Mapping the Slums of Dhaka from 2006 to 2010

Abstract

Background. Rapid urban growth in low and middle income countries is frequently characterized by informal developments. The resulting social segregation and slums show disparities in health outcomes for the populations of the world’s megacities. To address these challenges, information on the spatial distribution of slums is necessary, yet the data are rarely available. The goal of this study was to use a remote sensing based approach to map urban slums in Dhaka, the second fastest growing megacity in the world. Methods. Slums were mapped through the visual interpretation of Quickbird satellite imagery between the years 2006 and 2010. Ancillary references included the 2005 census and mapping of slums, Google Earth, and geolocated photographs. The 2006 slums were first delineated and filtered in GIS to avoid small, isolated slums. For 2010, changes to the 2006 slums were defined over the latter’s polygons to retain border consistency. Conclusions. The dataset presented here can be considered a stepping stone for further research on slums and urban expansion in Dhaka. The slum distribution dataset is useful to be pooled with other data to reveal trends of informal settlement growth for local health policy advice in Dhaka.

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

“…Such information allows compensating for the non-availability of socioeconomic information (e.g., income levels) in many cities of the Global South [31]. Besides its importance for urban development [68], the where question is also relevant within a humanitarian context, for which several studies [67,76,109] developed methods to map refugee camps (e.g., under Copernicus) [125].…”

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

“…Vertical densification of slum dwellings may occur at this stage [58]. Moreover, it is also important to note that while slums in most cases have been known to develop from informal building practices, this development can also start from formal land development, which may become increasingly degraded over time [59]. It is, therefore, equally important to not only monitor the growth of slums, but to also to monitor the growth of different parts of cities that are likely to be transformed into slums in the future.…”

A Holistic Concept to Design Optimal Water Supply Infrastructures for Informal Settlements Using Remote Sensing Data

“…To illustrate our method, we apply the approach to two small slum clusters in Dhaka, Bangladesh (for the years 2006 and 2010) [11], and Dar es Salaam, Tanzania (for the years 1998 and 2002) [16]. Both input datasets are open-access and have been created using remote sensing data.…”

“…The classification for Dhaka is based on QuickBird satellite images with a resolution of 0.6 m, complemented by data from the 2005 census and mapping of slums, Google Earth data (IKONOS; SPOT and QuickBird) and geolocated photographs. To avoid small and isolated slums, the data were filtered in GIS [11]. We used the data about the informal settlements provided in shapefiles and calculated the centers of the slums to identify the distance between the slums, as well as the area of the slums via MATLAB [17].…”

“…Additionally, censuses are often not accurate enough; for example, political influences can lead to distorted results [9]. Satellite data have proven to be a useful alternative for the detection and observation of slums due to the very high spatial and temporal resolution [10][11][12].…”