Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.scs.2024.105695 |
Licence  |
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| Title (Primary) | Interpretable spatial machine learning insights into urban sanitation challenges: A case study of human feces distribution in San Francisco |
| Author | Yi, S.; Li, X.; Wang, R.; Guo, Z.; Dong, X.; Liu, Y.; Xu, Q. |
| Source Titel | Sustainable Cities and Society |
| Year | 2024 |
| Department | CLE |
| Volume | 113 |
| Page From | art. 105695 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Urban sanitation; Human feces; Open defecation; Interpretable spatial machine learning; SHAP |
| Abstract | Urban sanitation is critical for public health, with the management of human feces presenting significant challenges in growing urban areas. While prior research has concentrated on the health impacts of fecal contaminants, the spatial distribution and determinants of open defecation in urban contexts have received less attention. To address these gaps, this study proposed an interpretable spatial machine learning framework integrating Geographically Weighted Random Forest (GW-RF) and SHapley Additive exPlanations (SHAP) analysis to reveal the complex spatial heterogeneity and factors influencing feces density in cities, taking San Francisco as a case study. Our findings highlight that homelessness, population density, and building density are critical drivers of feces distribution. Importantly, higher restroom density was linked to increased feces density, underscoring the need for urban planning to focus on improving restroom accessibility rather than merely increasing their number. Additionally, our research suggests that green spaces serve as a mitigating factor, indicating that enhancing urban greenery could be an effective strategy for addressing sanitation challenges. This study not only offers insights into San Francisco’s urban sanitation management but also provides practical implications for urban development strategies globally, advocating for targeted, evidence-based interventions to foster healthier and more sustainable cities. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29541 |
| Yi, S., Li, X., Wang, R., Guo, Z., Dong, X., Liu, Y., Xu, Q. (2024): Interpretable spatial machine learning insights into urban sanitation challenges: A case study of human feces distribution in San Francisco Sust. Cities Soc. 113 , art. 105695 10.1016/j.scs.2024.105695 |
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