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  • Abascal, AngelaUniversity of Navarra, Spain; University of Twente, Netherlands (author)

Making Urban Slum Population Visible : Citizens and Satellites to Reinforce Slum Censuses

  • Article/chapterEnglish2024

Publisher, publication year, extent ...

  • Springer,2024
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:kau-100350
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-100350URI
  • https://doi.org/10.1007/978-3-031-49183-2_14DOI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:kap swepub-publicationtype

Notes

  • In response to the “Leave No One Behind” principle (the central promise of the 2030 Agenda for Sustainable Development), reliable estimate of the total number of citizens living in slums is urgently needed but not available for some of the most vulnerable communities. Not having a reliable estimate of the number of poor urban dwellers limits evidence-based decision-making for proper resource allocation in the fight against urban inequalities. From a geographical perspective, urban population distribution maps in many low- and middle-income cities are most often derived from outdated or unreliable census data disaggregated by coarse administrative units. Moreover, slum populations are presented as aggregated within bigger administrative areas, leading to a large diffuse in the estimates. Existing global and open population databases provide homogeneously disaggregated information (i.e. in a spatial grid), but they mostly rely on census data to generate their estimates, so they do not provide additional information on the slum population. While a few studies have focused on bottom-up geospatial models for slum population mapping using survey data, geospatial covariates, and earth observation imagery, there is still a significant gap in methodological approaches for producing precise estimates within slums. To address this issue, we designed a pilot experiment to explore new avenues. We conducted this study in the slums of Nairobi, where we collected in situ data together with slum dwellers using a novel data collection protocol. Our results show that the combination of satellite imagery with in situ data collected by citizen science paves the way for generalisable, gridded estimates of slum populations. Furthermore, we find that the urban physiognomy of slums and population distribution patterns are related, which allows for highlighting the diversity of such patterns using earth observation within and between slums of the same city. 

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Georganos, StefanosKarlstads universitet,Institutionen för miljö- och livsvetenskaper (from 2013)(Swepub:kau)sgeorgan (author)
  • Kuffer, MonikaUniversity of Twente, Netherlands (author)
  • Vanhuysse, SabineUniversité libre de Bruxelles (ULB), Belgium (author)
  • Thomson, DanaUniversity of Twente, Netherlands (author)
  • Wang, JonUniversity of Twente, Netherlands (author)
  • Manyasi, LawrenceCommunity Researcher Non-affiliated, Nairobi, Kenya (author)
  • Otunga, Daniel ManyasiCommunity Researcher Single Mother Association of Kenya, Nairobi, Kenya (author)
  • Ochieng, BrightonCommunity Researcher Non-affiliated, Nairobi, Kenya (author)
  • Ochieng, TrevaCommunity Researcher Non-affiliated, Nairobi, Kenya (author)
  • Klinnert, JorgeUniversity of Maryland, USA (author)
  • Wolff, EléonoreUniversité libre de Bruxelles (ULB), Belgium (author)
  • University of Navarra, Spain; University of Twente, NetherlandsInstitutionen för miljö- och livsvetenskaper (from 2013) (creator_code:org_t)

Related titles

  • In:Urban Inequalities from Space: Springer, s. 287-30297830314918569783031491832

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