Data visualisation for time series in environmental epidemiology.
BACKGROUND
Data visualisation has become an integral part of statistical modelling.


METHODS
We present visualisation methods for preliminary exploration of time-series data, and graphical diagnostic methods for modelling relationships between time-series data in medicine. We use exploratory graphical methods to better understand the relationship between a time-series reponse and a number of potential covariates. Graphical methods are also used to examine any remaining information in the residuals from these models.


RESULTS
We applied exploratory graphical methods to a time-series data set consisting of daily counts of hospital admissions for asthma, and pollution and climatic variables. We provide an overview of the most recent and widely applicable data-visualisation methods for portraying and analysing epidemiological time series.


CONCLUSIONS
Exploratory graphical analysis allows insight into the underlying structure of observations in a data set, and graphical methods for diagnostic purposes after model-fitting provide insight into the fitted model and its inadequacies.
Erbas
B
B
Department of Public Health, The University of Melbourne, VIC, Australia
Hyndman
R
R
D003196:Computer Graphics; D004782:Environmental Health / Q000706:statistics & numerical data*; D004787:Environmental Pollution / Q000706:statistics & numerical data; D004813:Epidemiology / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D018709:Statistics, Nonparametric; D013997:Time Factors; D013996:Time and Motion Studies