Urban development has acquired an important magnitude in touristic places in Greece. Many villages, especially in seaside areas have adapted to touristic requirements by the necessary infrastructures and activities. P...Urban development has acquired an important magnitude in touristic places in Greece. Many villages, especially in seaside areas have adapted to touristic requirements by the necessary infrastructures and activities. Pogonia, located in Vonitsa Etoloakarnanias, is a village which has welcomed the opportunity of touristic development. As a result, the house settlements increased 57.5% during the last 8 years. Urban growth modelling using Artificial Neural Networks (ANNs) was applied in order to simulate the urban development in Pogonia village using two methods: determinism and stochasticity. The variables used for deterministic simulation were: distances to roads, urban areas and coastline, slope and elevation. It was found that urban development can be better described using the network of distances between all urban settlements (stochastic approach) rather than using determinism. This can be explained by the importance of the neighbourhood relationships and the interaction between urban settlements, occurred within the interconnected network of the self-organized urban system.展开更多
Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban gr...Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.展开更多
文摘Urban development has acquired an important magnitude in touristic places in Greece. Many villages, especially in seaside areas have adapted to touristic requirements by the necessary infrastructures and activities. Pogonia, located in Vonitsa Etoloakarnanias, is a village which has welcomed the opportunity of touristic development. As a result, the house settlements increased 57.5% during the last 8 years. Urban growth modelling using Artificial Neural Networks (ANNs) was applied in order to simulate the urban development in Pogonia village using two methods: determinism and stochasticity. The variables used for deterministic simulation were: distances to roads, urban areas and coastline, slope and elevation. It was found that urban development can be better described using the network of distances between all urban settlements (stochastic approach) rather than using determinism. This can be explained by the importance of the neighbourhood relationships and the interaction between urban settlements, occurred within the interconnected network of the self-organized urban system.
文摘Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.
