Mapping entire cities,
in minutes instead of weeks.
Transparency: Equip Sport is where our founders work. We built this tool end to end, and the numbers below come from real usage.
$0.60 a pin, one at a time.
Every time Equip expanded into a new city, the same slow process started. An outsourced agency received a 17-page operational guide and crawled Google Maps, Street View, and satellite imagery to inventory every park, sports court, and green space in the city, one pin at a time.
Each location was catalogued in a spreadsheet: sport type, public or private, accessibility, coordinates, zip code, number of courts. Then the filtering started. Remove private clubs. Remove irrelevant sports. Copy the survivors to a new Excel sheet. Import it as a layer on MyMaps. Research the city's best neighbourhoods and crime hotspots via Google and ChatGPT. Draw a polygon for the proposed impact zone. Filter by zip code. Then review each remaining pin individually on satellite imagery: is the park too small? Full of trees? A memorial garden? Delete it from both the spreadsheet and the map.
The agency delivered a multi-sheet Excel file, a MyMaps with half a dozen hand-built layers, and an optional PowerPoint summarising the methodology, and charged roughly $0.60 per pin. A mid-size city with 800+ locations to scan meant hundreds of dollars and days of manual work, per city, and every new city started the whole process from scratch.
The same pipeline, automated.
Equip Scout replaces the outsourced mapping workflow with one internal tool. Give it a city and a set of constraints (target sports, maximum zone size, desired pin count) and it runs the same steps the agency did, in minutes instead of days.
The Scout pipeline has four steps. First, it finds every sports facility and park in the target city from multiple data sources. Second, AI classifies each location by sport type, public or private access, and suitability for a station. Third, it filters out private courts, irrelevant sports, and unsafe areas using city-specific criteria. Fourth, it proposes the optimal impact zone, weighing density, safety, and neighbourhood activity, with a map and data export.
Source. The tool automatically pulls every sports facility, park, and public court in the target city from multiple data sources, so nobody crawls Google Maps by hand.
Classify. AI tags each location from its available metadata: sport type, public or private, court count, size, and suitability for a station. The calls the agency made by eye (is this park big enough? is it covered in trees? is it a memorial garden?) now happen automatically.
Filter. The city-specific criteria that used to require manual Excel filtering and MyMaps layer gymnastics (priority sports, public-only, unsafe-area exclusion) are applied in a single pass.
Zone. Instead of someone researching neighbourhoods on Google and drawing a polygon by hand, Scout scores locations on density, neighbourhood activity, accessibility, and safety data, then proposes the optimal impact zone with a map and data export.
From an outsourced process, to an in-house tool.
Mapping a city used to be a multi-day outsourced job with several deliverables. Now it runs in minutes. A new city that would have cost hundreds of dollars in per-pin agency fees costs roughly $20 in compute and API calls, and the 17-page operational guide the agency worked from is built into the tool.
Quality also became consistent. The old process depended on whoever did the mapping that week: their judgement on which parks were "too small," their Google searches for crime data, their hand-drawn polygons. Scout applies the same criteria in every city.
We shipped the tool in three weeks, and Equip now uses it to prepare every new city deployment.
Want one of these?
Tell us where you're starting from and we'll suggest where to begin: a full build, a focused feature, or a quick production-readiness pass.