The Influencing Factors of a Polycentric Employment System on Jobs-Housing Matching—A Case Study of Hangzhou, China

Abstract

This paper discusses the relationship between the polycentric spatial structure and jobs-housing matching and its influencing factors in the urbanized area of Hangzhou City in China. First, 42 employment centers are identified while using the method of the threshold value of employment density and employment to residence ratio based on the places of employment and residence that were identified from cellphone signaling data. This study found that Hangzhou have remarkable features of a polycentric system. Second, we use the jobs-housing matching rate index to measure the jobs-housing matching characteristics of employment centers. We found that the polycentric structure does not guarantee jobs-housing matching of all centers, although nearly half of the employment centers had good jobs-housing matching and 14.29% do not achieve jobs-housing matching, which indicates the uncertainty of "co-location hypothesis" in China. Employment centers of different levels, industry types, and locations also display certain regularity in their jobs-housing matching level. Third, by using correlation analysis and stepwise multiple regression analysis, we found that, besides the polycentric factor, the spatial and industrial factors also exert a certain effect on jobs-housing matching, reasonable control of the employment center size, employment to resident ratio, and industrial diversity are the determinants that affect the jobs-housing matching level.

Polycentric Urban Spatial Structure Identification Based on Morphological and Functional Dimensions: Evidence from Three Chinese Cities

“…To this end, Muñiz and Garcia-Lopez [11] proposed the relative threshold method, which identifies continuous units that are greater than the average employment density and 1% of the total employed population of metropolitan areas as employment centers. This method can adopt the same criteria in different cities without repeatedly evaluating the threshold value [22,23]. The employment-to-residence ratio believes that industrial areas do not have high employment density while attracting a large number of commuter inflows, but these areas should also be considered as employment concentrations [20,21], for example, Shearmur and Coffey [21] identify continuous units with a employment-to-residence (E/R) ratio greater than 1 and a total employed population greater than 500 as urban employment centers in Canada.…”

“…In China, most empirical studies on polycentric urban spatial structure adopt one morphological approach [22,40], rarely used multi-method comparative studies from the two dimensions of morphology and function. Mobile signaling data can be located to the user's base station location, and effective algorithms can be used to obtain population and employment distribution data similar to census data, as well as work-residence commuting contact data of traffic surveys, which are a kind of large-sample real-time data, providing a good data source for identifying urban spatial structure [23]. As in this study, the mobile phone signaling data of the three cities were acquired in 2017; the relevant data analysis was conducted in similar years.…”

“…(1) Threshold method This paper compares Giuliano and Small [7], Muñiz and Garcia-Lopez [11], Shearmur and Coffey [21], and Zhu et al [23] with the absolute threshold (GS), relative threshold (GL), employment-residence ratio (SC), and comprehensive threshold (GL_SC.) methods, and it found that the GL_SC method has the best result.…”

Identification and characterization of urban polycentric structure based on points of interest in Shenyang, China

“…In the process of researching the polycentric urban structure, many classic urban spatial‐structure theories have been developed, such as multicore, central‐place, and organic‐dispersion theories (Hsu, 2012; Sang & Xiao, 2003). According to these theories, research on the polycentric structure mainly includes the following: identification method of a polycentric structure (Montejano et al., 2016; Taubenbock et al., 2017), formation mechanism of a polycentric structure (Meng et al., 2021), polycentric organization pattern (Veneri, 2018), effect of multiple centers on relief from traffic congestion, and relationship among the polycentric network nodes (Ozus et al., 2012; Salvati et al., 2018; Wei et al., 2020; Zhu et al., 2019). In terms of structure recognition, scholars mainly use methods such as geospatial and morphological analyses to identify the form and function of multiple centers from different perspectives, such as polycentric structure analysis based on network commuting traffic and polycentric urban structure based on urban agglomeration characteristics and economic levels (Burger et al., 2014; Liu et al., 2021; Zhang et al., 2020; Zhou et al., 2021).…”

The Impact of Digital Technology Use on Passive Entrepreneurial Exit in Rural Households: Empirical Evidence from China

“…In terms of individual characteristics, they mostly focus on age, gender, marriage, and whether they are party members, etc. [5,6]. In terms of household characteristics, they mainly focus on household assets, the proportion of older people, etc.…”