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.

中文翻译：

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在半全球范围内分析建成区的时空度量与社会经济指标之间的联系

多种多样的社会经济过程塑造着城市地区的自然要素。因此，它们在许多方面都影响着城市居民的居住环境和可持续性。监测城市结构的发展及其与社会经济和环境过程之间的关系将有助于阐明它们之间的联系，从而有助于制定新战略以实现更可持续的发展。在这项研究中，我们确定了功能性市区中收入，不平等，GDP，空气污染和就业指标之间的经验关系和显着关系，以及它们随时间的变化与建筑物和自然要素的空间组织。我们能够使用时空指标在32个国家/地区中使用来自全球可用数据库的地理信息来证明这一点。我们采用了随机森林回归法，并且能够解释32％至68％的社会经济变量的变异性。这证实了空间格局及其变化与社会经济指标有关。我们还确定了在模型中更相关的时空指标：我们发现城市紧凑度，集中度，分散指数，密集增长的密度，可及性以及土地利用/土地覆盖密度和变化可能可以用作某些社会经济指标的代理。这项研究是在全球范围内确定这种关系的第一步和基础步骤。所提出的方法用途广泛，包含新数据集也很简单，