Guides & examples
How-to guides and customer stories. Pick a goal or browse by theme.
How-to guides
6What can be done with the data: concept-level guides to each analysis, independent of tools.
Brand Comparison
Compare brands with location insights (scale, typical store performance, and development over time) in support of competitive intelligence and market understanding.
Store Profiling
Build a complete picture of a single location: visit volume against the chain and category, weekly rhythm, who visits, and where they come from.
Area Identification
Score every area in a market on explicit criteria (activity, mix, momentum, seasonality, timing, audience, presence) and combine them into a ranked shortlist.
Examples
9Worked, runnable recipes, country-specific and run end-to-end on real data.
Matching a store list to location identifiers
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.
Grocery brand comparison
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.
Profiling an H-E-B store in San Antonio
One store, full picture in lean Python: trend vs. chain, competitors and category, home and work catchment, audience, journeys, and the weekly rhythm.
Grocery penetration block by block in Denver
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
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.
Finding candidate malls for an in-mall retailer
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.
Finding Denver's up-and-coming neighborhoods
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.
Reporting the 16th Street reopening in Denver
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
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.
Using the platform
5First steps and capability walkthroughs, from your first query to bulk exports and custom locations.
Make your first query
Get access, create an API key, and run your first query against the catalog API, with cURL, and the response shapes explained.
Build with AI
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.
Use Pine59 without code
Browse the catalog, build dashboards, and download samples through Insights and Console. No SDK or API key required.