Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to livable, healthy, sustainable development cities. In this study, we focused on potential effect human daily activities LST from a short-term perspective. Beijing was selected as case city, Weibo check-in data were employed measure intensity activities. MODIS analyzed used for urban measurement. We adopted spatial autocorrelation analysis, Pearson correlation autoregressive model explore influence mechanism LST, study performed at both pixel scale subdistrict scale. The results show that there significant positive LSTs, landscape components are strong explainers LST. captured night, can last accumulate over few hours. variables use functions building forms varying impacts daytime nighttime. Moreover, comparison different scales indicates relationships some sensitive current enriches literature offers meaningful practical suggestions monitoring, early warning, management thermal environment with remote sensing technology big sources.