Assessing the Heat Vulnerability Index (HVI) in Klang Valley Using Principal Component Analysis (PCA) and an Equal-Weighted Approach

Normaisurah Samsuddin; Siti Aekbal Salleh; Nafisah Khalid; Noraain Mohamed Saraf; Rostam Yaman; Lynlei Pintor; Girlie Rueda; Normalyana Samsuddin

doi:10.23917/forgeo.v39i2.8633

Authors

Normaisurah Samsuddin Faculty of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor https://orcid.org/0009-0003-1333-398X
Siti Aekbal Salleh Faculty of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor. Institute for Biodiversity and Sustainable Development (IBSD), Universiti Teknologi MARA, 40450, Shah Alam, Selangor https://orcid.org/0000-0002-2812-1897
Nafisah Khalid Faculty of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor https://orcid.org/0000-0001-8205-3859
Noraain Mohamed Saraf Faculty of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor https://orcid.org/0000-0001-5562-6711
Rostam Yaman Institute for Biodiversity and Sustainable Development (IBSD), Universiti Teknologi MARA, 40450, Shah Alam, Selangor https://orcid.org/0000-0002-9951-6677
Lynlei Pintor Ecosystems Research and Development Bureau (ERDB), Department of Environment and Natural Resources, College, Laguna https://orcid.org/0000-0002-7209-3184
Girlie Rueda Ecosystems Research and Development Bureau (ERDB), Department of Environment and Natural Resources, College, Laguna https://orcid.org/0000-0002-1630-1823
Normalyana Samsuddin Department of Quantity Surveying, College of Built Environment, Universiti Teknologi MARA (UiTM), 94300, Kota Samarahan, Sarawak https://orcid.org/0009-0008-3586-290X

DOI:

https://doi.org/10.23917/forgeo.v39i2.8633

Keywords:

Heat Vulnerability Index (HVI), Principal Component Analysis (PCA), Equal Weighted Index (EWI)

Abstract

Urbanisasi telah menyebabkan lebih dari 50% populasi global tinggal di daerah perkotaan, berkontribusi pada modifikasi signifikan dalam iklim perkotaan yang memperburuk efek buruk perubahan iklim, khususnya pada kesehatan manusia. Studi ini bertujuan untuk menilai Indeks Kerentanan Panas (HVI) di Lembah Klang, Malaysia, menggunakan Analisis Komponen Utama (PCA) dan Indeks Tertimbang Setara (EWI). Selanjutnya membandingkan kedua metode untuk menentukan mana yang lebih efektif mencerminkan konteks lingkungan dan sosial-ekonomi wilayah tersebut. Berfokus pada suhu permukaan tanah sebagai paparan, variabel sensitivitas dan adaptif lainnya ditriangulasi di mana indeks kerentanan panas dikembangkan. Temuan menunjukkan wilayah lembah Klang memiliki tingkat kerentanan yang bervariasi terhadap tekanan panas, dengan kota yang paling terdampak adalah Kuala Lumpur dan Petaling karena faktor sensitivitas paparan dan kapasitas adaptif. Seperti yang dapat dilihat, HVI-PCA mencakup 7,06% untuk Sangat Rendah, 12,87% untuk Rendah, 54,82% untuk Sedang, 14,99% untuk Tinggi, dan 10,26% untuk kerentanan Sangat Tinggi, sementara HVI-EWI mewakili Sangat Rendah pada 6,62%, Rendah pada 51,29%, Sedang pada 26,86%, Tinggi pada 15,22% dan Sangat Tinggi pada 0,02%. Perbedaan-perbedaan ini menggarisbawahi pentingnya pilihan metodologis, di mana PCA mendukung ketahanan empiris berdasarkan metode berbasis data. Pada saat yang sama, EWI cocok ketika konsensus ahli kurang atau ketika kesederhanaan dan dukungan pemangku kepentingan yang luas diperlukan. Rekomendasi termasuk memaksimalkan representasi indikator untuk penilaian yang akurat dan perencanaan perkotaan yang tangguh terhadap stresor terkait panas. Studi ini dapat berfungsi sebagai HVI dasar dari area studi, yang memungkinkan pengembangan teknik pencegahan untuk mengurangi kondisi panas ekstrem.

