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<p>In this study, we propose – likely for the first time in this context – a novel operational approach using Zipf's Law to distinguish between urban, rural, and intermediate areas in a given country. Traditionally, the classification of these areas has relied on demographic criteria or predefined socioeconomic thresholds, which are often not always adaptable to specific territorial dynamics. Our approach, instead, analyzes population size and spatial distribution in a given ensemble of territorial units, allowing for a more objective and accurate land classification. By testing two complementary methodologies across three different spatial scales (333, 1037, and 6126 spatial units) in Greece, we found that the intermediate scale (1037 municipalities) best captures economies of scale and agglomeration, providing a more precise representation of urban dynamics and rural districts. Our findings offer an innovative tool for policymakers, supporting more effective and informed spatial planning based on dynamic models rather than more subjective (threshold) criteria. Additionally, this approach enables a clearer identification of transitional areas between urban and rural regions (e.g. peri-urban districts), which have traditionally been difficult to classify operationally. The use of models verifying the rank-size rule for land classification purposes not only enhances the precision of territorial analysis but also provides a robust foundation for future investigations, and a new perspective for spatial data collection. Finally, the proposed method can be applied to similar contexts, expanding opportunities for improved land resource management.</p>

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An operational classification of rural and urban areas based on Zipf’s Law

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Luca Salvati,
  • Leonardo Salvatore Alaimo,
  • Alessandro Muolo

摘要

In this study, we propose – likely for the first time in this context – a novel operational approach using Zipf's Law to distinguish between urban, rural, and intermediate areas in a given country. Traditionally, the classification of these areas has relied on demographic criteria or predefined socioeconomic thresholds, which are often not always adaptable to specific territorial dynamics. Our approach, instead, analyzes population size and spatial distribution in a given ensemble of territorial units, allowing for a more objective and accurate land classification. By testing two complementary methodologies across three different spatial scales (333, 1037, and 6126 spatial units) in Greece, we found that the intermediate scale (1037 municipalities) best captures economies of scale and agglomeration, providing a more precise representation of urban dynamics and rural districts. Our findings offer an innovative tool for policymakers, supporting more effective and informed spatial planning based on dynamic models rather than more subjective (threshold) criteria. Additionally, this approach enables a clearer identification of transitional areas between urban and rural regions (e.g. peri-urban districts), which have traditionally been difficult to classify operationally. The use of models verifying the rank-size rule for land classification purposes not only enhances the precision of territorial analysis but also provides a robust foundation for future investigations, and a new perspective for spatial data collection. Finally, the proposed method can be applied to similar contexts, expanding opportunities for improved land resource management.