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Multiscale Spatial Distribution Pattern and Influencing Factors on Inland Fishing Gardens in China

Author

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  • Yong Huang

    (College of Nature Resources and Environment, Northwest A&F University, Xianyang 712100, China)

  • Qinjun Kang

    (College of Nature Resources and Environment, Northwest A&F University, Xianyang 712100, China)

  • Qi Wang

    (College of Nature Resources and Environment, Northwest A&F University, Xianyang 712100, China)

  • Lili Luo

    (College of Nature Resources and Environment, Northwest A&F University, Xianyang 712100, China)

  • Tingting Wang

    (Yellow River’s Soil and Water Conservation, Xifeng Governance Bureau, Qingyang 745000, China)

  • Qingrui Chang

    (College of Nature Resources and Environment, Northwest A&F University, Xianyang 712100, China)

Abstract

Recently, a significant number of freshwater fishing gardens have sprouted up across mainland China. These recreational facilities are an important component in promoting the upgrading of the fishing industry and rural revitalization, and they are a key component in the high-quality development of rural tourism. This paper uses fishing gardens points of interest (POI) in China as data sources and employs kernel density estimation and geographical detectors to systematically uncover the multiscale spatial distribution pattern of these gardens, as well as the factors influencing their distribution. The results show that: (1) There are 15,090 fishing gardens in inland China. The spatial distribution of Chinese fishing gardens corresponds well with the “Hu-Line”, with a greater number of gardens clustered in the southeast and few in the northwest. The density distribution exhibits a polarized pattern with multiple high-density centers. (2) The number of fishing gardens varies significantly across regions, with the eastern > central > western > northeastern; Guangdong has the most fishing gardens. The top five provinces have 43.4% of the total number of fishing gardens in the country. Large-scale fishing gardens are common in developed cities such as the Pearl River Delta, Beijing-Tianjin-Hebei, and the Yangtze River economic belt. (3) In natural environmental factors, land altitude and contour are negatively correlated with the distribution of fishing gardens, whereas winter temperature is positively correlated with the distribution. More than 50% of fishing gardens are located within 6 km of urban built-up areas. (4) GDP, population, and tourism revenue are the most important social development factors influencing the distribution of fishing gardens. The moderate factors are per capita income and the rate of urbanization; the weak factors are fishery output value and freshwater products production. In the discussion, suggestions on how to guide the rational layout and healthy development of the fishing garden industry in the region are put forward. We believe that these suggestions could be part of the pursuit to improve the fishing garden industrial policy in China.

Suggested Citation

  • Yong Huang & Qinjun Kang & Qi Wang & Lili Luo & Tingting Wang & Qingrui Chang, 2022. "Multiscale Spatial Distribution Pattern and Influencing Factors on Inland Fishing Gardens in China," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6542-:d:825430
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    References listed on IDEAS

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    1. John Rolfe & Prabha Prayaga, 2007. "Estimating values for recreational fishing at freshwater dams in Queensland ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(2), pages 157-174, June.
    2. Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    3. Anton Paulrud & Thomas Laitila, 2013. "A cost-benefit analysis of restoring the Em River in Sweden: valuation of angling site characteristics and visitation frequency," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2255-2266, June.
    4. Christos Polykretis & Manolis G. Grillakis & Athanasios V. Argyriou & Nikos Papadopoulos & Dimitrios D. Alexakis, 2021. "Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece," Land, MDPI, vol. 10(9), pages 1-23, September.
    5. Rolfe, John & Prayaga, Prabha, 2007. "Estimating values for recreational fishing at freshwater dams in Queensland," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(2), pages 1-18.
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    2. Wenxin Wang & Xia Yang & Xia Gan & Chang Yang & Xiangwen Gong & Si Chen, 2023. "The Differences in the Impact of Economic Structure Adjustment on the Ecological Carrying Capacity of County Education—A Case Study of Chongqing, China," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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