Methodology for a human-centric CIM: Translating non-geocoded citizen perceptions into spatial indicators
Abstract
To address the growing complexity between the built environment and urban life, modern urban planning has increasingly relied on digital technologies for decision-making, including City Information Modeling (CIM). While CIM has matured from standards and data integration to real-time analytics and digital twins, most initiatives optimize physical systems while struggling to incorporate “intangible” citizen-generated data. This paper addresses this gap by presenting a replicable methodology to embed a customized Environment, Social, and Governance (ESG) framework with a CIM workflow by joining both tangible and intangible city layers to inform planning. The general methodology includes: (1) identifying a French priority neighborhood (QPV) and prioritizing local ESG indicators with a focus group, (2) conceptualizing and designing a data model that integrates urban geometry with subjective human perceptions, and (3) prototyping and developing the CIM model. This approach allows for a multi-scale assessment of socio-environmental sustainability, accounting for uncertainty in human perception. The novelty of this research lies in creating a replicable CIM method that converts non-geocoded citizen perceptions into spatially weighted and probabilistic indicators. Future work will complete the CIM workflow, detailing the integration of the ESG indicators in a practical CIM tool designed to inform decisions through reporting and recommendations.
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BibTeX
@article{jurdi2026methodology,
title = {{Methodology for a human-centric CIM: Translating non-geocoded citizen perceptions into spatial indicators}},
author = {Majd Al Jurdi and Vedrana Ikalovic and Rania Wehbe and Hussein Mroueh},
journal = {Sustainable Cities and Society},
year = {2026},
month = oct,
doi = {10.1016/j.scs.2026.107982},
url = {https://hal.science/hal-05794936v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Methodology for a human-centric CIM: Translating non-geocoded citizen perceptions into spatial indicators
AU - Majd Al Jurdi
AU - Vedrana Ikalovic
AU - Rania Wehbe
AU - Hussein Mroueh
PY - 2026
DA - 2026/10/12
JO - Sustainable Cities and Society
DO - 10.1016/j.scs.2026.107982
UR - https://hal.science/hal-05794936v1
AB - To address the growing complexity between the built environment and urban life, modern urban planning has increasingly relied on digital technologies for decision-making, including City Information Modeling (CIM). While CIM has matured from standards and data integration to real-time analytics and digital twins, most initiatives optimize physical systems while struggling to incorporate “intangible” citizen-generated data. This paper addresses this gap by presenting a replicable methodology to embed a customized Environment, Social, and Governance (ESG) framework with a CIM workflow by joining both tangible and intangible city layers to inform planning. The general methodology includes: (1) identifying a French priority neighborhood (QPV) and prioritizing local ESG indicators with a focus group, (2) conceptualizing and designing a data model that integrates urban geometry with subjective human perceptions, and (3) prototyping and developing the CIM model. This approach allows for a multi-scale assessment of socio-environmental sustainability, accounting for uncertainty in human perception. The novelty of this research lies in creating a replicable CIM method that converts non-geocoded citizen perceptions into spatially weighted and probabilistic indicators. Future work will complete the CIM workflow, detailing the integration of the ESG indicators in a practical CIM tool designed to inform decisions through reporting and recommendations.
SN - 2210-6707
ER -