The Technopolitics of Mapping Dar es Salaam: An examination of the technological and political motivations of the Humanitarian OpenStreetMap Team

Mapping has long formed a key part of development work, from recording household surveys, participatory mapping exercises, and PRA projects. Now though the sector is full of new actors- mapping and tech companies as well as NGOs- monitoring through drones and satellite images, alongside employing more traditional methods. Many of these new players were born from NGOs and companies who started as ‘crisis mappers. Short-term ‘crisis mapping’ projects have become a regular part of humanitarian response following a disaster. The short-term nature of such actions, and the need for stable employment/profits, has led to an increasing trend for the same organizations and companies to either remain on the ground producing maps or to move into new areas as part of a pre-emptive mapping practice, inserting themselves into the wider international development ecosystem. This research, centered on Tanzania, examines how HOTOSM has attempted to pivot towards working as a development organization that creates maps for prevention of crisis, but also wider socio-economic outputs. The research used interviews to explore the interplay between technology and micro/macro politics around the mapping of Dar es Salaam. Examining how HOTOSM its role, and how they position their map-making within the context of Dar es Salaam. Findings suggest that HOTOSM is still underdeveloped as an organization and lacks the maturity to create true participatory models of working.

Rise of Digital Humanitarian Network (DHN) in Southeast Asia: Social Media Insights for Crisis Mapping in Disaster Risk Reduction (DRR)

Digital technologies and big data speedily change humanitarian crisis response and transform the processes from traditional to digital. Digital Humanitarian Network (DHN) for disaster risk reduction (DRR) using crisis mapping of the vulnerable population is becoming increasingly common during any disaster response process. To get the information and provide in time support, the critical Source of data is social media. In Southeast Asia, Facebook is the most used social media platform. Communities often rely on social media to seek in time assistance and guidance. Emerging social media and networks are remarkably well-compatible with intelligent data-centric systems, which foster an effective disaster management plan under disaster scenarios. During previous disasters in Southeast Asia, it was believed to be the fastest response medium. However, validation is essential to obtain important data, and after years of research, there are still many undiscovered features of social media that can be used in emergencies. This paper aims to determine Southeast Asian countries' readiness to utilise social media for DRR activities and understand the criteria of DHN by integrating crisis mapping. A qualitative research design is applied to gain an insight into the humanitarian disaster network for disaster risk reduction. Data were collected through document analysis. I argue that digital humanitarians can offer a unique combination of speed and safe access while escaping some of the traditional constraints of the aid-media relationship. The study concluded that DHN provides a collaborative environment for the organizations to collaborate and act fast to assist.

CIME: Context-aware geolocation of emergency-related posts

Information extracted from social media has proven to be very useful in the domain of emergency management. An important task in emergency management is rapid crisis mapping, which aims to produce timely and reliable maps of affected areas. During an emergency, the volume of emergency-related posts is typically large, but only a small fraction is relevant and help rapid mapping effectively. Furthermore, posts are not useful for mapping purposes unless they are correctly geolocated and, on average, less than 2% of posts are natively georeferenced. This paper presents an algorithm, called CIME, that aims to identify and geolocate emergency-related posts that are relevant for mapping purposes. While native geocoordinates are most often missing, many posts contain geographical references in their metadata, such as texts or links that can be used by CIME to filter and geolocate information. In addition, social media creates a social network and each post can be enhanced with indirect information from the post’s network of relationships with other posts (for example, a retweet can be associated with other geographical references which are useful to geolocate the original tweet). To exploit all this information, CIME uses the concept of context, defined as the information characterizing a post both directly (the post’s metadata) and indirectly (the post’s network of relationships). The algorithm was evaluated on a recent major emergency event demonstrating better performance with respect to the state of the art in terms of total number of geolocated posts, geolocation accuracy and relevance for rapid mapping.

Crisis Mapping and Crowdsourcing in Complex Emergencies

Crisis mappers secure satellite imagery, photos, video, event data, incident data, and other documentary evidence to create an operational picture of a disaster in order to facilitate improved humanitarian response and assistance in a crisis. The era of human-powered crisis mapping between 2009 and 2014 was a bootstrapped effort very much a function of the peculiar state of technological development at the time—available but not yet formalized, streamlined, and automated. Humans filled the gap until machine assistance could catch up. These efforts, often mundane (e.g., cut and paste over and over for hours), were more reflective of the state of technology at the time than anything else. Another precondition that enabled the field to grow is the often taken-for-granted public good provided by the GPS satellites maintained by the U.S. Air Force. Without this service, the project at the time would not have emerged where and when it did. The future will be shaped as a result of improvements in automated forms of data collection; improved machine learning techniques to help filter, identify, visualize, and analyze the data; and the proliferation of low-cost drones and other forms of sensors, to name a few.