Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings
In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in sel...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/931 |
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| author | Özge Öztürk Hacar Müslüm Hacar Fatih Gülgen Luca Pappalardo |
| author_facet | Özge Öztürk Hacar Müslüm Hacar Fatih Gülgen Luca Pappalardo |
| author_sort | Özge Öztürk Hacar |
| collection | DOAJ |
| description | In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making. |
| format | Article |
| id | doaj-art-fb54e277833047dabf7442d13e8dd0f7 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fb54e277833047dabf7442d13e8dd0f72025-01-24T13:21:22ZengMDPI AGApplied Sciences2076-34172025-01-0115293110.3390/app15020931Where We Rate: The Impact of Urban Characteristics on Digital Reviews and RatingsÖzge Öztürk Hacar0Müslüm Hacar1Fatih Gülgen2Luca Pappalardo3Department of Geomatic Engineering, Yildiz Technical University, 34220 Istanbul, TürkiyeDepartment of Geomatic Engineering, Yildiz Technical University, 34220 Istanbul, TürkiyeDepartment of Geomatic Engineering, Yildiz Technical University, 34220 Istanbul, TürkiyeKnowledge Discovery and Data Mining Laboratory, ISTI-CNR, 56124 Pisa, ItalyIn urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making.https://www.mdpi.com/2076-3417/15/2/931digital footprintsurban dynamicspedestrian densitycentrality metricsspatial analysisurban street networks |
| spellingShingle | Özge Öztürk Hacar Müslüm Hacar Fatih Gülgen Luca Pappalardo Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings Applied Sciences digital footprints urban dynamics pedestrian density centrality metrics spatial analysis urban street networks |
| title | Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings |
| title_full | Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings |
| title_fullStr | Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings |
| title_full_unstemmed | Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings |
| title_short | Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings |
| title_sort | where we rate the impact of urban characteristics on digital reviews and ratings |
| topic | digital footprints urban dynamics pedestrian density centrality metrics spatial analysis urban street networks |
| url | https://www.mdpi.com/2076-3417/15/2/931 |
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