Using the EZMT to Equitably Plan for Electric Vehicle Charging Stations (Text Version)

This is a text version of the video for Using the EZMT to Equitably Plan for Electric Vehicle Charging Stations presented on Jan. 11, 2022.

Sandra Loi: Okay. Well, welcome, everyone. Welcome to today's webinar. I'm Sandra Loi from the National Renewable Energy Laboratory. Our team at NREL provides technical assistance to the Department of Energy's Technology Integration Program, and our network of more than 75 Clean Cities coalitions located all around the United States.

Today, you'll hear about the Department of Energy's Energy Zones Mapping Tool, or EZMT, developed by Argonne National Laboratory. EZMT is a free online mapping tool with a large mapping library of energy resource, energy infrastructure, and siting data, modeling to identify areas suitable for power plants or energy corridor paths and other analytical tools. It was recently updated for EV charging station mapping and modeling, including an emphasis on equity.

Argonne's Jim Kuiper will highlight the new mapping data and how to use the new models to help identify potential locations for EV charging stations. Equity data, such as percent low income, percent minority, household transportation energy burden, multi-family housing density, manufacturing housing density, and many others can be included in the EV analysis or any of the other models in the system.

Today, we also have Margaret Smith from the Department of Energy's Vehicle Technologies Office, who'll say a few words to kick things off.

Before we get started, I'd like to review a few items so you know how to participate in today's webinar.

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Now I'd like to introduce today's speakers. Jim Kuiper is a principal geospatial engineer at Argonne National Laboratory, with over 30 years of experience in geospatial science applied to a broad range of environmental and energy disciplines. He is the technical coordinator of the Energy Zones Mapping Tool featured in today's webinar.

Before I pass things over to Jim, I'd like to also introduce and welcome Margaret Smith. Margaret's a technology manager in the US Department of Energy's Vehicle Technologies Office. Margaret has an engineering degree from the University of Virginia. She has been working with the Vehicle Technologies Office for over a decade, initially as a contractor, and started in 2020 as a federal employee. She manages a broad portfolio of DOE funded transportation projects, provides programmatic direction for the Clean Cities Coalition Network, and oversees work performed by DOE's national laboratories. She also leads energy equity and environmental justice initiatives at the Vehicle Technologies Office, including the Justice 40 pilot program. Margaret, over to you.

Margaret Smith: Thank you, Sandra. I am happy to introduce the Department of Energy's Energy Zones Mapping Tool, EZMT, which is designed for analyzing and planning energy projects throughout the United States. The work Argonne will be describing today is a partnership between DOE's Office of Electricity and the Vehicle Technologies Office. Specifically for the Vehicle Technologies Office or VTO, Argonne has enhanced the tool with multiple setting factors for electric vehicle charging stations, with a focus on equity.

The tool is being used in two VTO-funded efforts, including one of our EV community partner projects from 2020 called the Mid-Atlantic Electrification Partnership, and a joint effort between VTO and the Federal Highway Administration for the I80 Mid-America Corridor Plan. Geographic location provides key insights for understanding the current EV charging landscape and planning for the future. EZMT provides quick and easy to access data plus models that will help with EV charging siting.

The tool is being recognized by state agencies and local Clean Cities coalitions as having relevant variables to help identify gaps in corridors and low access to EV charging in underserved communities. DOE offers this tool as one of the valuable resources available to states and localities for implementing the charging and other alternative fuel infrastructure provisions of the bipartisan infrastructure law. Back to you, Sandra.

Sandra Loi: Wonderful. Thank you, Margaret. Appreciate that. And now on to Jim.

Jim Kuiper: Right. Thanks, everyone, for participating today. This is a versatile tool, and I think there'll be something of interest to all of you. I won't be able to cover every dimension of it today, so included in the slides at the end of the deck are a whole series of examples of the use cases I'll be mentioning in one of the slides. If you would like to kind of dig into those, I suggest getting a copy of the slides afterwards, and there'll be some content there that you can use for that beyond what I'll have time to cover.

