## 2 Study Area and Data
### Data  
The research framework of this study is illustrated in Figure 1. To measure the 15-min walking range, Point of Interest (POI) data was adopted as one of the core data types. POI data refers to a collection of specific geographic locations that hold significant value or attraction for individuals, with attribute information typically including names, latitude and longitude coordinates, etc. [17; 66]. The POI data used in this study was obtained from the Baidu Map API (Application Programming Interface) for developers in 2024, collected through a Python-based program (version 3.9.7), totaling 387,109 entries. The POI data used in this study was sourced from the Baidu Map API (Application Programming Interface) provided for developers in 2024, collected via a Python-based program, totaling 387,109 entries. From this dataset, eight POI categories closely aligned with the needs of older adults were filtered, encompassing catering, shopping, healthcare, transportation, and other daily living service facilities. After manual cleaning and deduplication, 711 service facility POI entries related to elderly care facilities were obtained, including types such as nursing homes, elderly care centers, senior apartments, and elderly service centers [11; 30]. The 2024 road network data of Changsha City was downloaded from OpenStreetMap [17]. To quantify the environmental characteristics of 15-min life circles at the neighborhood scale, this study integrates Baidu Street View images with semantic segmentation technology. Baidu Street View images were collected for specified latitude and longitude coordinates, capturing urban scenes in four cardinal directions (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)). Figure 1: Research Framework. For the semantic segmentation algorithm, we employed the DeepLab-V3 model [67], a high-performance architecture developed by Google using the TensorFlow framework and Convolutional Neural Networks (CNNs). The model's robustness for complex urban scene parsing was rigorously validated on the Cityscapes benchmark [68], which provides pixel-level annotations across 30 semantic categories in diverse street environments. This study utilized the Cityscapes dataset for semantic parsing of street view imagery. Drawing upon established research methodologies, ten critical street-level elements relevant to pedestrian environments were identified and analyzed: road surfaces, sidewalks, building facades, walls, fences, vegetation, terrain features, sky visibility, pedestrians, and traffic vehicles. The population data were derived from China's 2020 Seventh National Population Census [69], covering 172 subdistricts (townships) within Changsha's administrative boundaries. Our analysis revealed notable aging population patterns: 18 subdistricts contained elderly populations (>65 years) exceeding 10,000 individuals; 98 subdistricts (56.97% of the total) had elderly populations surpassing 6000.The research data and sources are shown in Table 1.  
As shown in Figure 2a, the spatial distribution of elderly residents demonstrates a distinct diffusion pattern from the main urban area to peripheral regions. Notably, Liuyang and Ningxiang emerged as substantial growth poles with concentrated elderly populations. These high-density areas, primarily located in central urban zones and selected rural peripheries, face substantial elderly care pressure. Figure 2b illustrates two distinct aging patterns: absolute numbers peak in central urban districts and remote townships, while higher proportions of elderly residents cluster in surrounding townships. The pronounced growth poles in Liuyang and Ningxiang highlight regions requiring prioritized attention for elderly care facility placement due to intensified service demands.