You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Incorporating geographic identifiers requires dealing with three thorny data volume problems:
Finding a postal code for each interval for each person. It is not entirely clear what heuristic to use to choose a postal code in the case of multiple choices, particularly because heuristics based on time sorting will be computationally expensive to do at scale.
Finding the local geography code for each postal code requires a non-trivial look-up against a large reference table. Again running this at scale will be computationally punishing.
Finally mapping the local geography code to a geometry requires only a look-up against a relatively small reference table, but then requires incorporating a geometry field.
The text was updated successfully, but these errors were encountered:
The strategy for efficiently tabulating geography:
Find all unique combinations of days and putative postal codes.
Filter against the postal code list to pull only valid postal codes.
To resolve day collisions take the alphabetically highest postal code because of the behavior to enter unknown postal codes as alphabetically lower postal codes, e.g. T0T0T0.
For each persons semi-annual interval find the postal code at the beginning and the end of the period.
Incorporating geographic identifiers requires dealing with three thorny data volume problems:
The text was updated successfully, but these errors were encountered: