# Pine59 docs

> Documentation for the Pine59 platform: foot traffic, visit length, trade areas, and related metrics across the Nordics and the USA. Every page below is also available as Markdown by appending `.md` to the URL.

## Get started
- [Welcome](http://console.pine59.com/docs/get-started.md): Onboarding hub. Pick a starter guide.

## Guides
- [Guides & examples](http://console.pine59.com/docs/guides.md): How-to guides and customer stories. Pick a goal or browse by industry.
- [Make your first query](http://console.pine59.com/docs/guides/first-query.md): Get access, create an API key, and run your first query against the catalog API, with cURL, and the response shapes explained.
- [Match your locations](http://console.pine59.com/docs/guides/match-locations.md): Bring your own list of stores or sites and match them to Pine59's location identifiers so you can query our data using your IDs.
- [Matching a store list to location identifiers](http://console.pine59.com/docs/guides/match-locations/match-addresses-api.md): Four San Antonio addresses matched in Python: exact address, disambiguation by brand, a value search for the one the list misspelled, and the join the mapping enables.
- [Build with AI](http://console.pine59.com/docs/guides/build-with-ai.md): Point Claude, Cursor, or other AI clients at the Pine59 docs and API to list datasets, query metrics, and explain results without writing glue code.
- [Export data in bulk with Python](http://console.pine59.com/docs/guides/bulk-export.md): Run a query server-side as an asynchronous export job and download the results as files: create, poll, download, straight into pandas.
- [Use Pine59 without code](http://console.pine59.com/docs/guides/no-code.md): Browse the catalog, build dashboards, and download samples through Insights and Console. No SDK or API key required.
- [Brand Comparison](http://console.pine59.com/docs/guides/brand-comparison.md): Compare brands with location insights (scale, typical store performance, and development over time) in support of competitive intelligence and market understanding.
- [Grocery brand comparison](http://console.pine59.com/docs/guides/brand-comparison/us-grocery-brand-comparison.md): Four grocery chains head to head in Node.js: overall vs. typical store, same-store development with the share of stores growing, and the audience fingerprint.
- [Store Profiling](http://console.pine59.com/docs/guides/store-profiling.md): Build a complete picture of a single location: visit volume against the chain and category, weekly rhythm, who visits, and where they come from.
- [Profiling an H-E-B store in San Antonio](http://console.pine59.com/docs/guides/store-profiling/us-heb-san-antonio-store-profile.md): One store, full picture in lean Python: trend vs. chain, competitors and category, home and work catchment, audience, journeys, and the weekly rhythm.
- [Void Analysis](http://console.pine59.com/docs/guides/void-analysis.md): Find what a market lacks: compare an area's measured demand against references such as analog areas, competitor networks, or your own stores, then validate each gap with real movement.
- [Grocery penetration block by block in Denver](http://console.pine59.com/docs/guides/void-analysis/us-grocery-penetration-denver.md): Trade areas times store visits, joined in DuckDB: captured visits, the leading grocer, and saturation per block group, ending in a headroom shortlist.
- [Mapping Sprouts' white space in Denver](http://console.pine59.com/docs/guides/void-analysis/us-sprouts-white-space-denver.md): The brand white-space variant in Python: profile Sprouts' strongest catchments, screen the competitor network for look-alike areas the brand has not entered (16 candidates in Denver, 2025), then test each against measured journeys.
- [Analog Modeling: Look-Alike Site Selection](http://console.pine59.com/docs/guides/analog-modeling.md): Let your own results set the criteria: profile the locations where your stores perform, then screen a market for look-alike locations you haven't entered.
- [Finding candidate malls for an in-mall retailer](http://console.pine59.com/docs/guides/analog-modeling/us-candidate-malls-colorado.md): Classify a state's malls into a handful of transparent classes, join explicitly fictional internal sales, and shortlist the look-alike centers with no store yet, in Python on calendar-2025 data.
- [Area Identification](http://console.pine59.com/docs/guides/area-identification.md): Score every area in a market on explicit criteria (activity, mix, momentum, seasonality, timing, audience, presence) and combine them into a ranked shortlist.
- [Finding Denver's up-and-coming neighborhoods](http://console.pine59.com/docs/guides/area-identification/us-cbg-scoring.md): Score every Denver block group on 2025 activity, destination share, momentum (Jul–Nov 2025 vs. Jul–Nov 2024), summer index and audience, combine them with explicit weights, and stress-test the shortlist, in Python.
- [Area Development Reporting](http://console.pine59.com/docs/guides/area-development-reporting.md): Report on the areas you answer for, either evaluating what an investment changed or continuously measuring an existing asset, with activity, mix, timing, and audience developments against a benchmark.
- [Reporting the 16th Street reopening in Denver](http://console.pine59.com/docs/guides/area-development-reporting/us-16th-street-denver.md): The before-and-after lens on a real intervention, in bash and jq: the corridor's share of metro visits through construction and reopening, the quarterly spread against the benchmark, and the four-line report.
- [Tracking Grünerløkka's district trend](http://console.pine59.com/docs/guides/area-development-reporting/no-grunerlokka-tracking.md): The continuous lens on Oslo's canonical district: 3.5 years of daily data resolved by name, the standing scorecard (the district up against a flat Oslo benchmark, reversing two soft years, visitor share at a four-year high), dwell stability, and the consortium read.

