Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta

Research on disaster response frequently uses volunteered geographic information (VGI), due to its capability to provide near real-time information during and after a disaster. It is much less commonly used in spatial planning related to disaster management. However, VGI appears to have considerable potential for use in spatial planning and offers some advantages over traditional methods. For example, VGI can capture residents' preferences in a much faster, more timely, and more comprehensive fashion than is possible with, for example, questionnaires and surveys. This research investigates the usefulness of VGI for planning flood evacuation shelters. Using Jakarta, Indonesia, as a case study, we use VGI to capture the locations of flood evacuation shelters based on residents' preferences during flood periods in 2013-2014 and 2014-2015 and compare these with the locations of official shelters. Floods frequently affect Jakarta and the city administration uses VGI in flood emergency responses. Moreover, Jakarta has been identified as having the largest number of active Twitter users among cities worldwide. Thus, Jakarta is an appropriate place to study the use of VGI for planning evacuation shelters. VGI generated by Twitter users was used to identify the shelter locations preferred by Jakarta residents, and more precisely the flood evacuees. Of 171,046 tweets using keywords relating to flood evacuation, the content of 306 tweets indicated that they had been sent from inside or near evacuation shelters. The spatial pattern showed that those tweets were sent from 215 locations, mostly located near flooded areas. The analysis further showed that 35.6% of these shelter locations preferred by residents intersected with the locations of official evacuation shelters. As a general conclusion, our study demonstrates the advantages of using VGI for spatial planning, which mainly relates to the ease of capturing community preferences over a large area. (C) 2017 The Authors. Published by Elsevier Ltd.