# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 3 Methodology  
### Study Area and Data  
The study area is defined as central Nanjing city, encompassing six administrative districts, namely the Xuanwu, Qinhuai, Jianye, Gulou, Yuhuatai, and Qixia districts. These districts collectively cover an area of 787.45 km\({}^{2}\). Each district holds unique significance within the city. Xuanwu district serves as a hub for science, education, and tourism. The Qinhuai district stands out as a historical and cultural center. The Jianye district represents a newly developed international region. The Gulou district serves as the economic, cultural, educational, and administrative core. The Yuhuatai district has witnessed significant growth in high-tech industries and serves as a demonstration zone for a renowned software city. The Qixia district acts as a significant shipping logistics center. Hence, this study area effectively captures the essence of urban life in Nanjing. To account for the modifiable areal unit problem (MAUP), we divide the study area into homogenous square grids measuring 500 m \(\times\) 500 m, rather than relying on traffic analysis zones or administrative units. This grid size aligns with past research conducted on Chinese metropolises (2018, 2019, 2020). In total, our analysis utilizes 1555 grids, as depicted in Figure 1.  
The endogenous variable is the number of ride-hailing pick-ups in each grid. To better understand the link between built environment variables and ride-hailing ridership, the  
Figure 1: Study area. impacts of the corresponding taxi ridership are considered in this study. The ride-hailing and taxi order data are obtained from the regulatory platform of the Nanjing Transport Bureau. In order to focus on the impact of the built environment on ride-hailing ridership on weekdays, avoid holiday interference, and consider that ride-hailing ridership is cyclical, the data of ride-hailing and taxi orders data from April 11 th (Monday) to April 15 th (Friday) 2022 are selected for research. In the dataset, each record contains some key information, including order ID, vehicle ID, times, longitudes, and latitudes of pick-ups and pick-offs (Table 1). After removing the records beyond the study area and those with order times less than 2 min or more than 2 h, 835,370 ride-hailing records and 195,691 taxi records are used in this study. The spatial and temporal distributions of ride-hailing and taxi ridership are visualized in Figure 2. From a spatial standpoint, both ride-hailing and taxi ridership exhibit higher concentrations in urban central areas, gradually decreasing as one moves away from these central regions. Regarding the temporal aspect, ride-hailing and taxi ridership demonstrate similar patterns during workdays. Generally, two peaks can be observed at approximately 8:00 and 18:00, respectively. Consequently, to gain a deeper understanding of the spatiotemporal variations in the relationships, this analysis focuses on two peak periods (7:00-9:00 and 17:00-19:00) as well as an off-peak period (11:00-13:00) on weekdays. As shown in Table 2, the total number of ride-hailing and taxi trips is computed for each grid within these three periods over the course of one week. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Description** & **Mean** & **S.D.** \\ \hline \multirow{2}{*}{Ride-hailing ridership at morning peak} & Number of ride-hailing pick-ups in each grid at & 86.33 & 110.177 \\  & weekday morning peak within one week & & 22.16 & 34.7 \\  & Number of taxi picking-ups in each grid at weekday & & 49.15 & 73.376 \\  & weekday off-peak within one week & & & \\  & Number of taxi pick-ups in each grid at weekday & & 11.49 & 35.73 \\  & weekday off-peak within one week & & 80.03 & 114.719 \\  & weekday evening peak within one week & & 13.8 & 33.102 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical descriptions of endogenous variables.