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A Relation of Urbanization Entropy and Urban Heat Phenomenon

도시화 엔트로피와 도시 열현상과의 관계성

  • 강상준 (국립 강릉원주대학교 도시계획.부동산학과)
  • Received : 2023.07.06
  • Accepted : 2023.08.19
  • Published : 2023.09.30

Abstract

The issue to be discussed is set as the relationship between urban fragmentation and urban heat phenomena. The fragmentation is recognized as a negative form that commonly occurs in the process of urbanization. The purpose of this study is to examine the relationship between urbanization entropy and heat phenomenon by looking at the five major cities in Korea. The employed methods are InVEST Urban Cooling Model and MSPA (Morphological Spatial Pattern Analysis) by using the meteological data for the July 2018. The major results are as follows; First, a low rank correlation(rho=-0.3) is found in the relation between entropy and Cooling Capacity Index (CCi). Second, a very high level of rank correlation is observed between entropy and Average Temperature(℃)(rho=0.9). The implications are that 1) a city with a large degree of sprawling development can have a negative effect on urban heat phenomena; 2) the composition of land use including dispersion and concentration in non-urbanized areas, which has the characteristics of open space, can affect the urban thermal environment. Due to the limited number of case studies, it is appropriate to understand that a possibility, not generalization, is observed between entropy and heat phenomena in urbanized areas.

본 연구에서 다루게 될 도시계획 이슈는 도시화 과정에서 흔히 부정적인 스프롤 현상의 물리적 형태 중 하나로 인식되는 도시 파편화 지역들과 도시 열현상의 관계성으로 설정하였다. 연구목적은 시가화 지역 Entropy와 열 현상과의 관계성을 국내 5개 주요 도시 사례를 통해 살펴보는 것이다. 연구대상지 토지피복자료 촬영시기와 동일한 해인 2018년 7월 여름 기상자료를 바탕으로 InVEST Urban Cooling Model을 이용하였고, MSPA(Morphological Spatial Pattern Analysis) 모형을 이용하여 Entropy를 계산하였다. 주요결과로 첫째, Entropy와 Cooling Capacity Index(CCi) 순위 상관성은 낮은 순위 상관성을 보이고 있다(rho=-0.3). 둘째, Entropy와 Average temperature(℃) 사이에는 매우 높은 수준의 순위 상관성이 관찰된다(rho=0.9). 연구함의는 첫째, 난개발 정도가 큰 도시는 도시 열 현상에 부정적일 수 있다는 점이다. 둘째, 오픈스페이스의 성격을 갖는 비시가화 지역의 분산·집중 등의 토지이용 공간 구성이 도시 열환경에 영향을 미칠 수 있음을 의미한다. 본 연구는 일부 사례지역에 한정되어 진행되었다는 점에서 시가화 지역 Entropy와 열 현상 사이에서 일반화가 아닌 가능성이 관찰되고 있음으로 이해하는 것이 적절하다.

