The number of visitors to museums is a key indicator of the performance of cultural institutions. Being able to have early estimates of visits is of crucial importance for cultural policy makers who need to allocate resources and funding, or to evaluate the impact of cultural policies.
Traditional data gathering and processing methods result in delayed estimates on the number of visitors to free museums. Here, we show how our information gathering process using the online search engine Google could help policy makers have early and accurate estimates of the number of people visiting a museum or gallery. We use time series methods, as well as artificial neural networks, to combine historical visits data with data on museums search volume on Google to generate rapid and accurate estimates of the number of visitors to a range of UK-based museums and galleries. We also provide an extensive validation of our analysis to reduce the risk of our findings being the result of a spurious correlation.
Our results provide further evidence that publicly available data sets detailing our online behaviour can be used to better understand the current state of society.