Quantifying Slumness with Remote Sensing Data

Documentos de trabajo Economía y Finanzas No 13-23

23 Pages Posted: 10 Feb 2014

See all articles by Juan C. Duque

Juan C. Duque

Universidad EAFIT - School of Economics and Finance - Center for Research in Economic & Finance (CIEF)

Jorge Patino

Universidad EAFIT; Universidad EAFIT - School of Economics and Finance - Center for Research in Economic & Finance (CIEF)

Luis Ruiz

Polytechnic University of Valencia

Josep Pardo

Polytechnic University of Valencia

Date Written: June 26, 2013

Abstract

The presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008; Taubenb¨ock et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all.

Keywords: Regional Science, Remote Sensing, Slum, GEOBIA

JEL Classification: C8, R14

Suggested Citation

Duque, Juan Carlos C. and Patino, Jorge and Patino, Jorge and Ruiz, Luis and Pardo, Josep, Quantifying Slumness with Remote Sensing Data (June 26, 2013). Documentos de trabajo Economía y Finanzas No 13-23, Available at SSRN: https://ssrn.com/abstract=2390737 or http://dx.doi.org/10.2139/ssrn.2390737

Juan Carlos C. Duque (Contact Author)

Universidad EAFIT - School of Economics and Finance - Center for Research in Economic & Finance (CIEF) ( email )

Carrera 49 No. 7 South - 50
Bogotá
Colombia

Jorge Patino

Universidad EAFIT - School of Economics and Finance - Center for Research in Economic & Finance (CIEF) ( email )

Carrera 49 No. 7 South - 50
Bogotá
Colombia

Universidad EAFIT ( email )

Carrera 49 N° 7 sur – 50
Bogotá, Antioquia 00000
Colombia

Luis Ruiz

Polytechnic University of Valencia ( email )

Camino de Vera, s/n
Valencia, Valencia 46022
Spain

Josep Pardo

Polytechnic University of Valencia ( email )

Camino de Vera, s/n
Valencia, Valencia 46022
Spain

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