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References

Akbari, M., Fuladi, A., Shamsoddini, A., Jafarpour Ghalehteimouri, K., & Bidel, H. (2024). Enhancing Urban Livability in District 22 of Tehran: A Study on the Impact of Efficient Fuels Using Fuzzy Delphi and Cross-Impact Analysis Methods. Forum Geografi, 38(3), 395–412. doi: 10.23917/forgeo.v38i3.5185 [Google Scholar] [CrossRef]

Bayomi, N., & Fernandez, J. E. (2023). Quantification of heat vulnerability using system dynamics. Frontiers in Built Environment, 9. doi: 10.3389/fbuil.2023.102548 [Google Scholar] [CrossRef]

Cresswell, K. (2023). A Florida Urban Heat Vulnerability Index: Assessing Weighting and Aggregation Approaches. SSRN, 4454161. doi: 10.2139/ssrn.4454161 [Google Scholar] [CrossRef]

Dewa, H. P. N., Nirwansyah, A. W., Dewi, R. S., & Demirdag, I. (2023). Vulnerability Analysis of School Buildings to Tsunami in the Cilacap Coastal Area. Forum Geografi, 37(2). doi: 10.23917/forgeo.v37i2.23269 [Google Scholar] [CrossRef]

Helbling, M. and Meierrieks, D. (2023). Global warming and urbanization. J Popul Econ, 36, 1187–1223. doi: 10.1007/s00148-022-00924-y [Google Scholar] [CrossRef]

Isa, N. A., Wan Mohd, W. M. N., and Salleh, S. A. (2017). The effects of built-up and green areas on the land surface temperature of the Kuala Lumpur City. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 107–112. doi: 10.5194/isprs-archives-XLII-4-W5-107-2017 [Google Scholar] [CrossRef]

Isa, N. A., Wan Mohd, W. M. N., Salleh, S. A., and Ooi, M. C. G. (2018). The effects of green areas on air surface temperature of the Kuala Lumpur city using WRF-ARW modelling and Remote Sensing technique. IOP Conference Series: Earth and Environmental Science, 117(1). doi: 10.1088/1755-1315/117/1/012012 [Google Scholar] [CrossRef]

Jimenez-Munoz, J. C., Cristobal, J., Sobrino, J. A., Soria, G., Ninyerola, M. and Pons, X. (2009). Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data, IEEE Transactions on Geoscience and Remote Sensing, 47 (1), 339-349, doi: 10.1109/TGRS.2008.2007125. [Google Scholar] [CrossRef]

Kasihairani, D., Hidayat, R., & Supari, S. (2024). Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia. Forum Geografi, 38(3), 413–425. doi: 10.23917/forgeo.v38i3.5402 [Google Scholar] [CrossRef]

Kayal, P., & Chowdhury, I. R. (2025a). Understanding climate change effects on integrated agricultural livelihoods: a PCA-based vulnerability assessment in Gosaba Block, West Bengal, India. Environment, Development and Sustainability, 1-36. doi: 10.1007/s10668-025-05970-6 [Google Scholar] [CrossRef]

Kayal, P., & Chowdhury, I. R. (2025b). From challenges to opportunities: uncovering spatial patterns and key determinants of sustainable livelihood development in rural West Bengal. SN Social Sciences, 5(3). doi: 10.1007/s43545-025-01058-0 [Google Scholar] [CrossRef]

Kotak, Y., Gul, M. S., Muneer, T., & Ivanova, S. M. (2015, April). Investigating the impact of ground albedo on the performance of PV systems. In Proceedings of the CIBSE Technical Symposium, London, UK (pp. 16-17).

