Abstract
Abstract. Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, and specifically by region of origin, for the capital region of Copenhagen, Denmark, by combining various socio-demographic and topographic input layers.
Highlights
Cities have been expanding rapidly for the last decades, with migration playing a determining role in their development and shaping their image
Cities expand rapidly with international migration significantly contributing to urban growth and urban population change
The Leaky Rectified Linear Unit (Leaky ReLU) is used as activation function to receive back only the positive values and the Mean Absolute Error (MAE) as the cost function for the evaluation of the projected data by comparing the model’s spatial projections to the projected regional population of the demographic group in question
Summary
Cities have been expanding rapidly for the last decades, with migration playing a determining role in their development and shaping their image. Various local features determine population distribution and these variations in density of cultural expressions implies that some areas are more attractive for immigrants. This fact generates substantial questions around the residential choices of immigrants, the discernible spatial characteristics that affect their choices, and most importantly about their future distribution in the urban fabric. Nowadays, increased interest around these research issues is expressed especially due to the rising inequalities, socio-spatial segregation and the continuous risk of ghettoization that cities suffer. Issues arise due to the lack of established methods and tools that could utilize the existing knowledge on the determinants of migrant settlement in order to help cities assess where future migrants are likely to set up home
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