All right. Let me switch to the presentation here.

All right. So today we'll be covering just kind of a bit of a background about the project without getting in a lot of depth, the methodology behind the modeling part of the tool. We'll be showing some of the content that applies to this particular use case in the tool, and then the actual models pertaining to the EVSE siting.

Then Sydney, one of my colleagues, will be talking about some specific examples, and we'll follow that up with a demonstration of the tool, including those examples.

So as was mentioned already, the EZMT is funded by the DOE Office of Electricity, and it was first launched in 2012. It's a kind of – at the time, it was – had a smaller scope, and also only extended to the eastern side of the United States, but since then, it's been extended and enhanced in many ways, and it's become kind of a broader and more flexible tool related to energy analysis. And as indicated there, the most recent updates have to do with electrical vehicle service equipment or charging stations, and a lot of emphasis on equity and environmental justice metrics.

The main scope of the tool is energy resources. You know, where do we get our energy from? What do we harness for energy? The infrastructure itself, and then a large amount of siting factors that pertain to finding good locations.

As was mentioned also, so we're partnering with – these two projects listed here are partnering with the EZMT team to add these features to the EZMT, and the Mid-Atlantic project concerns a four-state area, and – well, three states and Washington, DC, and they involve funding for actually installing charging stations, public outreach, and other aspects. Argonne is providing some of the analytical support for that project.

And then for the I-80 Mid-America Alternative Fuel Corridor, we'll be creating a plan for I-80 that extends actually from Nebraska to New Jersey, including both EVSE and compressed natural gas stations.

We are focused on these two projects, but wherever possible, we're making updates nationally to the tool in terms of data layer extents, modeling extents, etcetera.

So one of the key things I want you to think about today are the kind of questions that you can answer with this system, as well as some of the things that are beyond the scope of the tool. So here's a list of a whole variety of things, and again, there's slides in the deck that show how you can address these questions with the interface.

So some of the key ones I'll be talking about today are transportation energy burden layer in terms of using the tool to gather data for your own use, looking at the models which will help decide priorities for siting investments or find kind of a prospecting tool to find locations that are suitable for a particular use case for installing chargers, and then some of these other questions I'll answer if I can in the time we have.

So as a context, the current administration has a strong emphasis on these two main topics for today, and I've just pulled a couple of quotes from these two documents. In the infrastructure initiatives, there's a goal to accelerate and deploy electric vehicles and charging stations. That's a strong emphasis. And then also an equity emphasis, so the federal agencies that have any connection to equity are being required and are moving towards assessing how they can better address equity.

Today's demonstration is more technical, how to use the tool, what you can do with it. I just want to say up front, equity has many dimensions, and it's a very complex issue. It's currently being studied. There are a lot of experts on it, and a lot of information beyond my expertise. So the examples I've provided here are for illustration purposes only, to show what can be done, and then as you learn more about equity yourself, you can apply the tool for your uses.

So one of the key elements of the EZMT is the data content. And in the category of EVSE, electric vehicles, and transportation related layers, here's some of the main ones that either previously existed in the tool or have been recently added. In particular, we've got traffic data for the roads. Those are mostly for the project area only, but covered quite a few states for that. We have the designated EV corridors and alternative fuel corridors. And then the current stations themselves can be depicted as shown in the map on the right there.

Then for equity, there are quite a few different metrics in the tool and available. The EPA EJScreen data, if you're not familiar with that, you should look up EPA Screen. They have their own mapping tool with great capabilities for addressing equity and great reporting capability. So we have that data in this tool, and you can download it from us, but I suggest going to the EPA to get the original and most current data. But in this tool, you can display it on the map and click on the map and get information, as well as this whole array of other equity related data sets.

And then getting at kind of the broader tool itself, a lot of the electrical infrastructure pertains to this topic, but also, as you go down the list here, there are just a wide variety of other information that's available. There's over 350 layers in the tool.

