BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//speaker//GH QYZE 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-TEDKSY@talks.staging.osgeo.org DTSTART;TZID=CET:20250716T110000 DTEND;TZID=CET:20250716T113000 DESCRIPTION:INTRODUCTION\nBiodiversity is a crucial yet complex concept in ecological research. Beta diversity\, representing species turnover across spatial gradients\, plays a key role in understanding ecosystem functioni ng and conservation planning. Studies suggest that environmental factors s uch as altitude\, latitude\, and geographical distance drive beta diversit y patterns. However\, large-scale analyses may not directly inform local c onservation efforts. Therefore\, fine-scale assessments within specific ad ministrative regions are essential for effective biodiversity management a nd protected area planning. In FORCING project (Geri et al.2016)\, the Edm und Mach Foundation and the University of Trento recovered a huge database of vegetation surveys\, build in the 1970s to represent the "Schmid's veg etation belts" in the forests of Province of Trento a region of about 6.21 2 km² in the northeastern Italian Alps with a huge flora and fauna biodiv ersity (Tattoni et al. 2021). The surveys and the cartographic materials w ere digitized and organized in a geographic geodatabase with QGIS and post GIS. The sampling design of this archive lends itself perfectly to being a nalyzed from a beta diversity perspective\, permitting to compare several environmental gradients in terms of species turnover and species richness. The archive was created using FOSS4G and is permanently available and sto red in a web-GIS hosted on servers maintained by Fondazione Edmund Mach an d is accessible at http://meteogis.fmach.it/forcing/ (unfortunately due to a technical problem related to an ongoing general software update the acc ess maybe unavailable until the end of 2025).\nThe aim of this paper is to test the use of FOSS4G software (Ciolli et al. 2017) and the FORCING geod atabase (Geri et al.2016) to perform an exploratory analysis of the floris tic species turnover in a relative small area\, and to try to underline so me patterns and driving forces. \n\nMETHODS\nThe data coming from the orig inal sampling project were managed and prepared using Qgis software\, usin g the Spatialite format geographic database and georeferenced in the WGS84 UTM 32N coordinate reference system (srid: 32632). 517 linear transects a nd a total of 190761 species records were analysed (Geri et al. 2016). The statistical analysis were performed with R software. In each linear trans ect\, considered a single ecological community\, the beta diversity using site as simple point were calculated evaluating in this way the degree of species turnover across the environmental gradient that is created along t he transect (Tuomisto\, 2010). The basic statistical properties extracted for each transect were put in relation with the beta diversity index and w ith the corresponding values of species richness\, producing graphs that s hows the various relations trend. The significance of the linear relations were tested using the Pearson correlation coefficient. Each belt was comp ared in terms of species composition using the Sørensen’s coefficient o f similarity. The behavior of the beta diversity and species richness were deepened in terms of variance partitioning. It was tested the variance ex plained by the four variables: mean altitude\, mean slope\, range of altit ude and range of slope against beta diversity and species richness. The an alysis should stress the role of the variable in single or in multiple way to drive the species turnover. The variance partitioning analysis were pr ocessed using the Vegan library of the R statistical software (R Core Team \, 2024)\, and in particular the module “varpart”. This function parti tions the variation of response data table with respect to two\, three\, o r four explanatory tables\, using redundancy analysis ordination (RDA). To simplify the results interpretation the variance partitioning in combina tions of group of three variables was applied. Both terrain altitude and s lope data and both vegetation beta diversity and species richness data wer e transformed with a log transformation in order to obtain a normal distri bution of data. QGIS was used also for data exploration and representation .\n\nRESULTS\nPearson indices show that all the variables are significativ e except the slope variance for both beta diversity and species richness a nd the mean altitude only for species richness. Generally both species ric hness and beta diversity grow increasing altitudinal range\, slope range a nd slope mean while variance doesn’t show a definite trend considering i n particular way the species richness. Sorensen statistic shows how the si milarity decreases with increasing the altitude separation from the lower level\, and highlights the pairwise comparison between altitude adjacent b elts. The latter statistic shows how the similarity presents a different b ehavior with the increase of altitude\, rising very fast in the first step (between 0 and 600 meters) then leveled off and finally decrease in corre spondence to the last two steps\, between 1500 meters to 2100 meters. The variable that explain much more variance is the altitude variance for both beta diversity and species richness. Regarding beta diversity the greater joined effect is due to the combination of altitude range and slope mean while altitude range\, slope mean and slope range presents an higher joine d effect for three variables. \n\nDISCUSSIONS AND CONCLUSIONS\nThe results confirm that the transects characterized by a wider range of slope and el evations show a higher rate of beta diversity. This is reasonable\, since the more are the different environments the transects cross\, the more pro nounced should be beta diversity. This is also confirmed by the linear rel ation of the single variables highlighted both as graphical trend and by t he pearson tests and moreover\, by the fact that the variance is explained as a joint action of variables. \nFinally this work confirmed that FOSS4G software is perfectly suitable to be used to perform spatial statistical analysis to study beta diversity both from the point of view of numerical statistic and from the point of view of geostatistics (Ciolli et al. 2017) showcasing the power and versatility of these tools.\nFurther future deve lopments and analysis will include the comparison of beta diversity of the present vegetation with other historical floristic archives sampling (Lel li et al 2023)\, statistical analysis of the data using different set of s tatistical and geostatistical techniques and finally to include remote sen sed data. DTSTAMP:20260530T061657Z LOCATION:PA01 (Quarticle) SUMMARY:Exploratory analysis of beta diversity across altitude gradients in an Alpine region (Trentino) using FOSS4G and a historical floristic archi ve - Marco Ciolli URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/TEDKSY/ END:VEVENT 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:20260530T061657Z 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