Ítem
Acceso Abierto

Nightlight, landcover and buildings: understanding intracity socioeconomic differences

dc.contributor.gruplacGrupo de investigaciones. Facultad de Economía. Universidad del Rosario
dc.creatorGarcia Suaza, Andrés Felipe
dc.creatorVarela, Daniela
dc.date.accessioned2024-02-13T15:51:06Z
dc.date.available2024-02-13T15:51:06Z
dc.date.created2024-02-12
dc.date.issued2024-02-13
dc.description.abstractMonitoring patterns of segregation and inequality at small-area geographic levels is extremely costly. However, the increased availability of data through non-traditional sources such as satellite imagery facilitates this task. This paper assess the relevance of data from nightlight and day-time satellite imagery as well as building footprints and localization of points of interest for mapping variability in socio-economic outcomes, i.e., household income, labor formality, life quality perception and household informality. The outcomes are computed at a granular level by combining census data, survey data, and small area estimation. The results reveal that non-traditional sources are important to predict spatial differences socio-economic outcomes. Furthermore, the combination of all sources creates complementarities that enable a more accurate spatial distribution of the studied variables.
dc.format.extent26 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_42233
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/42233
dc.language.isoeng
dc.publisherUniversidad del Rosario
dc.publisher.departmentFacultad de Economía
dc.relation.urihttps://ideas.repec.org/p/col/000092/021025.html
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.source.bibliographicCitationAbascal, A., Rodríguez-Carreño, I., Vanhuysse, S., Georganos, S., Sliuzas, R., Wolff, E., and Kuffer, M. (2022). Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas. Computers, environment and urban systems, 95:101820.
dc.source.bibliographicCitationAddison, D. M. and Stewart, B. (2015). Nighttime lights revisited: the use of nighttime lights data as a proxy for economic variables. World Bank Policy Research Working Paper, (7496).
dc.source.bibliographicCitationAkbar, P., Couture, V., Duranton, G., and Storeygard, A. (2023a). Mobility and Congestion in Urban India. American Economic Review, 113(4):1083–1111.
dc.source.bibliographicCitationAkbar, P. A., Couture, V., Duranton, G., and Storeygard, A. (2023b). The fast, the slow, and the congested: Urban transportation in rich and poor countries. Technical report, National Bureau of Economic Research.
dc.source.bibliographicCitationAsian Development Bank (2020). Introduction to Small Area Estimation Techniques:: A Practical Guide for National Statistics Offices. Technical report, Asian Development Bank, Manila, Philippines. Edition: 0 ISBN: 9789292622237 9789292622220.
dc.source.bibliographicCitationBaragwanath, K., Goldblatt, R., Hanson, G., and Khandelwal, A. K. (2021). Detecting urban markets with satellite imagery: An application to india. Journal of Urban Economics, 125:103173.
dc.source.bibliographicCitationBattese, G. E., Harter, R. M., and Fuller, W. A. (1988). An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data. Journal of the American Statistical Association, 83(401):28–36. Publisher: [American Statistical Association, Taylor & Francis, Ltd.].
dc.source.bibliographicCitationBaum-Snow, N. and Turner, M. A. (2017). Transport infrastructure and the decentralization of cities in the people’s republic of china. Asian development review, 34(2):25–50.
dc.source.bibliographicCitationBurchfield, M., Overman, H. G., Puga, D., and Turner, M. A. (2006). Causes of sprawl: A portrait from space. The Quarterly Journal of Economics, 121(2):587–633.
dc.source.bibliographicCitationCh, R., Martin, D. A., and Vargas, J. F. (2021). Measuring the size and growth of cities using nighttime light. Journal of Urban Economics, 125:103254.
dc.source.bibliographicCitationCharris, C., Velilla, R., and Chaves, L. (2019). Mapping the human development index using nighttime lights inside brazil. XVII ENABER—Encontro Nacional da Associação Brasileira de Estudos Regionais e Urbanos. https://brsa. org. br/wpcontent/uploads/wpcf7-submissions/990/manuscript_Iden. pdf.
dc.source.bibliographicCitationChe, M. and Gamba, P. (2019). Intra-urban change analysis using sentinel-1 and nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4):1134–1142.
dc.source.bibliographicCitationChen, X. and Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 108(21):8589–8594.
dc.source.bibliographicCitationDoll, C. N., Muller, J.-P., and Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1):75–92.
dc.source.bibliographicCitationDonaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4):171–198.
dc.source.bibliographicCitationDuque, J. C., Patino, J., Ruiz, L., and Pardo, J. (2013). Quantifying Slumness with Remote Sensing Data.
dc.source.bibliographicCitationDurst, N. J., Sullivan, E., Huang, H., and Park, H. (2021). Building footprint-derived landscape metrics for the identification of informal subdivisions and manufactured home communities: A pilot application in hidalgo county, texas. Land Use Policy, 101:105158.
dc.source.bibliographicCitationElbers, C., Lanjouw, J. O., and Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1):355–364.
dc.source.bibliographicCitationElbers, C. and van der Weide, R. (2014). Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality. World Bank Policy Research Working Paper, (6962).
dc.source.bibliographicCitationElvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., and Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6):1373–1379.
dc.source.bibliographicCitationEngstrom, R., Hersh, J., and Newhouse, D. (2022). Poverty from space: Using high resolution satellite imagery for estimating economic well-being. The World Bank Economic Review, 36(2):382–412.
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.jelR12, E26, C21
dc.subject.keywordRemote sensing
dc.subject.keywordSatellite imagery
dc.subject.keywordNightlights
dc.subject.keywordPoints of interest
dc.subject.keywordSpatial segregation
dc.subject.keywordUrban footprints
dc.subject.keywordInformal housing
dc.titleNightlight, landcover and buildings: understanding intracity socioeconomic differences
dc.typeworkingPaper
dc.type.hasVersioninfo:eu-repo/semantics/draft
dc.type.spaPre-Preprint
Archivos
Bloque original
Mostrando1 - 2 de 2
Cargando...
Miniatura
Nombre:
dt314R.pdf
Tamaño:
3.53 MB
Formato:
Adobe Portable Document Format
Descripción:
Cargando...
Miniatura
Nombre:
index314.html
Tamaño:
225 B
Formato:
Hypertext Markup Language
Descripción: