What are the best methodologies for normalizing retail storefront foot traffic data to account for seasonal tourism spikes in dense pedestrian corridors like Times Square?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the best methodologies for normalizing retail storefront foot traffic data to account for seasonal tourism spikes in dense pedestrian corridors like Times Square?
5 real searches · 1 real-search pass
Times Square pedestrian counts tourism seasonality NYC foot traffic data methodology seasonal adjustment tourism indicatorsGoogle ↗
OBSERVEDReason
seasonal adjustment time series retail foot traffic weather holidays events tourism econometric methodologyGoogle ↗
OBSERVEDReason
NYC tourism monthly visitation data official NYC tourism Times Square pedestrian countsGoogle ↗
OBSERVEDReason
retail foot traffic seasonal adjustment tourism pedestrian counts time series normalization methodology STL regression weather eventsGoogle ↗
OBSERVEDReason
Times Square pedestrian counts tourism seasonal patterns NYC data pedestrian volumeGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (5)
Classes come from the query-classifier (a local Laya model). 3 of 5 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT5 real searches · gpt-6
Times Square pedestrian counts tourism seasonality NYC foot traffic data methodology seasonal adjustment tourism indicators Google ↗
seasonal adjustment time series retail foot traffic weather holidays events tourism econometric methodology Google ↗
NYC tourism monthly visitation data official NYC tourism Times Square pedestrian counts Google ↗
retail foot traffic seasonal adjustment tourism pedestrian counts time series normalization methodology STL regression weather events Google ↗
Times Square pedestrian counts tourism seasonal patterns NYC data pedestrian volume Google ↗
- Exploring Pedestrian Counting Procedures (FHWA-HPL-16-026) · fhwa.dot.gov
- Guide for Seasonal Adjustment and Crowdsourced Data Scaling · rosap.ntl.bts.gov
- Seasonal Adjustment Questions and Answers · census.gov
- Seasonal Adjustment Methodology for National Labor Force Statistics from the CPS : U.S. Bureau of Labor Statistics · bls.gov
Queries
5 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Times Square pedestrian counts tourism seasonality NYC foot traffic data methodology seasonal adjustment tourism indicators OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
seasonal adjustment time series retail foot traffic weather holidays events tourism econometric methodology OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
NYC tourism monthly visitation data official NYC tourism Times Square pedestrian counts OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
retail foot traffic seasonal adjustment tourism pedestrian counts time series normalization methodology STL regression weather events OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
Times Square pedestrian counts tourism seasonal patterns NYC data pedestrian volume OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |