Nightlight, landcover and buildings: understanding intracity socioeconomic differences

Monitoring 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 sat...

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Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/42233
Acceso en línea:
https://repository.urosario.edu.co/handle/10336/42233
Palabra clave:
R12, E26, C21
Remote sensing
Satellite imagery
Nightlights
Points of interest
Spatial segregation
Urban footprints
Informal housing
Rights
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id EDOCUR2_4e67d5c85e4ab413191dbb36b8926ad3
oai_identifier_str oai:repository.urosario.edu.co:10336/42233
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.none.fl_str_mv Nightlight, landcover and buildings: understanding intracity socioeconomic differences
title Nightlight, landcover and buildings: understanding intracity socioeconomic differences
spellingShingle Nightlight, landcover and buildings: understanding intracity socioeconomic differences
R12, E26, C21
Remote sensing
Satellite imagery
Nightlights
Points of interest
Spatial segregation
Urban footprints
Informal housing
title_short Nightlight, landcover and buildings: understanding intracity socioeconomic differences
title_full Nightlight, landcover and buildings: understanding intracity socioeconomic differences
title_fullStr Nightlight, landcover and buildings: understanding intracity socioeconomic differences
title_full_unstemmed Nightlight, landcover and buildings: understanding intracity socioeconomic differences
title_sort Nightlight, landcover and buildings: understanding intracity socioeconomic differences
dc.subject.none.fl_str_mv R12, E26, C21
Remote sensing
Satellite imagery
Nightlights
Points of interest
Spatial segregation
Urban footprints
Informal housing
topic R12, E26, C21
Remote sensing
Satellite imagery
Nightlights
Points of interest
Spatial segregation
Urban footprints
Informal housing
description Monitoring 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.
publishDate 2024
dc.date.none.fl_str_mv 2024-02-12
2024-02-13T15:51:06Z
dc.type.none.fl_str_mv info:eu-repo/semantics/workingPaper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
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dc.identifier.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/42233
url https://repository.urosario.edu.co/handle/10336/42233
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://ideas.repec.org/p/col/000092/021025.html
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv 26 pp
application/pdf
dc.publisher.none.fl_str_mv Universidad del Rosario
Facultad de Economía
publisher.none.fl_str_mv Universidad del Rosario
Facultad de Economía
dc.source.none.fl_str_mv Abascal, 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.
Addison, 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).
Akbar, P., Couture, V., Duranton, G., and Storeygard, A. (2023a). Mobility and Congestion in Urban India. American Economic Review, 113(4):1083–1111.
Akbar, 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.
Asian 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.
Baragwanath, 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.
Battese, 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.].
Baum-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.
Burchfield, 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.
Ch, 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.
Charris, 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.
Che, 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.
Chen, 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.
Doll, 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.
Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4):171–198.
Duque, J. C., Patino, J., Ruiz, L., and Pardo, J. (2013). Quantifying Slumness with Remote Sensing Data.
Durst, 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.
Elbers, C., Lanjouw, J. O., and Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1):355–364.
Elbers, 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).
Elvidge, 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.
Engstrom, 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.
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repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Nightlight, landcover and buildings: understanding intracity socioeconomic differencesR12, E26, C21Remote sensingSatellite imageryNightlightsPoints of interestSpatial segregationUrban footprintsInformal housingMonitoring 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.Universidad del RosarioFacultad de Economía2024-02-122024-02-13T15:51:06Zinfo:eu-repo/semantics/workingPaperhttp://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_804226 ppapplication/pdfhttps://repository.urosario.edu.co/handle/10336/42233Abascal, 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.Addison, 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).Akbar, P., Couture, V., Duranton, G., and Storeygard, A. (2023a). Mobility and Congestion in Urban India. American Economic Review, 113(4):1083–1111.Akbar, 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.Asian 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.Baragwanath, 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.Battese, 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.].Baum-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.Burchfield, 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.Ch, 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.Charris, 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.Che, 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.Chen, 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.Doll, 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.Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4):171–198.Duque, J. C., Patino, J., Ruiz, L., and Pardo, J. (2013). Quantifying Slumness with Remote Sensing Data.Durst, 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.Elbers, C., Lanjouw, J. O., and Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1):355–364.Elbers, 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).Elvidge, 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.Engstrom, 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.instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://ideas.repec.org/p/col/000092/021025.htmlhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Garcia Suaza, Andrés FelipeVarela, Danielaoai:repository.urosario.edu.co:10336/422332024-02-16T03:00:39Z

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