Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities.
Zhenyu HanTong XiaYanxin XiYong LiPublished in: Scientific data (2023)
This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city.
Keyphrases
- healthcare
- public health
- mental health
- big data
- human health
- health information
- deep learning
- machine learning
- artificial intelligence
- coronavirus disease
- sars cov
- primary care
- social media
- health promotion
- drinking water
- air pollution
- climate change
- adverse drug
- genome wide
- molecular dynamics
- global health
- electronic health record