PDF(Pubmed)
Abstract：
The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity\'s spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.
摘要：
传染性COVID-19最近出现并演变成威胁世界的大流行。在两年前采取了严格的预防措施之后，尽管出现了致命的遗传菌株，但全球大多数活动都重新开放。在这种情况下，评估和绘制基于活动特征的热点区域促进传染病传播至关重要。因此，我们的研究问题是：如何根据商业活动的空间规划在城市内部定义COVID-19风险的潜在热点？在我们的研究中，扎耶德和十月的城市，埃及,以各种商业活动为特征，被选为试验台。首先,我们根据疾病控制和预防中心(CDCP)标准和Kriging插值方法分析了每种活动的空间和形态特征以及潜在的感染风险.然后，使用谷歌移动，以前的报告，和半结构化面试，定义了兴趣点和人口流动，并将其与最后一步相结合，作为确定热点的相互关联的水平层。验证研究比较了生成的活动风险图，空间COVID-19病例，土地利用分布采用逻辑回归(LR)和皮尔逊系数(Rxy)。通过可视化分析，我们的研究结果表明，这两个城市的中心地区，包括不相容和集中的商业活动，尽管地区的城市密度中等，但风险峰值(LR=0.903，rxy=0.78)，表明仅靠城市密度不足以降低公共卫生风险。建议将基于健康观点的活动空间配置与城市密度一起作为风险评估工具，以制定适当的决策来应对大流行的城市。
