SAMPLE POI Point of Interest Data for all Businesses in Peru

Techsalerator/SAMPLE POI Point of Interest Data for all Businesses in Peru is a Foot Traffic & Mobility data product published on databricks-marketplace and indexed by The Alternative Data Encyclopedia.

Techsalerator publishes SAMPLE POI Point of Interest Data for all Businesses in Peru as a databricks-marketplace offering in the Foot Traffic & Mobility signal family.

The data

Techsalerator maintains it as part of its Foot Traffic & Mobility portfolio and lists it through databricks-marketplace.

Structure, access and licensing

License: other. Pricing: Free (Databricks Marketplace).

Access is through a dataset that is obtained from the databricks-marketplace listing. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Mobility data derived from opt-in app panels and aggregated geolocation, converted into visit counts at points of interest such as stores, restaurants, malls and workplaces. The signal approximates physical-world activity — above all store traffic — before companies publish comparable figures.

Foot traffic leads reported revenue for location-based businesses, so visit trends front-run retail comps, restaurant sales and mall performance by weeks. Analysts track pandemic-style recoveries block by block, validate expansion or closure decisions through openings data, and screen REIT tenants on footfall momentum. Because visits are observed for competitors simultaneously, the data also reveals share shifts between physical chains that neither discloses directly. Raw pings are filtered into a stable device panel, attributed to point-of-interest polygons, and delivered as venue-level series of visits, unique visitors and dwell time. Venue metadata includes brand, category chain affiliation, opening status and precise building footprints; leading providers catalog tens of millions of POIs across nearly every country. Derived layers add trade-area demographics, cross-shopping matrices, migration patterns and indexed same-store traffic.

Location pings arrive from software-development kits embedded in consumer apps under consent frameworks, then pass device-stability filters and demographic weighting to correct panel skew. Vendors map coordinates to curated POI geometries, deduplicate multi-device users, and calibrate raw visit counts against benchmarks such as reported sales where available. Delivery is typically daily or weekly as indexes rather than absolute counts, with suppression rules applied to small venues and sensitive categories.

Caveats and compliance

Panels inherit the demographic and geographic biases of their source apps, underweighting rural and older populations. POI matching errors and venue closures inject structural breaks into time series. Dwell time is noisy across device types, and absolute visit counts should be treated as indexes rather than headcounts.

Precise location is sensitive personal data under GDPR and CCPA, so buyers must verify documented opt-in consent, minimum aggregation thresholds and re-identification testing. Reputable vendors suppress sensitive venues such as clinics and places of worship and restrict export of device-level traces.

Who uses this signal

Retail, restaurant and REIT analysts estimate same-store trajectories and tenant health from visit series; consumer funds use them as a real-time read on discretionary spending. Corporate users apply the same tools to site selection and field marketing.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading