Latent trajectory models for space-time analysis: An application in deciphering spatial panel data

This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space-time analysis. Using the space-time data collected at county level for the whole United States through webpage search on the keyword climate change, we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage-data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space-time trajectories through linking the webpage-data derived popularity measure about climate change to a set of socio-demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space-time analysis and more generally in GIScience.