Exploring the Determinants of Urban Green Space Utilization Based on Microblog Check-In Data in Shanghai, China

Enhanced Automatic Identification of Urban Community Green Space Based on Semantic Segmentation

At the neighborhood scale, recognizing urban community green space (UCGS) is important for residential living condition assessment and urban planning. However, current studies have embodied two key issues. Firstly, existing studies have focused on large geographic scales, mixing urban and rural areas, neglecting the accuracy of green space contours at fine geographic scales. Secondly, the green spaces covered by shadows often suffer misclassification. To address these issues, we created a neighborhood-scale urban community green space (UCGS) dataset and proposed a segmentation decoder for HRNet backbone with two auxiliary decoders. Our proposed model adds two additional branches to the low-resolution representations to improve their discriminative ability, thus enhancing the overall performance when the high- and low-resolution representations are fused. To evaluate the performance of the model, we tested it on a dataset that includes satellite images of Shanghai, China. The model outperformed the other nine models in UCGS extraction, with a precision of 83.01, recall of 85.69, IoU of 72.91, F1-score of 84.33, and OA of 89.31. Our model also improved the integrity of the identification of shaded green spaces over HRNetV2. The proposed method could offer a useful tool for efficient UCGS detection and mapping in urban planning.

Unraveling Visiting-Activity Patterns of Heterogeneous Communities for Urban-Park Planning and Design

Urban parks provide essential outdoor recreation space, especially for high-density cities. This study evaluated the park-visiting activity profiles of residents to inform the planning and design of community-relevant parks. The visiting and activity patterns of 465 Hong Kong adult residents were collected using a structured questionnaire. The correlations of visiting and activity patterns of the different socio-demographic groups were analyzed. Varying features of visiting and activity patterns were observed for different socio-demographic groups. Older patrons visited parks intensively for nature-enjoyment activities and had shorter travel if intended for social and physical-exercise activities. The middle-aged respondents with children mainly conducted family based recreation, visited parks more frequently, and traveled farther. The young adults reported lower patronage, but the visit frequency increased with the engagement level in outdoor and physical-exercise activities. The homemakers reported a high visit frequency and enthusiastic participation in social activities. They tended to visit more frequently and stay longer in parks for physical-exercise activities. Our study revealed the urban parks’ divergent patronage behavior and unique roles to disparate user groups. They furnished evidence to apply continually precision park planning, design, and promotion to achieve socially responsive and age-friendly parks.