Disaster Mapping 2.0 blog

How can VGI be integrated with authoritative data for disaster risk management?

Nov 10th, 2014 by j0p

By Sophie Crommelinck

In case of a natural disaster authoritative data often does not provide enough information necessary for coping with the situation: For humanitarian aid, affected areas need to be rapidly identified, so help can be offered where it is most needed. Therefore aid authorities prioritize areas in a so-called damage assessment ranging from “unaffected” to “destroyed”. Satellite remote sensing is one major tool used for assigning these categories. However, in case of hurricanes for example, high-resolution remote sensing data from satellites might be unavailable for days because of cloud cover or orbital limitations of revisit time (Schnebele et al., S. 1007). In cases of flood, limited spatial resolution of satellite data may hinder the detection of small ﬂooded areas in vegetated, commercial or residential areas (Triglav-Cekad, S. 2753). To gain additional insight of ground conditions during and after the disaster, volunteered geographical information (VGI) is increasingly used.

VGI as an “emerging and quickly growing data source” (Goodchill in Schnebele et al., S. 1008) can be further divided into two groups of relevant data for disaster risk management includable in authoritative data:

1) Data that is collected and offered by volunteers for a specific purpose (participatory sensing, collaborative mapping and analysis). An example for this is a mission launched by the Civil Air Patrol, the civilian branch of the US Air Force, after Hurricane Sandy in 2012: Volunteers flew along the coastline of New Jersey capturing thousands of aerial photos documenting heavily flooded areas. These were then published through the Federal Emergency Management Agency for residents to check by street address if their house had been damaged (Schnebele et al., 2014).

2) Secondly VGI as a large amount of anonymous data is analysed for a specific purpose (collective sensing). The data can for instance consist of images, videos, sounds and text messages extracted from social media. In contrast to the first group of data, the volunteers did not initially share the content for this purpose and should therefore be considered contributors rather than volunteers. This geographical information is nevertheless defined under the label VGI. An example for this is an analysis of twitter data during the Elbe flood, prioritizing the data according to its geographical relation to the flood phenomenon. Thereby its credibility can be assessed; a step required before integrating the data in authoritative data (Herfort et al., 2014).

Despite these positive examples, VGI is mostly regarded as insufficiently structured, documented and validated according to scientific standards (Schade et al., S. 3). It therefore needs to be integrated into the existing Spatial Data Infrastructure (SDI), an institutionally sanctioned framework for posting, discovering, evaluating and exchanging geospatial information. These efforts are summoned under the term VGI Sensing (De Longueville in Schade et al., S. 4).

With VGI relying on humans reporting changes in their environment together with traditional authoritative data, gaps in the spatial and temporal coverage of information could be reduced. Reducing the volume of VGI to credible and relevant content is of major importance for a successful usage of VGI. But how can the volume of VGI be reduced to representative data? Which aspects of disaster risk management can be addressed by incorporating VGI in authoritative data? How much is VGI data already seen as a mandatory part of authoritative data? Do you know of any examples?

Questions to be answered during the seminar Disaster Mapping 2.0…

Sources:

Herfort, B., Albuquerque, J. P. De, Schelhorn, S., & Zipf, A. (2014). Exploring the geographical relations between social media and flood phenomena to improve situation awareness A study about the River Elbe Flood in June 2013. In Proceedings of the 17th AGILE Conference on Geographic Information Science (pp. 1–19).

Kamel Boulos, M. N., Resch, B., Crowley, D. N., Breslin, J. G., Sohn, G., Burtner, R., … Chuang, K.- Y. S. (2011). Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. International Journal of Health Geographics, 10(1), 67. doi:10.1186/1476-072X-10- 67

Lue, E., Wilson, J. P., & Curtis, A. (2014). Conducting disaster damage assessments with Spatial Video, experts, and citizens. Applied Geography, 52, 46–54. doi:10.1016/j.apgeog.2014.04.014

Schade, S., Díaz, L., Ostermann, F., Spinsanti, L., Luraschi, G., Cox, S., … Longueville, B. (2011). Citizen-based sensing of crisis events: sensor web enablement for volunteered geographic information. Applied Geomatics, 5(1), 3–18. doi:10.1007/s12518-011-0056-y

Schnebele, E., Cervone, G., & Waters, N. (2014). Road assessment after flood events using non- authoritative data. Natural Hazards and Earth System Science, 14(4), 1007–1015. doi:10.5194/nhess-14-1007-2014

Spinsanti, L., & Ostermann, F. (2013). Automated geographic context analysis for volunteered information. Applied Geography, 43(null), 36–44. doi:10.1016/j.apgeog.2013.05.005

Triglav-Čekada, M., & Radovan, D. (2013). Using volunteered geographical information to map the November 2012 floods in Slovenia. Natural Hazards and Earth System Science, 13(11), 2753– 2762. doi:10.5194/nhess-13-2753-2013

www .esri .com /library /brochures /pdfs /spatial -data -infrastructure .pdf, 20th October, 2014.