opk12
commented
3 weeks ago I am not an expert at mapping charging locations. I used visidata, especially the Frequency table menu item.
POI status
Where stato is in OUTOFORDER, INOPERATIVE, BLOCKED, RESERVED, I propose that Osmose either maps to disused:... or is silent.
CSV-to-OSM mapping cardinality
I think that id_evse is the primary key (has no duplicates) and maps to ref:EU:EVSE.
The column name numero_connettori means number of connectors.
- Most records have
numero_connettori == 1. - The 18 cases where
numero_connettori == 2need custom parsing for at leastformato_del_connettoreandstandard_del_connettore. Most of them havestato == INOPERATIVE. I think they can be ignored for simplicity.
Group types
Records can have the same id_evse from the beginning until the last asterisk, and the number after the last asterisk is different. I guess that that number identifies connectors on the same object. 22 groups are larger than 4 records, which seems wrong.
Records can have the exact same coordinates x, y. I guess this means connectors on the same object. 192 groups are larger than 4, which seems wrong. For example, 36 records have x, y == '8.02017100000006', '43.925331' and 194 records have x, y == '14.2848320000001', '40.858953'.
Grouping by ref or by coordinates returns different groups. I'm not sure which one is better.
Groups example
For example, I'm copy-pasting 3 rows. They have refs IT*BEC*EH000026*1; IT*BEC*EH000026*2; IT*BEC*EH000026*3, exact same x, y, but different last update date (data_ultimo_aggiornamento) and different power (potenza_erogabile).
x,y,objectid,id_location,nome_location,indirizzo,regione,citta,codice_postale,provincia,orario_d_apertura,id_univoco_evse,id_evse,stato,numero_connettori,id_connettore,standard_del_connettore,formato_del_connettore,tipologia_di_alimentazione,voltaggio_massimo,amperaggio_massimo,potenza_erogabile,stato_di_pubblicazione,longitudine_evse,latitudine_evse,capabilities,data_ultimo_aggiornamento,data_di_caricamento,fasce_di_potenza,comune,regione_cleaned,comune_cleaned,codice_comune_formato_alfanumerico
2.5954570000001,44.0550230000001,17523,0106aa9b-f26b-431c-9a6f-5eb2d415b10c,L.mare G. di Vittorio Ang. Città delle Donne,L.mare G. di Vittorio Ang. Città delle Donne,Emilia-Romagna,Rimini,47921,Rimini,Aperto 24/7,0024324f-feee-4f02-aafb-76a9c55779da,IT*BEC*EH000026*2,AVAILABLE,1,ac563b36-7740-41b7-84bc-f0fa622535a3,IEC_62196_T2_COMBO,CABLE,DC,550,200,110000,PUBLISHED,12.59546,44.05502,"REMOTE_START_STOP_CAPABLE, RFID_READER",1709834285000,1711383890000,Ultraveloce o Ultra-fast,Rimini,Emilia-Romagna,Rimini,099014
12.5954570000001,44.0550230000001,17521,0106aa9b-f26b-431c-9a6f-5eb2d415b10c,L.mare G. di Vittorio Ang. Città delle Donne,L.mare G. di Vittorio Ang. Città delle Donne,Emilia-Romagna,Rimini,47921,Rimini,Aperto 24/7,aa76dcb4-bf1f-457f-9843-f0b4f13c4e43,IT*BEC*EH000026*3,AVAILABLE,1,c8ba808c-6895-4a77-9874-7d18ece11e19,IEC_62196_T2,CABLE,AC_3_PHASE,400,32,22144,PUBLISHED,12.59546,44.05502,"REMOTE_START_STOP_CAPABLE, RFID_READER",1709824107000,1711383890000,Veloce o Fast,Rimini,Emilia-Romagna,Rimini,099014
12.5954570000001,44.0550230000001,17524,0106aa9b-f26b-431c-9a6f-5eb2d415b10c,L.mare G. di Vittorio Ang. Città delle Donne,L.mare G. di Vittorio Ang. Città delle Donne,Emilia-Romagna,Rimini,47921,Rimini,Aperto 24/7,bc5e4aa2-65b2-47be-97c4-67170732f5fb,IT*BEC*EH000026*1,AVAILABLE,1,951f88f0-c8d3-4d9f-b468-0ce1a3c05c1b,IEC_62196_T2_COMBO,CABLE,DC,550,200,110000,PUBLISHED,12.59546,44.05502,"REMOTE_START_STOP_CAPABLE, RFID_READER",1709831904000,1711383890000,Ultraveloce o Ultra-fast,Rimini,Emilia-Romagna,Rimini,099014Groups code
This example finds the groups.
