- -

Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Sapena;Ruz;Taubenböck ...
Tamaño: 3.705Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Sapena Moll, Marta es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Taubenböck, Hannes es_ES
dc.date.accessioned 2021-05-22T03:32:18Z
dc.date.available 2021-05-22T03:32:18Z
dc.date.issued 2020-07-11 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166661
dc.description.abstract [EN] Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof ISPRS International Journal of Geo-Information es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Urban growth es_ES
dc.subject Socio-economic variables es_ES
dc.subject Spatio-temporal metrics es_ES
dc.subject Global analysis es_ES
dc.subject IndiFrag es_ES
dc.subject GHSL es_ES
dc.subject OECD es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/ijgi9070436 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Sapena Moll, M.; Ruiz Fernández, LÁ.; Taubenböck, H. (2020). Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale. ISPRS International Journal of Geo-Information. 9(7):1-22. https://doi.org/10.3390/ijgi9070436 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/ijgi9070436 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 7 es_ES
dc.identifier.eissn 2220-9964 es_ES
dc.relation.pasarela S\415497 es_ES
dc.description.references Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T. A., & Taubenböck, H. (2019). Understanding an urbanizing planet: Strategic directions for remote sensing. Remote Sensing of Environment, 228, 164-182. doi:10.1016/j.rse.2019.04.020 es_ES
dc.description.references Wentz, E. A., York, A. M., Alberti, M., Conrow, L., Fischer, H., Inostroza, L., … Taubenböck, H. (2018). Six fundamental aspects for conceptualizing multidimensional urban form: A spatial mapping perspective. Landscape and Urban Planning, 179, 55-62. doi:10.1016/j.landurbplan.2018.07.007 es_ES
dc.description.references Wentz, E., Anderson, S., Fragkias, M., Netzband, M., Mesev, V., Myint, S., … Seto, K. (2014). Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing. Remote Sensing, 6(5), 3879-3905. doi:10.3390/rs6053879 es_ES
dc.description.references Allen, L., Williams, J., Townsend, N., Mikkelsen, B., Roberts, N., Foster, C., & Wickramasinghe, K. (2017). Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. The Lancet Global Health, 5(3), e277-e289. doi:10.1016/s2214-109x(17)30058-x es_ES
dc.description.references Belsky, D. W., Caspi, A., Arseneault, L., Corcoran, D. L., Domingue, B. W., Harris, K. M., … Odgers, C. L. (2019). Genetics and the geography of health, behaviour and attainment. Nature Human Behaviour, 3(6), 576-586. doi:10.1038/s41562-019-0562-1 es_ES
dc.description.references Villeneuve, P. J., Jerrett, M., G. Su, J., Burnett, R. T., Chen, H., Wheeler, A. J., & Goldberg, M. S. (2012). A cohort study relating urban green space with mortality in Ontario, Canada. Environmental Research, 115, 51-58. doi:10.1016/j.envres.2012.03.003 es_ES
dc.description.references Patz, J. A., Daszak, P., Tabor, G. M., Aguirre, A. A., Pearl, M., … Epstein, J. (2004). Unhealthy Landscapes: Policy Recommendations on Land Use Change and Infectious Disease Emergence. Environmental Health Perspectives, 112(10), 1092-1098. doi:10.1289/ehp.6877 es_ES
dc.description.references Wilkinson, D. A., Marshall, J. C., French, N. P., & Hayman, D. T. S. (2018). Habitat fragmentation, biodiversity loss and the risk of novel infectious disease emergence. Journal of The Royal Society Interface, 15(149), 20180403. doi:10.1098/rsif.2018.0403 es_ES
dc.description.references Zohdy, S., Schwartz, T. S., & Oaks, J. R. (2019). The Coevolution Effect as a Driver of Spillover. Trends in Parasitology, 35(6), 399-408. doi:10.1016/j.pt.2019.03.010 es_ES
