A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Shicong Cao; Hao Zheng

doi:10.1007/978-981-16-5983-6_13

A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Shicong Cao, Hao Zheng^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2021 DigitalFUTURES
Subtitle of host publication	The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
Editors	Philip F. Yuan, Hua Chai, Chao Yan, Neil Leach
Publisher	Springer Singapore
Pages	139-147
Number of pages	9
ISBN (Electronic)	978-981-16-5985-0
ISBN (Print)	978-981-16-5982-9
DOIs	https://doi.org/10.1007/978-981-16-5983-6_13
Publication status	Published - 26 Jun 2021
Externally published	Yes
Event	DigitalFUTURES-CDRF 2021: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) - Tongji University, Shanghai, China Duration: 26 Jun 2021 → 4 Jul 2021

Conference

Conference	DigitalFUTURES-CDRF 2021
Place	China
City	Shanghai
Period	26/06/21 → 4/07/21

Research Keywords

Health environment
Point of interest
Local data
Health-related outcome
Machine learning

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

Access Output

10.1007/978-981-16-5983-6_13

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Cao, S., & Zheng, H. (2021). A POI-Based Machine Learning Method for Predicting Residents’ Health Status. In P. F. Yuan, H. Chai, C. Yan, & N. Leach (Eds.), Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) (pp. 139-147). Springer Singapore. https://doi.org/10.1007/978-981-16-5983-6_13

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title = "A POI-Based Machine Learning Method for Predicting Residents{\textquoteright} Health Status",

abstract = "Health environment is a key factor in public health. Since people{\textquoteright}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{\textquoteright}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.",

keywords = "Health environment, Point of interest, Local data, Health-related outcome, Machine learning",

author = "Shicong Cao and Hao Zheng",

year = "2021",

month = jun,

day = "26",

doi = "10.1007/978-981-16-5983-6_13",

language = "English",

isbn = "978-981-16-5982-9",

pages = "139--147",

editor = "Yuan, {Philip F. } and Hua Chai and Chao Yan and Neil Leach",

booktitle = "Proceedings of the 2021 DigitalFUTURES",

publisher = "Springer Singapore",

address = "Singapore",

note = "DigitalFUTURES-CDRF 2021 : The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) ; Conference date: 26-06-2021 Through 04-07-2021",

}

Cao, S & Zheng, H 2021, A POI-Based Machine Learning Method for Predicting Residents’ Health Status. in PF Yuan, H Chai, C Yan & N Leach (eds), Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). Springer Singapore, pp. 139-147, DigitalFUTURES-CDRF 2021, Shanghai, China, 26/06/21. https://doi.org/10.1007/978-981-16-5983-6_13

A POI-Based Machine Learning Method for Predicting Residents’ Health Status. / Cao, Shicong; Zheng, Hao.
Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). ed. / Philip F. Yuan; Hua Chai; Chao Yan; Neil Leach. Springer Singapore, 2021. p. 139-147.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A POI-Based Machine Learning Method for Predicting Residents’ Health Status

AU - Cao, Shicong

AU - Zheng, Hao

PY - 2021/6/26

Y1 - 2021/6/26

N2 - 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.

AB - 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.

KW - Health environment

KW - Point of interest

KW - Local data

KW - Health-related outcome

KW - Machine learning

U2 - 10.1007/978-981-16-5983-6_13

DO - 10.1007/978-981-16-5983-6_13

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-981-16-5982-9

SP - 139

EP - 147

BT - Proceedings of the 2021 DigitalFUTURES

A2 - Yuan, Philip F.

A2 - Chai, Hua

A2 - Yan, Chao

A2 - Leach, Neil

PB - Springer Singapore

T2 - DigitalFUTURES-CDRF 2021

Y2 - 26 June 2021 through 4 July 2021

ER -

A POI-Based Machine Learning Method for Predicting Residents’ Health Status

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Research Keywords

Publisher's Copyright Statement

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