Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
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DOI：
10.3390/su132212640
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发表时间：
2021-11-01
期刊：
影响因子：
3.9
通讯作者：
Lustosa Brito, Patricia
中科院分区：
文献类型：
--
作者：
Merodio Gomez, Paloma;Juarez Carrillo, Olivia Jimena;Lustosa Brito, Patricia
大多数中低收入国家（LMIC）城市的持续城市化伴随着城市和城市周边地区的快速社会经济变化。城市转型进程，如中产阶级化以及城市贫民区的增加（例如，贫民窟）产生新的城市模式。非常迅速的社会经济和人口动态的交叉点往往没有得到充分的理解，用于理解它们的相关数据通常不可用，过时或过于粗糙（分辨率）。传统的基于调查的方法（例如，人口普查）是在低时间粒度下进行的，不允许对大城市地区进行频繁更新。研究人员和决策者通常使用非常过时的数据，这些数据不能反映实地的现实情况，数据汇总掩盖了社会经济差距。因此，需要释放地球观测（EO）的潜力。地球观测数据能够提供详细的时空尺度信息，以支持监测转换。在本文中，我们展示了EO和人工智能（AI）的最新创新如何在需要大规模和/或多时相数据时提供有关社会经济状况的相关快速信息，特别是关于贫困城市街区的信息，例如，支持可持续发展目标（SDG）监测。我们为关键挑战提供解决方案，包括提供多尺度数据、降低数据成本以及绘制社会经济状况图。这些创新填补了编制统计信息的数据空白，解决了COVID-19下获取实地数据的问题。
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.