Exposure to green spaces contributes to residents’ physical and mental health and well-being. The equitable allocation of green space has also become an increasingly important issue for society and the government. This study takes 3281 communities in Shenzhen as the analysis units. Using web crawlers, semantic segmentation based on deep learning, web map path planning and entropy weighting methods, four types of residents’ daily green exposure indicators are calculated, including community green space ratio, green view index (GVI), park accessibility, and the weighted composite green exposure index. The results reveal inequalities in the level of green exposure in Shenzhen’s communities across economic classes, mainly in GVI and comprehensive green exposure. We also found that the level of composite green exposure is relatively stable; however, green space ratio attainment levels for newer communities are increasing and GVI and park accessibility attainment levels are decreasing. Finally, among the newly built communities: compared to the low-income level communities, the high-income level communities have a significant advantage in green space, but the mid-income level communities do not have such an advantage. The main findings of this study can provide policy implications for urban green space planning, including the need to prioritize the addition of public green space near older communities with poor levels of green exposure, the addition of street greenery near communities with poor levels of composite green exposure, and ensuring that parks have entrances in all four directions as far as possible.

The Jianghan Basin is a lacustrine basin in central China developing multiple salt bearing deposits in the Eocene with the success of shale oil exploration in the Qianjiang Formation. The lower member of the Xingouzui Formation in the Chentuokou Depression has become another exploration target. However, rapid changes in lithofacies and strong sedimentary heterogeneity limit the exploration progress. This study aimed to explore the sequence division, lithofacies characteristics, and sedimentary environment using sedimentological, X-ray fluorescence (XRF) and X-ray diffraction (XRD) analyses. The sequence stratigraphic analysis indicates that the low member of the Xingouzui Formation is divided into two third-order sequences, namely SQ1 and SQ2, and four system tracts, including highstand systems tract (HST), lake expanding system tract (EST), early highstand system tract (EHST), and late highstand system tract (LHST). Moreover, a total of nine major lithofacies and five lithofacies associations (LA1-5) were identified. The organic geochemical data show that the laminated argillaceous dolomite in EST and EHST developed the best oil content with an average TOC of 1.18% and S1 of 3.18 mg/g, The laminated argillaceous dolomite deposited in anoxic conditions with a humid climate, moderate salinity, and stratified deep waterbody is a favorable exploration facies for shale oil.

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Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women's walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: 1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and more dispersed distribution of scores. 2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. 3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety.

Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women’s walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: (1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and a more dispersed distribution of scores. (2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. (3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety.