Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

Soman, Satej; Beukes, Anni; Nederhood, Cooper; Marchio, Nicholas; Bettencourt, Luís M. A.

doi:10.2139/ssrn.3701930

Cited by 4 publications

(4 citation statements)

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“…The second group of open data are mainly used to detect informal settlements based on their physical features. Soman, Beukes, Nederhood, Marchio, and Bettencourt (2020) have conducted street block analysis in 120 low and middle income countries to detect informal settlements based on the lack of public street access (Soman et al, 2020). The method relies on OSM data that can have different levels of accuracy in different countries.…”

Section: Open Data For Detecting Informal Settlementsmentioning

confidence: 99%

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

2021

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The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta's informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.

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Section: Open Data For Detecting Informal Settlementsmentioning

confidence: 99%

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

2021

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“…Another valuable source of data is derived via digital (often crowd-sourced) maps, e.g., Open Street Map (OSM). For example, Baruah et al (2021) combine satellite based population and night lights data with OSM data on the road network to compare the urban form of cities formally under anglophone and francophone colonial rule, while Soman et al (2020) rely on OSM data to detect informal settlements.…”

Section: Introductionmentioning

confidence: 99%

Where are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries

Barzin¹,

Avner²,

Rentschler³

et al. 2022

Policy Research Working Papers

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9979Globally, both people and economic activity are increasingly concentrated in urban areas. Yet, for the vast majority of developing country cities, little is known about the granular spatial organization of such activity despite its key importance to policy and urban planning. This paper adapts a machine learning based algorithm to predict the spatial distribution of employment using input data from open access sources such as Open Street Map and Google Earth Engine. The algorithm is trained on 14 test cities, ranging from Buenos Aires in Argentina to Dakar in Senegal. A spatial adaptation of the random forest algorithm is used to predict within-city cells in the 14 test cities with extremely high accuracy (R-squared greater than 95 percent), and cells in out-of-sample "unseen" cities with high accuracy (mean R-squared of 63 percent). This approach uses open data to produce high resolution estimates of the distribution of urban employment for cities where such information does not exist, making evidence-based planning more accessible than ever before.

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“…The primary aim of classification is to reduce the complexity of the world around us. Many urban form classification methods exist at building (Schirmer and Axhausen, 2015; (Steadman et al, 2000, 2009)), street (Marshall, 2005) neighbourhood (Soman et al, 2020) and city (Louf and Barthelemy, 2014) scales, varying conceptually and analytically both in terms of focus scale – for example, global, (Angel et al, 2012) versus local (Guyot et al, 2021); analytical approach – for example, quantitative versus qualitative and aim of the classification. Structurally, the simplest forms involve flat classifications, where the relationship between types is unknown.…”

Section: Introductionmentioning

confidence: 99%

Methodological foundation of a numerical taxonomy of urban form

Fleischmann

Feliciotti

Rómice

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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Cities are complex products of human culture, characterised by a startling diversity of visible traits. Their form is constantly evolving, reflecting changing human needs and local contingencies, manifested in space by many urban patterns. Urban morphology laid the foundation for understanding many such patterns, largely relying on qualitative research methods to extract distinct spatial identities of urban areas. However, the manual, labour-intensive and subjective nature of such approaches represents an impediment to the development of a scalable, replicable and data-driven urban form characterisation. Recently, advances in geographic data science and the availability of digital mapping products open the opportunity to overcome such limitations. And yet, our current capacity to systematically capture the heterogeneity of spatial patterns remains limited in terms of spatial parameters included in the analysis and hardly scalable due to the highly labour-intensive nature of the task. In this paper, we present a method for numerical taxonomy of urban form derived from biological systematics, which allows the rigorous detection and classification of urban types. Initially, we produce a rich numerical characterisation of urban space from minimal data input, minimising limitations due to inconsistent data quality and availability. These are street network, building footprint and morphological tessellation, a spatial unit derivative of Voronoi tessellation, obtained from building footprints. Hence, we derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities – Prague and Amsterdam – and discuss potential applications and further developments. The proposed classification method represents a step towards the development of an extensive, scalable numerical taxonomy of urban form and opens the way to more rigorous comparative morphological studies and explorations into the relationship between urban space and phenomena as diverse as environmental performance, health and place attractiveness.

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Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

Cited by 4 publications

References 0 publications

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

Where are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries

Methodological foundation of a numerical taxonomy of urban form

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