Characteristics and Influencing Factors of Spatial Differentiation of Market Service Industries in Rural Areas around Metropolises—A Case Study of Wuhan City’s New Urban Districts

Chen, Jie; Jiang, Liang; Luo, Jiancheng; Tian, Li; Tian, Ye; Chen, Guolei

doi:10.3390/ijgi11030170

Cited by 11 publications

(8 citation statements)

References 40 publications

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“…3) statistics of entrepreneurial service information and optimization of the entrepreneurial environment, 4) establishment of multisectoral and multifunctional collaboration mechanisms to facilitate more effective pooling of resources by broadening the range of actors, and 5) a talent pool to enhance the minds of farmers to start businesses back home. (6) We should build high-quality resources for rural, human resource security, market supervision, and other departments; create a cloud platform for migrant workers to return home and start businesses; improve information exchange between departments; promote the effective integration of all departments' resources; and provide timely feedback to relevant subjects. We will work together to establish an agricultural and industrial enterprise base, mobilize resources for projects in the agricultural and industrial complex's location, and promote business environment structure optimization.…”

Section: Discussionmentioning

confidence: 99%

“…The method of determining the number and weight of a single prediction model is also proposed [ 5 ]. The combined prediction model based on the difference algorithm is compared to the prediction model composed of the first prediction algorithm with the most effective prediction effect [ 6 ] by studying the difference algorithm and using it to select subprojects. The experimental results show that the model offset algorithm improves the combined model's prediction accuracy and approximate weight distribution.…”

Section: Research Backgroundmentioning

confidence: 99%

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Research on the Linkage Mechanism between Migrant Workers Returning Home to Start Businesses and Rural Industry Revitalization Based on the Combination Prediction and Dynamic Simulation Model

Wang

2022

Computational Intelligence and Neuroscience

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Township and rural migrant workers are returning to the business personnel’s main force. Party 18 proposed a rural revitalization strategy, and the central committee of the State Council issued a series of encouraging policy measures to bring them back. The study found that migrant workers’ return-home entrepreneurship and the connection between the rural industries on the basis of factor resources flow are related. As a result, this study analyzed the practical situation of migrant workers’ return-home entrepreneurship. Next, it used a combination forecast method and dynamic simulation model of the migrant workers’ return-home entrepreneurship and revitalization of the relationship with the rural industry. The resuscitation of rural industries can be efficiently facilitated by the return of rural migrant workers to launch firms. Their success can attract more migrant workers to launch businesses. The total regeneration of rural areas and the prosperity of farmers can be achieved by effectively linking the return of migrant workers to launch businesses and the revival of rural industries.

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Section: Discussionmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Research on the Linkage Mechanism between Migrant Workers Returning Home to Start Businesses and Rural Industry Revitalization Based on the Combination Prediction and Dynamic Simulation Model

Wang

2022

Computational Intelligence and Neuroscience

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“…The kernel density estimation method can reflect the spatial distribution density and aggregation characteristics of elements. The formula is as follows [33]:…”

Section: Kernel Density Estimation Methodsmentioning

confidence: 99%

Spatial Differentiation, Influencing Factors, and Development Paths of Rural Tourism Resources in Guangdong Province

Liao

Zuo

Law³

et al. 2022

Land

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Rural tourism resources are the core carriers of rural tourism. It is, therefore, beneficial to further optimize the layout of rural tourism and to explore the spatial differentiation of rural tourism resources and their influencing factors. Taking 4670 rural tourism resources in Guangdong Province in China as the research object, this study explores the spatial distribution patterns of rural tourism resources through the nearest neighbor index, grid dimension analysis, kernel density analysis, and standard deviation ellipse method. Geodetectors are used to identify the influencing factors of the spatial heterogeneity of these resources in Guangdong Province. The findings reveal the following: (1) The distribution of rural tourism resources in Guangdong Province shows a tendency of agglomeration along the Tropic of Cancer, and the spatial distribution is unbalanced. The hot and cold spots show a “northeast-southwest” distribution trend. Furthermore, most of the hotspots form three high-density core areas, the sub-dense stretch zones connect into a w-shaped belt, and the sub-cold areas and sub-hot areas show a large expansion trend, thus forming five radiation areas. (2) The distribution of rural tourism resources in Guangdong Province is affected by multiple factors. In particular, the force of agricultural resource endowment, tourism resource endowment and transportation location are relatively strong, and social economy and tourist source market are the weak factors.

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“…Further analysis of the correlation between the number of pro-poor tourism villages within the buffer zone and the distance of the buffer zone shows that the correlation coefficient between them is 0.976 and passes the test. This shows that as the buffer zone continues to expand, the more the pro-poor tourism villages within its buffer zone are distributed, and they are distributed roughly along the perimeter of the water course [38]. A good water source is conducive to improving the local climate and integrating with the surrounding environment to form a beautiful ornamental landscape and a unique rural tourism style, which becomes an important resource support for rural tourism development.…”

Section: River Systemmentioning

confidence: 99%

Spatial Distribution Characteristics and Influencing Factors of Pro-Poor Tourism Villages in China

Zhu

et al. 2022

Sustainability

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This paper aims to contribute to the effectiveness of pro-poor tourism in rural areas. We use 5770 pro-poor tourism villages in China as the research objects; the spatial distribution characteristics of pro-poor tourism villages in China are analyzed using a combination of disequilibrium index, kernel density analysis, and spatial autocorrelation; their influencing factors are detected using a geographical detector and overlay analysis. The study results show the following: (1) The distribution of pro-poor tourism villages is exceptionally uneven in three zones, eight regions, and inter-provincial levels, forming a high-density cluster belt that includes Hebei, Henan, Anhui, and Hubei, and five high-density cluster cores that include southern Gansu, Sichuan–Gansu–Shaanxi border area; Guizhou, Hunan, and Chongqing border area; southern Sichuan; and southwest Guizhou. (2) Regarding spatial correlations, the pro-poor tourism villages in central and western regions are in hot spots, while those in eastern regions are in cold spots. The hot spots gradually increase, while cold spots gradually decrease, and the clustering trend of the distribution of the pro-poor tourism villages is increasingly apparent. (3) Pro-poor tourism villages are affected by social, economic, industrial, and other human factors as well as natural geographical factors such as terrain, precipitation, river, and climate, among which the industrial factors have a more significant impact. Pro-poor tourism villages are concentrated in humid mountainous areas with an altitude of about 1000 m and an annual precipitation of more than 800 mm, and they are mostly distributed in the subtropical monsoon climate zone closer to the river and more suitable climate. (4) It is suggested that pro-poor tourism villages can be divided into four types: the resource underutilization type, mountain environment restriction type, traffic location non-optimization type, and industrial development lagging type, and the sustainable development strategies of different types of pro-poor tourism villages are proposed.

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Characteristics and Influencing Factors of Spatial Differentiation of Market Service Industries in Rural Areas around Metropolises—A Case Study of Wuhan City’s New Urban Districts

Cited by 11 publications

References 40 publications

Research on the Linkage Mechanism between Migrant Workers Returning Home to Start Businesses and Rural Industry Revitalization Based on the Combination Prediction and Dynamic Simulation Model

Research on the Linkage Mechanism between Migrant Workers Returning Home to Start Businesses and Rural Industry Revitalization Based on the Combination Prediction and Dynamic Simulation Model

Spatial Differentiation, Influencing Factors, and Development Paths of Rural Tourism Resources in Guangdong Province

Spatial Distribution Characteristics and Influencing Factors of Pro-Poor Tourism Villages in China

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