Urban flood modelling using geo-social intelligence
Social media is not only a way to share information among a group of people but also an emerging source of rich primary data that can be crowdsourced for good. The primary function of social media is to allow people to network near real-time, yet the repository of amassed data can also be applied to decision support systems in response to extreme weather events. In this paper, Twitter is used to crowdsource information about several monsoon periods that caused flooding in the megacity of Jakarta, Indonesia. Tweets from two previous monsoons related to flooding were collected and analysed using the hashtag # 'banjir'. By analysing the relationship between the tweets and the flood events, this study aims to create 'trigger metrics' of flooding based on Twitter activity. Such trigger metrics have the advantage of being able to provide a situational overview of flood conditions in near real-time, as opposed to formal government flood maps that are produced on a six to twelve hourly schedule alone. The aim is to provide continuous intelligence, rather than make decisions on outdated data gathered between extended discrete intervals.