Multiscale administrative representation of population density at sub-municipal level in Spain, the Galician case

• Autor(a) principal: Nobajas, Alex
• Data de Publicação: 2022
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.18055/Finis26947
• topic: Artigos

The disparity between total population values and how that population is spread across an area can generate issues of perception, as though choropleth maps are usually used to represent population, its density may not be equally spread across the territory. A prime example of this situation is Galicia in NW Spain, as it has the highest number of population settlements of the country with almost 50% of the total, and yet at the same time it has only around 4% of municipalities, which means that each local authority has on average dozens of borderless settlements within its limits. However, population maps of the region are normally produced at the municipal level, providing a poor representation of the demographic complexity of the area. Consequently, this paper provides an alternative way of displaying population density by using three sub-municipal division levels obtained from official sources: census tracts, parishes and settlements. While census tracts and parishes have defined limits, settlements do not, so they are normally mapped by using punctual symbology. It is therefore necessary to generate some borders for those population entities which do not have them, as it can help understand how the population is distributed in more detail. In order to provide a suitable means of comparison with the other administrative divisions this paper presents a method to convert settlement data into polygons that includes the use of Voronoi tessellation, producing for the first time the most detailed population density choropleth map of the region at settlement level. Furthermore, the method can be applied anywhere in Spain and with non-census data.