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o2omismatch.github.io · 616 words · saved by 1 readers

"As one of many yelp users, searching food online is part of my exploratory life." However, does popular stores among yelp reviewers really contribute to the livelyhood of physical space? Inspired by this question, we download review data from 15,000+ stores in Manhattan from Yelp, and then we aggregate these reviews by physical street segments. In this way we could compare the aggregated online popularity with the number of actual visits along these streets, here is what we found: To test how much does online reviews actually contribute to physical acitivites, we include other variables in the model. * Physical Characteristics: Does Beautiful Space Matter? * Business Diversity and Transit: Jane Jacobs still right? * Local Business: How are local businesses doing? * X: We control other variables. The graph below shows our preliminary results. The height of the bar indicates the significance of each variable (how precise they are), and the length of the bar shows the effectiveness of e

Methods Methods How do we calculate the street O2O index? 1. Observations "As one of many yelp users, searching food online is part of my exploratory life." However, does popular stores among yelp reviewers really contribute to the livelyhood of physical space? Inspired by this question, we download review data from 15,000+ stores in Manhattan from Yelp, and then we aggregate these reviews by physical street segments. In this way we could compare the aggregated online popularity with the number of actual visits along these streets, here is what we found: 2. Model Construction To test how much

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