dc.description.references Watmough, G. R., Atkinson, P. M., Saikia, A., & Hutton, C. W. (2016). Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India. World Development, 78, 188-203. doi:10.1016/j.worlddev.2015.10.031 es_ES
dc.description.references Duque, J. C., Patino, J. E., Ruiz, L. A., & Pardo-Pascual, J. E. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning, 135, 11-21. doi:10.1016/j.landurbplan.2014.11.009 es_ES
dc.description.references Venerandi, A., Quattrone, G., & Capra, L. (2018). A scalable method to quantify the relationship between urban form and socio-economic indexes. EPJ Data Science, 7(1). doi:10.1140/epjds/s13688-018-0132-1 es_ES
dc.description.references Arribas-Bel, D., Patino, J. E., & Duque, J. C. (2017). Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning. PLOS ONE, 12(5), e0176684. doi:10.1371/journal.pone.0176684 es_ES
dc.description.references Faisal, K., Shaker, A., & Habbani, S. (2016). Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada. ISPRS International Journal of Geo-Information, 5(3), 23. doi:10.3390/ijgi5030023 es_ES
dc.description.references Liang, H., Guo, Z., Wu, J., & Chen, Z. (2020). GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression. Advances in Space Research, 65(1), 481-493. doi:10.1016/j.asr.2019.09.035 es_ES
dc.description.references Weigand, M., Wurm, M., Dech, S., & Taubenböck, H. (2019). Remote Sensing in Environmental Justice Research—A Review. ISPRS International Journal of Geo-Information, 8(1), 20. doi:10.3390/ijgi8010020 es_ES
dc.description.references McCarty, J., & Kaza, N. (2015). Urban form and air quality in the United States. Landscape and Urban Planning, 139, 168-179. doi:10.1016/j.landurbplan.2015.03.008 es_ES
dc.description.references Hankey, S., & Marshall, J. D. (2017). Urban Form, Air Pollution, and Health. Current Environmental Health Reports, 4(4), 491-503. doi:10.1007/s40572-017-0167-7 es_ES
dc.description.references Olsen, J. R., Nicholls, N., & Mitchell, R. (2019). Are urban landscapes associated with reported life satisfaction and inequalities in life satisfaction at the city level? A cross-sectional study of 66 European cities. Social Science & Medicine, 226, 263-274. doi:10.1016/j.socscimed.2019.03.009 es_ES
dc.description.references Sapena, M., Ruiz, L. A., & Goerlich, F. J. (2016). ANALYSING RELATIONSHIPS BETWEEN URBAN LAND USE FRAGMENTATION METRICS AND SOCIO-ECONOMIC VARIABLES. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 1029-1036. doi:10.5194/isprsarchives-xli-b8-1029-2016 es_ES
dc.description.references Stokes, E. C., & Seto, K. C. (2019). Characterizing and measuring urban landscapes for sustainability. Environmental Research Letters, 14(4), 045002. doi:10.1088/1748-9326/aafab8 es_ES
dc.description.references De Leeuw, J., Georgiadou, Y., Kerle, N., De Gier, A., Inoue, Y., Ferwerda, J., … Narantuya, D. (2010). The Function of Remote Sensing in Support of Environmental Policy. Remote Sensing, 2(7), 1731-1750. doi:10.3390/rs2071731 es_ES
dc.description.references Taubenböck, H., Ferstl, J., & Dech, S. (2017). Regions Set in Stone—Delimiting and Categorizing Regions in Europe by Settlement Patterns Derived from EO-Data. ISPRS International Journal of Geo-Information, 6(2), 55. doi:10.3390/ijgi6020055 es_ES
dc.description.references Chen, X., & Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 108(21), 8589-8594. doi:10.1073/pnas.1017031108 es_ES
dc.description.references Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017). Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model. ISPRS International Journal of Geo-Information, 6(9), 288. doi:10.3390/ijgi6090288 es_ES
dc.description.references Oldekop, J. A., Sims, K. R. E., Karna, B. K., Whittingham, M. J., & Agrawal, A. (2019). Reductions in deforestation and poverty from decentralized forest management in Nepal. Nature Sustainability, 2(5), 421-428. doi:10.1038/s41893-019-0277-3 es_ES
