Some analytics lessons from the homeless along the Caltrain tracks
For the past year I have been commuting to the Palo Alto office from Monterey, a distance of 90 miles. Part of the journey is completed by train from Gilroy to Palo Alto on Caltrain, a Bay Area commuter rail line. During the first six months I tended to gaze out at scenery in the distance and be taken by the beauty of the golden foothills decked by stately oak trees.
But lately my attention has been focused on the untidy region near the tracks, where there is much evidence of vigorous, edge-of society activity, and a lot of wisdom to be gained by watching with an analytical eye.
Sometimes the beautiful is not nearly as instructive as the ugly. And likewise, the grungy world of rail lines reveals some worthwhile wisdom about analytics.
Like desperados waiting for a train?
One of the few areas in the US where the unemployment rate has been falling in 2012 is Silicon Valley and the Bay Area in general. Yet as Bay Area employment picks up there has also been a curious increase of homeless persons camping regularly at the Gilroy station.
Surprise! They are not unemployed; they are the working poor. Broadly reduced unemployment cuts across all income levels, and in the Bay Area subminimum wage jobs and well-paid skilled tech sector jobs have been increasing simultaneously.
Inhabiting the Gilroy station also facilitates labor force participation. The station is a regional transit center served by local buses, Greyhound, and even charters to Mexico, enabling station inhabitants to be mobile. Inhabitants can head to another town to find employment on a moment’s notice. At the railway station they can be easily fetched for day labor, and they can develop a symbiotic relationship with taxi drivers, who may offer local transport or information in exchange for tasks. The station also is patrolled regularly by the local police and Amtrak employees, and is therefore safer than most other areas where they could camp.
Lesson: Don’t uncritically accept two observations appearing together as fact, or as evidence that one relates to the other in a meaningful way. Let the data tell you what the relationships are.
Things get even more interesting as the train leaves the countryside and approaches San Jose. Find out how by reading the full text version on Forbes.com