  The burst of demand for TNCs has significantly changed the transportation
landscape and dramatically disrupted the Vehicle for Hire (VFH) market that
used to be dominated by taxicabs for many years. Since first being introduced
by Uber in 2009, ridesourcing companies have rapidly penetrated the market.
This paper aims to investigate temporal and spatial patterns in taxi and TNC
usage based on data at the taxi zone level in New York City. Specifically, we
fit suitable time series models to estimate the temporal patterns. Next, we
filter out the temporal effects and investigate spatial dependence in the
residuals using global and local Moran's I statistics. We discuss the
relationship between the spatial correlations and the demographic and land use
effects at the taxi zone level. Estimating and removing these effects via a
multiple linear regression (MLR) model and recomputing the Moran's I statistics
on the resulting residuals enables us to investigate spatial dependence after
accounting for these effects. Our analysis indicates interesting patterns in
spatial correlations between taxi zones in NYC and over time, indicating that
predictive modeling of ridesourcing usage must incorporate both temporal and
spatial dependence.
