| # EV Charging Stations in Finland — Merged Dataset |
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| **File:** `ev_stations_merged.csv` |
| **Rows:** 5,410 (one row per charging location) · **Columns:** 25 |
| **Companion file:** `merge_review.csv` (3,158 rows - the matched pairs only, for manual review) |
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| This dataset combines two independently collected lists of public EV charging locations in Finland into a single table. It is the base table for the DSP project: recommending 50 new fast-charging locations. |
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| ## 1. Source datasets |
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| | | Google Places API | Digitraffic | |
| |---|---|---| |
| | Records | 4,944 | 3,848 | |
| | Unit | One Google place | One operator-listed charging station | |
| | Strengths | Address, opening hours, ratings, reviews | Operator, connector counts, AC/DC split, max power | |
| | ID used | Google Place ID (`ChIJ…`) | Operator's own station ID | |
| | Columns prefixed/named | `google_name`, `formattedAddress`, `rating`, … | `hf_*`, `total_*`, `operator_name`, `max_power_kw`, … | |
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| The two datasets describe the same physical sites from different angles, so they were matched by location and name and combined. |
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| **Naming note:** in column names and `source` values, the prefix `hf` refers to the Digitraffic dataset (`hf_only`, `hf_station_ids`, `hf_rows`, …). |
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| ## 2. How the merge works |
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| Each Google place was matched to Digitraffic stations that lie **within 100 m** of it (the largest matched distance is 99.8 m). Name similarity was recorded as well, but it is low for almost all pairs (median 0.29). That's because Google names are brand labels ("Virta Charging Station"), while Digitraffic names are site names ("Turun Toriparkki - K1"). **In practice, distance is what decided the matches.** |
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| Every record from both datasets ends up in the merged file. Nothing was dropped. |
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| | `source` | Rows | Google records | Digitraffic records | Meaning | |
| |---|---|---|---|---| |
| | `both` | 3,158 | 3,158 | 3,382 | Google place matched to ≥1 Digitraffic station | |
| | `google_only` | 1,786 | 1,786 | — | Google place with no match in Digitraffic data | |
| | `hf_only` | 466 | — | 466 | Digitraffic station with no match in Google data | |
| | **Total** | **5,410** | **4,944** | **3,848** | | |
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| ### Why the mismatch in Digitraffic: |
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| 3,848 - 3,158 = 690, but there are only 466 `hf_only` rows. The other **224** Digitraffic records weren't lost. **175 Google places matched 2–9 Digitraffic stations each**, and those stations were folded into the one row: |
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| - names and IDs are joined with `; ` in `hf_station_names` / `hf_station_ids` |
| - connector and EVSE counts are **summed** |
| - `hf_rows` records how many Digitraffic records were combined (1–9) |
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| Example: one Google place at Turun Toriparkki carries 5 Digitraffic records (separate parking levels), totalling 34 connectors. |
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| ### Reconciliation checks |
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| ```python |
| import pandas as pd |
| ev = pd.read_csv('ev_stations_merged.csv') |
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| ev['source'].value_counts() # both 3158, google_only 1786, hf_only 466 |
| (ev['source'] != 'hf_only').sum() # 4944 = Google total |
| ev['hf_rows'].sum() # 3848 = Digitraffic total |
| ev.loc[ev.source == 'both', 'hf_rows'].sum() # 3382 Digitraffic records inside matches |
| ``` |
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| --- |
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| ## 3. Columns |
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| **Where each column comes from:** **G** = Google Places API · **D** = Digitraffic (the `hf_*` columns) · **Merge** = created during the merge |
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| ### Identity & location |
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| | Column | From | Type | Description | |
| |---|---|---|---| |
| | `place_key` | Merge | str | Row identifier. Google Place ID for `both` and `google_only` rows. For `hf_only` rows, the Digitraffic station ID copied as-is. **Not fully unique**, see §5. | |
| | `source` | Merge | str | `both`, `google_only` or `hf_only` (see §2). | |
| | `name` | G / D | str | Display name. Google's name for `both`/`google_only` rows. Digitraffic station name for `hf_only` rows. Google names are **brand labels** ("K-Lataus Charging Station"), not site names. | |
| | `lat`, `lng` | G / D | float | WGS84 coordinates. Always filled. For matched rows it isn't documented which dataset supplied them. | |
| | `city` | D | str | Municipality/town. **Null for all `google_only` rows**, but recoverable from `formattedAddress`. | |
| | `postalCode` | G / D | str | 5-digit Finnish postal code. Missing on 27% of `hf_only` rows. | |
| | `formattedAddress` | G | str | Full Google address, e.g. `Kauppamiehentie 1, 21710 Pargas`. | |
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| ### Google attributes (Google Places API) |
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| | Column | Type | Range | Description | |
| |---|---|---|---| |
| | `google_name` | str | — | Google's place name (same as `name` where present). | |
| | `openingHours` | str | — | Weekly hours as text. 4,022 rows are "Open 24 hours" every day. | |