Keywords

Acknowledgement

이 논문은 2023년도 국립 강릉원주대학교 학술연구조성비 지원에 의하여 수행되었음

References

  1. 강상준, 2022, 교외개발의 재조명: 도시의 진화와 계획으로 의 함의, 「환경영향평가」, 31(3), pp.161-172.
  2. 강상준, 2021, 시가화지역 공간상 위치분배와 폭염현상과의 관계성: 5개 광역시사례, 「환경영향평가」, 30(3), pp. 175-185.
  3. 기경석.이경재, 2009, 대도시 외곽지역 논경작지의 토지이용 및 피복변화에 따른 온도변화 모형 연구, 「한국조경학회지」, 37(1), pp.18-27.
  4. 명수정, 2009, 도시 토지이용변화에 따른 수문기상 변화 분석, 「한국환경정책평가연구원」.
  5. 안재현, 2006, 도시 토지이용변화에 따른 수문기상 변화 분석, 「한국수자원학회 학술발표회 논문집」.
  6. 홍석환.이경재, 2004, 서울 강남지역 아파트단지의 녹지면적에 따른 온도변화 모형, 「한국환경생태학회지」, 18(1), pp.53-60.
  7. Ackley J., Angilletta, M., J., DeNardo, D., Sullivan, B., and J. Wu, 2015, Urban heat island mitigation strategies and lizard thermal ecology: landscaping can quadruple potential activity time in an arid city. Urban ecosystems, 18, pp.1447-1459. https://doi.org/10.1007/s11252-015-0460-x
  8. Angel, S., Parent, J., and D. Civco, 2010, The fragmentation of urban footprints: Global evidence of urban sprawl 1990-2000. Lincoln Institute of Land Policy Working Paper. Cambridge, MA.
  9. Bakarman, M., and Chang, J., 2015, The influence of height/width ratio on urban heat island in hot-arid climates, Procedia Engineering, 118, pp.101-108. https://doi.org/10.1016/j.proeng.2015.08.408
  10. Batty, M., Steadman P., and Y. Xie, 2004, Visualisation in spatial modelling, CASA Working Paper 79. Centre for Advanced Spatial Analysis, University Collage London. London.
  11. Das Chatterjee N., Chatterjee S., and Khan A. 2016. Spatial modelling of urban sprawl around Greater Bhubaneswar city, India, Modelling Earth System environmental biology, 2(14).
  12. Huang, H., Deng, X., Yang H, and S. Li, 2020, Spatial evolution of the effects of urban heat island on residents' health. Technical Gazette, 27(5), pp.1427-1435. https://doi.org/10.17559/TV-20200503211912
  13. Kunapo, J., Fletcher, T., Ladson, A. Cunningham, L., and M. Burns, 2018, A spatially explicit framework for climate adaptation. Urban Water Journal, 15(2), pp.159-166. https://doi.org/10.1080/1573062X.2018.1424216
  14. Li, Y., Feng, Q., De-Xuan, S., and Z. Ke-Jia, 2016, Research on urban heat-island effect, Procedia Engineering, 169, pp.11-18. https://doi.org/10.1016/j.proeng.2016.10.002
  15. Luis, I., Rolf., and C., Elmar, 2013, Urban sprawl and fragmentation in Latin America: A dynamic quantification and characterization of spatial patterns, Journal of Environmental Management, 115, pp.87-97. https://doi.org/10.1016/j.jenvman.2012.11.007
  16. McDonald, R. I., Kroeger, T., Boucher, T., Wang, L., and R., Salem, 2016, Planting healthy air: A global analysis of the role of urban trees in addressing particulate matter pollution and extreme heat. CAB International, pp.128-139.
  17. Melaas, E., Wang, J., Miller, D., and M. Friedl, 2016, Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan region. Environmental Research Letters, 11(5), pp.054020.
  18. Montavez, J., Rodriguez A., and J. Jimenez, 2000, A study of the urban heat island of Granada. International Journal of Climatology: A Journal of the Royal Meteorological Society, 20(8), pp.899-911. https://doi.org/10.1002/1097-0088(20000630)20:8<899::AID-JOC433>3.0.CO;2-I
  19. Niu, L., Zhang, Z,, Peng, Z., Liang, Y., Liu, M., Jiang, Y., and R. Tang, 2021, Identifying surface urban heat island drivers and their spatial heterogeneity in China's 281 cities: An empirical study based on multiscale geographically weighted regression. Remote Sensing, 13(21), pp.4428.
  20. Phelan, P., Kaloush, K., Miner, M., Golden, J., Phelan, B., Iii, H. S., and R., Taylor, 2015, Urban Heat Island: Mechanisms, Implications, and Possible Remedies. Annual Review of Environment and Resources, pp.285-309.
  21. Priyadarsini, R., Hien, W., and C. David, 2008, Microclimatic modeling of the urban thermal environment of Singapore to mitigate urban heat island. Solar energy, 82(8), pp.727-745. https://doi.org/10.1016/j.solener.2008.02.008
  22. Rovai, A., Jason, D. Baker, and K. Michael, 2013, Social science research design and statistics: A practitioner's guide to research methods and IBM SPSS. Watertree Press.
  23. Schneider, A., and Woodcock, C, 2008, Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Studies, pp.659-692.
  24. Schwarz, N., 2010, Urban form revisited-Selecting indicators for characterising European cities. Landscape and Urban Planning, 96, pp.29-47. https://doi.org/10.1016/j.landurbplan.2010.01.007
  25. Sudhira, H., Ramachandra, T. and K., Jagadish, 2004, Urban sprawl: metrics and modeling using GIS, International Journal of Applied Earth Observation and Geoinformation, 5(1), pp.29-39. https://doi.org/10.1016/j.jag.2003.08.002
  26. Taha, H., 1997, Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and buildings, 25(2), pp.99-103. https://doi.org/10.1016/S0378-7788(96)00999-1
  27. Yang, C., Zhan, Q., Gao, S., and H. Liu, 2019, How do the multi-temporal centroid trajectories of urban heat island correspond to impervious surface changes: a case study in Wuhan, China. International Journal of Environmental Research and Public Health, 16(20), pp.3865.
  28. Yeh, A. and Li, X. 1998. Sustainable land development model for rapid growth areas using GIS, International Journal of Geographical Information Science, 12(2), pp.169-189.
  29. Vogt, P., 2010, User Guide of GUIDOS, Institute for Environment and Sustainability European Commission, Joint Research Centre.
  30. Wilczek, F, 2021, Fundamentals: Ten keys to reality, Penguin Press.
  31. Zardo, L., Geneletti, D., Prez-soba, M., and M., Eupen, 2017, Estimating the cooling capacity of green infrastructures to support urban planning. Ecosystem Services, 26, pp.225-235. https://doi.org/10.1016/j.ecoser.2017.06.016