Latif, S. Z. A., Salleh, S. A., Salim, P. M., Saraf, N. M., Halim, M. A., Idris, A. N., Mustaha, E., & Pintor, L. (2023). The Exploratory Study of Normalized Indicator of Heat Vulnerability Index (HVI) By Using Functional Relationship. IOP Conference Series: Earth and Environmental Science, 1240(1), 012009. doi: 10.1088/1755-1315/1240/1/012009 [Google Scholar] [CrossRef]

Li, F., Yigitcanlar, T., Nepal, M., Thanh, K. N., & Dur, F. (2024). A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights. Remote Sensing, 16(16), 3032. doi: 10.3390/rs16163032 [Google Scholar] [CrossRef]

Lourdes K. T., Gibbins, C. N., Sherrouse, B. C., Semmens, D. J., Hamel, P., Sanusi, R., Azhar, B., Diffendorfer, J., Lechner, A. M. (2024). Mapping development preferences on the perceived value of ecosystem services and land use conflict and compatibility in Greater Kuala Lumpur. Urban Forestry & Urban Greening, 92 (2024), 128183, doi: 10.1016/j.ufug.2023.128183 [Google Scholar] [CrossRef]

Man, N & Abd Majid, N. (2024). Urban Landscape Changes and Land Use Patterns: The Impact of Mass Rapid Transit (MRT) System Construction in the Context of Development in the Klang Valley between 2010 and 2020. International Journal of Academic Research in Business and Social Sciences, 14, doi: 10.6007/IJARBSS/v14-i4/20838 [Google Scholar] [CrossRef]

Muhamad, S. N., How, V., Lim, F. L., Md Akim, A., Karuppiah, K., & Mohd Shabri, N. S. A. (2024). Assessment of heat stress contributing factors in the indoor environment among vulnerable populations in Klang Valley using principal component analysis (PCA). Scientific Reports, 14(1). doi: 10.1038/s41598-024-67110-w [Google Scholar] [CrossRef]

Nayak, S. G., Shrestha, S., Kinney, P. L., Ross, Z., Sheridan, S. C., Pantea, C. I., ... & Hwang, S. A. (2018). Development of a heat vulnerability index for New York State. Public health, 161, 127-137. doi: 10.1016/j.puhe.2017.09.006 [Google Scholar] [CrossRef]

Noori, S., Mohammadi, A., Miguel Ferreira, T., Ghaffari Gilandeh, A., & Mirahmadzadeh Ardabili, S. J. (2023). Modelling and Mapping Urban Vulnerability Index against Potential Structural Fire-Related Risks: An Integrated GIS-MCDM Approach. Fire, 6(3), 107. doi: 10.3390/fire6030107 [Google Scholar] [CrossRef]

Paterson, S.K. and Godsmark C. N. (2020). Heat-health vulnerability in temperate climates: lessons and response options from Ireland. Global Health, 16(29). doi: 10.1186/s12992-020-00554-7 [Google Scholar] [CrossRef]

Piczak, M. L., Perry, D., Cooke, S. J., Harrison, I., Benitez, S., Koning, A., Peng, L., Limbu, P., Smokorowski, K. E., Salinas-Rodriguez, S., Koehn, J. D., and Creed, I. F. (2023). Protecting and restoring habitats to benefit freshwater biodiversity. Environmental Reviews, 32(3), 438-456. doi: 10.1139/er-2023-0034 [Google Scholar] [CrossRef]

Qureshi, A. M., & Rachid, A. (2022). Heat Vulnerability Index Mapping: A Case Study of a Medium-Sized City (Amiens). Climate, 10(8), 113. doi: 10.3390/cli10080113 [Google Scholar] [CrossRef]

Ramli, M., & Alias, N. (2024). Multidimensional Vulnerability Mapping Using GIS and Catastrophe Theory. International Journal of Geoinformatics, 20(8), 1–16. doi: 10.52939/ijg.v20i8.3443 [Google Scholar] [CrossRef]

Salleh, S. A., Isa, N. A., Siman, N. A., Zakaria, N. H., Pintor, L. L., Yaman, R., & Che Dom, N. (2023). The Development of the Vulnerability Index (VI) using Principal Component Analysis (PCA). International Journal of Sustainable Construction Engineering and Technology, 14(5), 16-36. [Google Scholar]

