brain of mat kelcey

consider the graph below which shows the proportion of tweets per 10 min slot of the day (GMT0)it compares 4.7e6 tweets with any location vs  320e3 tweets with identifiable lat lonssome interesting observations with unanswered questions...the ebb and flow is not just a result of the time of day for high twitter traffic areas. the reduction between 06:00 and 10:00 comes close to zero. this is false, there is never a worldwide time when internet traffic hits zero. does twitter turn down it's gatdenhose for capacity reasons?the number of tweets with lat lons are correlated to those without EXCEPT past...

for v3 lets aggregate by time of the day, should make for an interesting animationbrowsing the data there are lots of other lat longs in data, not just iPhone: and ÜT: there are also one tagged with Coppó:, Pre:, etc perhaps should just try to take anything that looks like a lat long.furthermore lets switch to a bigger dataset again, 4.7e6 tweets from Oct 13 07:00 thru Oct 19 17:00,i've been streaming all my tweets ( as previously discussed ) and been storing them in a directory json_streamhere are the steps...use a streaming script to take a tweet in json...

let's rewrite v1 using hadoop tooling, code is on githubwe'll run hadoop in non distributed standalone mode. in this mode everything runs in a single jvm so it's nice and simple to dev against.in v1 it wasbzcat sample.bz2 | ./extract_locations.pl > locationsusing the the awesome hadoop streaming interface it's not too different. this interface allows you to specify any app as the mapper or reducer. the main difference is that it works on directories not just files.for the mapper we'll use exactly the same script as before; extract_locations.pl and since there is no reduce component of this job so we...

was discussing the streaming twitter api with steve and though i knew about the private firehose i didn't know there was a lighter weight public gardenhose interface!since discovering this my pvr has basically been runningcurl -u mat_kelcey:XXX http://stream.twitter.com/1/statuses/sample.json |\  gzip -9 - > sample.json.gzbut what am i going to do with all this data?while poking around i noticed there was a fair number of iPhone: and ÜT: lat long tagged locations (eg iPhone: 35.670086,139.740766) so as a first hack let's do some work extracing lat longs and displaying them as heat map points on a map.all the code is...