Understanding the spatial interaction among residents, cooling service, and heat risk area in complex urban areas is conducive to developing targeted management. However, traditional urban thermal environment assessments typically relied on simple linear integration of associated indicators, often neglecting the spatial interaction effect. To explore the spatial interaction among the three elements, this study proposes an accessibility-based urban thermal environment assessment framework. Using Zhengzhou, a rapidly urbanizing city, as an example, remotely sensed images from three periods (2010, 2015 and 2020) were applied to extract urban green space (UGS) and hot island area (HIA). An improved two-step floating catchment area (2SFCA) method and bivariate local Moran’s I were employed to explore whether residents’ clustering locations are more likely to access cooling service or to be exposed to heat risk. The results demonstrate that the UGS in the city has been expanding, whereas the HIA shrank within the inner city in 2015 and then increased in 2020. Even though the urban thermal environment may have improved in the last decade, the spatial interaction among the residents, cooling service and heat risk area could be exacerbated. Spatial autocorrelation shows an increase in locations that are disadvantageous for resident congregation. Even when sufficient cooling services were provided, residents in these areas could still be exposed to high heat risk. The developed urban thermal environment framework provides a novel insight into the residents’ heat risk exposure and cooling service accessibility, and the findings could assist urban planners in targeting the improvement of extra heat exposure risk locations.}

There are urgent calls for new approaches to map the global urban conditions of complexity, diffuseness, diversity, and connectivity. However, existing methods mostly focus on mapping urbanized areas as bio physical entities. Here, based on the continuum of urbanity framework, we developed an approach for cross-scale urbanity mapping from town to city and urban megaregion with different spatial resolutions using the Google Earth Engine. This approach was developed based on multi-source remote sensing data, Points of Interest – Open Street Map (POIs-OSM) big data, and the random forest regression model. This approach is scale-independent and revealed significant spatial variations in urbanity, underscoring differences in urbanization patterns across megaregions and between urban and rural areas. Urbanity was observed transcending traditional urban boundaries, diffusing into rural settlements within non-urban locales. The finding of urbanity in rural communities far from urban areas challenges the gradient theory of urban-rural development and distribution. By mapping livelihoods, lifestyles, and connectivity simultaneously, urbanity maps present a more comprehensive characterization of the complexity, diffuseness, diversity, and connectivity of urbanized areas than that by land cover or population density alone. It helps enhance the understanding of urbanization beyond biophysical form. This approach can provide a multifaceted understanding of urbanization, and thereby insights on urban and regional sustainability.}

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.}