» Articles » PMID: 19478993

Can We Spot a Neighborhood from the Air? Defining Neighborhood Structure in Accra, Ghana

Overview
Journal GeoJournal
Date 2009 May 30
PMID 19478993
Citations 18
Authors
Affiliations
Soon will be listed here.
Abstract

Slums are home to a large fraction of urban residents in cities of developing nations, but little attempt has been made to go beyond a simple slum/non-slum dichotomy, nor to identify slums more quantitatively than through local reputation. We use census data from Accra, Ghana, to create an index that applies the UN-Habitat criteria for a place to be a slum. We use this index to identify neighborhoods on a continuum of slum characteristics and on that basis are able to locate the worst slums in Accra. These do include the areas with a local reputation for being slums, lending qualitative validation to the index. We show that slums also have footprints that can be identified from data classified from satellite imagery. However, variability among slums in Accra is also associated with some variability in the land cover characteristics of slums.

Citing Articles

Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning.

Metzler A, Nathvani R, Sharmanska V, Bai W, Muller E, Moulds S Sci Total Environ. 2023; 893:164794.

PMID: 37315611 PMC: 7615085. DOI: 10.1016/j.scitotenv.2023.164794.


Managing urban solid waste in Ghana: Perspectives and experiences of municipal waste company managers and supervisors in an urban municipality.

Lissah S, Ayanore M, Krugu J, Aberese-Ako M, Ruiter R PLoS One. 2021; 16(3):e0248392.

PMID: 33705483 PMC: 7951920. DOI: 10.1371/journal.pone.0248392.


Measuring social, environmental and health inequalities using deep learning and street imagery.

Suel E, Polak J, Bennett J, Ezzati M Sci Rep. 2019; 9(1):6229.

PMID: 31000744 PMC: 6473002. DOI: 10.1038/s41598-019-42036-w.


A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability.

Dotse-Gborgbortsi W, Wardrop N, Adewole A, Thomas M, Wright J Int J Health Geogr. 2018; 17(1):14.

PMID: 29792189 PMC: 5966850. DOI: 10.1186/s12942-018-0134-z.


Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

Arribas-Bel D, Patino J, Duque J PLoS One. 2017; 12(5):e0176684.

PMID: 28464010 PMC: 5413026. DOI: 10.1371/journal.pone.0176684.


References
1.
Diez Roux A . Investigating neighborhood and area effects on health. Am J Public Health. 2001; 91(11):1783-9. PMC: 1446876. DOI: 10.2105/ajph.91.11.1783. View

2.
Songsore J, Goldstein G . Health and Environment Analysis for Decision-Making (HEADLAMP): field study in Accra, Ghana. World Health Stat Q. 1995; 48(2):108-17. View

3.
Diez-Roux A . Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 1998; 88(2):216-22. PMC: 1508189. DOI: 10.2105/ajph.88.2.216. View

4.
Montgomery M, Hewett P . Urban poverty and health in developing countries: household and neighborhood effects. Demography. 2005; 42(3):397-425. DOI: 10.1353/dem.2005.0020. View

5.
Openshaw S, Rao L . Algorithms for reengineering 1991 census geography. Environ Plan A. 1995; 27(3):425-46. DOI: 10.1068/a270425. View