## Datasets
- [Datasets](http://console.pine59.com/docs/datasets.md): Pick a country to browse the datasets available there.
- [Coverage matrix](http://console.pine59.com/docs/datasets/coverage.md): Flat list of every dataset currently available, one row per (country × metric × location set × cadence) combination.
- [USA — datasets](http://console.pine59.com/docs/datasets/usa.md): 
- [USA — metric models](http://console.pine59.com/docs/datasets/usa/metric-models.md): Country-specific metric models for USA.
- [USA — location sets](http://console.pine59.com/docs/datasets/usa/location-sets.md): Concrete collections of locations available in USA.
- [USA — signal sources](http://console.pine59.com/docs/datasets/usa/supply.md): Underlying signals feeding USA metric models.
- [Foot Traffic Annual — Safegraph Places (USA)](http://console.pine59.com/docs/datasets/usa/foot-traffic-annual-safegraph-places-usa-yearly.md): Annual aggregated foot-traffic counts at Safegraph places across the United States. Suitable for year-over-year trend analysis at the national or regional level.
- [Foot Traffic — USA metric model](http://console.pine59.com/docs/datasets/usa/metric-models/foot-traffic-place-usa.md): Aggregated foot traffic from panel-based visit signals against Safegraph place geometry.
- [Safegraph Places — USA](http://console.pine59.com/docs/datasets/usa/location-sets/safegraph_places_usa.md): The Safegraph point-of-interest set covers the United States with several million places including retail, dining, services, and public venues.
- [GPS — USA supply source](http://console.pine59.com/docs/datasets/usa/supply/gps-usa.md): Panel-based GPS observations from a representative sample of US mobile users. Used as the primary signal for USA foot-traffic and movement metrics.

