We love traveling and wanted to build something where people can input which activities they like to do and which kinds of places they want to see. Often it is also important which time of year one travels to a certain place to make the activities available there enjoyable. We wanted to integrate all this into one interactive map to be sure to make the right choice of when to travel where.

What it does

It shows a map with different activities someone might be interested in that can be checked or unchecked. These activities include details when you hover over them and for example show the website of a campground or the difficulty of a climbing route. Not only can you find places that have a lot to offer, but you can also see the temperature there depending on the time of year. The temperature preferences can be adjusted to how warm or cold you like it and the map then shows you the areas that have your preferred climate in green and the ones that don't in red.

How we built it

We built the site in javascript. All the data has been processed and cleaned in R in order to make it all work smoothly.

Challenges we ran into

Integrating the weather data and activity data was a bigger problem than anticipated. It was hard to find a good resolution for the temperature data that covered the surface of the world in a manner that doesn’t leave gaps at certain locations and overlaps at others. Then when the activity data was added simply clustering them as single dots in the map was not an option, because that way one could barely tell what types of activities there are in a cluster. We solved this by having two different versions of the map depending on the level of zoom. When the user zooms out, they can see where there are clusters of activities by the height of the bars, when they zoom in, one can see by the type of icon which activities exist in a location and can hover over them to get more information.

Accomplishments that we're proud of

We were able to overcome many hurdles along the way, such as finding good data and cleaning up messy one. We also learned to work with new technologies such as for visualisation and R packages to handle geospatial information.

What we learned

We experimented with web-scraping in R because for some of the activities or sights, there simply weren’t usable datasets to download even though all the information is freely available on the internet.

What's next for Where to go when

We would like to include more weather data to make the “When” part of our project more prominent. For example it would be interesting to know the humidity or likelihood of natural disaster at the given locations. Snow data would be interesting as well. If we could include likelihood of snow and data on ski resorts for example, that could be another interesting feature in the project.

Demo Video

Team members (Slack)

@[Hacker] Jan @Sophie

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