Data supporting: Clustering Approach to Typo-Morphologies Across Multiple Dutch Cities for Urban Heat Island Intensity Assessment in Urbanized Residential Settings

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Authors: Maha Moustafa Habib Abdelraouf, Doruntina Zendeli, Marjolein van Esch, Wim J. Timmermans, Maarten van Ham
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Abstract

OverviewThis repository contains datasets on residential typo-morholgoies across 99 Dutch cities, including a focused subset covering four major urban areas: Amsterdam, Rotterdam, Utrecht, and The Hague. These datasets were developed to facilitate research on the relationship between detailed urban morphologies and maximum Urban Heat Island intensity (UHImax). Unlike traditional classification frameworks such as the Local Climate Zone (LCZ), the typologies presented here are derived through an unsupervised clustering approach that identifies unique residential typo-morphologies based on four key morphological parameters. These typologies offer refined, data-driven subclasses that better capture the complexity of urban forms and their thermal characteristics in the Dutch context, while maintaining compatibility with existing LCZ classifications for cross-referencing. MethodologyMethodologies applied in generating this geospatial dataset include Principal Component Analysis, K-means clustering for typology identification, evaluation metrics like the Davies-Bouldin Index, as well as analyzing relationships between typo-morphologies and UHImax intensity. All analytical code and workflows are documented and available to ensure reproducibility and support further research. Spatial data are projected using the Amersfoort / RD New coordinate system (EPSG:28992), consistent with Dutch national geospatial standards. The dataset’s spatial granularity is at a 100 × 100 meter grid scale, enabling linkage with census and other socio-demographic datasets provided by Statistics Netherlands (CBS). This makes the dataset a valuable resource for integrated urban climate studies and targeted heat adaptation planning.

Publication details

Journal / source
4TU.ResearchData
Publication date
5 January 2026
Language
EN
Document type
Dataset