Counting the camouflaged: the Homeless Census – Part 1 of 2

A couple of weeks ago, Martina forwarded an email solicitation to Anne and I. Volunteers were needed to conduct a countywide homeless census. Martina herself wouldn’t be able to volunteer (nor Anne, both of them had schedule conflicts) but she was VERY curious about how it was set up, especially the sensitivity training for the volunteers. Since I didn’t have anything more pressing to do; and as I am always looking for new experiences, I signed up. I told Martina and Anne I’d tell them how it went. So this is it. There’s a also good account of the census in San Jose from the Mercury News.

In Silicon Valley, the supply of housing has lagged far behind demand. We need more housing to accommodate people at all income levels: the software engineer; the barrista who makes the software engineer’s daily latte; the venture capitalist who funds the software engineer’s product development. But affordable housing, for the ‘barrista’, is the most critical aspect of the housing shortage — and homelessness. For those with lower incomes, the pressure to continue living here must be like a vise, as rents have skyrocketed. Even for tech workers making six figures, housing prices should be considered stratospheric.[1] The median monthly rent for a 2-bedroom is around $2,500.

The Mountain View Voice weekly newspaper recently ran two excellent stories on homelessness back to back. It was eye-opening for me — the how’s and why’s of being homeless in this area became understandable and relatable.

I became curious: how many homeless people were there in my neighbourhood? Were there even any? I had no idea what to expect. At this rate, zero seemed as likely as many.[2]

ON A MILD WEEKDAY AFTERNOON, I biked over to the Opportunity Center on Encina Avenue in Palo Alto. It was wedged between the PAMF hospital and the Town and Country shopping center. I’d never known until then there was a one-stop facility providing services for the homeless on Encina Avenue, in the midst of such prosperity. It also explained why I had often seen homeless people while biking along the Embarcadero bike path (which is accessible from Encina Avenue).

I had come for one of the six 1-hour volunteer training sessions being held around Santa Clara county. The meeting room where the training was being held was filled. The turnout was higher than had been expected, as the organizers (from a professional research company) kept going out to other rooms to bring in more chairs. Those already seated around the big conference table nudged their chairs closer to their neighbors to make room for newcomers. About two-thirds of the folks in the room seemed like clients or residents of the Opportunity Center. The rest were the likes of me (or Anne or Martina, if they’d been there.)

It was a good sign that so many people were willing to help to count the homeless.
It was a dismal sign that there were so many homeless people to be counted.

FOR MOST PEOPLE, ‘homeless’, in its extreme image, brings to mind a panhandler on a downtown sidewalk, clothes greasy from prolonged grime, pungent from the lack of hygiene. Anchored by plastic bags holding all their worldly goods. Under the influence of something, they shout loudly or mumble incoherently, intimidating passers-by like you or me avoiding or ignoring them.

There are more types of homeless people beyond that stereotype in Silicon Valley. Some of them have jobs, they are even a part of our everyday lives. The clerk at the check-out counter. The waxer at the car wash. The security guard in the lobby. They don’t look like what we think of as ‘homeless’.

But their jobs are at hourly wages, part-time, no benefits. A medical emergency hits, rent hikes, a lay-off — such events may ping-pong the financially fragile between housed and homelessness. The woman assembling your gluten-free wrap or vacuuming your office carpet could be involuntarily couchsurfing, or living in her car.

EVEN THOUGH THIS WAS THE FIRST TIME I (or Martina or Anne) had heard of the homeless census, it’s been conducted systematically for over a decade.

The Department of Housing and Urban Development (HUD) requires a point-in-time (PIT) count of the homeless every two years during the last 10 days of January. This takes place over the entire country. HUD does not pay for the data collection — it is an unfunded mandate. The 2013 census found 7,631 homeless in Santa Clara County — an increase of 8 percent from 2011.

The data is used to help determine the level of funding HUD will provide to local agencies for homeless services. The data is also used for analysis and understanding of the current homeless situation, as well as a record to be compared over time with previous and future counts.

The legal definition of ‘unsheltered homelessness’ is “individuals and families with a primary night time residence that is a public or private place not designated for or ordinarily used as a regular sleeping accommodation for human beings.”

This would include people spending the night in:

      • makeshift shelters
      • in the open
      • vehicles
      • parks
      • abandoned buildings
      • bus/train stations
      • airports
      • camping grounds

Those following would not be counted:

  • couch-surfers
  • doubling-up
  • in homes awaiting eviction or foreclosure
  • in jail
  • in rehabilitation/mental heath facilities

In Santa Clara county, this point-in-time homeless count was to be a 24-hour exposure snapshot on January 27-28. The count included three components:

  1. a shelter count of people staying the homeless shelters or (subsidized) hotel rooms;[3]
  2. general street count (for which the 400 volunteers were needed); and
  3. a follow up survey in February.

For the General Street Count, the surveyors would be divided into teams. Each team consisted of a ‘guide’ and 1-2 volunteers. The guide was typically someone who was, or had been homeless. The guide would know better who might be homeless, and where they were likely to be. The volunteers would help drive and look. Someone on the team would tally.

Each team was assigned two census tracts.[4] Using maps provided, the team would drive along every street in our assigned tracts to spot the homeless. Where it was applicable and safe to do so, we would park our cars and walk as a team to see where the homeless were.

Teams would fan out starting around 6 AM — before people who were in homeless shelters would be forced out by lock-out hours, to minimize double counting. Most teams would complete their survey between 10 to 11 AM.

