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Mentor: Dr. Shion Guha
Lab Github:
Personal Github:

Week One

  • Orientation
  • Data discussion meeting and updating new team members on previous work and lab goals
  • Background reading on various spatial clustering algorithms and use of data science concepts in police departments

Week Two

  • Compiled a preliminary list of clustering algorithms sorted into family, with a note of potential sources of biases and a summary of the steps
  • Demonstrated a variety of clustering algorithms on random data
  • Explored Kernel Density Estimates

Week Three

  • Expanding a dictionary for sanitizing address data
  • RCR Training
  • Made a poster for upcoming Northwestern Mutual event
  • Opened API keys

Week Four

  • Added basemap to DBScan visualization
  • Created several DBScan clustering models of crime in Milwaukee
  • Presented research at Northwestern Mutual technology showcase at announcement of the Data Science Institute

Week Five

  • Reporting halfway progress
  • More DBScan and nearest neighbors based calculation of epsilon
  • Began developing theory and equation for a "wastage index"

Week Six

  • Implemented a K Nearest Neighbors plot to help identify optimal eps given a minPT value on certain subsections of the data
  • Added hulls around individual clusters to make more user friendly

Week Seven

  • Further background reading
  • Cultivating literature for paper

Week Eight

  • Began write up about DBScan findings
  • Made poster for end of summer poster session

Week Nine

  • Continued final paper writing

Week Ten

  • Poster session
  • Final presentations
  • Finished final write-up