PlotAnalyzer¶

Analyzes the surroundings of a Plot using OpenStreetMap data (via Overpass API).

Getting Started¶

from plot_finder import Plot, PlotAnalyzer

plot = Plot(plot_id="141201_1.0001.6509")
analyzer = PlotAnalyzer(plot, radius=2000)  # default radius: 1000m

Every method accepts an optional radius parameter to override the default.

Education¶

Find schools, kindergartens, universities, and colleges.

places = analyzer.education()
places = analyzer.education(radius=5000)

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`school`	Primary and secondary schools
`kindergarten`	Kindergartens and preschools
`university`	Universities
`college`	Colleges

Finance¶

Find ATMs and banks.

places = analyzer.finance()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`atm`	ATMs / cash machines
`bank`	Bank branches

Transport¶

Find public transport stops, train stations, and airports.

places = analyzer.transport()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`bus_stop`	Bus stops
`bus_station`	Bus stations
`tram_stop`	Tram stops
`station`	Train stations
`halt`	Small railway stops
`ferry_terminal`	Ferry terminals
`aerodrome`	Airports

Infrastructure¶

Find shops, healthcare, restaurants, services, and public buildings.

places = analyzer.infrastructure()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`supermarket`	Supermarkets
`convenience`	Convenience stores
`mall`	Shopping malls
`pharmacy`	Pharmacies
`hospital`	Hospitals
`clinic`	Medical clinics
`doctors`	Doctor offices
`dentist`	Dental clinics
`post_office`	Post offices
`fuel`	Gas / fuel stations
`police`	Police stations
`fire_station`	Fire stations
`place_of_worship`	Churches, mosques, etc.
`restaurant`	Restaurants
`cafe`	Cafes

Green Areas¶

Find parks, gardens, playgrounds, and forests.

places = analyzer.green_areas()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`park`	Public parks
`garden`	Gardens
`nature_reserve`	Nature reserves
`playground`	Playgrounds
`forest`	Forests
`wood`	Woodlands

Water¶

Find rivers, lakes, ponds, and other water bodies.

places = analyzer.water()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`water`	Generic water body
`river`	Rivers
`lake`	Lakes
`pond`	Ponds
`reservoir`	Reservoirs
`stream`	Streams
`canal`	Canals

Nuisances¶

Find power lines, transformers, industrial zones, and factories.

places = analyzer.nuisances()

for p in places:
    print(f"{p.kind}: {p.name} — {p.distance_m}m")

Kind	Description
`line`	Power lines
`transformer`	Power transformers
`industrial`	Industrial zones
`works`	Factories and plants

Climate¶

Get climate data for the plot location (last 365 days). Uses the Open-Meteo Archive API — no API key needed.

from plot_finder import Climate

climate = analyzer.climate()

print(f"Avg temp: {climate.avg_temp}°C")
print(f"Frost days: {climate.frost_days}")
print(f"Sunshine: {climate.sunshine_hours}h")
print(f"Total rain: {climate.total_rain_mm}mm")

Returns a Climate model. See the Climate docs for all fields.

Raises: OpenMeteoError

All Places (Bulk)¶

Fetch all categories in a single Overpass query instead of calling each method separately.

places = analyzer.all_places()

for category, items in places.items():
    print(f"{category}: {len(items)} places")
    for p in items:
        print(f"  {p.name} — {p.distance_m}m")

Returns dict[str, list[Place]] with keys: education, finance, transport, infrastructure, green_areas, water, nuisances.

Async

For faster execution, use AsyncPlotAnalyzer — see Async API.

How It Works¶

The plot's centroid is converted from EPSG:2180 to WGS84
An Overpass API query searches OpenStreetMap within the radius
Haversine distance is calculated from the plot centroid to each result
Results are sorted by distance (closest first)