import pandas as pd
def until_last_asterisk(self: str) -> str:
return self[:self.rindex('*')]
csv = pd.read_csv('rete_ricarica_veicoli_elettrici_cleaned.csv', dtype=str)
# Group by the prefix of id_evse and by the couple of coordinates.
csv['base_of_id_evse'] = csv.id_evse.map(until_last_asterisk)
csv['xy'] = csv[['x', 'y']].apply(tuple, axis=1)
# Groups with more than 100 entries, more than 4 entries, ...
[group for group in map(tuple, csv.groupby('xy').groups.values()) if len(group) > 100]
[group for group in map(tuple, csv.groupby('base_of_id_evse').groups.values()) if len(group) > 4]
x1, y1 = '14.2848320000001', '40.858953'
csv.loc[(csv.x == x1) & (csv.y == y1)] # 194 rowsForeign key
The ref:EU:EVSE syntax is like ref:EU:EVSE=DE*EBW*E4032*1; DE*EBW*E4032*2; DE*EBW*E4032*3, from the first example here. Note that the semicolon is followed by a space.
import itertools
def until_last_asterisk(self: str) -> str:
return self[:self.rindex('*')]
test_group = ['XX*F2X*ABCDITO1234*1', 'IT*F2X*EF2XITO1051*1', 'IT*F2X*EF2XITO1051*2']
['; '.join(group) for _, group in itertools.groupby(test_group, until_last_asterisk)]Coordinates
The coordinates columns are x, y and latitudine_evse, longitudine_evse.
Feature tag
I propose amenity=charging_station rather than man_made=charge_point, because Tag:amenity=charging_station and man_made=charge_point say that
- mapping the
charging_stationis mandatory anyway, mappingcharge_points is optional - a station with only one charge point has to be mapped as a station, according to the examples from Tag:amenity=charging_station and to Key:ref:EU:EVSE
Disclaimer: I'm not an expert at mapping charging locations.
source
source = https://www.piattaformaunicanazionale.it/
The column name data_ultimo_aggiornamento means date of the last update, data_di_caricamento means date of publishing.
Maybe something like
# Dividing by 1000, because the format seems to be milliseconds from the Unix epoch.
print(datetime.date.fromtimestamp(int("1709834285000") / 1000).strftime("%Y-%m-%d"))
# Output:
# 2024-03-08
frodrigo
The Italian community discussed this dataset of charging locations, which is updated regularly and derived from the government data. The Italian community assumes CC-BY-4-equivalent licensing, to be safe.
Osmose can
ref:EU:EVSE. Importsource,source:date.Thank you @aborruso for improving and maintaining the dataset on OnData. Thank you @cascafico for help with understanding the dataset and for independent verification of its quality.
Additional information The CSV is missing many (surveyed) charging locations. This should improve over time, as more companies submit their locations to the gov.
Point 2 helps against some uncooperative mass-importers of non-existent charging locations, that were discussed here.
As the dataset lacks an explicit license, Italian law assigns open by default, which Italian law constrains to at most attribution, at least gratis use (even gratis commercial use). More details at Wikipedia.
You can find the original gov dataset's URL here in the transformation script. I think the gov URL's shape looks more prone to breakage than the GitHub URL. That script is explained here.
Keywords - rete_ricarica_veicoli_elettrici_cleaned, rete_ricarica_veicoli_elettrici, rete ricarica veicoli elettrici, colonnine di ricarica elettrica per automobili (auto) elettriche.