Analyzing Charging and Petrol Station Distribution with FOSS4G: Implications for Energy Transition Monitoring in European Regions
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FOSS4G Europe 2024 Tartu13 / 156
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Transcript: English(auto-generated)
00:00
Thank you for the opportunity. So I would like to talk to you about this topic that I found particularly interesting, that is about the connection between GIS and the renewable energy sector. So I believe this presentation is a start of some ideas, some projects that I would like to continue in the future. And this is kind of, I believe, a very important topic
00:22
because just in the last years, maybe you heard about the Paris Agreement between the more than 196 nations and the new European Green Deal that put very ambitious goals of reduction of emissions by 50% by 2030
00:41
and the climate neutrality by 2050. So these are very, very important goals. And to be rich is very important, a big reduction of green and grass emissions in particular from the transport sector. We heard also yesterday the presentation, the final keynote, how this is important.
01:02
And so this is also because there is a big impact of this sector inside the emissions. So if we move just to the transport sector, we see that cars and passenger cars are the major polluters. And we can have two ways to reduce the emission of CO2. So it's making a vehicle more efficient or changing the fuel use.
01:23
And as now, say, five years ago, the majority of cars were still diesel or petrol cars, but it thinks it is needed to evolution of this idea and to pass to more other types of fuel, so to pass to charging the electric vehicles.
01:44
So for that, we should, for sure, it's important to take into consideration not only the emission during the usage of the cars, but also the production and the transmission of the cars. But still, there are benefits that we can see in the different graphs
02:00
from the passage and the moving to electric vehicles. But we have a problem because if we are not charging station, there is no diffusion of the electric cars. But if there are no electric cars, it's not needed for a charging station network. So what I'm going to talk next is about which is now the state of this charging station in Europe,
02:24
how we can emphasize and we can identify the state of the art and which are the future predictions that we can find. So which data we should use? The idea was to have a global vision, at least an European vision.
02:42
So we go over national data set, then we tested also some of them. So the idea is to use OpenStreetMap that maybe all of you know. It's a global database, more than just a map, with open license data. In that, there are volunteers that map charging station.
03:03
So they are mapped using this, as you say, amenity charging station. And what has been done has been used the open service that is a routing engine that allows to interrogate the data of OpenStreetMap with this FOS4G software and to identify it to compute different parameters.
03:21
It could be used for routing, but also to create isochrones. Now we are going to see what, you can see in the picture here, sorry. In the picture here, at one point you can see which is there that could be reached from that point. So the methodology is composed mainly of four steps. So there is the download of the data from OpenStreetMap.
03:42
I tested in the Lombardy region that is where Milan is located in Italy. There is wide area with more than 10 million people living there. So there is this first downloading of the data. Then there is the creation of the isochrone at one, five and ten kilometers. We choose this distance because we would like to test different distances from the charging station.
04:07
And this can be easily done. Let's say one kilometer means that it is very close to every point that you have. So that it is very reachable. And to arrive to ten that means that you need to travel a little bit to reach it. So then we, this is kind of zoom on a city of Cremona.
04:23
You see that there is the bigger one and then smaller and smaller meaning that they are closer to the charging station. Then we can intersect all the isochrone created before with the rotor network. You can see here that there are mainly some orange areas that are the areas where there are no closer,
04:44
there are further charging station than ten kilometer. Or other area you can see here in the purple there is this center area that is the center of Milano, where you can see that there are more station. Then, so we can also have a zoom, for example, as before in the city of Cremona,
05:02
you can see that the city center is in purple so it is one kilometer. Going further outside the city there are less or no charging station mapped. Then we can create some statistics so we can identify those. So divided by class we can see that the 17 percentage of the street network is inside one kilometer from the charging station.
05:23
While you can see that the majority is, however, under the five kilometer area. For all the region you can see also in the graph below. So what has been done, this test done in the Lombardy region, has been repeated in other different areas. So we choose four different regions.
05:43
You can, let's say we have Lombardy as before, we have Piedmont where there is touring, we have Lazio where is Rome and Sicily. So we can see that the distribution of the charging station is very different from the different region. So we can see that also the number is very different.
