# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 2 Literature Review
### Built Environment Effects on Ride-Hailing ridership  
As an emerging transport mode, ride-hailing ridership and its relationship to built environment variables have received increasing research attention [14; 18; 19]. However, the debate on the link between the built environment and ride-hailing ridership has not reached a consensus. Most prior studies suggest that the built environment plays an essential role in affecting ride-hailing ridership [6; 9; 11; 20]. For instance, [PERSON] and [PERSON] [20] used a structural equation model (SEM) to explore the impacts of built environment variables on ride-hailing ridership and found that population density and land use mix show positive relationships with ride-hailing ridership. Similarly, [PERSON] et al. [9] found that activity density and land use mix are positively correlated with ride-hailing ridership based on Uber order data. In contrast, [PERSON] and [PERSON] [1] found a negative correlation between land use mix and ride-hailing ridership. An essential reason for the dissonance in this theme of studies is that researchers often ignore the spatiotemporal heterogeneity in the link between the built environment and ride-hailing ridership [11]. In the previous literature, ordinary least square (OLS) regression is widely used in various fields of research [21; 22]. It is also commonly used as a global model for regression in the study of the impact of the built environment and ride-hailing ridership. They assume that the relationship between ride-hailing ridership and influencing factors is spatially constant, and cannot address spatiotemporal issues [2; 7]. To account for the spatial effects, [PERSON] and [PERSON] [23] considered the spatial error model and spatial autoregressive model to explore the effects of the built environment on ride-hailing ridership. The results showed that the increase of jobs in the retail and entertainment industries had a positive effect on ride-hailing ridership, while pedestrian infrastructure and areas far away from public transport sites had reduced the ride-hailing service. In another example, [PERSON] [4] explored the effects of neighborhood-built environment variables on Uber demand based on hierarchical linear models, identifying socioeconomic and built environment factors most associated with any changes to Uber service area size. As these models assume that the coefficients of the independent variables are constant in space, and only capture part of the spatially varying effects, GWR models are then introduced in exploring the spatially heterogeneous effects of the built environment on ride-hailing ridership [2]. For instance, [PERSON] et al. [24] used GWR models to explore the relationship between the built environment and ride-hailing ridership. The results confirm that the effects of the built environment vary across spatial locations of the study area. [PERSON] et al. [11] found that built environment variables, such as road density and population density, showed spatially heterogeneous effects on ride-hailing ridership based on GWR. The literature suggests that GWR often performs better than global models in studies examining the spatial patterns and determinants of ridership [25]; however, as a local regression model, GWR regards the spatial scale constant across space. Built environment variables may have multiscale spatial effects, and such an assumption may cause biased estimations. In order to overcome this issue, [PERSON] et al. (2019) proposed a MGWR model that can relax the limitation of fixed bandwidths and consider spatial heterogeneity and scale effects. [PERSON] et al. (2019) applied the MGWR model to examine the relationship between sociodemographic characteristics and COVID-19 incidence rates. [PERSON] et al. (2019) investigated the association between the built environment and public transportation preferences. The above research verified the superiority of the MGWR model.