Internal Differentiation within the Rural Migrant Population from the Sustainable Urban Development Perspective: Evidence from China

Abstract

Population mobility and attendant issues, especially housing issues, have a major impact on sustainable urban development. In the urbanization process, a number of micro-communities with various social characteristics have come to compose the rural migrant population (RMP), resulting in internal differentiation. This study aims to reveal the demographic structure of this specific group, and to analyze the effects of the mechanism between population flow trends and sustainable urban development, taking housing demand as a starting point. To this end, a clustering model for mixed-type data based on partitioning around the medoid is proposed, and the linked characteristics and potential laws of the RMP are analyzed, based on the dynamic data of the migrant population in eastern China. To achieve sustainable urban development, the locational preferences and coping strategies of inflowing micro-communities based on city types are demonstrated. The results show that the RMP can be divided into four groups that have strong representativeness and that show significant differences in population structure and housing demand. Super-large and medium-sized cities are the main migration destinations. Several suggestions are proposed, based on these results. Housing security policies should be designed according to the housing demand characteristics and the spatial distribution of different groups. Housing security policies should play a full and positive role in reasonably guiding RMP movement.

Heterogeneity of Internal Migrant Household Consumption in Host Cities: A Comparison of Skilled Migrants and Labor Migrants in China

“…Therefore, a new category of migrant workers has appeared: skilled migrant workers. Scholars have recognized the heterogeneity of the migrant population [11][12][13]. In this paper, we divide migrants into two groups: skilled migrants and labor migrants.…”

“…According to the 6th population census in 2010, skilled migrants comprised 14.4% of total migrants [14], and this proportion rose to 25.7% according to the 1% population sampling survey in 2015. Studies have reported that skilled migrants' occupations, earnings, consumption habits, and lifestyles are significantly different from their less-educated counterparts because they received a higher education [11][12][13]. Although skilled migrants are also mainly employed in the manufacturing, transportation, low-end service, and wholesale and retail trade industries, similar to labor migrants, skilled migrants mostly work in management, professional, or technical positions, which are higher positions than those occupied by labor migrants.…”

“…Most studies on migrant consumption have treated Chinese internal migrants as a homogenous group. However, the heterogeneity of the migrant population becomes more obvious [11][12][13]. The householder has a large effect on household consumption [16] and education helps to improve.…”

Research on Urban and Rural Coordination Development and Its Driving Force Based on the Space-time Evolvement Taking Guangdong Province as an Example

“…In the early stage of development, there is a special binary opposition between urban and rural areas. Cities absorb a large amount of rural labor through the industrialization process and the gap between urban and rural areas gradually becomes prominent [1][2][3]. With the process of industrialization, cities further spread their development effects to the countryside and drive rural development, gradually forming the "core-periphery" [4], "growth pole" [5] and "circle layer" models [6].…”

Contributions to the mixed data clustering problem: from a conceptual codification and classification proposal to the usage of optimization methods

“…Davoodi and Moradi (2018) used a mixed data clustering algorithm based on fuzzy k-prototypes algorithm to determine the number of rules in each layer from a deep learning framework, called deep rule-based fuzzy system (DRBFS), with the objective of predicting mortality at an emergency unit. Deng et al (2018) proposed a new method based on the k-medoids algorithm to analyze the rural migrant population in China. The authors used PAM algorithm with the Gower distance.…”