Chris Shughrue

Chris Shughrue

Chris is a quantitative urban geographer. At StreetCred he designs data science approaches to uncover patterns in the location of interesting places, and to most reliably represent those places. In his previous academic research, he developed global analyses of systemic risks to cities using approaches from network science and geospatial analysis. Chris holds a PhD from Yale in urbanization and environmental change.

Blog Posts

  • Around the World in 2.2 Million Stops

    There may be 150 million places in the world. What if you wanted to visit all of them? How would you plan this epic trip—and make sure it didn’t take a lifetime?

  • Illuminating Points of Interest From Space

    At StreetCred, we’re on a mission to get all the place data in the world. This presents a major challenge: starting from a blank map of the world, how do you predict where points of interest exist?

  • Fishing for POIs

    The StreetCred community created, validated, and enriched more than 25,000 places during MapNYC and MapLA. On the face of it, this is a lot of places. But it begs the question, how many more have yet to be mapped? How much further do we need to go to map not just a lot of places, but all the places?

  • Validating Validation

    Core to the StreetCred data collection process is bringing together multiple, independent users to create and validate the existence of places. Validation is a multi-step process requiring a consensus among user submission. We wanted to follow up on the intuition behind this validation system to assess the accuracy of user-generated data and the combined accuracy of multiply validated POIs.

  • Seeing Across City Lines

    The StreetCred community enriched more than half of the ~15,000 data points imported from partners in the course of the month-long competition. Our partners rated their confidence in their initial data as high, low, or likely out of business. This created a new opportunity for the StreetCred community to prune off bad points from otherwise good datasets, as well as to find the gems buried in murky or possibly outdated data.