06:01
We can see that in Lombardy there are more than 1,000 while it's one sixth of all the charging station mapped in Italy. So what we could see here in the graph is that we can have very different situation because we have almost more than 50 percentage of the street network in Lombardy
06:23
is within five kilometer from the charging station. So this means that this is a really reachable network so it's very easy for people to charge in their cars because they are very close to them. Then we will be later some discussion about the fact that
06:43
we are now considering only public charging stations. There will be for sure for electric cars there is also the pros with respect to fuel that you have the possibility to charge cars at home. This is something that should be also taken into consideration. And there are also other regions where we have less diffusion of the charging station.
07:07
So then we do also comparison between different European regions. We choose a part of Italy for the size. Almost of the other are around let's say there are like province level apart from Estonia and Belgium that are national level.
07:24
And what we can see here is that there is very, very different situation. We can say that almost also the density of the street are around from one to four kilometers of street per areas. So there are more dense network and less dense.
07:45
We can see that there are situation where the coverage is very, very high. We can see for example in Belgium that more than 75% of the street are reachable within five kilometer from a charging station.
08:01
That means that there is a very diffuse network. However, the different region could have different needs and goals. So it could be needed to reach different because we can see for example just because we are here in Estonia we can see that there are very, very low coverage of the network.
08:24
So this can be something that let's say could be very different from region to region. So maybe a comparison between European region doesn't make sense because we are comparing situation maybe with the same size but with very different number of inhabitants, very different also charging station network
08:45
but related to always really the network identified. We can see here a more clear division because we can see that there are like really dense area like the Paris area and also Madrid one. But we can have also for example Estonia that have 29 inhabitants per square kilometer.
09:05
So let's say we are talking size similar but also we reach another network. Also we can see that like for Estonia we have 11 charging station per 101,000 inhabitants. So there are very, very different numbers that we can see represented in the graph
09:24
how the situation is varying between the different. So we need to compare this network with something that could be more local and could be more related to what is really the situation in that place. So obviously the network we are talking about is the fuel station network.
09:41
So we need to compare it with something that have a similar coverage, a similar goal but for sure there will be different results to be obtained because we are talking to something that have also different time that needs to spend in that location. We have different procedure that needs to be done
10:03
because you can put fuel in your car for a fast time and then you can leave. For the charging station maybe you need to plan a little bit more. But then this can provide, let's say we can set it as goal. So this could be the goal to be reached to have a very distributed by the network.
10:22
So we can see here some examples regarding Italy. So we can see that for every area the petrol network is widely more diffuse and cover more areas with respect to the charging station.
10:41
We can see more for the whole Italy that between inside 5 km there are almost more than 80% while for the charging station we are less than 50%. But we can still see that there are areas with different behaviors. So even if more similar the behavior of the petrol station there is also difference.
11:01
So we can see that in Italy there is still some work to be done to reach the same distribution. Comparing instead the result in Europe what we can see is that in Estonia and Belgium what we see before that Belgium was the biggest, the widest network and in Estonia we see that it was the lowest distribution.
11:23
We can see very similar behavior in the sense that if we check Estonia we see that even if the petrol distribution is low because maybe there is a wider network and it's not needed to reach that kind of coverage of petrol station. As the same for the charging station, moreover as I said before
11:42
charging electrical car could be charged also at home. For Belgium we can see very similar result while for Brandenburg, so Berlin in the area is quite similar and also for Paris, for Madrid we can see that as for all the area in Italy it's needed a little bit more to reach the same coverage.
12:03
So also using OSM data what we could be using the OSM APIs what we could be studying the evolution in time of the number of charging station in petrol station. Starting from the petrol station we can see that the path are very different
12:21
but in almost all the case there have been a peak of the number of petrol station mapped and then a decrease, so meaning that maybe station are closed we can see that there are some imports because we have a very high increase of the number in a very short time but almost all of them were reaching a high level around 2010.
12:42
So this means that open street is really representing in particular about these topics what is on the ground, so is that representing what is now, what are mapping and if something is mapping it could be easily inserted into the database. While for the charging station what we see is that something is still increasing
13:02
that could represent two things because we still see some peak, some increase rapidly that means that there are imports, mainly this one is for Belgium so there have been an import of charging station in Belgium meaning that this increase is covering something that was already present before
13:24
and was not present after. But we can see that the general trend in all the areas is that there is a coming interest into the development of the charging station network so what could be interesting is that really OSM could be used to study the evolution in this wide area.