dc.description.references Sims, K. R. E., Thompson, J. R., Meyer, S. R., Nolte, C., & Plisinski, J. S. (2019). Assessing the local economic impacts of land protection. Conservation Biology, 33(5), 1035-1044. doi:10.1111/cobi.13318 es_ES
dc.description.references Lobo, J., Alberti, M., Allen-Dumas, M., Arcaute, E., Barthelemy, M., Bojorquez Tapia, L. A., … Youn, H. (2020). Urban Science: Integrated Theory from the First Cities to Sustainable Metropolises. SSRN Electronic Journal. doi:10.2139/ssrn.3526940 es_ES
dc.description.references Seto, K. C., Golden, J. S., Alberti, M., & Turner, B. L. (2017). Sustainability in an urbanizing planet. Proceedings of the National Academy of Sciences, 114(34), 8935-8938. doi:10.1073/pnas.1606037114 es_ES
dc.description.references Cities (Urban Audit)https://ec.europa.eu/eurostat/web/cities/background es_ES
dc.description.references Metropolitan Areas, OECD Regional Statistics [Database]http://dx.doi.org/10.1787/data-00531-en es_ES
dc.description.references Eurostat, Geographical Information and Mapshttps://ec.europa.eu/eurostat/web/gisco/gisco-activities/integrating-statistics-geospatial-information/geostat-initiative es_ES
dc.description.references NASA Socioeconomic Data and Applications Center. U.S. Census Gridshttps://sedac.ciesin.columbia.edu/ es_ES
dc.description.references Esch, T., Taubenböck, H., Roth, A., Heldens, W., Felbier, A., Thiel, M., … Dech, S. (2012). TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns. Journal of Applied Remote Sensing, 6(1), 061702-1. doi:10.1117/1.jrs.6.061702 es_ES
dc.description.references GHS-BUILT R2018A—GHS Built-Up Grid, Derived from Landsat, Multitemporal (1975-1990-2000-2014). European Commission, Joint Research Centre (JRC) [Dataset]http://data.europa.eu/89h/jrc-ghsl-10007 es_ES
dc.description.references Chen, J., Cao, X., Peng, S., & Ren, H. (2017). Analysis and Applications of GlobeLand30: A Review. ISPRS International Journal of Geo-Information, 6(8), 230. doi:10.3390/ijgi6080230 es_ES
dc.description.references Land Cover CCI Product User Guide Version 2http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf es_ES
dc.description.references Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema, J., … Stewart, I. (2015). Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities. ISPRS International Journal of Geo-Information, 4(1), 199-219. doi:10.3390/ijgi4010199 es_ES
dc.description.references Cao, W., Dong, L., Wu, L., & Liu, Y. (2020). Quantifying urban areas with multi-source data based on percolation theory. Remote Sensing of Environment, 241, 111730. doi:10.1016/j.rse.2020.111730 es_ES
dc.description.references Qiu, C., Schmitt, M., Geiß, C., Chen, T.-H. K., & Zhu, X. X. (2020). A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 152-170. doi:10.1016/j.isprsjprs.2020.01.028 es_ES
dc.description.references The Metropolitan Database. Metadata and Release Noteshttp://stats.oecd.org/wbos/fileview2.aspx?IDFile=4aed3009-6020-48f3-8eeb-e01a8e5f61c4 es_ES
dc.description.references Gross Domestic Product (GDP) (Indicator)https://doi.org/10.1787/dc2f7aec-en es_ES
dc.description.references Income Inequality (Indicator)https://doi.org/10.1787/459aa7f1-en es_ES
dc.description.references Air pollution Exposure (Indicator)https://doi.org/10.1787/8d9dcc33-en es_ES
dc.description.references Employment Rate (Indicator)https://doi.org/10.1787/1de68a9b-en es_ES
dc.description.references Redefining “Urban”: A New Way to Measure Metropolitan Areas, OECD Publishinghttps://doi.org/10.1787/9789264174108-en es_ES
dc.description.references Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., & Schipper, A. M. (2018). Global patterns of current and future road infrastructure. Environmental Research Letters, 13(6), 064006. doi:10.1088/1748-9326/aabd42 es_ES
dc.description.references Sapena Moll, M., & Ruiz Fernández, L. Á. (2015). Descripción y cálculo de índices de fragmentación urbana: Herramienta IndiFrag. Revista de Teledetección, (43), 77. doi:10.4995/raet.2015.3476 es_ES
