Ítem
Acceso Abierto
Nightlight, landcover and buildings: understanding intracity socioeconomic differences
| dc.contributor.gruplac | Grupo de investigaciones. Facultad de Economía. Universidad del Rosario | |
| dc.creator | Garcia Suaza, Andrés Felipe | |
| dc.creator | Varela, Daniela | |
| dc.date.accessioned | 2024-02-13T15:51:06Z | |
| dc.date.available | 2024-02-13T15:51:06Z | |
| dc.date.created | 2024-02-12 | |
| dc.date.issued | 2024-02-13 | |
| dc.description.abstract | 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. | |
| dc.format.extent | 26 pp | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.48713/10336_42233 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/42233 | |
| dc.language.iso | eng | |
| dc.publisher | Universidad del Rosario | |
| dc.publisher.department | Facultad de Economía | |
| dc.relation.uri | https://ideas.repec.org/p/col/000092/021025.html | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.accesRights | info:eu-repo/semantics/openAccess | |
| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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). | |
| dc.source.bibliographicCitation | Akbar, P., Couture, V., Duranton, G., and Storeygard, A. (2023a). Mobility and Congestion in Urban India. American Economic Review, 113(4):1083–1111. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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.]. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | Donaldson, 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.bibliographicCitation | Duque, J. C., Patino, J., Ruiz, L., and Pardo, J. (2013). Quantifying Slumness with Remote Sensing Data. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | Elbers, C., Lanjouw, J. O., and Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1):355–364. | |
| dc.source.bibliographicCitation | 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). | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.bibliographicCitation | 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. | |
| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject.jel | R12, E26, C21 | |
| dc.subject.keyword | Remote sensing | |
| dc.subject.keyword | Satellite imagery | |
| dc.subject.keyword | Nightlights | |
| dc.subject.keyword | Points of interest | |
| dc.subject.keyword | Spatial segregation | |
| dc.subject.keyword | Urban footprints | |
| dc.subject.keyword | Informal housing | |
| dc.title | Nightlight, landcover and buildings: understanding intracity socioeconomic differences | |
| dc.type | workingPaper | |
| dc.type.hasVersion | info:eu-repo/semantics/draft | |
| dc.type.spa | Pre-print |
