Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques
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 analy...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Firenze University Press
2022-03-01
|
| Series: | Aestimum |
| Subjects: | |
| Online Access: | https://oaj.fupress.net/index.php/ceset/article/view/12093 |
| _version_ | 1818308971915640832 |
|---|---|
| author | Veronica Alampi Sottini Elena Barbierato Irene Capecchi Tommaso Borghini Claudio Saragosa |
| author_facet | Veronica Alampi Sottini Elena Barbierato Irene Capecchi Tommaso Borghini Claudio Saragosa |
| author_sort | Veronica Alampi Sottini |
| collection | DOAJ |
| description | 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.
|
| first_indexed | 2024-12-13T07:22:45Z |
| format | Article |
| id | doaj.art-9aae0be1100d47c6a3a97965737a322e |
| institution | Directory Open Access Journal |
| issn | 1592-6117 1724-2118 |
| language | English |
| last_indexed | 2024-12-13T07:22:45Z |
| publishDate | 2022-03-01 |
| publisher | Firenze University Press |
| record_format | Article |
| series | Aestimum |
| spelling | doaj.art-9aae0be1100d47c6a3a97965737a322e2022-12-21T23:55:23ZengFirenze University PressAestimum1592-61171724-21182022-03-017910.36253/aestim-12093Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniquesVeronica Alampi Sottini0Elena Barbierato1Irene Capecchi2Tommaso Borghini3Claudio Saragosa4Department of Agriculture, Food, Environment and Forestry (DAGRI), University of FlorenceDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of FlorenceDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of FlorenceDepartment of Architecture (DIDA), University of FlorenceDepartment of Architecture (DIDA), University of FlorenceHuman 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. https://oaj.fupress.net/index.php/ceset/article/view/12093urban visual qualityurban indicatorsGeographically Weighted RegressionRandom ForestGoogle Street ViewFlickr |
| spellingShingle | Veronica Alampi Sottini Elena Barbierato Irene Capecchi Tommaso Borghini Claudio Saragosa Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques Aestimum urban visual quality urban indicators Geographically Weighted Regression Random Forest Google Street View Flickr |
| title | Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques |
| title_full | Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques |
| title_fullStr | Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques |
| title_full_unstemmed | Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques |
| title_short | Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques |
| title_sort | assessing the perception of urban visual quality an approach integrating big data and geostatistical techniques |
| topic | urban visual quality urban indicators Geographically Weighted Regression Random Forest Google Street View Flickr |
| url | https://oaj.fupress.net/index.php/ceset/article/view/12093 |
| work_keys_str_mv | AT veronicaalampisottini assessingtheperceptionofurbanvisualqualityanapproachintegratingbigdataandgeostatisticaltechniques AT elenabarbierato assessingtheperceptionofurbanvisualqualityanapproachintegratingbigdataandgeostatisticaltechniques AT irenecapecchi assessingtheperceptionofurbanvisualqualityanapproachintegratingbigdataandgeostatisticaltechniques AT tommasoborghini assessingtheperceptionofurbanvisualqualityanapproachintegratingbigdataandgeostatisticaltechniques AT claudiosaragosa assessingtheperceptionofurbanvisualqualityanapproachintegratingbigdataandgeostatisticaltechniques |