Optimal Intra-Urban Hierarchy of Activity Centers—A Minimized Household Travel Energy Consumption Approach

Abstract

An intra-urban hierarchy of activity centers interconnected by non-motorized and public transportation is broadly believed to be the ideal urban spatial structure for sustainable cities. However, the proper hinterland area for centers at each level lacks empirical study. Based on the concentric structure of everyday travel distances, working centers, shopping centers, and neighborhood centers are extracted from corresponding types of POIs in 286 Chinese cities at the prefectural level and above. A U-shaped curve between Household Transportation Energy Consumption (HTEC) per capita and center density at each of the three levels has been found through regression analysis. An optimal intra-urban hierarchy of activity centers is suggested to construct energy-efficient cities.

Urban Spatial Structure and Travel in China

“…Again, the empirical findings are inconclusive. In the ten comparative studies, five found no effect or mixed effects of increasing polycentricity (J. Zhang and Xie 2015; B.…”

“…Many studies recognize subcenters based on local knowledge or the cities’ master plans, which could be different from the subcenters identified based on economics rationales: the former methods could include subcenters that do not have significant impacts on urban functions or those that reflect spatial clustering of both employment and population. Increasingly, researchers use quantitative methods to detect subcenters by estimating the number of businesses (J. Zhang and Xie 2015), job density (J.…”

“…Liu and Wang 2011). Identifying subcenters is particularly challenging for comparative studies, and therefore, some of them use new data sources, such as point of interest (POI) data (J. Zhang and Xie 2015) and satellite image (P.…”

“…We notice in our scoping review that not until after the 2010s when China Economic Census data became available, studies that quantitatively identify employment subcenters had emerged. In recent years, researchers have started to explore new data sources to describe urban structure or to estimate travel; these new sources include smart card data (Long, Zhang, and Cui 2012; Yuanyuan Xu, Ta, and Li 2017; Long and Thill 2015), Open Street Map (J. Zhang and Xie 2015), crowdsourcing data (Engelfriet and Koomen 2017), which, despite their shortcomings, can complement existing data sources.…”

“…In the ten comparative studies, five found no effect or mixed effects of increasing polycentricity (J. Zhang and Xie 2015;B. Sun et al 2017;Wan, Chen, and Wang 2007;Engelfriet and Koomen 2017;P.…”