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Towards better visualizations of urban sound environments: insights from interviews

Modan Tailleur, Pierre Aumond, Vincent Tourre, Mathieu Lagrange

TL;DR

The paper examines how urban noise visualization can be made more perceptually accurate by incorporating sound-source information. Through qualitative interviews with diverse city stakeholders, it evaluates current NM- and OBS-platforms, identifies three key use cases for source-aware representations, and outlines design guidelines toward audience-tailored indicators and greater data transparency. The study proposes leveraging multi-source visualizations (MS-platforms) like Lasso alongside traditional noise maps, to support residents, planners, and policymakers with ultra-localized analyses. The work highlights practical implications for index design, data interpretation, and public engagement, aiming to improve the relevance and accessibility of urban soundscape representations.

Abstract

Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility.

Towards better visualizations of urban sound environments: insights from interviews

TL;DR

The paper examines how urban noise visualization can be made more perceptually accurate by incorporating sound-source information. Through qualitative interviews with diverse city stakeholders, it evaluates current NM- and OBS-platforms, identifies three key use cases for source-aware representations, and outlines design guidelines toward audience-tailored indicators and greater data transparency. The study proposes leveraging multi-source visualizations (MS-platforms) like Lasso alongside traditional noise maps, to support residents, planners, and policymakers with ultra-localized analyses. The work highlights practical implications for index design, data interpretation, and public engagement, aiming to improve the relevance and accessibility of urban soundscape representations.

Abstract

Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility.
Paper Structure (19 sections, 4 figures)

This paper contains 19 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Interviews
  3. Selection of participants
  4. Structure of Interview
  5. Gathering information about past experience (10 minutes)
  6. Demonstration of existing platforms (5 minutes)
  7. Strengths and Weaknesses of existing platforms (10 minutes)
  8. Visualization of the presence of multiple sound sources (5 minutes)
  9. Ideas for Visualization and Analysis (10 minutes)
  10. Exploring Additional Ideas (10 minutes)
  11. Results
  12. Noise-Related Complaints
  13. Soundscape Exploration
  14. Guidance for urban planners
  15. Discussion
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Noise map of the city of Lorientbruitparifwebsite
  • Figure 2: Rumeur platformbruitparifwebsite
  • Figure 3: Example of Harmonica index throughout the day bruitparifwebsite
  • Figure 4: Lasso platformlassowebsite