Okay. So the modeling in the tool, I just want to give you a sense of what it does, how it works, and we'll be showing examples and showing a demo of it as well.

So as a base, we have a range of suitability from zero, unsuitable, to 100, which would be fully suitable. And we have a series of layers that we can apply. These would be siting criteria that pertain to a particular objective that you're trying to create a map of.

So in this case, population density, how far is it to the nearest EV charging station, and these other variables are all symbolized with a separate modeling layer in the tool. And then within each layer, particular areas are scored with a value from 0 to 100. For example, if you're close to an EV charging station, you might have a low suitability score, because you don't want to site another station right there. You want to site it within a gap, depending on your objectives. You might want to stay close to the EV corridors, etcetera.

And what the tool does is let you combine those, and if you look at that dialogue there, there's a column for weight. So you can say this layer is more important than the others, and it'll be weighted stronger in the model. And then that gear icon, if you click that, you can fine tune the suitability settings within each layer.

Once you've kind of got your model configuration done, you click launch, and the system computers weighted geometric mean for every 250 meter cell. Currently, the modeling extent is the lower 48 states, and we – Alaska and Hawaii are not in the modeling system at this time. Just the lower 48 states.

This modeling approach, which is also called multi-criteria decision analysis in some settings, is really an iterative process. So at the left, you would typically define your model objective and requirements. So these are related to the thing you're trying to map, as well as data availability. So here, we've got an objective, as an example, and then within those different criteria, you need to rank them – in other words, give them weights, if you don't want to weight them equally, and then finetune the suitability settings within them.

That model can be configured and run in the tool. All of these models are preconfigured with some default settings, so you don't have to start from scratch., You might want to start with a model and then adjust it for your own interests.

When you run the model, it creates this map of suitability, and you can overlay it, examine it, and learn more about how it fared in different areas on the map, and then typically make some refinements to the model and run it again, until you're satisfied with the results, and it's giving you information that's helpful.

Okay, at this time, I'd like to hand it over to Sydney, and she's going to talk about some specific models that we've configured and set up in this project.

Sydney Wu: Okay. Thank you, Jim. Okay, so I'm going to briefly introduce three case studies that we did using EZMT, focusing on the siting of electric vehicle charging facilities. As Jim mentioned earlier, all these case studies are in progress. They're not perfect. The goal here is to show how we typically do these type of analyses in EZMT, so you can build your own model and adjust them based on your preferences.

Okay. The first example, example A, is focusing on transportation network company or TNCs, including companies like Uber and Lyft. So our goal here is to identify high suitability locations to build EV charging facilities that can support the usage of electric TNC vehicles, so that people who drive TNC vehicles would have chargers near where they live or work so they can charge their vehicles between assignments and shifts. Next slide, please.

Thank you. Okay. So like Jim talked about, in order to achieve this objective, we will need to select some – a series of modeling layers based on our objective. So here's a table of the modeling layers we included in this example.

So the first three are population density, road traffic density, and land cover, which describes the intensity of land development. So all these three modeling layers indicate areas with a larger number of drivers and riders and development. So these would be the places we want to prioritize, because there will be just more people using the charger, potentially.

And then we will consider distance to substations, and this would help us to identify areas with convenient access to electricity supply. As you might have noticed, there are these arrows on the right column of this table. These arrows indicate the relation, the direction, between the suitability of EV charging in any specific location with the changes in these modeling layers.

For the first three, we would want to put EV charging stations in areas with higher population density, higher road traffic density, and higher land development. So we see a upward arrow, as green. But for distance to substations, we want the charging facility to be close to electricity supply, so the arrow is pointing downward. Same with number of existing EV chargers within a ten mile radius. We want to put in areas that already lack EV charging infrastructures, so this one also has an arrow pointing downward.

And after that, there are five equity-related modeling layers. Some of them are related to the demographics of the residents living in the area, and some others are related to the transportation options, whether there is transit, and whether people living here have vehicles of their own. So we want to put chargers in where these modeling layers are high in the area, because many studies have shown that areas with more transportation disadvantaged population would potentially also have more TNC drivers and riders. So this is the rationale of why we chose these modeling layers and how we configure them to work in the model. Next slide, please.