13:46
So the other point was, as we said before, we have open street but we also have an alternative data set. This is something that, for example, I just used the case of Italy in March 2024 there was a release of this platform that you can see on the right
14:06
this is a platform that means like a single national platform for charging points for electric vehicles so there was this public product distributed but it provides just the list like that
14:22
or the map where you can see where are the charging stations. This is something against every law that proposed and invited the public agency because it's the ministry to release that data with a machine readable format and with a compatible license, the license was compatible
14:44
but then they just keep it in this format using this was a proprietary software. So there is, luckily, we have this association in Italy on data that before asked to the legislation to change something
15:00
in the meanwhile, it just created a script that daily update and download the data that are available so using the data was possible also to make a comparison between the data that we saw about Italy about OSM and the data that are officially data. So what we can see is that for the official data we have in Italy
15:20
the coverage is almost the 50%. So in MOPEN Stipitomo there are 50% of the stations that are into the official data set so that means that there is still some work to do for mapping them and moreover this connects also to the fact that the network that we measured before
15:47
is better, the real network is better with respect to the one that we have in OpenStreetMap. So you can see that using the official data we reach for example for Rambardi region
16:01
there is a region where there is Milan almost the 80-90% of the coverage between 5 km. So the official data are promising and we have some just few case where they are different for example for the Lazio region where there are just the one quarter of the data in OSM with respect to the official one but in fact we see that is where there is the biggest improvement
16:25
and we go up more significantly. That means also that the missing station are in locations different from the one related to OpenStreetMap. So there could be some municipality where we have the official data but the data is missing in OpenStreetMap.
16:44
So we can see that there is some kind of non-trend about OpenStreetMap of local knowledge of mappers that map their area where they know and maybe there are some areas that are left let's say blank where there are no active mappers that completed this kind of activity.
17:00
So going and see all the different areas of Italy we can see that there are very different behaviors. We see before Lombardi that considering the 5 km distance reach over the 80% while we have area for example like Basilicata Calabria where we have a very low coverage that is around the 30%.
17:21
So also the situation in Italy is very different comparing using the official data. So this means that it will be needed an improvement, a new network to cover the whole areas. So this is some start. There are some ideas to the future.
17:41
For sure there will be interesting to identify some real trends that are present so the one related to the difference location could be something that could support the mapping where there are missing data because this data that we have official is not licensed compliant with OpenStreetMap so could not directly import in them.
18:00
Another important aspect to be considered is the capacity of the charging station. So we can have a station where it can be just charged in one car and we can reach up to five or more. So this can be something significant to be considered considering also the time that is the other point of the time needed for charging the cars. And also we should take in consideration how could we evaluate the fact that the car can be charged
18:24
also at home because this is requiring a less number of charging stations because with respect to the fuel for sure you cannot fuel your car at home and so you need a different maybe a different network. So also the isochron that we see before could be used to identify
18:42
which are the locations where the charging stations are needed more and where it is easier for using just one charging network station to increase the network covering that spot of area. Also because for example for highways we have for sure different behavior between highways and local streets.
19:03
So going to conclusions, the goal of the capitalization we see that is bigger. There is a very big high impact on the transport sector and the number of is that we see this connection between the charging network and the number of cars, electric cars. So if the network will increase also the number of electric cars will increase.
19:24
The official data also are real state of the art and could be used to identify where more data are needed and also you can decode it for sure is available and you can use it to test it and to test it in your area and to identify or improve the methodology.
19:42
So thank you for your attention. Grazie mille. Really nice, thank you very much. Do we have questions from the audience?
20:03
Thank you Lorenzo for this interesting presentation. I have a question and a comment. The question is I didn't exactly catch your definition of coverage because I have a point which is the charging station
20:22
and I have a line which is the road so I wanted to understand better what do you mean when you say that it's covered. And then the comment is that you calculate the density and you compare the line of road that is covered over an area
20:47
but the road network is uneven. So I personally would understand it better if you would compare the covered road over the whole network.
