Urban Lighting Infrastructure Analysis Using Topology Density Maps

Zhicheng Hu*, Claudia V. López-Alfaro*, Ricardo da Silva Torres*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

1 Citation (Scopus)

Abstract

Digital twins that simulate urban environments have emerged as promising tools to support informed decision-making. In the context of lighting infrastructure analysis using such digital twins, the use of suitable information visualization methods for supporting the identification of the pros and cons of different alternatives is of paramount importance. This paper introduces the use of Topology Density Maps in the assessment of simulated lighting infrastructure designs. Our analysis also considers the evaluation of temporal changes of density maps, using the recently proposed Temporal Topology Density Map algorithm. We demonstrate the potential of exploring such visualization methods in the context of walkability assessment using real and simulated data associated with an urban green area in Alesund, Norway.

Original languageEnglish
Title of host publicationProceedings of the 38th ECMS International Conference on Modelling and Simulation ECMS 2024
EditorsDaniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev
PublisherEuropean Council for Modelling and Simulation (ECMS)
Pages225-233
Number of pages9
ISBN (Electronic)9783937436838
ISBN (Print)9783937436845
Publication statusPublished - Jun 2024
Event38th ECMS International Conference on Modelling and Simulation, ECMS 2024 - Cracow, Poland
Duration: 4 Jun 20247 Jun 2024

Publication series

NameProceedings - European Council for Modelling and Simulation, ECMS
Volume38
ISSN (Print)2522-2414

Conference/symposium

Conference/symposium38th ECMS International Conference on Modelling and Simulation, ECMS 2024
Country/TerritoryPoland
CityCracow
Period4/06/247/06/24

Keywords

  • Digital Twin
  • lighting infrastructure
  • spatiotemporal data
  • topology density map
  • urban data visualization

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