Okay, so this is the result of the – in the EZMT after we configure the model. So the left on is an overall map of the study area, which is the mid-Atlantic states. So the areas with yellow-ish shades are the areas that we – that the model determined is suitable for – to place an EV charging facility for this purpose. It's a little hard to see, so we zoomed into one location, Richmond, Virginia, as shown on the map to the right.

So this is a high suitability area in the Whitcomb neighborhood of Richmond, Virginia. So as shown by the different colors and shades, if it's in red, then that's a area with the highest suitability, and then followed by orange and yellow.

So we looked into this area and found that this area is mainly residential. About two-thirds of the families here are single family housing, and one-third is multi-family housing. And most of the multi-family housing belonged to a public housing authority, and there are some local businesses along a major road in the – to the right side of the map. And this place also has some major highways and major roads.

And this neighborhood is one of the neighborhoods in Richmond that has the lowest household income. So this place with lower income, no existing charging facilities available, and a bunch of multi-family housing, is – the model deems this to be suitable to place an EV charging station, potentially can help the potential TNC drivers or riders to better – to have better transportation options. Next one. Thank you.

The second example is focusing on rural areas. So for this one, we want to fill in the gaps in rural areas and provide DC fast charging stations for rural residents. Thank you.

So for this one, because of the different objectives, we also choose different modeling layers to do the analysis. The first three might seem a little bit familiar to you guys. We also have population density, land cover, and road traffic density. But what's different between the rural model and the TNC model is that because we need to identify rural areas in the United States, so in this case, we would not be prioritizing places with higher – the highest population density and the highest land development. Instead, we would select regions with relatively low population density and low intensity of land development to help us identify rural areas effectively. So these two arrows are pointing down, because the relationship here – the direction of the relationship here is reversed.

But for road traffic density, it's still a positive factor, because we still want to identify places that have a lot of crossing traffic. And then distance to substation, we want to – we want the station to be close to substations, and then close to electricity supply. And finally, we identify areas that lack EV charging infrastructures, with the number of EV charges within a ten mile radius, with that modeling layer. Next one.

Okay, so this is very similar to the results of example A. We have the modeling results of the study area, the entire study area, to the left, and all these dispersed yellow indicate that these areas are somewhat suitable to place a charger. And then we zoom into one high suitability area in Milton, Virginia, as shown on the map to the right. So this area is mostly residential, and it's next to two highways, a highway junction, so it has a lot of crossing traffic. But the residential population density here is rural, so it's not very dense. But because of the highway, there's a lot of road traffic coming through this area.

And this area also has some destinations, gas stations that can help us play – that can locate – that a EV charging station can locate. So in this area, the nearest non-Tesla charge is located about three miles away, so we can see this place as a gap of – is lacking EV charging station as of now. So the model identified this area to be a potential high suitability area for EV charging station.

So the last example is a corridor analysis. For this one, we want to identify high suitability locations for DC fast charging within five miles of designated EV alternative fuel corridors. In most cases, there are charging stations along the corridors already, but we still found some gaps in between these charging stations, and those are the gaps we want to fill in. Next one.

Okay, so this one, we also have these modeling layers. The first one is distance to designated EV corridors. Of course, we want the charger to be close as possible to these corridors. And we also have the two modeling layers that appeared in the previous models as well. And finally, we have road traffic density and population density. Those are positive factors, because we want to place the chargers in an area where there are people living and vehicles crossing. And finally, we also want the chargers to be placed in areas with relatively more population of minorities, so that it can benefit more transportation disadvantaged population. Next.

Okay, so this is the results of the corridor model analysis. So the map to the left is the – you can see a lot of the suitability areas along highways. So select Cumberland, Maryland, as an example of a high suitability area, as shown on the map to the right. So this place has interstate highway crossing, 68, and also has some local businesses, restaurants, diners, gas stations nearby, and also has a very major truck stop adjacent to the highway. And this place currently doesn't have an EV charging facility available. So this place potentially could be a good place to build an EV charging facility that can really benefit the vehicles coming across this highway.

