An urban heat island functional zoning approach based on weighted kernel K-means

Dongchao Wang; Baolei Zhang

doi:10.1117/12.3010749

15 November 2023 An urban heat island functional zoning approach based on weighted kernel K-means

Dongchao Wang, Baolei Zhang

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281518 (2023) https://doi.org/10.1117/12.3010749
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

The urban heat island theory plays a crucial role in the domains of urban climate and urban planning. However, conventional Urban Heat Island Functional Zoning (UHIFZ) methods often rely on empirical common sense, neglecting the spatial effects that can better explain the essence of UHIFZ. To address this gap, this paper proposes a novel method, named Geographically Weighted Kernel K-Means for UHIFZ (GWKKM-UHIFZ). By integrating spatial weights into the feature spatial distance metric process, this study demonstrates that the combination of feature spatial distance and machine learning classification algorithms can effectively achieve flexible urban heat island functional zoning and provide accurate data support for subsequent quantitative analysis. Additionally, the evaluation indexes can be adjusted according to specific requirements, thereby acquiring valuable thematic information for future urban planning and design upgrades.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongchao Wang and Baolei Zhang "An urban heat island functional zoning approach based on weighted kernel K-means", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281518 (15 November 2023); https://doi.org/10.1117/12.3010749

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