Spatio-temporal Data Analysis for Development of Microclimate Prediction Models

Tageui Hong; Yeonsook Heo

doi:10.26868/25222708.2021.30781

Spatio-temporal Data Analysis for Development of Microclimate Prediction Models

Tageui Hong
, Yeonsook Heo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.

Original language	English
Title of host publication	BS 2021 - Proceedings of Building Simulation 2021
Subtitle of host publication	17th Conference of IBPSA
Editors	Dirk Saelens, Jelle Laverge, Wim Boydens, Lieve Helsen
Publisher	International Building Performance Simulation Association
Pages	878-885
Number of pages	8
ISBN (Electronic)	9781775052029
DOIs	https://doi.org/10.26868/25222708.2021.30781
Publication status	Published - 2022
Event	17th IBPSA Conference on Building Simulation, BS 2021 - Bruges, Belgium Duration: 2021 Sept 1 → 2021 Sept 3

Publication series

Name	Building Simulation Conference Proceedings
ISSN (Print)	2522-2708

Conference

Conference	17th IBPSA Conference on Building Simulation, BS 2021
Country/Territory	Belgium
City	Bruges
Period	21/9/1 → 21/9/3

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1003751).

Publisher Copyright:
© International Building Performance Simulation Association, 2022

ASJC Scopus subject areas

Building and Construction
Architecture
Modelling and Simulation
Computer Science Applications

Access to Document

10.26868/25222708.2021.30781

Cite this

Hong, T., & Heo, Y. (2022). Spatio-temporal Data Analysis for Development of Microclimate Prediction Models. In D. Saelens, J. Laverge, W. Boydens, & L. Helsen (Eds.), BS 2021 - Proceedings of Building Simulation 2021: 17th Conference of IBPSA (pp. 878-885). (Building Simulation Conference Proceedings). International Building Performance Simulation Association. https://doi.org/10.26868/25222708.2021.30781

Spatio-temporal Data Analysis for Development of Microclimate Prediction Models. / Hong, Tageui; Heo, Yeonsook.
BS 2021 - Proceedings of Building Simulation 2021: 17th Conference of IBPSA. ed. / Dirk Saelens; Jelle Laverge; Wim Boydens; Lieve Helsen. International Building Performance Simulation Association, 2022. p. 878-885 (Building Simulation Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hong, T & Heo, Y 2022, Spatio-temporal Data Analysis for Development of Microclimate Prediction Models. in D Saelens, J Laverge, W Boydens & L Helsen (eds), BS 2021 - Proceedings of Building Simulation 2021: 17th Conference of IBPSA. Building Simulation Conference Proceedings, International Building Performance Simulation Association, pp. 878-885, 17th IBPSA Conference on Building Simulation, BS 2021, Bruges, Belgium, 21/9/1. https://doi.org/10.26868/25222708.2021.30781

Hong T, Heo Y. Spatio-temporal Data Analysis for Development of Microclimate Prediction Models. In Saelens D, Laverge J, Boydens W, Helsen L, editors, BS 2021 - Proceedings of Building Simulation 2021: 17th Conference of IBPSA. International Building Performance Simulation Association. 2022. p. 878-885. (Building Simulation Conference Proceedings). doi: 10.26868/25222708.2021.30781

Hong, Tageui ; Heo, Yeonsook. / Spatio-temporal Data Analysis for Development of Microclimate Prediction Models. BS 2021 - Proceedings of Building Simulation 2021: 17th Conference of IBPSA. editor / Dirk Saelens ; Jelle Laverge ; Wim Boydens ; Lieve Helsen. International Building Performance Simulation Association, 2022. pp. 878-885 (Building Simulation Conference Proceedings).

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title = "Spatio-temporal Data Analysis for Development of Microclimate Prediction Models",

abstract = "Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.",

author = "Tageui Hong and Yeonsook Heo",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1003751). Publisher Copyright: {\textcopyright} International Building Performance Simulation Association, 2022; 17th IBPSA Conference on Building Simulation, BS 2021 ; Conference date: 01-09-2021 Through 03-09-2021",

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doi = "10.26868/25222708.2021.30781",

language = "English",

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N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1003751). Publisher Copyright: © International Building Performance Simulation Association, 2022

PY - 2022

Y1 - 2022

N2 - Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.

AB - Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.

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ER -

Spatio-temporal Data Analysis for Development of Microclimate Prediction Models

Abstract

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