And with that, I'll hand it back to Jim.

Jim Kuiper: All right. Thank you, Sydney. Let me just – this is the first slide of the kind of example deck, but it's worth just showing you this first before I get into the demo. EZMT uses accounts, and that's mainly just so you can save your work. There's many things that get saved in your sessions, and you can return to the tool and pick up where you left off and have your results there. So it's not intended to prevent anybody from using it, and we hope that you can smoothly get through the registration process.

Just the one thing I wanted to emphasize is the accounts need to be enabled, so even though – we have some folks that once they've confirmed their email, they try to log in, we do have to take a moment and check the registration information and enable the account. So that's step three there. And you'll get an email message when that's done.

If there's any delays or problems that you encounter, like you don't see the email confirmation message, or you don't get a response, you can always contact EZMT.ANL.gov, and that goes to several of the site administrators, so you should get a response promptly, if you need to do that.

Once you're registered, return to the home page. There's a link in the confirmation message that you can hit, or you can just go to the home page, click launch tool, and then be prompted to log in and start.

Okay. Switching over, this is the home page for the Energy Zones Mapping Tool. And as an aside, I just wanted to say that we're working on a complete update of the interface and the tool itself, and that should be rolled out in late spring. So even the name is going to change. But I think you're going to find it much more easy to learn and understand, but for now, I'll go over some of the key points here.

When you reach the home page initially, there’ll be a register link at this top right, and when you're logged in, it'll say My Account there. And in terms of help information, without logging in and registering, you can access help about the tool in this menu at the top right. And there's also a link to our YouTube channel here at the left. There's this little news section. If you click on these links, it'll go to a page that has just the news items, so you can see recent updates and things there. And there's an introductory video. So there are lots of help facilities right at the home page.

Once you return to the site, you can click this Launch Tool button that appears on each page of the website. And I've already launched it in this second tab here. There'll be a couple of steps to log in, and there's click on terms and conditions, which mostly just acknowledges how we use any information that you provide in the tool, just as a privacy disclosure.

One thing that might be a little disconcerting, when you launch the tool, typically, it just has a blank map. And the first thing to do for most users is to click library. That's the data library. It has two tabs. Map layers are raw GIS layers that you can display on the map, and model layers can also be displayed on the map, but those are used for suitability modeling as well. So just know that you can access and display the individual layers there if you like. Most of the time, you'd be working with the map layer catalogue.

As an example, for some of the data in the catalogue that pertains to our topic today, if you hover over the title for any of these categories, you'll see this drop down arrow, and that's used to filter. So I'm just going to give an example of looking up something. If we wanted to find the household transportation energy burden layer that we've been talking about, we can use the filter, and type in burden, hit return, and then you're presented with just that one line. You can clear the filters by unchecking either box here at the bottom by the way.

When you're looking at data, if you click that little icon on the left, it opens up with a little bit more information about the layer as well as the publication date of the data and when it was last updated in the tool. If you want a copy of just about any of the mapping content, you can click this download data link, and the first time you do that, there's a dialogue that appears which you can optionally disable, and which I've done. But right now, it's downloading that data set for me, and I've got a copy of it that fast. Usually, though, you click this left-most icon, and that adds the layer to the map, and it adds it to the table of contents at the left.

This third icon is metadata, which describes all kinds of details about the data and its data source. That's really important to understand the background of the data set.

Okay, so that's just adding one layer to a map. One thing I can do is right click under layer properties. I can adjust the opacity, and that will allow me to see the base map through the layer.

All right. Let me add a few other layers quickly. Now I'm going to filter by transportation in the category, okay? So now it's isolating just the transportation layers, and here we have the alternative fuel corridors and a series of charging stations categorized by some of their attributes. So let's just look at non-Tesla stations today. The Tesla network is proprietary, although they're making some moves. I don't know all the details about it. But we've subdivided the data. So you can look at the Tesla network or the network without it or all. So again, these layers have all been added to the map.

