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

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

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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

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Title: Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale
Author: Sapena Moll, Marta Ruiz Fernández, Luis Ángel Taubenböck, Hannes
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria
Issued date:
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 ...[+]
Subjects: Urban growth , Socio-economic variables , Spatio-temporal metrics , Global analysis , IndiFrag , GHSL , OECD
Copyrigths: Reconocimiento (by)
Source:
ISPRS International Journal of Geo-Information. (eissn: 2220-9964 )
DOI: 10.3390/ijgi9070436
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/ijgi9070436
Type: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cities (Urban Audit)https://ec.europa.eu/eurostat/web/cities/background

Metropolitan Areas, OECD Regional Statistics [Database]http://dx.doi.org/10.1787/data-00531-en

Eurostat, Geographical Information and Mapshttps://ec.europa.eu/eurostat/web/gisco/gisco-activities/integrating-statistics-geospatial-information/geostat-initiative

NASA Socioeconomic Data and Applications Center. U.S. Census Gridshttps://sedac.ciesin.columbia.edu/

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

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

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

Land Cover CCI Product User Guide Version 2http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

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

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

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

The Metropolitan Database. Metadata and Release Noteshttp://stats.oecd.org/wbos/fileview2.aspx?IDFile=4aed3009-6020-48f3-8eeb-e01a8e5f61c4

Gross Domestic Product (GDP) (Indicator)https://doi.org/10.1787/dc2f7aec-en

Income Inequality (Indicator)https://doi.org/10.1787/459aa7f1-en

Air pollution Exposure (Indicator)https://doi.org/10.1787/8d9dcc33-en

Employment Rate (Indicator)https://doi.org/10.1787/1de68a9b-en

Redefining “Urban”: A New Way to Measure Metropolitan Areas, OECD Publishinghttps://doi.org/10.1787/9789264174108-en

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

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

Urban morphological zones 2006. European Environment Agencyhttps://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2006-1

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

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

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

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

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

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

Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324

How to Normalize the RMSEhttps://www.marinedatascience.co/blog/2019/01/07/normalizing-the-rmse/

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

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

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

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

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

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

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

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

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