This page has only limited features, please log in for full access.
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.
Peng Zeng; Zongyao Sun; Yuqi Chen; Zhi Qiao; Liangwa Cai. COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China. International Journal of Environmental Research and Public Health 2021, 18, 2135 .
AMA StylePeng Zeng, Zongyao Sun, Yuqi Chen, Zhi Qiao, Liangwa Cai. COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China. International Journal of Environmental Research and Public Health. 2021; 18 (4):2135.
Chicago/Turabian StylePeng Zeng; Zongyao Sun; Yuqi Chen; Zhi Qiao; Liangwa Cai. 2021. "COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China." International Journal of Environmental Research and Public Health 18, no. 4: 2135.
In recent decades, the availability of diverse location-based service (LBS) data has largely stimulated the research in individual human mobility. However, less attention has been paid on the intra-city movement of cyclists coupled with their spatiotemporal dynamics. To fill the knowledge gap, drawing on bicycle-sharing data over one week in Shanghai, China, this study investigates the dynamics of bicycle-sharing users at two spatial scales (i.e., city level and subdistrict level) and explores the intra-city spatial interactions by those cyclists. At the city level, by applying the analysis of variance (ANOVA) test and the Wilcoxon signed-rank test, this study examines the temporal variation of cyclists across a seven-day period. At the subdistrict level, we develop a new index to capture the urban vitality using bicycle-sharing data with the consideration of trip flow allied with spatial weights. In terms of the computed urban vitality over the course of a day, 98 subdistricts are partitioned into 7 groups by using K-means clustering. In addition, spatial autocorrelation and hot spot analysis are also applied to examine the spatial features of urban vitality at different periods. Our results reveal that urban vitality has an obvious character of the spatial cluster and this cluster feature varies markedly over the course of a day. By shedding new lights on intra-city movement, we argue our results are important in informing urban planners on how to better allocate public facilities and increase bicycle usage as a way to progress towards more sustainable urban areas.
Peng Zeng; Ming Wei; Xiaoyang Liu. Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data. Sustainability 2020, 12, 1714 .
AMA StylePeng Zeng, Ming Wei, Xiaoyang Liu. Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data. Sustainability. 2020; 12 (5):1714.
Chicago/Turabian StylePeng Zeng; Ming Wei; Xiaoyang Liu. 2020. "Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data." Sustainability 12, no. 5: 1714.
The effects of human activities and land cover changes on urban thermal field patterns are closely related to the land surface temperature (LST) and air temperature. At present, the number of studies on the quantitative relationship between these two indexes and the effect of the observational scale on their influence is insufficient. In this study, spatial analysis methods such as geographic modeling were combined with remote sensing images, meteorological data, and points of insert and used to investigate the composition and scale of the factors influencing the temperature field in Beijing. The results showed that there are differences in the positive and negative correlations between LST and air temperature and various influencing factors. At a spatial resolution of 90 m, LST had a strong linear relationship with the average air temperature. Indicators reflecting elements of human activity, such as buildings, roads, and entertainment, were easily measured by meteorological stations at a small scale, and the natural green space ratio could also be easily captured by satellite thermal sensors at small scales. These results have substantial implications for environmental impact assessments in areas experiencing an increasing urban heat island effect due to rapid urbanization.
Huang Huanchun; Yang Hailin; Deng Xin; Hao Cui; Liu Zhifeng; Liu Wei; Zeng Peng. Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS. Sustainable Cities and Society 2020, 55, 102024 .
AMA StyleHuang Huanchun, Yang Hailin, Deng Xin, Hao Cui, Liu Zhifeng, Liu Wei, Zeng Peng. Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS. Sustainable Cities and Society. 2020; 55 ():102024.
Chicago/Turabian StyleHuang Huanchun; Yang Hailin; Deng Xin; Hao Cui; Liu Zhifeng; Liu Wei; Zeng Peng. 2020. "Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS." Sustainable Cities and Society 55, no. : 102024.