data-sci-project/osm_point_of_interest

Finland EV charging amenity POIs

This CSV is a point-of-interest (POI) dataset for exploring amenities near potential EV charging sites in Finland. It was derived from an Overpass export of OSM nodes, ways, and relations. Each record represents one existing OSM element which is relevant to an ev charger placement.

Data collection

The Overpass query selected Finnish elements tagged amenity=cafe, fast_food, fuel, or restaurant; shop=mall, supermarket, or convenience; tourism=attraction; or leisure=park, sports_centre, or fitness_centre. It used out center tags;. The dataset reflects the OSM export at the time of 16/10/2026.

CSV schema

Column	Meaning	GeoJSON source
osm_id	Numeric OSM identifier; use with type to identify an element	Numeric part of properties["@id"]
type	OSM element type (node, way, relation)	Prefix of properties["@id"]
name	POI name; all retained rows have a nonblank value	properties["name"]
latitude	Latitude in decimal degrees	geometry.coordinates[1]
longitude	Longitude in decimal degrees	geometry.coordinates[0]
address_city	Tagged city, null if unavailable	properties["addr:city"]
category	Selected OSM tag value	First present value among amenity, leisure, tourism, shop
posti_alue	Tagged postcode, this property was taken from the paavo postal codes dataset due to missing values in the original dataset	----

GeoJSON stores positions as [longitude, latitude]. For ways and relations, the exported point is the Overpass center, which is a representative coordinate; it is not the full geometry or necessarily an entrance.

Preliminary EDA

import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv("osm_postal_codes.csv")
df.info()

counts = df["category"].value_counts()
counts.sort_values().plot(kind="barh")
plt.xlabel("Number of POIs")
plt.ylabel("Category")
plt.title("Distribution of POI Categories in Finland")
plt.tight_layout()
plt.show()

plt.figure(figsize=(12,16))
plt.scatter(df["longitude"],df["latitude"],s=2,alpha=0.3)
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title("Spatial Distribution of Relevant POIs in Finland")
plt.show()

top_cities = df["address_city"].value_counts().head(10).index
city_category = (df[df["address_city"].isin(top_cities)].groupby(["address_city", "category"]).size().unstack(fill_value=0))
city_category.plot(kind="bar",stacked=True,figsize=(12, 6))

plt.ylabel("Number of POIs")
plt.xlabel("City")
plt.title("POI Composition of Major Finnish Cities")
plt.legend(title="Category", bbox_to_anchor=(1.05, 1))
plt.tight_layout()
plt.show()