The Naturmuseum in St Gallen and Meteomatics AG embarked upon a collaboration to see how weather information could help better predict the number of visitors to the Museum, rather than just guessing that people go to museums on days with bad weather.
Meteomatics were excited to make its TV Index and other parameters available within its Weather API to Data Scientists working with the Naturmuseum St Gallen, to help the museum better plan its resources by understanding the relationship between weather and visitor numbers.
- Meteomatics TV Index identifies "bad weather" which might make people watch more TV
- The index varies between 0 and 1, where o describes nice weather conditions and 1, rainy and stormy weather.
Meteomatics' TV index provides a correlation between weather and the numbers of people estimated to watch TV. The index includes a combination of weather types (rainfall, visibility, wind speeds, sunshine duration and windchill) that could lead to more people staying in and watching TV as a result of "bad weather".
Meteomatics has observed the impact of bad weather on people and the likelihood of them staying indoors to watch TV creating the following rules:
- if: precipitation_1h > 3 mm or wind_speed_10m > 25 m/s or windchill_temperature < –20 °C - then: TV_Index = 1 (bad weather)
- if: sunshine_duration_1h = 60 min or precipiation_1h = 0 mm or wind_speed_10m < 3 m/s - then: TV_Index = 0 (good weather)
else: set index_sum = 0 and let
The Data Scientists working on the project were able to combine Meteomatics TV Index with two years of historic visitor numbers to train the machine learning prediction model, treating weekdays, weekends/holidays differently, using a non -linear regression method to model the relationship (including other weather parameters that also have a role).
The Project Team identified a strong correlation between adverse weather conditions and the number of visitors to the museum (weekends & holidays 0.71 and weekdays & no holidays is weaker at 0.53).
These insights have transformed the Museum's operational planning, allowing Naturmuseum St Gallen to more accurately predict visitor numbers: improve visitors' experience: staffing levels and safety.
Metomatics' TV Index is a good example of how Meteomatics supports machine learning methods by correlating weather insights with other secondary data sources to predict demand and footfall. In fact, Meteomatics are seeing companies across industry applying Meteomatics API to their own business demand intelligence, to create or tune insights and improve their own demand forecasts
Meteomatics specializes in making weather data more useful and relevant to Meteomatics' API customers, by creating weather parameters that more accurately represent use cases for weather data, with indices that look to correlate impacts of weather on human behavior globally, examples include; favorable weather for a BBQ: going to the beach: cycling: skiing, gardening and many others.
Please contact [email protected] if you would like to discuss applying weather data to your machine learning or artificial intelligence application(s)