5 research outputs found
Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution
There is growing evidence in the epidemiologic literature of the relationship
between air pollution and adverse health outcomes. Prediction of individual air
pollution exposure in the Environmental Protection Agency (EPA) funded
Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study
relies on a flexible spatio-temporal prediction model that integrates land-use
regression with kriging to account for spatial dependence in pollutant
concentrations. Temporal variability is captured using temporal trends
estimated via modified singular value decomposition and temporally varying
spatial residuals. This model utilizes monitoring data from existing regulatory
networks and supplementary MESA Air monitoring data to predict concentrations
for individual cohort members. In general, spatio-temporal models are limited
in their efficacy for large data sets due to computational intractability. We
develop reduced-rank versions of the MESA Air spatio-temporal model. To do so,
we apply low-rank kriging to account for spatial variation in the mean process
and discuss the limitations of this approach. As an alternative, we represent
spatial variation using thin plate regression splines. We compare the
performance of the outlined models using EPA and MESA Air monitoring data for
predicting concentrations of oxides of nitrogen (NO)-a pollutant of primary
interest in MESA Air-in the Los Angeles metropolitan area via cross-validated
. Our findings suggest that use of reduced-rank models can improve
computational efficiency in certain cases. Low-rank kriging and thin plate
regression splines were competitive across the formulations considered,
although TPRS appeared to be more robust in some settings.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS786 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software
This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.
Recommended from our members
The spatial impact of employment centres on housing markets
The spatial impact of employment centres on housing markets. Spatial Economic Analysis. Local economic growth tends to affect neighbourhood house prices unevenly. It has been observed that prime locations experience price hikes far in excess of the surrounding local area. Yet, this phenomenon is not well captured by existing economic models. This research provides a model of spatial and temporal interactions between housing and employment markets. The results show that rapid growth of employment centres increases house prices in neighbouring locations even after adjusting for fundamentals. It is concluded that spatial clustering of companies creates an option value for existing and potential employees that goes beyond ease of access for commuting purposes.The authors thank the Higher Education Funding Council for England (HEFCE) for providing funding for a project which inspired this study
Thirty Years of Spatial Econometrics
In this paper, I give a personal view on the development of the field of spatial econometrics during the past thirty years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, takeoff and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions.
A sampling approach to estimate the log determinant used in spatial likelihood problems
Spatial statistics, Spatial autoregression, Maximum likelihood, Sparse matrices, Log-determinants, Spatial econometrics, Parallel processing, C11, C21, C23, R11,