Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.

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High carbon emissions played significant role in global climate change, which made cities with rapid urbanization responsible for local carbon mitigation. In this study, a land-based CFN framework was established by taking 15 land use types as different network nodes. It engaged in exploring the spatio-temporal patterns of the carbon change from a land use perspective, which was followed by the analysis of network utilities to reveal the complex carbon change mechanism. By taking Guangzhou city as an empirical study, the established framework was tested, which showed that the high-level carbon emission patches were found extended from the city center to the city limits with diminished size from 2000 to 2015. Transitions that increased/reduced carbon emissions were detected and revealed the different carbon change mechanism in three time-spans. Exploitation was found significantly contributed to the carbon emissions in 2000–2005 and fell over time. In the built-up area, the dominant carbon relationship has changed from exploitation to mutualism with enlarged carbon emissions. Competition was found continuously increased throughout the study period with favorable mutual restriction effects on carbon emissions, which provided valuable insight for the carbon mitigation through urban planning.

The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China.

High carbon emissions played significant role in global climate change, which made cities with rapid urbanization responsible for local carbon mitigation. In this study, a land-based CFN framework was established by taking 15 land use types as different network nodes. It engaged in exploring the spatio-temporal patterns of the carbon change from a land use perspective, which was followed by the analysis of network utilities to reveal the complex carbon change mechanism. By taking Guangzhou city as an empirical study, the established framework was tested, which showed that the highlevel carbon emission patches were found extended from the city center to the city limits with diminished size from 2000 to 2015. Transitions that increased/reduced carbon emissions were detected and revealed the different carbon change mechanism in three time-spans. Exploitation was found significantly contributed to the carbon emissions in 2000-2005 and fell over time. In the built-up area, the dominant carbon relationship has changed from exploitation to mutualism with enlarged carbon emissions. Competition was found continuously increased throughout the study period with favorable mutual restriction effects on carbon emissions, which provided valuable insight for the carbon mitigation through urban planning.