Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Geo@Work, keynote from Carto Spatial Data Science conference

439 views

Published on

Keynote given at Carto Spatial Data Science conference (https://carto.com/spatial-data-conference/), December 1, 2017.

How and why is location intelligence important at WeWork? A global provider of flexible work space, WeWork is growing at an incredible pace. With 178 locations in 53 cities and 18 countries, and doubling each year, and taking on decade+ leases, it is crucial that we take site selection seriously. In this talk, we’ll cover our initial efforts to bring geospatial thinking, tooling, and predictive models to the organization. We’ll cover some use cases, models, as well as some of the challenges of tackling geocentric data science at global scale.

Published in: Data & Analytics
  • Login to see the comments

Geo@Work, keynote from Carto Spatial Data Science conference

  1. 1. GEO @ Carl Anderson Market Analytics Team
  2. 2. Growing Very Rapidly... # Open Locations Today: 200 2010: 0
  3. 3. 175,000+ Members Globally 200 Physical Locations 64 Cities 20 Countries ...and Globally
  4. 4. WHERE DO PEOPLE WANT TO WORK?
  5. 5. ? Where next? Why?
  6. 6. World Metro Neighborhood Building Floor Multiple Scales
  7. 7. What’s a good city, neighborhood, or location? What are the relevant metrics, trends? What are the firmographics for Bangkok? How do we determine and measure market potential and saturation? When will be too late to enter a given market? How do we determine comparable markets? Questions, Questions
  8. 8. 175,000+ Members Globally 200 Physical Locations 64 Cities 20 Countries
  9. 9. Space People & Business primarily to serve Sales, Marketing, Executives, Real Estate Location intelligence is the intersection of people and space
  10. 10. Responsibilities of the Market Analytics Team: ● Geospatial strategy ● Data sourcing, provisioning ● Ad hoc analyses ● Develop predictive models ● Develop APIs ● Tooling ● Geo-expertise resource
  11. 11. MODELS / DATA SCIENCE
  12. 12. Esri Tapestry Segmentation
  13. 13. Caveat: you have to define an area not a point location to attach a segment. There is no definitive segment for a location as it is a function of scale of the area being considered. Portland Zoomed out coarser-grained Portland Zoomed in finer-grained 500m radius around each location was the smallest radius in which Esri would assign a segment to each location (smaller radii led to missing data). There is no correct scale. Nevertheless, we find some interesting findings. Segments Vary With Scale
  14. 14. Enriched 140 WeWork US locations with 500m and 1km radius. Almost all were in handful of 67 segments. Goal: understand distribution of segments on current fleet Value: ● Filter ● Score Esri Tapestry Segmentation
  15. 15. WeWork (open) Other Goal: predict which ZIP codes should have a coworking space, and why Coworking Model As of June 2017
  16. 16. Score all ZIPs Opportunities: Where is there no coworking but there should be? Where is there coworking but there should not be? Train on balanced dataset Interpretable models: Decision trees, Logistic, NaiveBayes, kNN Enrich data Demographics, HHV, firmographics, daytime population... All Coworking spaces in US 3,600 Coworking spaces Aggregate by ZIP ~36,000 ZIP codes Test on unbalanced dataset Optimize for precision not accuracy or F1 Filter by population density ~16,000 Coworking spaces Goal: Predict which ZIP codes should have a coworking space, and why Value: ● List of ZIPs we should investigate ● Relative importance of features ● Validation of current fleet Coworking Model
  17. 17. Comps Proforma: document set out case for a new location, including projected financial performance Given new Building, which are most similar “comps” in our fleet? Predict “detrended occupancy” All WeWork locations open >6 mo Enrich data Mine combinations of 1-10 features kNN with leave one out cross-validation Output ranked table for each WeWork Goal: Provide a ranked list of comparable WeWork locations given some non-WeWork location. Which features are important? Value: ● Better predictions ● Insights into drivers
  18. 18. Comps Building Neighborhood Market Building # of floors # of businesses within Xm # of colleges within MSA Building total sq ft Distance to other WeWorks Undergrad, grad enrollment Year built How far can one walk, bike, drive in X seconds Population who commute by car, walk Building Class #businesses of different sizes Household income Building Rating Per capita income House values by ZIP WW #floors, #offices, #desks Walk, bike, transit score Daytime population Goal: Provide a ranked list of comparable WeWork locations given some non-WeWork location. Which features are important? Value: ● Better predictions ● Insights into drivers
  19. 19. NY LA SF DC Goal: Provide a ranked list of comparable WeWork locations given some non-WeWork location. Which features are important? Chord diagram of nearest neighbor of each location Comps
  20. 20. Goal: Determine whether # of amenities in different classes predict WeWork location success Value: ● Location Score / Feature ● Key categories? All WeWork locations open >6 mo List of all US storefront businesses For each business category & distance, how predictive is that category? lm(occupancy ~ #chinese restaurants within 200m) lm(occupancy ~ #pizza restaurants within 200m) ... lm(gyms ~ #pizza restaurants within 800m) Amenities
  21. 21. ● Score every reasonable location in U.S. ○ Location attributes ■ Across street: Western Union , Blue Bottle ? ■ Neighborhood: food, transport, river/parks, fitness... ● Output: simple thumbs up / down ● Value: ○ Heatmaps ○ Monitor locations (buildings, other businesses) Goal: score every urban intersection in US with thumbs up / thumbs down, and understand why Work In Progress
  22. 22. topos.ai
  23. 23. “I think the best technologies, and Twitter is included in this, disappear. They fade into the background, and they’re relevant when you want to use them, and they get out of the way when you don’t” JACK DORSEY 2012 Charlie Rose interview
  24. 24. CHALLENGES
  25. 25. Data Quality / Availability
  26. 26. Same for metros? Metro Identifiers
  27. 27. HiPPOs
  28. 28. carl.anderson@wework.com @leapingllamas We Are Hiring! https://www.wework.com/careers Questions?

×