| | `rating` | float | 1.0–5.0 | Average Google rating. Only filled on 28% of rows. | |
| | `userRatingCount` | float | 1–2,880 | Number of Google reviews. **Median is 2**, so most ratings are based on very few reviews. | |
| | `connectorCount` | float | 1–367 | Google's count of connectors. | |
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| ### Charger attributes (Digitraffic) |
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| | Column | Type | Range | Description | |
| |---|---|---|---| |
| | `operator_name` | str | 70 values | Charging operator, e.g. Liikennevirta, eParking, ABC, K Auto Oy. | |
| | `total_evses` | float | 1–264 | Charge points (EVSEs), summed across folded records. | |
| | `total_connectors` | float | 1–264 | Connectors, summed across folded records. | |
| | `total_dc_connectors` | float | 0–40 | DC (fast) connectors. | |
| | `total_ac_connectors` | float | 0–264 | AC (slow/medium) connectors. | |
| | `max_power_kw` | float | 0.9–1,200 | Highest charging power at the site, in kW. Median is 22. | |
| | `highest_power_type` | str | — | Power class: `AC_SLOW`, `AC_MEDIUM`, `DC_SLOW`, `DC_FAST`, `DC_ULTRA_FAST_LEVEL_1`, `DC_ULTRA_FAST_LEVEL_2`. Folded rows can list several classes separated by `; `. | |
| | `hf_station_names` | str | — | Digitraffic station name(s), `; `-separated if folded. | |
| | `hf_station_ids` | str | — | Digitraffic station ID(s), `; `-separated if folded. | |
| | `hf_rows` | float | 1–9 | Number of Digitraffic records combined into this row. | |
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| ### Match diagnostics (`both` rows only) |
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| | Column | Type | Range | Description | |
| |---|---|---|---| |
| | `dist_m` | float | 0–99.8 | Distance in metres between the Google place and the Digitraffic station. Median is 1.5. | |
| | `name_sim` | float | 0–1 | Name similarity score. Mostly low because of brand-vs-site naming, so it's weak evidence for or against a match. | |
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| ## 4. What's missing, and where |
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| Columns are missing in blocks, depending on `source`. Filter on `source` before treating a missing value as zero. |
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| | Columns | `both` | `google_only` | `hf_only` | |
| |---|---|---|---| |
| | Charger attributes (`operator_name`, `total_*`, `max_power_kw`, …) | ✓ | **all empty** | ✓ | |
| | Google attributes (`formattedAddress`, `openingHours`, `rating`, …) | ✓ | ✓ | **all empty** | |
| | `city` | ✓ | **all empty** | ✓ | |
| | `dist_m`, `name_sim` | ✓ | empty | empty | |
| | `rating` / `userRatingCount` | 37% filled | 20% filled | empty | |
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| **Power is unknown for 1,789 rows (33%).** These are the `google_only` rows, plus 3 `hf_only` rows. |
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| ## 5. Known issues |
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| 1. **Most rows aren't fast chargers.** Only **1,266 rows have `max_power_kw` ≥ 50** (829 are ≥ 150). The most common value is 7.36 kW (1,182 rows). For fast-charging analysis, filter first, for example with `total_dc_connectors > 0` or `max_power_kw >= 50`. Treat the power-unknown rows as a separate group rather than dropping them. |
| 2. **`place_key` has 6 duplicates** (`232`, `234`, `235`, `236`, `373`, `900`). Some operators number their stations with plain integers, and two operators can use the same number. Each pair is two different sites 85–333 km apart. Fix by adding the operator name to the Digitraffic IDs: |
| ```python |
| ev['place_key'] = ev['place_key'].where(ev.source != 'hf_only', |
| ev.operator_name + ':' + ev.place_key.astype(str)) |
| ``` |
| The same collision affects 30 IDs inside `hf_station_ids`. |
| 3. **Brand names are spelled inconsistently.** `Virta` / `VIRTA` / `Virta Global Charging Station` together cover 1,607 rows of one brand. Similar pairs appear in `operator_name` (`Helen Oy` / `Helen`, `Neste MY` / `Neste My`). `name` calls the brand "Virta", while `operator_name` calls it "Liikennevirta". |
| 4. **`highest_power_type` values in a different order count as different values.** `AC_SLOW; DC_FAST` and `DC_FAST; AC_SLOW` are the same thing. Split the string, sort, and rejoin before grouping. |
| 5. **Folded rows can hide separate sites.** 175 rows combine 2–9 Digitraffic stations. If those stations are far apart, the row overstates one location. Check rows where `hf_rows > 1`. |
| 6. **Matches 30 m or more apart:** there are 562. Their connector counts agree with Google's about as often as closer matches do (90% vs 89%), so distance alone doesn't mark a match as wrong. |
| 7. **`connectorCount` (G) and `total_connectors` (D) disagree** on about 11% of matched rows. Pick a rule for which one to trust, and record which source each value came from. |
| 8. **Minor:** 2 rows are outside Finland's bounding box. 14 coordinate pairs are shared by 28 rows. 6 names have leading/trailing spaces. 2 postal codes aren't 5 digits. |
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| ## 6. `merge_review.csv` |
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| Contains the same 3,158 matched pairs as the `source == 'both'` rows, sorted by `name_sim` ascending so the least similar names come first. Columns: `google_name`, `hf_station_names`, `dist_m`, `name_sim`, `operator_name`. |
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| It has **no `place_key`**, and `google_name` only has 97 distinct values. So don't join it back to the merged file on names. Filter the merged file's own `dist_m` / `name_sim` columns instead. |
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| ## 7. Compatibility with other datasets |
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| Can be combined with the OSM - Point of Interest dataset or with the traffic volume dataset for exploring demand. |