If we turn to equity, the equity data is under demographics, and so here you see again household transportation energy burden, but a whole slew of these other layers as well. So for example, if we pick low income as one of the categories, we can add that to the map as well.

Okay, now I wanted to just add a couple of the – let's switch to utilities, where we see a lot of the electrical infrastructure will be under there. So here, we have electrical substations, and maybe I'll just stop with that, because you get the idea. Worst case, you can just scroll through the 360 layers if you want, but these filters will help you as well, as you learn kind of what to look for and what the content contains.

If you uncheck these boxes, you're toggling the visibility of the layers on the map, and I'll just simplify the map here a second.

At the top, you've got the map navigation tools, and I'm just going to zoom the map so we can see a little bit more detail here. Okay, so here say we add the corridors back in. Any time we've got some features on the map, we can use this identify tool, and if we click on one of these features, it'll look up the information about that particular location on the map. And you'll notice there's a whole series of sections here. It picked up three records of the household transportation energy burden data, because, y, they're small units on the map, and also the corridor. So if I click this plus here, I can peruse the data for the corridor in that location, and these are the attributes that are in that GIS layer.

Okay. One thing you should know is that there's a series of base maps that you can toggle. So if you're looking in detail at a site, you might use the aerial topography and that's where you can really see kind of some of the infrastructure that's there and a lot of the context of the information. Now if I add electrical substations to the map, I can see, oh, yeah, there's one there. I might not have noticed it on the aerial photo, but I can highlight that and maybe see, hey, there's a good place to put a station near that substation that might be an especially good location.

Okay, I don't want to take too much more time. I want to allow time for questions. But one of the key things to demonstrate today are these models we've been talking about.

So if you click the analyze tool, it's in two panels. And by the way, the new interface is going to split these and make them a lot easier to peruse and understand. That's one of the main updates. But if you scroll through here, we've got all kinds of power plant types. Like here, we have a set of core models. These are exactly the same ones that Sydney was describing.

And so I'm going to click the corridor model by clicking this gear icon. The model launcher, you can just open it and click launch and it'll run the model, but I suggest that you take a little time to look at the siting criteria in their settings. So here we have another gear icon, and we can click on take a look at road traffic density. So this is based on average annual daily traffic within a quarter mile of where the road is.

And here, we're saying when we have very high traffic levels, we have high suitability, and we have no traffic, we have low suitability, so that kind of gets at that gradation that Sydney was talking about.

For equity, I can't emphasize enough that you can go both ways with equity. If your project is detrimental to underserved or disadvantaged communities, you need to reverse these suitability levels to get a good analysis result. Here, we're saying that we see it as an advantage to seek areas that have a higher percentage of minorities, and this is just as an example in this particular model. Any of the other equity criteria can be added to this model, and as we get feedback, I'm sure we'll be adding more and making refinements to them.

You can delete layers out of your model. So if you want to contrast a model with and without this criteria, you would click on delete, hit okay, and now you've only got these siting factors. And then you can use add, and this scrolls through about 110 different modeling layers that you can choose and add to your model.

So those are various ways you can make revisions. When you're done and satisfied with your initial settings, you could click launch. Okay. And then any things that run in the background or come up in your results panel – I've got quite a bit of stuff over the years that I've done in the tool, so I've got a long list here. And you can see this hourglass cursor. This model will run for a couple of minutes before it's completed.

Once it's done, usually you would click this icon at the left to add the results to the map. It's important to note that you can also click your gear icon here and get back to the same dialogue for that particular model. Here's all the settings that it had, and this is a good starting point if you want to revise the model, because if you revise these settings and click launch, it'll create a new copy of the model with revised settings. So that's a quick way to do that.

Let me think here. Okay. All right. So let's look at some of the results. I've pre-run these. I've got them ready to go in the table of contents to save time. And I'm going to switch the base map and zoom out a bit here.

