School of Architecture, Tianjin University, Tianjin 300272, China.
Tianjin University Research Institute of Architectural Design & Urban Planninng Co., Ltd, Tianjin 300350, China.
Int J Environ Res Public Health. 2021 Feb 22;18(4):2135. doi: 10.3390/ijerph18042135.
When a public health emergency occurs, a potential sanitation threat will directly change local residents' behavior patterns, especially in high-density urban areas. Their behavior pattern is typically transformed from demand-oriented to security-oriented. This is directly manifested as a differentiation in the population distribution. This study based on a typical area of high-density urban area in central Tianjin, China. We used Baidu heat map (BHM) data to calculate full-day and daytime/nighttime state population aggregation and employed a geographically weighted regression (GWR) model and Moran's I to analyze pre-epidemic/epidemic population aggregation patterns and pre-epidemic/epidemic population flow features. We found that during the COVID-19 epidemic, the population distribution of the study area tended to be homogenous clearly and the density decreased obviously. Compared with the pre-epidemic period: residents' demand for indoor activities increased (average correlation coefficient of the floor area ratio increased by 40.060%); traffic demand decreased (average correlation coefficient of the distance to a main road decreased by 272%); the intensity of the day-and-night population flow declined significantly (its extreme difference decreased by 53.608%); and the large-living-circle pattern of population distribution transformed to multiple small-living circles. This study identified different space utilization mechanisms during the pre-epidemic and epidemic periods. It conducted the minimum living security state of an epidemic-affected city to maintain the operation of a healthy city in the future.
当公共卫生紧急事件发生时，潜在的卫生威胁将直接改变当地居民的行为模式，尤其是在人口密度较高的城市地区。他们的行为模式通常从需求导向转变为安全导向。这直接表现为人口分布的分化。本研究以中国天津市中心的一个典型高密度城区为例。我们使用百度热力图（BHM）数据来计算全天和白天/夜间的人口聚集，并采用地理加权回归（GWR）模型和 Moran's I 分析流行前/流行期间的人口聚集模式和流行前/流行期间的人口流动特征。我们发现，在 COVID-19 疫情期间，研究区域的人口分布明显趋于同质化，密度明显下降。与流行前相比：居民对室内活动的需求增加（建筑面积比的平均相关系数增加了 40.060%）；交通需求减少（到主要道路的距离的平均相关系数下降了 272%）；昼夜人口流动强度显著下降（其极值差下降了 53.608%）；人口分布的大生活圈模式转变为多个小生活圈。本研究确定了流行前和流行期间不同的空间利用机制。它确定了受疫情影响城市的最低生活保障状态，以维持未来健康城市的运行。
