# Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings
## 2 Background of the Study
[34] proposed a machine learning model to predict app ratings based on user reviews, addressing challenges of biased feedback in user-generated content. Digital footprints also reveal a great deal about cultural and demographic diversity. [PERSON] et al. [35] analyzed Google reviews in Dublin to map cultural diversity, demonstrating the potential of user-generated data in cultural studies. The importance of such data in urban research is further emphasized by studies such as that of [PERSON] et al. [36], which highlighted the relationship between economic activity areas and location-based social network data. The ongoing research continues to explore these relationships, offering new insights into the spatial dimensions of visitor experiences [11; 32; 37]. Moreover, the influence of Google reviews on behavioral patterns has been widely documented in the literature [38; 39; 40; 41; 42; 43]. However, since these studies have primarily considered individuals as consumers, they focused only on the characteristics of the product itself or the business from which it is obtained. Digital interactions, such as reviews and ratings on platforms like Google Maps, offer valuable insights into how urban environments influence consumer decisions. As [PERSON] et al. [44] indicated, the understanding and management of digital footprints are critical for both individuals and organizations in Melbourne. While the existing research has extensively explored the effect of product and business attributes on consumer decisions, the spatial context has often been overlooked. For instance, studies focusing on centrality metrics or pedestrian dynamics tend to emphasize static spatial characteristics rather than the dynamic interplay between urban geometry and consumer behavior. Furthermore, the use of Google reviews in prior research has primarily been centered on consumer perspectives, without adequately considering how the spatial configuration of the urban environment can shape digital footprints. This gap underscores the need for more integrative approaches that account for both micro- (e.g., street geometry and venue proximity) and macro-level (e.g., network centrality and pedestrian flow) spatial factors. Although numerous studies have investigated the effects of online reviews and ratings on consumer decisions, most of this research predominantly focuses on product or business attributes (e.g., pricing, quality and brand reputation), with limited consideration for the spatial characteristics of the urban environment. Indeed, the existing literature often treats location merely as a static factor rather than a dynamic urban component that can shape user behaviors, movement patterns, and ultimately, online engagement. By explicitly examining how varying street configurations, pedestrian densities and geometric street features influence review volumes and rating tendencies, this study addresses a critical gap in understanding the spatial drivers of digital consumer behavior. Moreover, most of the studies examining Google reviews and ratings, as well as user evaluations on social media platforms, have a significant gap in understanding the influence of spatial characteristics of the environment on user behavior. This study aims to address this gap by investigating how the spatial features of urban environments impact user-generated reviews and ratings, thereby examining the influence of these reviews and ratings on behaviors within a spatial context. This study addresses these limitations by integrating dynamic pedestrian data, detailed geometric street attributes and urban network metrics into a unified model. By explicitly examining the significance of urban spatial characteristics on digital consumer behaviors, this research provides a novel contribution to the field, bridging the gap between urban planning and consumer analytics.