Utilizing Meetup’s API to Compare Boston, NYC, and Silicon Valley
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Utilizing Meetup’s API to Compare Boston, NYC, and Silicon Valley

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Major differences were found across these regions most notably that Boston has a lower than average number of business groups relative to Silicon Valley and NYC.

Major differences were found across these regions most notably that Boston has a lower than average number of business groups relative to Silicon Valley and NYC.

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Utilizing Meetup’s API to Compare Boston, NYC, and Silicon Valley Utilizing Meetup’s API to Compare Boston, NYC, and Silicon Valley Presentation Transcript

  • Utilizing Meetup’s API to Compare Boston, NYC, and Silicon Valley John Verostek February 21, 2012 Boston Predictive Analytics Meetup
  • Goals / Outline • Meetup Data Structure • Social Network Concepts • Software, Data Crunching: R (iGraph, tnet) • Software, Visualization: Gephi Utilize Meetup.com to: •compare Boston, NYC, and Silicon Valley
  • (Application Programming Interface) http://api.meetup.com/members.xml/?group_id=1676436&key= Interface for several languages including Python, Ruby
  • http://dataist.wordpress.com/ Jens Finnäs
  • Member 1 Event 2 Group 1 Group 2 Event 1 Interest 2Interest 1 Meetup Data City Member 2 Interest 3
  • GROUPS • What other meetup groups might be of interest to the Boston community? • Why have some groups not formed in Boston but exist in other cities? • How closely tied are groups to local industries? • What groups have common interests and/or members? EVENTS • What events happening in other cities might be interesting to members in Boston? MEMBERS • What groups or events are relevant to a member based on interests of similar members? (SNA) • Do people in cities like NYC, Palo Alto have similar or different interests versus Bostonians? (SNA) CITIES • Where does Boston rank relative to other cities such as NYC and SF? • What is the speed of growth in members, and diffusion of interests and/or groups across cities? • Where is Boston on the adoption curve? What’s the ceiling? What are the constraints? In the form of a Question
  • Region Center: Zip Code + Radius (25mi) Boston Silicon Valley NYC 02201 94041 10001
  • Members: 9.5 million Monthly RSVPs: 1.5 million Monthly Meetups: 280,000 Local Groups: 92,000 Cities: 45,000 Boston 2,139 Groups New York 7,992 Groups Silicon Valley 2,723 Groups City Summary
  • Number of Groups Created by Year
  • Year-to-Year Growth Rate (New Groups / Year)
  • How do Boston, NYC, and Silicon Valley look with respect to the different types of groups?
  • • Classify each group (1000’s of groups!) – Program/Machine Learning/Amazon Mechanical Turk – maybe if study expanded – Manually classify a subset of groups (n=300) • How to measure? – Use “member-group” dyads (i.e. # of members per group) – Calculate category size as a percentage of all groups Group Classification Meetup Group Category Members- Group Dyads Total Outdoors Social Non- Profit Hiking Group Outdoors 4504 4504 100% 0% 0% NERD Fun Social 4004 8508 53% 47% 0% Boston Volunteers Non-Profit 3789 12297 37% 33% 30% Social Fun Social 3575 15860 28% 48% 24%
  • Sample Pct. Largest 300 58% Largest 500 72% Largest 1000 90% 2,139 Groups 391,191 Dyads Sample Pct. Largest 300 56% Largest 500 69% Largest 1000 86% 2,723 Groups 550,189 Dyads Sample Pct. Largest 300 34% Largest 500 44% Largest 1000 59% 7,992 Groups 1,734,584 Dyads Boston Silicon Valley NYC
  • Boston Meetup Groups by CategoryPercentofAllMember-GroupDyads Decreasing Order of Group Size Category Pct. Technology 13% Gender 10% Ethnic 8% Age 8% Outdoors 7% Social 6% Business 4% Indoors 4% Recreation 4% Dating 3%
  • Silicon Valley Meetup Groups by CategoryPercentofAllMember-GroupDyads Decreasing Order of Group Size Category Pct. Technology 30% Business 12% Outdoors 10% Ethnic 6% Age 5% Social 4% Alcohol 4% Photography 4% Food 3% Movies 3%
  • NYC Meetup Groups by CategoryPercentofAllMember-GroupDyads Decreasing Order of Group Size Category Pct. Technology 15% Business 13% Ethnic 9% Culture 6% Social 5% Food 5% Dating 4% Outdoors 4% Travel 4% Movies 4%
  • “Group-Member” Summary (Top 300 Groups)
  • Robotics MFG EDU IT BioTech Health Care Professional Associations Mass Challenge Harvard Innovation Lab MIT Enterprise Forum NEU Tech CIC Boston Innovation District EventBrite VC’s
  • Web Design java python sql 2 R 1 1 1 1 Lean Startup 3 3 3 2
  • Edge List w/Weights Matrix Network Graph i j W 1 2 1 1 3 1 1 4 1 1 5 1 3 4 1 3 5 1 4 5 2 4 6 1 5 6 1 Matrix
  • Edge ListMatrix Network Graph I j W A B 1 B C 2
  • Group Member +
  • • Install igraph • library(igraph) • G <-read.graph("group-topic.txt",format="edgelist") • G<-as.undirected(G) • plot(G) i j 1 2 1 3 1 4 1 5 3 4 3 5 4 5 4 6 5 6
  • https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919 http://paulbutler.org/archives/visualizing-facebook-friends/
  • • Data Processing – Weights – Two Mode Analysis: ‘tnet’ • Visualization – Gephi – Graphviz Divide and Conquer
  • tnethttp://toreopsahl.com/tnet/http://toreopsahl.com/thesis
  • Matrix http://toreopsahl.com/tnet/two-mode-networks/projection/
  • Group - Member method=“newman”
  • Import
  • http://gephi.org/tutorials/gephi-tutorial-quick_start.pdf
  • Relationship of Characters in the book Les Miserables
  • 0 1000 2000 3000 4000 5000 6000 7000 0 5 10 15 20 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.000 0.020 0.040 0.060 0.080 Degree – Weighted Degree Hub - PageRank
  • Silicon Valley Members with first name A-D 15 Startup Meetups each with 1000+ Members
  • Software Guides Influencers: • Depends on who you follow. • LinkedIN Comparison Grid • http://www.slideshare.net/david.combe/a-comparative-study-of- social-network-analysis-tools • Overview of Common Social Network Analysis Software Platforms List with Description • train.ed.psu.edu/WFED-543/SocNet_TheoryApp.pdf • http://www.insna.org/software/index.html Los Links • http://www.kdnuggets.com/software/social-network-analysis.html
  • Drew Conway Presentation
  • http://paulbutler.org/archives/visualizing-facebook-friends/ “I defined weights for each pair of cities as a function of the Euclidean distance between them and the number of friends between them. “ “I plotted lines between the pairs by weight, so that pairs of cities with the most friendships between them were drawn on top of the others.” https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919 Paul Butler, from his Facebook page: