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Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.
Xucai Zhang; Yeran Sun; Ting Chan; Ying Huang; AnYao Zheng; Zhang Liu. Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou. Sustainability 2021, 13, 444 .
AMA StyleXucai Zhang, Yeran Sun, Ting Chan, Ying Huang, AnYao Zheng, Zhang Liu. Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou. Sustainability. 2021; 13 (2):444.
Chicago/Turabian StyleXucai Zhang; Yeran Sun; Ting Chan; Ying Huang; AnYao Zheng; Zhang Liu. 2021. "Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou." Sustainability 13, no. 2: 444.
The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.
Zhang Liu; Yunyan Du; Jiawei Yi; Fuyuan Liang; Ting Ma; Tao Pei. Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut. Remote Sensing 2019, 11, 2091 .
AMA StyleZhang Liu, Yunyan Du, Jiawei Yi, Fuyuan Liang, Ting Ma, Tao Pei. Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut. Remote Sensing. 2019; 11 (18):2091.
Chicago/Turabian StyleZhang Liu; Yunyan Du; Jiawei Yi; Fuyuan Liang; Ting Ma; Tao Pei. 2019. "Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut." Remote Sensing 11, no. 18: 2091.
Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.
Zhang Liu; Ting Ma; Yunyan Du; Tao Pei; Jiawei Yi; Hui Peng. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records. Transactions in GIS 2018, 22, 494 -513.
AMA StyleZhang Liu, Ting Ma, Yunyan Du, Tao Pei, Jiawei Yi, Hui Peng. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records. Transactions in GIS. 2018; 22 (2):494-513.
Chicago/Turabian StyleZhang Liu; Ting Ma; Yunyan Du; Tao Pei; Jiawei Yi; Hui Peng. 2018. "Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records." Transactions in GIS 22, no. 2: 494-513.