| id |
cdrf2021_139 |
| authors |
Shicong Cao1 and Hao Zheng |
| year |
2021 |
| title |
A POI-Based Machine Learning Method for Predicting Residents’ Health Status |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_13
|
| source |
Proceedings of the 2021 DigitalFUTURES
The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) |
| summary |
Health environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (2,508,548 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:53 |
