The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities.

In recent years, the waterway navigation and transportation industry has been developing rapidly, and the living environment of ship cabins has not received much attention. Using questionnaire surveys, data collection and computer simulations, this study explored the problems and causes related to thermal comfort that affect a crew living onboard. The survey showed differences in the thermal sensations of the crew. Cabins below the deck of a ship are usually more comfortable than those above deck. These differences were related to the range of frequent activities undertaken in the cabins. The data and calculations show that the thermal comfort in the stern winch cabin and the engine cabin was significantly higher than in the top living cabin and the meeting cabin. For cabins without windows in winter, the PMV and PPD indexes of those below deck were on average 11.95% higher and 7.03% lower, respectively, than those above deck, indicating better overall thermal comfort below deck. The simulation showed that the simulated PMV of an occupied cabin was up to 17.55% higher than the actual PMV, indicating that the number of crew members in the cabin significantly affected its level of thermal comfort. The results provide a reference for understanding and improving the thermal environment of ships and temporary water facilities.

Urban vitality is an important evaluation indicator for enhancing urban spatial efficiency and promoting sustainable development. However, few studies have systematically integrated steady-state and dynamic vitality perspectives. To address this gap, we integrate steady-state vitality and dynamic vitality indicators, and use geographically weighted regression (GWR) and geographically weighted logistic regression (GWLR) to quantify how the built environment, natural elements, and travel purposes influence urban vitality. The results reveal that: (1) From the steady-state perspective, urban vitality exhibits a distinct polycentric structure, with transportation POI and catering facilities serving as core driving factors; (2) From the dynamic perspective, areas where citizens are always highly concentrated are mainly influenced by floor area ratio and transportation POI. Green space coverage and building density significantly correspond to patterns of persistently low vitality, while periodic population fluctuations are associated with subway accessibility and proximity to waterfronts. This study provides a comprehensive analysis of the stable spatial distribution and dynamic changes in population aggregation, offering a theoretical and empirical basis for optimizing urban spatial layout and meeting citizens’ activity needs.