Abstract
The concept of smart cities encompasses not just technological advancement but also citizen well-being and sustainability. However, the increasing data availability often leads to a technology-centric focus, neglecting integration with citizen participation. The design could bridge this gap by facilitating data translation and accessibility. Therefore, this study aimed to test a process for co-analyzing mixed data through collaborative activities and data visualization tools, immersing participants in the impact of weather on urban mobility. The data sources included quantitative data from the transport providers, social networks, and qualitative data from a diary study. The process revealed significant potential, with participants reporting ease in analyzing substantial data volumes and finding the proposal innovative and enjoyable. Future steps may involve enhancing interactive visualizations and automating data-narrative integration for broader adaptability. The contribution of this study lies in a co-design process with data storytelling tools, for any project with a large volume of information.
Keywords
smart city; co-design; data visualization; storytelling
DOI
https://doi.org/10.21606/drs.2024.456
Citation
Corrêa Cordeiro, R., and Rupp Quaresma, M. (2024) Bridging the Gap: Data-Driven Design for Smart Cities, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.456
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Paper
Included in
Bridging the Gap: Data-Driven Design for Smart Cities
The concept of smart cities encompasses not just technological advancement but also citizen well-being and sustainability. However, the increasing data availability often leads to a technology-centric focus, neglecting integration with citizen participation. The design could bridge this gap by facilitating data translation and accessibility. Therefore, this study aimed to test a process for co-analyzing mixed data through collaborative activities and data visualization tools, immersing participants in the impact of weather on urban mobility. The data sources included quantitative data from the transport providers, social networks, and qualitative data from a diary study. The process revealed significant potential, with participants reporting ease in analyzing substantial data volumes and finding the proposal innovative and enjoyable. Future steps may involve enhancing interactive visualizations and automating data-narrative integration for broader adaptability. The contribution of this study lies in a co-design process with data storytelling tools, for any project with a large volume of information.