Open AccessAssessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques
Author(s) -
Veronica Alampi Sottini,
Elena Barbierato,
Irene Capecchi,
Tommaso Borghini,
Claudio Saragosa
Publication year - 2022
Publication title -
aestimum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.209
H-Index - 9
eISSN - 1724-2118
pISSN - 1592-6117
DOI - 10.36253/aestim-12093
Subject(s) - quality (philosophy) , perception , urban planning , data science , big data , work (physics) , urban design , computer science , environmental planning , geography , environmental resource management , data mining , environmental science , civil engineering , psychology , engineering , mechanical engineering , philosophy , epistemology , neuroscience
Human well-being is affected by the design quality of the city in which they live and walk. This depends primarily on specific physical characteristics and how they are aggregated together. Many studies have highlighted the great potential of photographic data shared on the Flickr platform for analyzing environmental perceptions in landscape and urban planning. Other researchers have used panoramic images from the Google Street View (GSV) web service to extract data on urban quality. However, at the urban level, there are no studies correlating quality perceptions detected by social media platforms with spatial geographic characteristics through geostatistical models. This work proposes the analysis of urban quality in different areas of the Livorno city through a methodological approach based on Geographical Random Forest regression. The result offers important insights into the physical characteristics of a street environment that contribute to the more abstract qualities of urban design.