dc.description.references Urban morphological zones 2006. European Environment Agencyhttps://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2006-1 es_ES
dc.description.references Taubenböck, H., Wiesner, M., Felbier, A., Marconcini, M., Esch, T., & Dech, S. (2014). New dimensions of urban landscapes: The spatio-temporal evolution from a polynuclei area to a mega-region based on remote sensing data. Applied Geography, 47, 137-153. doi:10.1016/j.apgeog.2013.12.002 es_ES
dc.description.references SCHUMM, S. A. (1956). EVOLUTION OF DRAINAGE SYSTEMS AND SLOPES IN BADLANDS AT PERTH AMBOY, NEW JERSEY. Geological Society of America Bulletin, 67(5), 597. doi:10.1130/0016-7606(1956)67[597:eodsas]2.0.co;2 es_ES
dc.description.references Sapena, M., & Ruiz, L. Á. (2019). Analysis of land use/land cover spatio-temporal metrics and population dynamics for urban growth characterization. Computers, Environment and Urban Systems, 73, 27-39. doi:10.1016/j.compenvurbsys.2018.08.001 es_ES
dc.description.references Breiman, L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science, 16(3). doi:10.1214/ss/1009213726 es_ES
dc.description.references GONZALEZ, J., & LEBOULLUEC, A. (2019). Crime Prediction and Socio-Demographic Factors: A Comparative Study of Machine Learning Regression-Based Algorithms. Journal of Applied Computer Science & Mathematics, 13(1), 13-18. doi:10.4316/jacsm.201901002 es_ES
dc.description.references Paul, S. S., Coops, N. C., Johnson, M. S., Krzic, M., Chandna, A., & Smukler, S. M. (2020). Mapping soil organic carbon and clay using remote sensing to predict soil workability for enhanced climate change adaptation. Geoderma, 363, 114177. doi:10.1016/j.geoderma.2020.114177 es_ES
dc.description.references Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324 es_ES
dc.description.references How to Normalize the RMSEhttps://www.marinedatascience.co/blog/2019/01/07/normalizing-the-rmse/ es_ES
dc.description.references Probst, P., Wright, M. N., & Boulesteix, A. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3). doi:10.1002/widm.1301 es_ES
dc.description.references Salvati, L., & Carlucci, M. (2015). Patterns of Sprawl: The Socioeconomic and Territorial Profile of Dispersed Urban Areas in Italy. Regional Studies, 50(8), 1346-1359. doi:10.1080/00343404.2015.1009435 es_ES
dc.description.references Weilenmann, B., Seidl, I., & Schulz, T. (2017). The socio-economic determinants of urban sprawl between 1980 and 2010 in Switzerland. Landscape and Urban Planning, 157, 468-482. doi:10.1016/j.landurbplan.2016.08.002 es_ES
dc.description.references Huang, J., Lu, X. X., & Sellers, J. M. (2007). A global comparative analysis of urban form: Applying spatial metrics and remote sensing. Landscape and Urban Planning, 82(4), 184-197. doi:10.1016/j.landurbplan.2007.02.010 es_ES
dc.description.references Angel, S., Arango Franco, S., Liu, Y., & Blei, A. M. (2020). The shape compactness of urban footprints. Progress in Planning, 139, 100429. doi:10.1016/j.progress.2018.12.001 es_ES
dc.description.references Bechle, M. J., Millet, D. B., & Marshall, J. D. (2011). Effects of Income and Urban Form on Urban NO2: Global Evidence from Satellites. Environmental Science & Technology, 45(11), 4914-4919. doi:10.1021/es103866b es_ES
dc.description.references Meneses, B., Reis, E., Pereira, S., Vale, M., & Reis, R. (2017). Understanding Driving Forces and Implications Associated with the Land Use and Land Cover Changes in Portugal. Sustainability, 9(3), 351. doi:10.3390/su9030351 es_ES
dc.description.references Corbane, C., Pesaresi, M., Kemper, T., Politis, P., Florczyk, A. J., Syrris, V., … Soille, P. (2019). Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data, 3(2), 140-169. doi:10.1080/20964471.2019.1625528 es_ES
dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem