Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
In anticipation of the 2032 climate-positive Olympic Games in Brisbane, we address the Urban Heat Island (UHI) effect optimizing the granularity of a sensor network in the Northshore Hamilton Priority Development Area (PDA), i.e., the location of the Athletes' Village, for efficient environmental monitoring and the provision of a Green Information System (IS). We use spatio-temporal sensor data and leverage advanced interpolation techniques to optimize both temporal and spatial granularity settings. Results and findings from our granularity analysis reveal an optimal temporal granularity at one-hour intervals, providing the optimal trade-off balance between computational efficiency and sufficient detail for urban planning. Finer temporal resolutions do not significantly enhance prediction accuracy. Spatial analysis further helps decision makers to balance trade-offs between economic costs and prediction accuracy, eliminating unnecessary sensors in the network.
Recommended Citation
Schoe, Celine; Tuczek, Matthias; Degirmenci, Kenan; and Breitner, Michael H., "Optimizing a Sensor Network's Granularity to Mitigate Urban Heat Island Effect at 2032 Brisbane Olympics" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 8.
https://aisel.aisnet.org/hicss-58/da/analytics_for_green_is/8
Optimizing a Sensor Network's Granularity to Mitigate Urban Heat Island Effect at 2032 Brisbane Olympics
Hilton Waikoloa Village, Hawaii
In anticipation of the 2032 climate-positive Olympic Games in Brisbane, we address the Urban Heat Island (UHI) effect optimizing the granularity of a sensor network in the Northshore Hamilton Priority Development Area (PDA), i.e., the location of the Athletes' Village, for efficient environmental monitoring and the provision of a Green Information System (IS). We use spatio-temporal sensor data and leverage advanced interpolation techniques to optimize both temporal and spatial granularity settings. Results and findings from our granularity analysis reveal an optimal temporal granularity at one-hour intervals, providing the optimal trade-off balance between computational efficiency and sufficient detail for urban planning. Finer temporal resolutions do not significantly enhance prediction accuracy. Spatial analysis further helps decision makers to balance trade-offs between economic costs and prediction accuracy, eliminating unnecessary sensors in the network.
https://aisel.aisnet.org/hicss-58/da/analytics_for_green_is/8