Studies of the spatial extent of surface urban heat island (SUHI or UHI_Surf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004–2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity.

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.

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

The growing demographic dividend is declining worldwide due to global population shrinkage. However, with the enhancement of spatial connection, industry-oriented population flow is becoming more and more active. The floating demographic dividend will bring new opportunities to regional development. Population flow has become an important field of urban research. We selected the Beijing-Tianjin-Hebei urban agglomeration as the study area and conducted the study of the interaction relationship between population flow and industry space supply. Considering that it takes time for space supplies to be industrial entities and then to affect population flow, this study built a fixed-effects model with annual space supply and “t + 1” population flow to perform regression analysis. The main findings of the research can be concluded as below. The regional population flow was mainly rural population and floated mainly to rural areas. It is difficult to complete the urbanization of floating population through land urbanization due to China’s household registration system. The population flow is sensitive to industry space supply and its spatial development conditions. As the study area is an important global urban agglomeration and China’s political, economic, and cultural center, its regional population flow is representative and typical. Guiding its population flow to form a coordinated spatial and social network can provide scientific support for regional industry space supply and allocation, and can provide a reference for the development of other urban agglomerations.

Production–Living–Ecological Space (PLES) is the functional projection of sustainable development in territory spatial planning. Its rational layout has become the most important task for developing countries to enhance ecological awareness and achieve sustainable goals. This study took the rural areas of Beijing-Tianjin-Hebei (BTH) as an example to analyze the relationship by means of quantitative cumulation between regional endowments (natural factors, location and facilities) and PLES to figure out the preference mechanism. The Boosted Regression Tree model (BRT) was used to obtain the contribution rate of factors and the internal marginal effect between 1980~2018. Our conclusions are as follows: Living space (LS) enjoyed the highest advantage of regional endowment level, followed by production space (PS). Except for the distance to water, other indicators were significantly different in the PLES, and the suitable range of various types was expanded from LS to PS and ecological space (ES). During the transfer, elevation had a universal effect. The process of increasing naturalness was affected by the distance of high-level urban areas, which verified the continuous effect of Chinese ecological civilization. This study clarified the selectivity of regional endowments to PLES, which will greatly guide the direction of regional territory spatial planning and the next step of regional sustainable development.

The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.