{"title":"哮喘易发区的时空建模:利用可解释人工智能(XAI)探索城市气候因素的影响","authors":"","doi":"10.1016/j.scs.2024.105889","DOIUrl":null,"url":null,"abstract":"<div><div>Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM<sub>2.5</sub>) in spring, carbon monoxide (CO) in summer, ozone (O<sub>3</sub>) in autumn, and PM<sub>10</sub> in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI)\",\"authors\":\"\",\"doi\":\"10.1016/j.scs.2024.105889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM<sub>2.5</sub>) in spring, carbon monoxide (CO) in summer, ozone (O<sub>3</sub>) in autumn, and PM<sub>10</sub> in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670724007133\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007133","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI)
Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM2.5) in spring, carbon monoxide (CO) in summer, ozone (O3) in autumn, and PM10 in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;