Riskmap is an app that uses data received by social media and projects it on a map of an uban environment during a crisis. It supports facebook messenger, twitter, sms, telegramm and it can be connected to official citizens grievance systems of city administration. It relies on social media bots that are filtering messages and identifies citizens that could report flooding. These individuals are then contacted by a message with a special link that enables citizens to communicate a crisis event through an interface with a message or a photograph. The app then geolocates this information and diffuses it as a map by grouping messages together and indicating dangerous areas with coloured zoning. First responders and authorities can access the data and the map through a special dashboard for providing additional information on the map. The app was also integrated in the Uber app for alerting drivers about flooded areas.
The app is also able to include data from sensor indicating flood levels or weather events
The localized data voluntarily provided by individuals helps to share information in a specific area and can aid to build community resilience. It also provides additional data to first responders and authorities for targeting specific areas.
The solutions requires the active participation of social media users that need to report flooding events via a specific interface.
Citizens share their own photographs or descriptions of a flooding event they witnessed.
Only flooding events that are accessible by social media users or measured by sensors can be directly reported.
Futhermore, the app focuses so far only on urban environments.
Only flood events that are recognized as such might be reported.
Citizens, authorities and emergency organizations need to actively consult the map for an effective use of the information provided.
The users of this solutions have to be an area or have to be interested in an area where information is provided by the map. Since information can vary
The information on the map need to be taken serious to influence the decisions of citizens and emergency organizations. On the other hand, the absence of flood warning in a specific area might be wrongfully interpretated as an indication of a safe environment.
The app is notably effective if its information is not only used by a single user but if it is embedded in larger social networks that amplify its message.
The app has been used in during high-intensity rainfall events in Jakarta and in Chennai in 2017. Its main use was enabling citizens to navigate through a flooded urban environment.
The app can have some limitations regarding the quality of the data. Its developers aim at widening its scope from flood events to other types of disasters after several tests that were considered successfull in terms of uses and distribution of the app and the map.
The quality of data, notably at the onset of a disasters, remains a problem. When a disaster begins, data sources for machine learning are still scarce.
The app also relies on the willingness of citizens to transmit data via it interface. Even though tests in Indonesia show a high interest in participating, the reliance on participatory of the app could be a limiting factor in other cases, if citizens are reluctant to share their information.
The app uses social media bots that analyze text and photographs for identifying flood related information. Consent to data uses is only given indirectly through the social media plattforms that are used.