Abstract:The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000-2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area-and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.Keywords: slums; informal areas; urban remote sensing; Global South; VHR imagery Global Urbanization and Slum Dynamics: The ContextCurrently, about one-quarter of the world's urban population lives in slums, which are defined by UN-Habitat as informal settlements [1] or areas deprived of access to safe water, acceptable sanitation, and durable housing; in addition to being areas that are overcrowded and lack land tenure security [2]. Over the last 15 years, there has been renewed interest in slum improvement and eradication by local and international organizations dealing with development issues. During this period, slums became a more prominent subject of remote sensing (RS) image analysis. Supported by increased availability of very-high-resolution (VHR) data and methodological advances, many RS studies [3][4][5][6][7][8] aimed to produce information on the geography and dynamics of slums. ...

Across the world, Transit Oriented Development (TOD) offers a strategy to integrate land use and transport systems by clustering urban developments around public transport nodes in functionally dense and diverse, pedestrian-and cycling-friendly areas. Even though the basic philosophy of TOD seems to be the same in all contexts, its specific applications greatly differ in form, function and impacts, calling for context-based TOD typologies that can help map these local specificities and better focus policy interventions. In recent years, TOD has also been widely advocated and applied in China; however, so far no study has systematically developed a TOD typology in a Chinese context. This paper fills this gap for the case of the Beijing metropolitan area. The approach is based on the node-place model, introduced by Bertolini (1996, 1999) to chart 'Transit' and 'Development' components, expanding it with a third, 'Oriented', dimension to quantify the degree of orientation of transit and development components towards each other. The paper reviewed the main TOD indicators in the international literature, selected those appropriate for the Beijing context, and classified the metro station areas into TOD types through a cluster analysis. The six identified types of metro station areas in Beijing demonstrate how the context-specific typology can support local urban and transport planners, designers and policymakers when considering future interventions.

The article maps urban poverty, using the `livelihoods assets framework' to develop a new index of multiple deprivation, examining the implications for area and sector targeting by policy-makers. This article deals with the index and the results for Delhi. The study maps: the spatial concentration of poverty; the diversity of deprivation at ward level; whether poverty is concentrated in slums; and correlations between voting patterns and poverty levels. The index uses census data disaggregated to electoral-ward level for multicriteria analysis, through GIS. Results show that hotspots of poverty are diverse in character, but are not concentrated in slum areas, with strong implications for policy-making and poverty studies methodology. These results suggest that the new index allows better insight into poverty with better targeting possibilities for policy-makers.

Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.