README.md

# 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

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


```python
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()
```

```python
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()
```

```python
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()
```