Exploring the Spatiotemporal Dynamics of Google Trends Data: An Application in Estimating Childcare Demand

Sean Reid, University Of California, Santa Barbara
Jacob A. Cooper, ChildCare Aware of America

This project presents a novel methodology for harnessing Google Trends data to approximate demand for childcare services across time and space. We build on a growing body of literature assessing the use of Google Trends for research purposes by providing a fresh understanding of the dynamics of Google’s reporting of search activity on its platform. We evaluate the spatial aspects of Google’s internet search trends from a GIScience perspective to broaden the versatility of how scholars and practitioners can use Google Trends to evaluate interest and demand across a range of topics. For industries like Early Childhood Education (ECE), where these data are scarce or come with questionable reliability, the approach presented here can provide relative search intensity data with a measurable quantification of error as a valuable surrogate. Early findings suggest geographic scale should be considered when correcting for bias and inconsistency in Google Trends data. We seek to build upon existing methodological frameworks to improve the utility of Google Trends in demographic and spatial research.

Keywords: Digital and computational demography, Geographic Information Systems (GIS), Spatial Demography, Big data

See extended abstract.