COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China

Abstract

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.

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

“…Relying on the technology of Location-Based Services (LBS), the Baidu heat map records the location data of application users every 15 min, displays this location information on a map [29], and reflects population aggregation with different colors and brightness, which is the main source of exploring the spatial-temporal dynamic distribution of the urban population [11][12][13]. Thus, the Baidu heat map has the potential to deliver reliable information regarding residents' behaviors [30][31][32]. Therefore, this study adopts the Baidu heat map to quantify population density distribution.…”

Exploring built environment factors on e-bike travel behavior in urban China: A case study of Jinan

“…Spatial autocorrelation is an essential indicator to examine whether the attribute value of a factor is significantly associated with its value of non-boring unit ( 62 ). Global Moran's I indicates the overall distribution of data within the study area, while local Moran's I assesses the similarities and differences between neighboring units ( 63 ). Global and local Moran's I are calculated as follows:…”

Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China

“…Theoretically, these two factors also have impacts on housing vacancies. Existing research focuses on the PA and FAR of residential quarters, mainly land price [14,15], land rents [16,17], population distribution [18], urban planning [19], public services, traffic conditions [20], indoor temperature [21], characteristics of spatial and temporal differences [19,22], their relationship with the city center [20,[23][24][25][26], and so on. For example, Zong and Ji found that the scale of Chongqing's residential quarters gradually increased from the city center to the peripheral areas, and planning, transportation, public services, and terrain factors constituted the driving forces for the expansion of residential quarters [20].…”