Urban carbon mapping with spatial BigData

The objective of this study is mapping carbons utilizing spatial BigData relating buildings and transportations. Regarding buildings, number of storeys, composition of use (e.g., residence and shops), and area of individual buildings are considered. Difference of hourly emission intensity from residence, commercial buildings, firms, and so on, also considered. Regarding transportations, vehicle location by one minutes are considered to capture dynamic fluctuation of transportation behavior. CO₂ emissions from individual buildings and road links in Sumida, Tokyo, Japan, are estimated by 30 minutes by a bottom-up approach. The results are visualized in a 3D manner. The result suggests the usefulness of our carbon mapping approach to detect hot spots, abnormal emissions, and so on, that helps efficient carbon management.