- We have two ways of measuring occupancy in large spaces:
Technique 1. Estimate occupancy using headcount. This technique would involve instrumenting the entryways with sensors in headcount mode and subtracting # of exits from # of entries to estimate occupancy. So if a headcount sensor has sensed 100 entries and 90 exits in a space, the estimated occupancy would be 10 people at that point in time.
Technique 2. Directly measured occupancy. This technique involves aggregating the outputs of sensors that cover the entirety of the space with sensors in activity mode. So if a conference room has 2 activity sensors installed, with 1 of them showing 2 people and the other showing 1 person, the occupancy of that room is measured to be 3 people.
Technique 1 vs Technique 2:
- Technique 1 only calls for instrumenting entries and exits vs Technique 2, which needs to cover the entire space so Technique 1 can have a cost advantage.
- Technique 1 is more effective when the sample size is large and is prone to error or drift when the sample size is small:
- The Butlr headcount sensor's accuracy spec is 95%. So if there are 10 entries and 10 exits, it can be expected to count 19 of 20 and so the estimated occupancy of the room would be 1 instead of 0.
- The error/drift could also accumulate throughout a period of time (e.g.: 1 day) if there's an asymmetrical amount of errors between in-counts and out-counts. Some environmental and behavioral conditions might increase the accumulated error, for instance: drastic temperature difference between both side of the door; user hanging around at the door.
- Whereas if Technique 1 is implemented in a larger space where there are, say, 200 entries and exits, the 5% error is manageable relative to the sample size and there is smaller possibility of asymmetry of error and smaller error to sample ratio.