Detecting the sinks and sources of transportation energy consumption and its forces driving at multiple spatiotemporal scales using trajectory data. (November 2022)

• Record Type: Journal Article
• Authors: Cao, Zheng; Guo, Guanhua; Xu, Yong; Wu, Zhifeng; Zhou, Weiqi
• Is Part Of: Applied geography. Volume 148(2022)
• Publication Date: 2022-11
• Subjects: Transportation anthropogenic heat release -- Trajectory big data -- Multi spatiotemporal scale -- Sink and source; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2022.102807
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: Accurate and detailed transportation anthropogenic heat release (TAHR) datasets would benefit atmospheric research and public health. However, detailed temporal and spatial descriptions are yet to be obtained because of the low spatiotemporal resolution of basic data in previous studies. Therefore, we here developed a low-cost and efficient TAHR construction method by introducing trajectory data. Furthermore, we constructed an index to examine TAHR sinks and sources. This enabled the quantification of spatial associations between workplace/residential functional zones and TAHR sinks/sources. The results showed that TAHR varied dramatically at multiple temporal scales. Peak‒trough shapes of TAHR variations were found at hourly levels, while increasing trends were found at daily levels. Central downtown areas experienced significant variation trends at both hourly and daily levels. Sinks of TAHR were more closely associated with workplaces, while TAHR sources were more closely associated with residential functional zones at daily levels. This study provides a reference for the explanation of ecological scientific questions using spatiotemporal big data and provides insights into the mechanisms underlying urban energy consumption, urban planning, and energy conservation. Highlights: Transportation anthropogenic heat release (TAHR) data were spatially reconstructed based on trajectory big data. A new index, the TAHR sink and source index, was built. The multi-temporal variation characteristics of TAHR and TAHR sinks and their sources, were quantified.