Sandra Loi: Jim, just wanted to let you know, we have about 13 minutes, and we do have a few questions, so just giving you a little bit of a time check.

Jim Kuiper: Yep. thank you.

Sandra Loi: Thank you.

Jim Kuiper: Okay. So in this case, I actually have two copies of the corridor model with and without minority that I've run, and if you compare them – there's a slide for this in the deck if you want to look at it later. You'll see that when you include minority in the model, there's certain areas that drop out along the corridors where minorities are not as prevalent. And you might use that as you're looking for stations along the corridor to say, you know, this particular community might be more interesting for investment.

As a last point about that, there's also – HUD has a program where they've defined with the states' coordination a whole set of opportunity zones, and these are designated for priority federal funding. So it might be worth taking a look at that overlay and correlating it with your high suitability areas, and seeing if you find some especially interesting locations.

Okay, I'm going to stop there and just open it up for questions. I'm happy to stay on the line beyond the time if we have further questions, and just I'd like to know your thoughts and how I can help answer them.

Sandra Loi: Great. Thank you so much. Thank you, Jim, and thank you to Sydney as well. And in addition to the recording, we will post the slides along with the recording, as Jim mentioned, so you'll have access to those as well. So there's a couple of questions that came in, and if you all have any other ones, feel free to keep posting them into the chat. Thank you for these.

So there was a couple of questions on the data, and I know, Jim, once you – these came in early on in the presentation, and then when you jumped into the tool, I know you were downloading some of that information. So two questions on the data. Can the data be used in ArcGIS? And can you export data files and output shape files?

Jim Kuiper: Yes. Well, the downloads are – typically, they are shape files, and ArcGIS is one of the GIS – the reason for shape files is that it's an open definition. It can be read in ArcGIS, but as well open source GIS systems, other systems, all support shape files, so that's kind of a ubiquitous data format that is useful.

A few of the layers are raster or they're images. Those will come out as tiffs. They're geo-tiffs, so they're geo-referenced. So that would be grid data. But otherwise, it's either tiffs or shape files.

Sandra Loi: Okay. Great. Thank you. What is the timeline for Alaska modeling updates?

Jim Kuiper: That has been on our proposed workplan for quite a few years now, so that – it might be worth a conversation about that, and we can include the sponsor and see if they're interested in funding that as an activity. We certainly have data content for Alaska. Some of the energy resource data is not available or appropriate for Alaska. You know, for example, solar might be less suitable there, at least during this time of year. However, it's mostly a matter of doing a work to create the modeling layers and operate the tool with them.

Sandra Loi: Great. Thank you. So what is the accuracy of the data for nationwide electrical substations? For example, hosting capacity, kilowatt or megawatt, of each substation? And also, is there an integrated data set combining all or most of the substations' utility areas across the country?

Jim Kuiper: Okay. I'm not sure I understand the last part of the question, but the data source is a key thing to look at for some of those questions. And if you right click in the table of contents, you get the metadata, or you can get it from the catalogue. So a key thing is this is coming from the Homeland Infrastructure Foundational Level database. It's a national database that is updated regularly, and so we're just hosting a copy of it in this tool. And we'll have some of the same questions about its quality and how current and what it contains.

If you click on one of these with the info tool, you'll see some of the fields that are in there. It typically does have data about – you know, here's maximum voltage in a range for the different substations. It doesn't have any information about whether it's at full capacity or whether it can accept additional connections and that sort of thing. So it has some limits. And what was the second part of the question?

Sandra Loi: Is there an integrated data set combining all or most of substations' utility areas across the country?

Jim Kuiper: Yeah. I guess I'm a little confused, but these are all coming across as separate layers. So you can collect multiple ones if you want, you know, service territories and electrical transmission lines and that sort of thing. And it is a national layer. If you right click on a layer, you can say zoom to layer extent, and this particular one includes Puerto Rico and Hawaii and some of the other territories. So it appears to have parts of Canada as well there. So hopefully that answers the question.

