Dynamic control of rural–urban migration

Itoh, Ryo

doi:10.1016/j.jue.2009.07.001

Cited by 17 publications

(5 citation statements)

References 19 publications

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“…Urbanization is undergoing rapid increases across the world, especially in some developing countries like China [1,2]. Although urbanized areas cover only a small portion of the earth's surface, they provide more advantageous conditions through better education, convenient healthcare, more job opportunities, and improved urban infrastructure than rural areas [3,4]. Urbanization is typically associated with an improved living infrastructure, such as convenient transportation networks and more commercial centers, as well as a synchronized process of spatial growth and scattering of built-up areas that used to be for agricultural purposes [5,6].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Sha

Ali

Wang

et al. 2018

IJGI

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Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.

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Section: Introductionmentioning

confidence: 99%

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Sha

Ali

Wang

et al. 2018

IJGI

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“…The depopulation of rural areas (excluding suburban areas) is observed all over the world [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data

Jing

Guo

2019

Sustainability

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Village hollowing is a growing policy problem globally, but accurately estimating housing vacancy rates is difficult and costly. In this study, we piloted the use of power consumption data to estimate the vacancy rate of rural housing. To illustrate the method used, we took power consumption data in 2014 and 2017 in an area of rural China to analyze the change in housing vacancies. Results indicated that the rural vacancy rates were 5.27% and 8.69%, respectively, while underutilization rates were around 10% in 2014 and 2017. Second, there was significant spatial clustering of vacant rural housing, and the hotspots were mainly distributed in western mountainous areas, whereas villages near urban areas had lower vacancy rates. Third, rural vacancies increased from 2014 to 2017. Compared with other methods, our method proved to be accurate, very cost-effective and scalable, and it can offer timely spatial and temporal information that can be used by policymakers to identify areas with significant village hollowing issues. However, there are challenges in setting the right thresholds that take into consideration regional differences. Therefore, there is also a need for more studies in different regions in order to scale up this method to the national level.

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“…Silveira et al [5] built a statistical mechanics model based on probability theory and ACE methods. Itoh [6] developed a scheme for the dynamic control of urbanization. Kenivetona et al [7] endeavored to predict population migration with considering the environmental influences.…”

Section: Introductionmentioning

confidence: 99%

Agent-based model for rural–urban migration: A dynamic consideration

Cai

Khan

2015

Physica A: Statistical Mechanics and its Applications

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This paper develops a dynamic agent-based model for rural-urban migration, based on the previous relevant works. The model conforms to the typical dynamic linear multi-agent systems model concerned extensively in systems science, in which the communication network is formulated as a digraph. Simulations reveal that consensus of certain variable could be harmful to the overall stability and should be avoided.

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Dynamic control of rural–urban migration

Cited by 17 publications

References 19 publications

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data

Agent-based model for rural–urban migration: A dynamic consideration

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