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

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 and Patricia Lustosa Brito
Additional contact information
Paloma Merodio Gómez: Instituto Nacional de Estadísticay Geografía (INEGI), Aguascalientes 20276, Mexico
Olivia Jimena Juarez Carrillo: Instituto Nacional de Estadísticay Geografía (INEGI), Aguascalientes 20276, Mexico
Monika Kuffer: Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Dana R. Thomson: Department of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Jose Luis Olarte Quiroz: Instituto Nacional de Estadísticay Geografía (INEGI), Aguascalientes 20276, Mexico
Elio Villaseñor García: Instituto Nacional de Estadísticay Geografía (INEGI), Aguascalientes 20276, Mexico
Sabine Vanhuysse: Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium
Ángela Abascal: School of Architecture, Navarra Center for International Development, University of Navarra, 31009 Navarra, Spain
Isaac Oluoch: Department of Philosophy, University of Twente, 7522 NB Enschede, The Netherlands
Michael Nagenborg: Department of Philosophy, University of Twente, 7522 NB Enschede, The Netherlands
Claudio Persello: Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Patricia Lustosa Brito: Politechnic School of Federal, University of Bahia (UFBA), Bahia 40210-630, Brazil

Sustainability, 2021, vol. 13, issue 22, 1-21

Abstract: 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.

Keywords: data cubes; deprivation; urban poverty; slums; data ecosystem; statistics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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