A Small Estimator for Neighborhood Parking

I've been working for some time on a high-grade parking estimator, and I'd like to share a simpler (albeit less accurate) variant. This estimator will give you, with a quick parameter valuation, a result to a single order of magnitude of accuracy; however, it is quick and useful to have on hand at, say, neighborhood debates.

It is written

where f stands for the count of faces on a unit block, b stands for the count of blocks in the area under estimation, l is the mean blocklength, τ (tau or "taff") is the length of a typical onstreet space, and þ ("thorn") is the ratio of space used.

Assuming FPS, I usually set τ to 15 feet (this I call my "template" parameter) and þ to 0.75 (the "G-Ho" parameter). b is geographically determined, and f by the overarching pattern of block development. Due to our use of grids, it is most often 4. This just leaves l, which is strongly locally determined. For example, in rectangular grids (like Manhattan's), it is the average length of a block from building line to building line; in more regular neighborhoods, a typical side should suffice.

Underestimation

Since this estimator is a highly simplified version of my estimator model, it follows it has a few inherent weaknesses. The largest is a tendency to underestimate, since it fails to count interstitial streets. Most attempts to rectify this without recourse to the full estimator would require significant transformations, subdivision, and general increasing unwieldiness. Because of this, the user must--especially in a neighborhood populated by a high count of interstitial streets--assume that the model has underestimated by an order of magnitude. Yet the results attained from this estimator are significantly higher what most people realize.

Overestimation

The need for τ is obvious, but (unlike the larger estimator) there is no way for this small estimator to autocorrect for deficiencies in its setting. Since the template setting of 15 (feet) is "tight", yielding the smallest standard parallel-parking space, this can also create overestimation, especially in the first part; changing the value of τ also has ripple effects that create larger and larger deviations from the larger estimator--particularly since þ, the G-Ho parameter, is a plug-in of a result attained from the larger estimator with a τ of 15.

In my judgment, however, the underestimation effect is an order of magnitude greater than the overestimation.

To a Higher Grade

While this "small" estimator is a derivation of a much higher-grade one, I must stipulate that use of the latter requires plugging in significantly more data, and therefore comes with a contract requirement. If you or your organization wants to have your parking supply estimated, feel free to contact me; this small estimator can get you into the ballpark, but I can bring you into the infield.