Imbricating Socioeconomic and Environmental Data to Model Spatial Patterns of Peri-Urbanisation and Their Consequences

Dear Colleagues,

The complex process of urban growth is regarded as a spatial diffusion that implies socioeconomic and environmental changes caused by the interaction of many factors that may be simultaneously analysed given the high availability of quantitative tools. It contributes to climate change and pollution, among other issues, on local, regional, continental, and global scales. Therefore, promoting sustainable urbanization, limiting natural land consumption, and ensuring the well-being of populations have become policy targets for urban and landscape planners.

The spatial structure of urban areas and their growth patterns determine how the physical, socio-economic, and environmental characteristics change over time. These interrelationships play a major role in the daily life of urban dwellers and leads decision makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding of the relationships between the spatial structure of urban areas and their socio-economic performance is of crucial relevance.

With the urban expansion based on dispersed settlement away from central urban areas, the traditional clear-cut division between urban and rural land has lost relevance, producing fragmented landscapes and transition zones between the city and the countryside with a mix of heterogeneous land uses, appearing as peri-urban expansion. Monitoring, quantifying, and characterizing the development of peri-urban areas enriches our understanding of past and present trends, provides evidence-based information, and supports decision-making processes, which allows us to anticipate unsustainable patterns and their potential consequences.

Therefore, this Special Issue focuses on the development of advanced quantitative processes to model the spatial patterns of peri-urban growth and their spatio-temporal consequences, imbricating socioeconomic and environmental data and including Earth observation (EO) and Geographic Information Systems (GIS) techniques.

Dr. Pere Serra
Dr. Albert Llausàs
Guest Editors

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