## 7 Limitation  
This study has several limitations that require further refinement in future research:  
First, the accuracy and completeness of point-of-interest (POI) data may partially influence the findings. Although data cleaning procedures were implemented, undetected duplicate entries may persist due to the massive dataset and the prevalence of small-scalefacilities. Additionally, the POI dataset's coverage might not fully represent the real-world distribution of all amenities, potentially leading to analytical biases.  
Second, the walkable service areas calculated via ArcGIS spatial network analysis may deviate from actual pedestrian environments. Such discrepancies could arise from algorithmic oversimplifications of real-world factors--such as terrain slope, sidewalk width, and traffic signal waiting times--which may compromise the precision of walkability assessments. To address this, walking time calculations could be enhanced through integration with Baidu Maps API route-planning functions [11]. Furthermore, the streetscape analysis was confined to central urban areas within Changsha's Third Ring Road due to limited Baidu Street View coverage in peripheral township regions. Future studies could supplement missing streetscape imagery through manual photographic surveys.  
While the finalized site selection predictions identify concentrated areas lacking senior care infrastructure--providing a scientifically grounded reference--subsequent analyses should incorporate additional metrics, such as regional elderly care demand pressures, to prioritize zones for urgent facility deployment.  
Finally, due to limitations in data collection, this study primarily focuses on the environmental characteristics of streets in central urban districts and does not fully account for other factors influencing elderly care facilities. Future research could incorporate approaches from existing literature, such as examining the relationship between socioeconomic factors and the distribution of elderly care facilities; analyzing the spatial correlation between facility attributes (e.g., number of beds, floor area, service categories) and surrounding amenities [58]; integrating housing price data and social media check-in data (e.g., Weibo) to evaluate disparities in equitable access to elderly care resources, particularly by applying the Gini coefficient to quantify spatial inequalities from a social justice perspective [82]; and conducting online and offline questionnaires to gather older adults' needs and provide targeted recommendations for improving facility services [60,83]. These multidimensional considerations could better capture seniors' actual usage experiences and refine existing analytical frameworks.  
Conceptualization, Y.Y. and T.D.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y., T.D.; formal analysis, Y.Y.; investigation, Y.Y.; resources, Y.Y.; data curation, Y.Y.; writing--original draft preparation, Y.Y. and T.D.; writing--review and editing, Y.Y. and T.D.; visualization, Y.Y.; supervision, T.D.; project administration, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study were obtained from public databases and can be found in Table 1.  
The authors declare no conflicts of interest.