BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//talk//E9Z8S W BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2025-E9Z8SW@talks.staging.osgeo.org DTSTART;TZID=CET:20250717T150000 DTEND;TZID=CET:20250717T150500 DESCRIPTION:The transition towards more sustainable transport together with a worldwide push for decarbonization promotes the adoption of light-duty electric vehicles (EVs). Nevertheless\, for EVs to run on par with or bett er than internal combustion engine vehicles\, they require convenient enou gh charging infrastructure (Knez et al.\, 2019). EV charging infrastructur e must accommodate shifting demands in terms of density (queuing)\, freque ncy (coverage gaps)\, and dependability (outage) (Hanig et al.\, 2025). Ev en if only a small fraction of all car trips are longer than 50 miles (wel l within the range of today's EVs)\, long-distance drivers' concerns about charging tend to have a disproportionate effect on their decision to buy a car (Haidar et al.\, 2022). Moreover\, changing stations availability ca n be critical when choosing a turistic destination.\nThis research project analyzes the availability of EV charging stations in the Provincia Autono ma di Trento (PAT)\, a region in the Italian eastern Alps\, a popular tour istic destination for Italians and northern Europeans.\nWhile an Italian n ational repository\, PUN\, "Piattaforma Unica Nazionale dei punti di ricar ica per i veicoli elettrici" of the Ministero dell'Ambiente e della Sicure zza Energetica (Single National Platform for Charging Points for Electric Vehicles of the Italian Ministry of Environment and Energy Security) is av ailable for consultation\, its dataset cannot be downloaded as a map or a table for processing. Therefore the Open Charge Map dataset\, available un der the Creative Commons Attribution 4.0 International license (CC-BY 4.0) license\, has been used. While this charging points database is far from complete\, it is fairly representative of the distribution and density of the charging stations. The JSON dataset for Italy has been converted to CS V and the points within the Provincia Autonoma di Trento have been extrac ted.\nThe road network has been provided by the local government\, Provinc ia Autonoma di Trento\, with a 1:10000 scale\, again under the CC-BY 4.0 l icense. Only the paved roads have been used.\nThe road network and the cha rging stations have been combined\, placing a node in each station\, at th e each road intersection and on each road extremity.\nWith this configurat ion\, the distance of each road to the closest charging station\, defined as the minimum distance of the starting or ending node of the arc represen ting the road\, has been evaluated: the minimum distance is below 1 km for most of the roads\, with only a few roads above 7 km.\nTo provide a bette r representation of the distance between charging stations and potential u sers a set of points has been created along the roads with a distance of 5 00m. The distance to charging points has been evaluated for these 8975 poi nts. Nodes belonging to roads shorter than 100m have been removed because they would have too mach influence on the distance distribution.\nThe mean distance from the charging points is 4749.4 m\, with a standard deviation of 4592.6 m. The maximum distance of 36766.6 m\, and\, as expected the mi nimum is 100 m. Only 3161 (35.22%) points have a distance above 5 km and 1 104 (12.30 %) above 10 km.\nTo analyze the distribution of the charging st ations their density has been evaluated by extracting the charging points for each municipality. The province has 166 municipalities\, ranging from relatively large cities in the main valleys to very small municipalities i n secondary valleys.\nThe number of charging stations per municipality is quite low\, 1.9 on average\, but 72 (43.4%) municipalities have no chargin g points at all. For the other 94 (56.6%) municipalities which do have at least one charging station\, the average number is of 3.32 charging point per municipality\, with a standard deviation of 3.79.\nResults are compati ble with a recent Italian national report (MOTUS-E\, 2025) indicating that more than 40% of the municipalities have no charging stations. Moreover\, around 30% of Italy has a distance to the nearest charging station above 5 km\, 6% above 10 km. However\, results are not really comparable because the national report does not employ network analysis but a coarse raster analysis with 1 km resolution and\, more importantly\, it takes advantage of the access to a more complete charging stations dataset.\nFuture develo pments include the repetition of the analysis for other Italian regions\, the differentiation of the analysis per types of EV chargers and the use o f a more comprehensive charging stations dataset. The availability of traf fic data is being investigated since it would make it possible to verify w hether the charging stations distribution match the traffic distribution o r it is possible to optimize its configuration to serve the largest number of vehicles.\nThe main limitations of the analysis come not from the proc essing tools but from the insufficient availability of data\, which are of ten in fragmented\, proprietary and inaccessible datasets.\nAll analyses a nd statistical and spatial processing were carried out using only FOSS\, d emonstrating the power and versatility of these software tools. In particu lar\, topological analysis has been implemented using python with numpy an d geopandas for data processing and igraph for network analysis. The Matpl otlib library has been used for data visualization. QGIS has been used for coordinate conversion\, map representation\, table processing and geoproc essing. DTSTAMP:20260527T064155Z LOCATION:PA01 (Quarticle) SUMMARY:Analysis of the electric vehicle charging station coverage in Itali an alpine region. - Marco Ciolli\, Paolo Zatelli URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/E9Z8SW/ END:VEVENT END:VCALENDAR