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Detecting village‐level regional development differences: A GIS and HLM method

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  • Yanhui Wang
  • Chenxia Liang
  • Jiacun Li

Abstract

To respond to the problems that the previous research mainly targeted the poverty at larger scale and ignored individual effect or contextual effect during exploring poverty contributing factors, we attempt to use spatial cluster analysis and multilevel linear regression model to target the poverty at village level from the perspective of spatial poverty, so as to identify where the poor villages are, and why they are so poor, thereby targeting poverty interventions. Specially, we adopt four types of spatial cluster indices to detect the spatial aggregation distribution of villages, and design HLM model to examine the poverty contributing factors from both village level and county level. The case study from Wuling contiguous destitute area show that: (1) The overall distribution shows a spatial pattern of large scattered but small concentration, scatters‐polar core‐axis‐clump coexisted. (2) Poverty contributing factors at village level from high to low are: per cultivated area, safe drinking water access ratio, terrain type, suffered frequency of natural disasters, road access ratio, and distance from the nearest town’s bazaar. The contribution degree of county‐level factors to the villages’ poverty from high to low are: second gross enrollment ratio, per capita GDP. (3) 45.1% of the difference among the villages’ poverty degrees comes from the development differences among poverty‐stricken villages themselves, and 54.9% from that among counties they belong to. Contributing factors at village level account for 61.4% of the variation of village‐level independent variables, and factors at county level contributed to 65.3% of the variation of county‐level independent variables.

Suggested Citation

  • Yanhui Wang & Chenxia Liang & Jiacun Li, 2019. "Detecting village‐level regional development differences: A GIS and HLM method," Growth and Change, Wiley Blackwell, vol. 50(1), pages 222-246, March.
  • Handle: RePEc:bla:growch:v:50:y:2019:i:1:p:222-246
    DOI: 10.1111/grow.12275
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    Cited by:

    1. Yizhen Zhang & Zhen Deng & Agus Supriyadi & Rui Song & Tao Wang, 2022. "Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1694-1715, December.
    2. Yuewen Jiang & Chong Huang & Duoduo Yin & Chenxia Liang & Yanhui Wang, 2020. "Constructing HLM to examine multi-level poverty-contributing factors of farmer households: Why and how?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    3. Mei Zhang & Jia Tang & Jun Gao, 2023. "Examining the Effects of Built Environments and Individual Characteristics on Commuting Time under Spatial Heterogeneity: An Empirical Study in China Using HLM," Land, MDPI, vol. 12(8), pages 1-20, August.
    4. Yuewen Jiang & Yanhui Wang & Wenping Qi & Benhe Cai & Chong Huang & Chenxia Liang, 2022. "Detecting Multilevel Poverty-Causing Factors of Farmer Households in Fugong County: A Hierarchical Spatial–Temporal Regressive Model," Agriculture, MDPI, vol. 12(11), pages 1-21, November.
    5. Venera Timiryanova & Dina Krasnoselskaya & Natalia Kuzminykh, 2022. "Applying the Multilevel Approach in Estimation of Income Population Differences," Stats, MDPI, vol. 6(1), pages 1-32, December.
    6. Yanhui Wang & Wenping Qi, 2021. "Multidimensional spatiotemporal evolution detection on China’s rural poverty alleviation," Journal of Geographical Systems, Springer, vol. 23(1), pages 63-96, January.
    7. Venera M. Timiryanova & Konstantin E. Grishin & Natalya Z. Solodilova & Rustam I. Malikov, 2022. "Economic Growth of Municipalities in Russia: Assessment of Unevenness in Time and Space," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 21(3), pages 514-544.

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