Straulino Daniel, Saldarriaga Juan C, Gómez Jairo A, Duque Juan C, O'Clery Neave
Centre for Advanced Spatial Analysis, University College London, London, UK.
Mathematical Institute, University of Oxford, Oxford, UK.
R Soc Open Sci. 2022 Nov 2;9(11):211841. doi: 10.1098/rsos.211841. eCollection 2022 Nov.
Knowledge of the spatial organization of economic activity within a city is a key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here, we propose an algorithm that can detect what we call using street view imagery. By using Medellín, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. By applying spatial analysis to our dataset, we detect a polycentric structure with five distinct clusters located in both the established centre and peripheral areas. Comparing the density of visible establishments with that of registered firms, we infer that informal activity concentrates in poor but densely populated areas. Our findings highlight the large gap between what is captured in official data and the reality on the ground.
了解城市内经济活动的空间组织是政策关注的关键。然而，在非正规程度较高的发展中城市，此类信息往往难以获取。机器学习的最新进展以及街景图像的可得性提供了一种经济实惠且易于自动化的解决方案。在此，我们提出一种算法，该算法能够利用街景图像检测我们所谓的[此处原文缺失具体所指内容]。通过将哥伦比亚麦德林作为案例研究，我们说明了如何运用这种方法来揭示此前未被发现的经济活动。通过对我们的数据集进行空间分析，我们检测到一个多中心结构，其中五个不同的集群分布在既有市中心和周边地区。将可见企业的密度与注册公司的密度进行比较，我们推断非正规活动集中在贫困但人口密集的地区。我们的研究结果凸显了官方数据所反映的情况与实际情况之间的巨大差距。
