Yiyi Wang

Anticipation of Land Use Change Through Use of Geographically Weighted Regression Models for Discrete Response

Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. The application of GWR to discrete-response data sets and land use change at the level of urban parcels has remained a novelty, however. This paper describes work that combined logit specifications with GWR techniques to anticipate five categories of land use change in Austin, Texas, and controlled for parcel geometry, slope, regional accessibility, local population density, and distances to Austins downtown and various roadway types. Results of this multinomial logit GWR model suggested spatial variations in—and significant influence of—these covariates, especially roadway vicinity and regional access. A 1% increase in the distance of an undeveloped parcel to the nearest freeway, for example, was estimated, on average, to increase the probability of residential development by 1.2%, while the same increase in distance to a major arterial was estimated to increase the probability by 1.8%. Conversely, proximity to roads (through reductions in such distances) was estimated to boost the likelihood of nonresidential development (e.g., 9.0% in the case of commercial development in response to a 1% decrease in distance to such arterials). The logsum accessibility index was estimated to exert an average positive influence on commercial, office, and industrial development tendencies, while it dampened land use transitions from an undeveloped state to residential uses. Comparisons of results with a spatial autoregressive binary probit (with the use of all developed land use categories as a single response) and GWR binary probit also provided some insights. The latter seemed to surpass the former in its account for spatial effects, as reflected by a lower Akaike information criterion value.

HYBRID ELECTRIC VEHICLE OWNERSHIP AND FUEL ECONOMY ACROSS TEXAS: AN APPLICATION OF SPATIAL MODELS

Policymakers, transport planners, automobile manufacturers, and others are interested in the factors that affect adoption rates of electric vehicles and other fuel-efficient vehicles. With tract-level data from the 2010 census and registered vehicle counts from Texas counties in 2010, this study investigated the impact of various built environment and demographic attributes, including land use balance, employment density, population density, median age, gender, race, education, household size, and income. Spatial autocorrelation (across census tracts) in unobserved components of vehicle counts by tract and cross-response correlation (both spatial and local–aspatial in nature) was allowed for by the estimation of models of ownership levels (vehicle counts by vehicle type and fuel economy level) with bivariate and trivariate Poisson–lognormal conditional autoregressive models. The presence of high spatial autocorrelations and local cross-response correlations was consistent in all models across all counties studied. Ownership rates for fuel-efficient vehicles were found to rise with household income, resident education levels, and the share of male residents and to fall in the presence of larger household sizes and higher job densities. The average fuel economy of each tracts light-duty vehicles was also analyzed with a spatial error model across all Texas tracts, and this variable was found to depend most on educational attainment levels, median age, income, and household size variables, though all covariates used were statistically significant. If households registering more fuel-efficient vehicles, including hybrid electric vehicles, are also more inclined to purchase plug-in electric vehicles, these findings can assist in spatial planning of charging infrastructure as well as other calculations (such as implications for the revenue from gas tax).

A Synthesis of Spatial Models for Multivariate Count Responses

This chapter provides a synthesis of spatial data mining models for analyzing multivariate count responses. Geo-referenced multivariate count responses are common in regional science (e.g., registered vehicle counts by body type and firm/job counts by industry type), but are computationally difficult to model—especially when sample size is large. This chapter synthesizes relevant research and offers lessons learned and best practices for future research.

Traffic Safety Along Tourist Routes in Rural Areas

Little is known about the safety of tourist drivers in the United States. Most domestic studies have focused on traffic deaths and injuries of U.S. citizens traveling abroad and cite factors such as driving on the left, lack of seat belt use, and alcohol consumption. U.S. states that have a number of tourist attractions and the roadways to reach them may be interested in whether traffic safety is problematic for drivers who are tourists. To that end, this research investigated the contributing factors for crash severity and crash likelihood of visiting drivers in or near three national parks in rural areas. Driver-level data from the Rocky Mountain National Park in Colorado and the Sequoia and Kings Canyon National Parks in California revealed risk factors for crash severity, including age, geometry, and seat belt use. The second data set offered a more microscopic view at the road level and was used to anticipate crash frequency of visiting drivers at the road link level. Moreover, the second data set contained road geometry, traffic volume, environment, and crash counts aggregated at the segment level along a 57.8-mi stretch of U.S. Hwy 89 (a primary route to the north gate of Yellowstone National Park) in Montana that is frequently used by tourists. Crash risk factors (e.g., geometry and traffic intensity) affected local and nonlocal (tourist) drivers in different ways. Further exploration of crash trends in specific parks would be valuable in understanding the overall trends and contributors to crashes in tourism areas and to determine effective improvement measures.

A Multivariate Spatial Analysis for Anticipating New Firm Counts

Abstract This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

Estimation of Seasonal Daily Traffic Flow of Agricultural Products and Implications for Implementation of Automatic Traffic Recorders

Reliable traffic counts on a highway system are critical for sound decision making about the maintenance, operation, and expansion of the system. Portable short-term automatic traffic recorders (ATRs) are a cost-efficient way to complement traffic counts from permanent ATR sites by performing temporary traffic counts on the highway system. Complicating the collection of traffic data with these short-term devices is the seasonal variation in vehicle operations throughout the year. This work focused on predicting the spatial distribution of seasonal traffic resulting from agricultural activities by using a new method that combines geographic information system spatial functions and the four-step travel demand model. This research collected information about township grids for Montana (as proxies for trip origins), grain elevators (trip destinations), agricultural ground cover, and crop yield estimates to estimate flows in tonnage at the grid level on the road network. Results suggest that the proposed method using the location of major crops and the locations of grain elevators can be used to predict tonnage of product that will be added to individual routes. The predicted values can then be compared with reported heavy-truck traffic to locate sites that may have underrepresented traffic flows. Although this work considered specifically three crops, the method can be applied to any resource flow that has known origin and destination information. The method can be enhanced by refining assumptions of the composition of heavy trucks transporting agricultural products and by field measurements of vehicle flows to better test the validity of the model.

Statistical Cause Analysis and Potential Countermeasures for Commercial Motor Vehicle Collisions in China

The rate of incidents involving commercial vehicles is a particular concern in China, accounting for an estimated 35.8% of all crashes in 2004 and over 30% in 2005. Commercial motor vehicle safety issues in China were studied by collecting data from Jingjintang freeway, which serves as the primary high-speed link between the cities of Beijing and Tianjin, China. For this study, heavy vehicles refer to vehicles with a gross weight of at least 20,000 ponds or vehicles designed to transport at least 40 passengers. This vehicle type is a surrogate for most Commercial Vehicles. Mid-sized vehicles are those the with a gross weight between 10,000 to 20,000 ponds, or vehicles designed to transport 20 to 39 passengers. Light/compact-sized vehicles are those smaller than the mid-size. Commercial vehicles are over-represented in collision involvement, and crashes that involve commercial vehicles have a higher severity level. Data collected from a survey of commercial vehicles and drivers was analyzed for statistical relationships between collision probability and causes related to driver behavior, driver training, and vehicle mechanical condition. Higher crash risk corresponds to lower driver experience, increased driver tendency to load a vehicle in excess of design capacity, operation of older or defective vehicles, and driver tendency to travel at excessive speeds. Based on the known factors influencing commercial vehicle crash probabilities, a number of potential safety countermeasures are suggested for further study in China.