As the General Street count would be a visual count, surveyors were not to approach or interact with the homeless people being counted. We were only to observe and tally, in order to maintain the dignity and respect of those being counted.

IN SILICON VALLEY, big data and metrics have become the new big thing. Algorithms track each and every click on the internet as grains of data, harvesting them insatiably, the haul insidiously monetized. If you surf the web looking for Elsa party invitations and Frozen ice-cream cakes, within five minutes your browser will push an ad your way for local bouncy house rentals shaped like ice palaces — before you even remembered you will need one.[5] To be tallying the homeless with pens on paper, using wooden clipboards for this census seemed quaint and antiquated. I found out later there was an app for that (of course), but it still required humans to go out in the field to observe and tally on their tablets.

At the training session, the organizers went over the scantron tally sheets, which would be correlated to the census tracts. Survey teams were to note

  • gender
  • age
  • single or families (at least one adult over 18 and one child under 18)
  • the type of dwelling (rent, building, car, RV/van)
  • if the location was an encampment

There were no street names.

Almost all the categories included an option for ‘unknown’ — it would often be hard to determine the age, gender or even number of people if they were huddled under blankets. The organizers stressed that it was better to have a smaller amount of imprecise actual data, rather than a large amount of data that was filled with assumptions.

There was lots of tittering over the options in the ‘gender’ section. It included

  • male
  • female
  • transgender female to male
  • transgender male to female
  • unknown

Apparently, data from this census would be cross analyzed with other data sets which included transgender information. So the transgender options were included on the scantrons to ensure consistency.[6]

A LITTLE AFTER 6 AM, I was not fully awake. I tried to remember the last time I was forced awake by an alarm clock as I drove to Fair Oaks Park in Sunnyvale to check in as a volunteer. Even though the parking lot was full of cars, there wasn’t a soul in sight. In the darkness, it was foreboding. Did I have the correct date? But in the distance a building was glowed flourescently through the windows from within.

The building was a single large room, reassuringly bustling with people. Even better, there were many to-go cartons of coffee, as well as snacks. I poured myself a cup. I wondered what the flake-out factor was — would-be volunteers might groggily tap the snooze button and roll over, unwilling to get out of a warm bed into pre-dawn chill. I could have been one of them.

The survey organizers gathered the surveyors, and described the census tracts up for assignment. We were encouraged to pick areas we were familiar with. Naturally I picked the census tract I lived in. It was paired with an adjoining census tract in Sunnyvale, which I frequently bike through.

The surveyors were then organized into teams, one guide paired with one volunteer. I was paired up with a guide who was a man. Randy was slight, middle-aged, white, with glasses and a muted air. He was neatly dressed, even if his clothes were dated. Like me, this was his first time surveying the homeless.

As we walked to the parking lot, I suggested I drive, and he tally.
“OK,” he paused. “But let me get my reading glasses.” To my mild surprise he walked to a car — his — and got what he needed.
“He has a car?” I wondered mentally.

SINCE NEITHER RANDY NOR I had any previous experience in looking for homeless to survey, we started methodically. The first area we drove through was all apartment and town home complexes. We drove slowly, squinting hard at the landscaping lining the perimeter walls/parking lots, to see if we could spot anyone sleeping in the bushes. There were none.

“Well it’s January,” Randy pointed out. “When it gets warmer, in the spring and summer, maybe more of them would be sleeping outdoors.” Nights are very chilly, even in California. Homeless people who sleep outside in warm weather would have sought more protected shelter during the winter. In Santa Clara county, some homeless shelters are only open during the cold season.

Then we spotted our first homeless: an RV parked next to a freeway sound wall at the edge of this residential neighborhood. We realized we should concentrate only vehicles, not persons. As it turned out, every single homeless person we came across was living in a vehicle.


BACK TO POST [1] Several doors down from where I live, a 3-bedroom townhouse was recently listed at 99% of $1 million. It was sold in less than a week, probably for over seven figures. When I first moved to this neighborhood, it felt outrageously overpriced. Enough years have passed that it seems reasonably priced. If we had to move here today, we wouldn’t. We probably couldn’t afford it.

BACK TO POST [2] “Wealthy people who live in economically diverse areas are more generous than those who live in exclusively wealthy areas” — this line in the 1/29/2015 NYT opinion piece on empathy by Nick Kristof struck a chord. I won’t comment on philanthropic tendencies, but I do feel that as much as people are open to diversity in in their social circles (ethnicity, race, religion, sexual orientation,etc), they should also be open to having neighbors of all income levels. It’s another element in keeping us real, a reminder of how varied the world is.

BACK TO POST [3] The Route 22 bus that runs between Palo Alto and San Joe operates 24 hours daily. The homeless who rode it as a ‘mobile shelter’ would also be counted. Likewise the creeks and adjacent trails were surveyed by others as part of this census.

BACK TO POST [4] Each census tract has an average of 4,000 residents. Census tracts can straddle city limits, but are always contained within a single county.

BACK TO POST [5] I made up this example about online shopping for a birthday off the top of my head. But today, I got a mail-order catalog from a high-end furniture store which I had never patronized — whose website I had searched a few months ago, when I was looking extensively for a dining room lamp. Big Data = Big Brother = truly omnipotent. Whoa.

BACK TO POST [6] I would think that there is a higher percentage of transgender people amongst the homeless than the general population. People who identify as transgender may be ostracized or discriminated against. If they are youths, they may run away from home, ending up homeless.


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