JKI4_​027_​Traveling_​in_​Europe

<p><strong>Challenge 27: Traveling in Europe</strong></p><p><strong>Level:</strong> Hard<br><br><strong>Description:&nbsp;</strong>The end of the year is coming and you are ready to organize a roadtrip around Europe. To facilitate its planning, you decide to create some interactive dashboards. Given a dataset with distances (in km) between many major European cities, you want to:<br><em><br>- </em>Create a component that identifies a network of cities that are connected by at most a given distance (e.g., a network of cities that are at most 200km apart from one another);<br>- Calculate and display different properties of each city in this network (e.g., which city is more central?); and<br>- Calculate the shortest paths between two selected cities in this network.<br><br>This can help you determine where your trip will start and which cities you will visit: two planning tools that come in handy if you want to check as many cities as possible within certain distance constraints. <strong>Hint: </strong>Use KNIME's <strong>Network Mining</strong> and <strong>Distance Matrix</strong> extensions to identify a network of cities and compute their properties and shortest distances.<br><em><br>Beginner-friendly objective: </em>Read the dataset on distances between cities in Europe.<em><br>Intermediate-friendly objective: </em>Create an interactive dashboard that allows you to specify a maximum distance <em>X</em> between two cities, creating a visual network of all identified cities that are at most <em>X</em>km apart from one another.<em><br>Advanced objectives: </em>(1) Gather network information for each city in your network (e.g., their degree, closeness, weight) and organize the information as a visual table. (2) Create an interactive visualization that allows you to select two cities from your network, returning the shortest path between them with all the cities that are in between them.</p>