The primary objective of this study was to conduct a spatial analysis for the usage of ride-sourcing services. This study collected the ride-sourcing trip data from Uber in New York City, as well as the road network attributes and the socio-demographic characteristics. The collected data were further aggregated into 167 ZIP code tabulation areas (ZCTA). The geographically weighted regression models were developed to establish the relationships between ride-sourcing usage and various contributing factors on weekday and weekend, respectively. The results suggested that the number of population with bachelors' degree or higher, the number of parking spaces, the ratio of floor area allocated for commercial use, and the subway accessibility in each ZCTA were positively correlated with the Uber usage. The number of unemployment population and the median household income in each ZCTA were found to be negatively correlated with the Uber usage. The results of the present study could provide useful insights for predicting the demand of zonal-level ride-sourcing services and developing effective policy initiatives for pricing and regulation of ride-sourcing industry.
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