Figure 2

Verifying the value of data on Google searches for museums or galleries. Are estimates of visitor numbers improved only when the Google Trends data relates to the museum or gallery in question, or would any data from Google Trends improve our estimates, suggesting that our findings might reflect a spurious correlation? To address this question, we repeat our analysis using data from Google Trends for control topics with limited or no relation to museums and galleries: England, Travel, Buckingham Palace, Hyde Park, London, United Kingdom, Holiday, and Color. Again, we generate monthly estimates of visitor numbers for all museums using rolling training windows between 30 months and 72 months, for both a baseline ARIMA model and a model enhanced with Google Trends data. (A) For comparison, we first depict the results across all museums when the actual Google Trends topics for each museum are used (for example, the Tate Modern topic for the Tate Modern gallery). We observe that the mean absolute scaled error (MASE) is lower when Google Trends data is included, regardless of training window size. (B) We compare these findings to results when Google Trends data for our eight control topics is used. Here, we find that the Google Trends model does not perform better than the baseline. Visual inspection suggests that adding irrelevant Google Trends data to the model in fact slightly increases the MASE. In the context of our previous findings, this provides further evidence that data on search queries for a specific museum or gallery contains valuable information that can be used to improve rapid estimates of visitor numbers