Application of the Dynamic Spatial Ordered Probit Model : Patterns of Ozone Concentration in Austin ,

While a wide variety of transportation data sets involve discrete values scattered across space and time, few techniques presently exist to properly analyze such data. A new dynamic spatial ordered probit model (DSOP) is described here, and its use is demonstrated for a case of ozone concentration categories. Using outputs of photochemical models for the Austin, Texas region over a 24-hour period, the model parameters were estimated using Bayesian techniques, and results illuminate key relationships, many of which are intuitive but generally obscured by complex upstream model systems. Relying on 132 4 km x 4 km surface grid cells as observational units, values are found to exhibit strong patterns of temporal autocorrelation, but appear strikingly random in a spatial context (after controlling for local land cover, transportation, and temperature conditions). While transportation and land cover conditions appear to influence ozone levels, their effects are not as instantaneous, nor as practically significant as the impact of temperature. The DSOP model proposed here is able to accommodate the unusual dynamics and spatial evolution of ordered response categories inherent in the ozone data.