21:03
So line over line. Thank you. So for the definition of error coverage is really the intersection between the network and the error coverage. So when we talk in the other point about an area is covered that means that the street in that area
21:22
could be reached inside the one kilometer. The purple area is one kilometer far away from a charging station. So every point on that street could be in one kilometer could reach a charging station. For the other point I always refer to this concept of
21:40
the area is covered means that the street on that area could be reached between a certain distance. It's always done considering the street network. So the streets, the distance, obviously I didn't say it but using the open route services what I've done is really calculating the distance
22:02
not in space but using the network. So it's really one kilometer far away from that point if there are one way or different also street with different speed. This is not taking consideration. This could be an improvement to consider also the time needed to reach one point. But this was more on the street distance
22:23
between the two points that we are considering. So also later when we consider I just put here the density just to have an idea of which area we are talking about because maybe there are more dense area where we have more street but just meaning that obviously you can see here
22:41
it's Paris and you can imagine that Paris and Estonia are very different areas. So we have also less people distributed in the area or more concentrated. That means that maybe it's needed a high number of charging stations because there are more people in that area that could be needed.
23:01
But also this could be on contrary because maybe like very dense area like Paris or big metropolitan cities the public transport could be more efficient to move people around the city or the suburbs with respect to the usage of cars. So this can be something to be taken into consideration.
23:21
Thank you. We have one more. Thanks. Thanks Lorenzo for the talk. I think I am the one who did that import that you noticed with fuel station in 2017. We got official data from Shell fuel company.
23:41
Yeah, that one. So really interesting talk. A lot of numbers and it really makes you think like how could you improve it. Like for example I really don't care how many charging stations are there. I am from Estonia. I know that there are long stretches of roads without any fuel stations.
24:01
But you are not thinking about the number. You must think like whether you would be stranded in a forest with no charging a car. So you could also apply like population density road network density to that and also like whether the spaces
24:22
commercial spaces to rent for charging stations. And it takes like a commercial angle from that like there are only a few steps until this becomes a system for fuel station companies
24:41
to learn where to put new stations, how to optimize the network. So did you have any commercial interest in that research? Thank you. This one topic that could be covered later in the future is for sure considering the capacity. For sure the connection with the population
25:00
will increase the interest. So if we will have official data population we will have for easy improve the research because we can try to identify if we can see before that the different network maybe that we have the coverage in Estonia. This 25 percentage within 5 kilometer is just maybe 100 percentage
25:23
if we consider for people because maybe the rest of the network is just highways connecting big small towns and the rest is just where it's not really needed or there could be just one that connect more point. For sure the 10 kilometer this maximum distance
25:42
from a charging station means that it's very close because for sure with every car you can do more than 10 kilometer to reach a charging station. So it was more to have an idea of the distribution more than the possibility to charge your car in that point. So the connection with the population could be useful but then you will need to consider also maybe wider distances
26:07
because you can better, it's not the time required to you to reach that point to charge your car. So maybe if you are travelling you can follow, you can go for a further distances
26:22
but then you will, it's just one occasion it's not the day by day where you can maybe go to work you can charge your car while you are working and then go back to home. So this could be some consideration that could be done in future to take the population but also maybe also place to work and this kind of activity that could be connected too.
26:43
Thank you. Okay, we would have time for one very fast question, very fast answer. And if I would not have to run this one. Thank you for your presentation.
27:02
You mentioned that your data could be in the future used to map the necessity for new charging stations and in that sense have you considered mapping on a smaller scale of course also the occupancy rate of charging stations
27:23
because I think that could be also a good indication to spot areas where more charging stations are needed if you notice that the few that are there are for example always occupied over a certain time scale
27:41
because this is something I have been working on myself and I would be very interested to get some input from you as well. Thank you for the question. Yeah, this could be something that could be in future related with the capacity and then with the occupancy so that there were some research on that but then let's say this also needed to understand
28:02
how to connect the capacity with the... Then you come back to the discussion about the population because maybe you can have... occupancy could represent the population but then will be needed also to add both the capacity and the occupancy to identify how also the network could be...
28:20
the coverage of the network could be influenced by that phenomenon. So this is also connected with the time spent at one location so you can maybe have in an office area you can have slower charging stations or faster in other areas so this can be something also to take into consideration for the future.
28:43
So to consider, to create two separate networks one related to slow charging cars for slow charging plugs and the one with high speed so you can also identify different needs that are the ones that you need to charge your car for during a long time because you spend in the office in a supermarket
29:00
or for fast because you need to travel or you spend less time in that. Thank you.
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