The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using more detailed indicators. The study utilized Google Street View and points of interest to depict the built environment. Building on previous work that segmented natural and artificial elements in streetscape images, this study further distinguished trees, bush, and grass. Additionally, it incorporated data from the Data Analysis and Visualization Unit of the DC Office of Planning to reflect a broader range of individual characteristics. Weighted least squares regression and Pearson correlation analysis were used to test the relationship between the built environment, sociodemographic, and crime, respectively. Some of the key findings are as follows. (1) Trees, bushes, and grass all reduce crime. (2) Urban functionality is hard to curb crime by enhancing informal public surveillance. (3) Among the sociodemographic variables, the walking commute rate is the variable most strongly positively correlated with crime. (4) Family relationships play an important role in suppressing crime. This study examined a more comprehensive range of indicators affecting urban crime in favor of safer cities.

Winter cities face the dual pressures of climate change and population aging, urgently requiring a shift from a singular focus on winter protection toward a development model adaptable to both winter and summer conditions. This shift is essential to enhance social resilience and safeguard the health of all age groups. This case study investigates how the thermal environment of life-sustaining streets in winter cities correlates with older adults’ daily activities. Employing Spearman correlation analysis, a heat exposure–pedestrian flow coupling matrix, and a comprehensive risk diagnostic model, the research analyzes the spatiotemporal variation patterns and underlying drivers of the street thermal environment. The key findings are: (1) All 15 surveyed streets exhibited Wet Bulb Globe Temperatures (WBGT) exceeding 28 °C during peak activity hours, with afternoon values (17:00–19:00) up to 2.7 °C higher than morning values. (2) On East Chaoyang Road, despite building shade and a high Visible Green Index (39.68%), the WBGT ranked second highest. This condition is attributed to a critically low average wind speed of 0.69 m/s (significantly below the city’s summer average of 2.67 m/s) and the widespread use of low-albedo asphalt, which collectively trap heat and negate the benefits of shading. (3) Using a dual-dimensional diagnostic framework, four streets were identified as dual-pressure streets with their combination of high elderly pedestrian flow (exceeding 126 persons/h) and high thermal risk (WBGT > 29 °C), marking them as priority intervention units. Based on these findings, the study proposes categorized street retrofit strategies that synergistically integrate climate adaptation and aging-friendliness. This provides an actionable, evidence-based foundation for planning decisions to support the sustainable renewal of winter cities amid climate change and population aging.