# Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings
## 6 Conclusions  
This study developed a prediction model to examine the influence of urban spatial characteristics on digital consumer behaviors. The model serves two primary purposes: assisting new EDO businesses in selecting optimal locations and providing existing EDObusinesses with valuable insights for situation analysis. By applying this model, new businesses can make informed location decisions based on predicted consumer engagement levels, while established businesses gain a clearer understanding of their current standing in terms of consumer interactions.  
The findings reveal that pedestrian flow and residential density are the most significant factors influencing review volumes and visitor scores, underscoring the importance of visibility and accessibility in consumer engagement. Centrality measures also played a critical role, positively impacting visitor scores but negatively influencing review volumes, suggesting that a broader network connectivity enhances the perceived satisfaction, while localized factors drive reviewing behavior. The geometric properties of streets, along with venue specific characteristics, further contribute to predicting review volumes, highlighting the relationship between urban design and digital consumer interactions.  
Academically, this study contributes to the literature by integrating dynamic pedestrian data and geometric street attributes into predictive modeling, offering a novel framework for analyzing the relationship between urban configurations and online consumer behavior. Practically, the research provides actionable insights for urban planners and business stakeholders. For instance, the findings emphasize the value of high-density pedestrian areas and well-connected locations in attracting consumer engagement. These implications can guide business owners in optimizing location selection and assist city planners in designing more consumer-friendly urban environments.  
Future research could explore methods for validating data to improve the robustness of findings. Expanding the scope of dependent variables and incorporating environmental characteristics, such as sidewalk width, greenery and lighting, would provide a more refined understanding of digital consumer behaviors. Researchers might also consider segmenting pedestrian data by demographic or user type and analyzing potential temporal variations to capture more detailed mobility patterns. Considering these suggestions, a more comprehensive framework that supports data-driven urban planning and more effective business strategies can be developed.  
Conceptualization, O.O.H.; methodology, O.O.H. and M.H.; software, O.O.H. and M.H.; validation, O.O.H., F.G. and M.H.; formal analysis, O.O.H.; investigation, O.O.H., M.H. and L.P.; resources, M.H.; data curation, M.H. and O.O.H.; writing--original draft preparation, O.O.H.; writing--review and editing, O.O.H., M.H., L.P. and F.G.; visualization, O.O.H.; supervision, F.G. and L.P.; project administration, O.O.H., L.P. and F.G.; funding acquisition, O.O.H., L.P. and F.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Scientific and Technological Research Council of Tarkiye (TUBITAK), grant number 1059B142200486.  
Not applicable.  
The authors declare no conflicts of interest.