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Mapping Population Dynamics at Local Scales Using Spatial Networks

Author

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  • José Balsa-Barreiro
  • Alfredo J. Morales
  • Rubén C. Lois-González
  • Ãtila Bueno

Abstract

Nowadays, around half of the global population lives in urban areas. This rate is expected to increase up to two-thirds by the year 2050. Most studies analyze urban dynamics in wide geographic ranges, focusing mainly on cities. According to them, the global population is spatially distributed (and polarized) in two extremes: large urban agglomerations and rural deserts. However, this remark is excessively general and imprecise. For this reason, it remains essential to analyze these dynamics at other spatial scales. A close-up look in thinly populated regions shows how urban dynamics are also noticeable. In this paper, we analyze spatiotemporal patterns of population distribution in a predominantly rural area by applying a local-scale approach. These patterns are represented by using spatial networks with nodes representing the human settlements and links showing hierarchies between nodes. This case study is conducted in a small municipality located in northwestern Spain. It is a predominantly rural area with a very particular spatial pattern of population distribution.

Suggested Citation

  • José Balsa-Barreiro & Alfredo J. Morales & Rubén C. Lois-González & Ãtila Bueno, 2021. "Mapping Population Dynamics at Local Scales Using Spatial Networks," Complexity, Hindawi, vol. 2021, pages 1-14, May.
    • Handle: RePEc:hin:complx:8632086
    DOI: 10.1155/2021/8632086
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    Cited by:

    1. Mei Sang & Jing Jiang & Xin Huang & Feifei Zhu & Qian Wang, 2024. "Spatial and temporal changes in population distribution and population projection at county level in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    2. Jingya Tang & Lichun Sui, 2022. "Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    3. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Yuri Cherkassov, 2023. "Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks," Mathematics, MDPI, vol. 11(5), pages 1-23, February.
    4. Carballosa, Alejandro & Balsa-Barreiro, José & Boullosa, Pablo & Garea, Adrián & Mira, Jorge & Miramontes, Ángel & Muñuzuri, Alberto P., 2022. "Assessing the risk of pandemic outbreaks across municipalities with mathematical descriptors based on age and mobility restrictions," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Morteza Maleki & Mohsen Bahrami & Monica Menendez & Jose Balsa-Barreiro, 2022. "Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States," IJERPH, MDPI, vol. 19(23), pages 1-26, November.
    6. Lifeng Tan & Huanjie Liu & Jiayi Liu & Jiayin Zhou & Pengfei Zhao & Yukun Zhang & Shuaishuai Zhao & Shenge Shen & Tong Li & Yinggang Wang & Linping Yang, 2022. "Influence of Environmental Factors on the Site Selection and Layout of Ancient Military Towns (Zhejiang Region)," Sustainability, MDPI, vol. 14(5), pages 1-20, February.

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