title: Computing estimated mobility needs of a given territory for bus route
body: <p>My doubt is more conceptual than programming-related but here we go.</p>
  <p>The goal is to place the most optimal set of stops for a bus route but first we need to compute the estimated demand in a territory.</p>
  <p>Input is not defined yet, but let's say we have an input of relevant weighted points of common interest of the population in a territory.</p>
  <p>We should compute the mobility needs in every point of all the roads in the territory in order to place the stops mentioned before.</p>
  <p>The approach I'm taking for this problem is:</p>
  <ul>
  <li><p>For every point in the road, compute the sum of weights of the N elements in a radius of R (something like the KNN algorithm logic).</p>
  </li>
  <li><p>After that, with KMeans (since its distance-based) we could compute the most relevant K centroids in the territory to get the first sample of stops. After that, optimize in order to fit with the optimization parameters given such as km's covered, route time, etc.</p>
  </li>
  </ul>
  <p>What do you guys think about this approach? Would you use other algorithm's? Would you try to approach the problem differently?</p>
  <p>In the end, the biggest mathematical challenge is to:</p>
  <ul>
  <li>Define mobility needs in a given territory.</li>
  <li>Place the stops based on the mobility needs.</li>
  </ul>