Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Int J Environ Res Public Health. 2020 Oct 12;17(20):7409. doi: 10.3390/ijerph17207409.
Various indicator systems have been developed to monitor and assess healthy cities. However, few of them contain spatially explicit indicators. In this study, we assessed four health determinants in Shenzhen, China, using both indicators commonly included in healthy city indicator systems and spatially explicit indicators. The spatially explicit indicators were developed using detailed building information or social media data. Our results showed that the evaluation results of districts and sub-districts in Shenzhen based on spatially explicit indicators could be positively, negatively, or not associated with the evaluation results based on conventional indicators. The discrepancy may be caused by the different information contained in the two types of indicators. The spatially explicit indicators measure the quantity of the determinants and the spatial accessibility of these determinants, while the conventional indicators only measure the quantity. Our results also showed that social media data have great potential to represent the high-resolution population distribution required to estimate spatially explicit indicators. Based on our findings, we recommend that spatially explicit indicators should be included in healthy city indicator systems to allow for a more comprehensive assessment of healthy cities.
已经开发了各种指标体系来监测和评估健康城市。然而,其中很少包含空间明确的指标。在这项研究中,我们使用健康城市指标体系中常见的指标和空间明确的指标来评估中国深圳的四个健康决定因素。空间明确的指标是使用详细的建筑物信息或社交媒体数据开发的。我们的结果表明,基于空间明确指标的深圳各区和街道的评估结果可能与基于传统指标的评估结果呈正相关、负相关或不相关。这种差异可能是由两种类型的指标所包含的不同信息引起的。空间明确的指标衡量决定因素的数量和这些决定因素的空间可达性,而传统指标仅衡量数量。我们的结果还表明,社交媒体数据具有很大的潜力来代表估计空间明确指标所需的高分辨率人口分布。基于我们的发现,我们建议在健康城市指标体系中纳入空间明确的指标,以更全面地评估健康城市。