The Morphology of African Cities

Antos, Sarah Elizabeth; Lall, Somik; Lozano‐Gracia, Nancy

doi:10.1596/1813-9450-7911

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…It is hypothesized that these cities have similar spectral signatures due to their similar urban morphologies [20], which are related to the infrastructure and housing in a city. In most African cities, a lower proportion of central land is dedicated to transport use, such as paved roads, and this proportion rapidly declines near the edges of the city [51]. Moreover, a great many African cities are characterized by low-rise buildings [52], and much land within the central business district remains unbuilt [53].…”

Section: Transferability Analysis Of Trained Models For Each Land Covmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

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Section: Transferability Analysis Of Trained Models For Each Land Covmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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“…Our study focuses on Nairobi, Kenya. A relatively monocentric city (Antos, Lall, and Lozano-Gracia 2016;Henderson et al 2016), Nairobi accommodates more than 1 million residents in informal settlements, most of whom are tenants (KNBS 2012). The severity of living conditions in Nairobi's informal settlements (particularly Kibera) has been widely documented (for example, Bird, Montebruno, and Regan 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatial Distributions of Job Accessibility, Housing Rents, and Poverty in Nairobi, Kenya

Nakamura¹,

Avner²

2018

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.

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“…However, 43% of low-income men and 31% of women still walk longer distances outside of the settlement to reach jobs. As a result, low-income settlements are strategically located near the CBD ( 50 , 51 ), a reality that is reflected in the accessibility analysis. Finally, many poor neighborhoods quite simply do not have roads that would allow for matatu service.…”

Section: Resultsmentioning

confidence: 99%

Measuring the Sustainable Development Goal (SDG) Transport Target and Accessibility of Nairobi’s Matatus

Fried

Tun

Klopp

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The urban Sustainable Development Goal (SDG) includes the target to provide “access to safe, affordable, accessible and sustainable transport systems for all” by 2030. However, debate exists around the best indicator to measure this target, and few actual measurements exist. This is in part because basic transit data are missing from many of the world’s cities, including in Africa where popular or “informal” systems dominate. This paper explores how to make progress in measuring indicators for the SDG transport target using Nairobi’s minibus system, matatus, as a case study. We partially measure the SDG indicator for the city as currently defined by the UN and then compare the SDG measurement to a location-based accessibility indicator that incorporates income data, travel times, and land-use considerations for Nairobi’s highly monocentric spatial urban form. We show that although the SDG analysis suggests generally favorable transit coverage, it also points to underlying transport inequalities for low-income residents. The more fine-grained location-based accessibility analysis reveals rapidly decreasing accessibility to opportunities as distance increases from the city’s central business district. This accessibility-based analysis further highlights income-based transport inequalities, identifying opportunities for improving integrated transport for residents living on the city’s near and far peripheries. Improving non-motorized transport access for those living in low-income areas with high access potential would also be important to improve access. We recommend that cities start using open-source software and open data to measure a variety of indicators needed for data-driven policy, to meet SDG 11.2 and go further to improve access to opportunities for all residents.

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The Morphology of African Cities

Cited by 12 publications

References 3 publications

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Spatial Distributions of Job Accessibility, Housing Rents, and Poverty in Nairobi, Kenya

Measuring the Sustainable Development Goal (SDG) Transport Target and Accessibility of Nairobi’s Matatus

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