Scenarios , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).

[19]  F. Norris,et al.  Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness , 2008, American journal of community psychology.

[20]  Abhas K. Jha,et al.  Safer Homes, Stronger Communities: A Handbook for Reconstructing After Natural Disasters , 2010 .

[21]  Gloria Bordogna,et al.  On predicting and improving the quality of Volunteer Geographic Information projects , 2016, Int. J. Digit. Earth.

[22]  Bernd Hellingrath,et al.  A Framework for the Integration of Volunteered Geographic Information into Humanitarian Logistics , 2014, AMCIS.

[23]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[24]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[25]  Torsten Braun,et al.  Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[26]  Jó Ueyama,et al.  Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks , 2015, Comput. Geosci..

[27]  Alexander Zipf,et al.  A Conceptual Quality Framework for Volunteered Geographic Information , 2015, COSIT.

[28]  Francis Harvey,et al.  To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced Geographic Information , 2013 .

[29]  Hermann Hellwagner,et al.  Supporting Crisis Management via Sub-event Detection in Social Networks , 2012, 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[30]  Alexander Zipf,et al.  A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management , 2015, Int. J. Geogr. Inf. Sci..

[31]  Miriam J. Metzger,et al.  The credibility of volunteered geographic information , 2008 .

[32]  Nigel Waters,et al.  Road assessment after flood events using non-authoritative data , 2013 .

[33]  Anna Zafeiris,et al.  Observations and Measurements , 2018 .

[34]  Christopher E. Oxendine,et al.  Using Non-authoritative Sources During Emergencies in Urban Areas , 2015 .

[35]  Kirsi Virrantaus,et al.  Spatial Data Quality: Problems and Prospects , 2009 .

[36]  Alexander Zipf,et al.  An Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data , 2015, Trans. GIS.

[37]  Maria Clara Fava,et al.  Modelo de alerta hidrológico com base participativa usando sistema de informações voluntárias para previsão de enchentes , 2015 .

[38]  Xiao Zhang,et al.  SensePlace2: GeoTwitter analytics support for situational awareness , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[39]  David S. Ebert,et al.  Public behavior response analysis in disaster events utilizing visual analytics of microblog data , 2014, Comput. Graph..

[40]  F. Ostermann,et al.  Automated geographic context analysis for volunteered information , 2013 .

[41]  João Porto de Albuquerque,et al.  A Software Architecture to Integrate Sensor Data and Volunteered Geographic Information for Flood Risk Management , 2016, ISCRAM.

[42]  Bin Jiang,et al.  Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information , 2016, ISPRS Int. J. Geo Inf..

[43]  Qi Li,et al.  A geospatial information portal for emergency management of natural disasters , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[44]  João Porto de Albuquerque,et al.  The Challenge of Transdisciplinarity in Information Systems Research: Towards an Integrative Platform , 2009 .

[45]  A. Rajabifard,et al.  Methods for assessing the credibility of volunteered geographic information in flood response: A case study in Brisbane, Australia , 2016 .

[46]  J. A. Quintanilha,et al.  Flooding and inundation collaborative mapping – use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil , 2018 .

[47]  Basabi Chakraborty,et al.  Discovering Topic Transition about the East Japan Great Earthquake in Dynamic Social Media , 2012, 2012 IEEE Global Humanitarian Technology Conference.

[48]  Marcos R. S. Borges,et al.  Resilience and brittleness in the ALERTA RIO system: a field study about the decision-making of forecasters , 2013, Natural Hazards.

[49]  S. Gorman,et al.  Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake , 2010 .