## Concepts
- [Concepts](http://console.pine59.com/docs/concepts.md): Abstract data model: location types, metrics, methodology, and privacy.
- [Location types — overview](http://console.pine59.com/docs/concepts/location-types.md): Place, Area, Way: the abstract categories that determine which metrics are available.
- [Place (location type)](http://console.pine59.com/docs/concepts/location-types/place.md): A point of interest: a building, a store, a venue. Place datasets describe the activity *at* a specific physical location.
- [Area (location type)](http://console.pine59.com/docs/concepts/location-types/area.md): A bounded geographical region: a neighborhood, a region, a grid cell, a ZIP code. Area datasets describe activity *within* an area, useful when point-of-interest granularity is too narrow.
- [Way (location type)](http://console.pine59.com/docs/concepts/location-types/way.md): A linear feature like a road segment or pedestrian path. Way datasets describe traffic *along* a route.
- [Techniques — overview](http://console.pine59.com/docs/concepts/techniques.md): The general techniques behind the data: privacy protections, visit detection, and modelling.
- [Visit assignment (technique)](http://console.pine59.com/docs/concepts/techniques/visit-assignment.md): What counts as a visit, how staypoints are detected, and how positions are expressed as H3 hexagon distributions.
- [Geolocation (technique)](http://console.pine59.com/docs/concepts/techniques/geolocation.md): Bayesian placement of devices within an antenna's coverage area, refined with contextual layers (land use, OSM, dwell models). Replaces the older population-weighted grid method.
- [Home & work inference (technique)](http://console.pine59.com/docs/concepts/techniques/home-and-work.md): Estimating the likely home and work areas of visitor groups, always at area level and never an address, to enable resident / worker / visitor breakdowns, trade areas, and visitor-only scoping for Visit Length.
- [Extrapolation (technique)](http://console.pine59.com/docs/concepts/techniques/extrapolation.md): Scaling the observed sample of devices up to population-level visit estimates, so counts reflect real-world volume rather than sample size.
- [Trips & journeys (technique)](http://console.pine59.com/docs/concepts/techniques/trips-and-journeys.md): How sequences of visits are grouped into trips and journeys for Way and ODM data.
- [Path inference (technique)](http://console.pine59.com/docs/concepts/techniques/path-inference.md): Determining the most likely route a device took through the road network, required for Traffic data and refines mode-of-transport assignment.
- [K filtering (technique)](http://console.pine59.com/docs/concepts/techniques/k-filtering.md): No statistic is published unless it represents at least a minimum number of visitors. Why category counts may not sum to totals.
- [Differential privacy (technique)](http://console.pine59.com/docs/concepts/techniques/differential-privacy.md): Small unbiased noise added to Trips and ODM aggregates so individual-level inference is impossible while statistical use is unaffected.
- [Privacy approach (technique)](http://console.pine59.com/docs/concepts/techniques/privacy-approach.md): Pine59 products are built for statistical use only, and no output can be linked to an individual person.
- [Foot Traffic (place metric)](http://console.pine59.com/docs/concepts/metrics/foot-traffic.md): Aggregated visit counts to a place over a time period. Foot traffic is the most widely used Pine59 metric and the foundation for site comparison, trend analysis, and competitive benchmarks.
- [Visit Length (place metric)](http://console.pine59.com/docs/concepts/metrics/visit-length.md): Distribution of visit durations at a place. Useful for distinguishing dwell-heavy venues (cafés, gyms) from quick-stop venues (gas stations, convenience).
- [Popular Times (place metric)](http://console.pine59.com/docs/concepts/metrics/popular-times.md): Hour-of-day and day-of-week visitation patterns at a place. Reveals peak hours, off-peak windows, and weekday/weekend differences.
- [Visitor Profile (place metric)](http://console.pine59.com/docs/concepts/metrics/visitor-profile.md): Aggregated profile of visitors to a place: home regions, demographics, behavioral cohorts. Useful for understanding catchment areas and audience composition.
- [Visitor Demographics (place metric)](http://console.pine59.com/docs/concepts/metrics/visitor-demographics.md): Demographic breakdown of visitors derived from census-linked home areas. Returns fractional shares (0.0–1.0), not absolute counts.
- [Visitor Journey (place metric)](http://console.pine59.com/docs/concepts/metrics/visitor-journey.md): Sequences of places visited before and after a target place. Reveals cross-shopping, shared catchments, and complementary venues.
- [Trade Area (place metric)](http://console.pine59.com/docs/concepts/metrics/trade-area.md): The geographic area from which a place draws its visitors. Useful for catchment sizing and competitive overlap analysis.
- [Foot Traffic w/ Visitor, Worker & Resident (area metric)](http://console.pine59.com/docs/concepts/metrics/foot-traffic-vwr.md): Foot-traffic at the area level split by visitor / worker / resident roles. Lets you separate transient activity from baseline residential activity.
- [Visit Length (area metric)](http://console.pine59.com/docs/concepts/metrics/visit-length-area.md): Distribution of visit durations within an area.
- [Visitor Profile (area metric)](http://console.pine59.com/docs/concepts/metrics/visitor-profile-area.md): Aggregated profile of visitors entering an area.
- [Visitor Demographics (area metric)](http://console.pine59.com/docs/concepts/metrics/visitor-demographics-area.md): Demographic breakdown of visitors to an area.
- [Origin-Destination Matrix (area metric)](http://console.pine59.com/docs/concepts/metrics/origin-destination.md): Flows of visitors between origin and destination areas over a time window. Foundational for transportation modeling and spatial-economy analysis. Currently Nordics-only.
- [Trade Area (area metric)](http://console.pine59.com/docs/concepts/metrics/trade-area-area.md): The geographic area that contributes the most visitors to a target area.
- [Traffic (way metric)](http://console.pine59.com/docs/concepts/metrics/traffic.md): Aggregated traffic counts on road segments. Segments come from OpenStreetMap (OSM), so every count is keyed to a public, stable way id you can join against your own network data. Coverage is the classified through-road network, from motorway down to tertiary. Currently Nordics-only.
- [Origin-Destination Matrix (Way) (way metric)](http://console.pine59.com/docs/concepts/metrics/origin-destination-way.md): Flows along way segments. Currently Nordics-only.

## Methodology
- [Methodology — USA](http://console.pine59.com/docs/datasets/usa/methodology.md): How USA data is produced. Scoped to the signals that actually serve USA.
- [Home & work inference — USA](http://console.pine59.com/docs/datasets/usa/methodology/home-and-work.md): Estimating the likely home and work areas of visitor groups, always at area level and never an address, to enable resident / worker / visitor breakdowns, trade areas, and visitor-only scoping for Visit Length.
- [Extrapolation — USA](http://console.pine59.com/docs/datasets/usa/methodology/extrapolation.md): Scaling the observed sample of devices up to population-level visit estimates, so counts reflect real-world volume rather than sample size.
- [Privacy approach — USA](http://console.pine59.com/docs/datasets/usa/methodology/privacy-approach.md): Pine59 products are built for statistical use only, and no output can be linked to an individual person.

## API
- [API reference (OpenAPI spec)](http://console.pine59.com/docs/api.md): Machine-readable OpenAPI 3 spec for the Pine59 HTTP API. Includes every endpoint, request/response schema, auth scheme, and curl examples. Fetch as JSON from https://unacastapis.com/openapi.json.