Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban pat...

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Autores principales:	Paloma Merodio Gómez, Olivia Jimena Juarez Carrillo, Monika Kuffer, Dana R. Thomson, Jose Luis Olarte Quiroz, Elio Villaseñor García, Sabine Vanhuysse, Ángela Abascal, Isaac Oluoch, Michael Nagenborg, Claudio Persello, Patricia Lustosa Brito
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data cubes deprivation urban poverty slums data ecosystem statistics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/78b898e3d89f42c5a48840ef383f061c
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id	oai:doaj.org-article:78b898e3d89f42c5a48840ef383f061c
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spelling	oai:doaj.org-article:78b898e3d89f42c5a48840ef383f061c2021-11-25T19:02:53ZEarth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images10.3390/su1322126402071-1050https://doaj.org/article/78b898e3d89f42c5a48840ef383f061c2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12640https://doaj.org/toc/2071-1050The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.Paloma Merodio GómezOlivia Jimena Juarez CarrilloMonika KufferDana R. ThomsonJose Luis Olarte QuirozElio Villaseñor GarcíaSabine VanhuysseÁngela AbascalIsaac OluochMichael NagenborgClaudio PerselloPatricia Lustosa BritoMDPI AGarticledata cubesdeprivationurban povertyslumsdata ecosystemstatisticsEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12640, p 12640 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data cubes deprivation urban poverty slums data ecosystem statistics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	data cubes deprivation urban poverty slums data ecosystem statistics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Paloma Merodio Gómez Olivia Jimena Juarez Carrillo Monika Kuffer Dana R. Thomson Jose Luis Olarte Quiroz Elio Villaseñor García Sabine Vanhuysse Ángela Abascal Isaac Oluoch Michael Nagenborg Claudio Persello Patricia Lustosa Brito Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
description	The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.
format	article
author	Paloma Merodio Gómez Olivia Jimena Juarez Carrillo Monika Kuffer Dana R. Thomson Jose Luis Olarte Quiroz Elio Villaseñor García Sabine Vanhuysse Ángela Abascal Isaac Oluoch Michael Nagenborg Claudio Persello Patricia Lustosa Brito
author_facet	Paloma Merodio Gómez Olivia Jimena Juarez Carrillo Monika Kuffer Dana R. Thomson Jose Luis Olarte Quiroz Elio Villaseñor García Sabine Vanhuysse Ángela Abascal Isaac Oluoch Michael Nagenborg Claudio Persello Patricia Lustosa Brito
author_sort	Paloma Merodio Gómez
title	Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
title_short	Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
title_full	Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
title_fullStr	Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
title_full_unstemmed	Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
title_sort	earth observations and statistics: unlocking sociodemographic knowledge through the power of satellite images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/78b898e3d89f42c5a48840ef383f061c
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Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

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