Salleh, S. A., Latif, Z. A., Pradhan, B., Wan Mohd, W. M. N., & Chan, A. (2013). Functional relation of land surface albedo with climatological variables: a review on remote sensing techniques and recent research developments. Geocarto International, 29(2), 147–163. doi: 10.1080/10106049.2012.748831 [Google Scholar] [CrossRef]

Salleh, S. A., Latif, Z. A., Chan, A., Morris, K. I., Ooi, M. C. G., & Mohd, W. M. N. W. (2015). Weather Research Forecast (WRF) modification of land surface albedo simulations for urban Near Surface Temperature. 2015 International Conference on Space Science and Communication (IconSpace), 243–247. doi: 10.1109/iconspace.2015.7283787 [Google Scholar] [CrossRef]

Soomar, S. M. and Soomar S. M. (2023). Identifying factors to develop and validate a heat vulnerability tool for Pakistan – A review. Clinical Epidemiology and Global Health, 19. doi: 10.1016/j.cegh.2023.101214 [Google Scholar] [CrossRef]

Szabó, S., Gácsi, Z., & Balázs, B. (2016). Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Landscape & Environment, 10(3–4), 194–202. doi: 10.21120/le/10/3-4/13 [Google Scholar] [CrossRef]

Tesfamariam, S., Govindu, V., & Uncha, A. (2024). Urban ecology in the context of urban heat island vulnerability potential zone mapping: the case of Mekelle city, Ethiopia. Frontiers in Climate, 6. doi: 10.3389/fclim.2024.1446048 [Google Scholar] [CrossRef]

Tomlinson, C. J., Chapman, L., Thornes, J. E., and Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: A review. In Meteorological Applications, 18(3), 296–306. doi: 10.1002/met.287 [Google Scholar] [CrossRef]

U.S. Environmental Protection Agency (EPA) and Centers for Disease Control and Prevention (CDC). (2016). Climate Change and Extreme Heat: What You Can Do to Prepare. Retrieved from https://www.epa.gov/sites/default/files/2016-10/documents/extreme-heat-guidebook.pdf

Wenwen Cheng, Dongying Li, and Zhixin Liu, R. D. B. (2021). Approaches for identifying heat-vulnerable populations and locations: A systematic review. Science of The Total Environment, 799. doi: 10.1016/j.scitotenv.2021.149417 [Google Scholar] [CrossRef]

Wong, L. P., Alias, H., Aghamohammadi, N., Aghazadeh, S., Nik Sulaiman, N. M. (2017). Urban heat island experience, control measures and health impact: A survey among working community in the city of Kuala Lumpur. Sustainable Cities and Society, 35, 660-668, doi: 10.1016/j.scs.2017.09.026 [Google Scholar] [CrossRef]

Xue Liu, Wenze Yue, Xuchao Yang, Kejia Hu andWei Zhang, M. H. (2020). Mapping Urban Heat Vulnerability of Extreme Heat in Hangzhou via Comparing Two Approaches. Complexity, 2020(1), 9717658. doi: 10.1155/2020/9717658 [Google Scholar] [CrossRef]

Zha, F., Lu, L., Wang, R., Zhang, S., Cao, S., Baqa, M. F., Li, Q., & Chen, F. (2024). Understanding fine-scale heat health risks and the role of green infrastructure based on remote sensing and socioeconomic data in the megacity of Beijing, China. Ecological Indicators, 160, 111847. doi: 10.1016/j.ecolind.2024.111847 [Google Scholar] [CrossRef]

Zhang, X., Long, Q., Kun, D., Yang, D., & Lei, L. (2022). Comprehensive Risk Assessment of Typical High-Temperature Cities in Various Provinces in China. International Journal of Environmental Research and Public Health, 19(7), 4292. doi: 10.3390/ijerph19074292 [Google Scholar] [CrossRef]