Sandra Loi: Okay. Great. So adding on to that a little bit, what are the data sources for land cover and transit desert index?

Jim Kuiper: Okay, so a quick way to look that up under the catalogue, University of Texas at Austin created the transit desert index data, so that's a quick way of just looking under the source. So here a brief detail about the source, and then the metadata will have a lot deeper information about it. And what were the other data sets?

Sandra Loi: Land cover was the other one.

Jim Kuiper: Okay. Yeah. We actually have two land cover data sets in the tool. One is the National Land Cover Database. That's from USGS. And the other is called LandFire – if I could find it. Maybe it's just – I think actually that one's only in the modeling library. We need to add it as a mapping layer. But it will look very similar. There it is.

Yeah, so these data sources, for those not familiar, this would have a series of categories of land use and land cover, and these are created from arial imagery or satellite imagery. So there's a computer algorithm that kind of scores different areas to see which category they're closest to, and it's mapped at a certain resolution. But these are the two major national sources of land use/land cover data, that at least I'm aware of.

Sandra Loi: Great. Thanks, Jim. How do you tie personal care use and transit services in terms of charging demands, or are you just trying to put chargers in transit stations and hubs?

Jim Kuiper: Yeah, we've sought data on kind of EV registrations, kind of like Uber driver or the ride share activity data, some of those things like that that get closer to the demand for chargers or the actual usage of them or the folks with these vehicles. So far, we don't – as a national tool, we'd like to have national data if possible. The ride share data that I've seen is proprietary, and we're not able to host it in the tool. The registration data we're expecting to add, and we're gathering some of that. But for the most part, the models to date involve more physical siting factors or a little bit more abstract ones, like population density, where you can maybe – it's maybe correlated, but not directly getting at some of those variables. Yeah.

Sandra Loi: All right. Thanks, Jim. So now on to a couple of questions about chargers. The first one, does the model suggest what kind of charger is appropriate, say level two versus a DC fast charge?

Jim Kuiper: In the modeling library, we have – we've divided those. Let me show you. I've got to get better at my filters here. Okay, so we have all those different choices for modeling layers. So it's not so much the model tells you, it's that you tell the model what you're interested in.

So if you're trying to fill in gaps between – like along a corridor people would be more interested in DC fast charging, you'd probably pick this third one. Or this one here if you wanted to include both Tesla and non-Tesla. Whereas, for example, residential charging overnight, you might be more interested in level two charging. So yeah, it's more in the phase where you define the objectives of your model and say this is the use case I'm trying to simulate, or these are the assumptions I want to have, and then we're trying our best to get siting factors that'll provide useful metrics to refine those maps. It's an ongoing process, and we definitely appreciate everybody's feedback and input, both in available data and ideas for siting factors. So this is kind of where we are today with it.

Sandra Loi: So then does the same apply – so does – sort of related, again, to the chargers, does it provide information as to how many chargers would be needed, or is the tool focused on suggesting candidate sites with suitability information?

Okay, note that this list has both kind of distance to the nearest station as well as the charger density, which if you have the location as multiple chargers, then it has higher density, even if there's only one station. So we're not able to directly get at that, but we're trying to – we're trying to provide siting factors that allow you to work with that sort of information.

Sandra Loi: And then, again, and it might be a similar response, but if it suggests quantity of chargers needed near term versus in the future, if there's any sort of distinctions there.

Jim Kuiper: Yeah, I guess what I would say is to the degree we can get metrics for – that get at charger demand, or even grid capacity, those sort of metrics vary across the landscape. So far, they've been a little bit elusive to get at, and some of them are dynamic. Even grid capacity varies across the day, and there's peak loads and things like that, same as traffic varies over time. So there's some level of complexity that we probably won't reach in the tool's data and capabilities.

And I guess what I will add is that if you have a model with these suitability factors, but there's a key one missing, you can still create a suitability map, and if you know some of the metrics or have other data, you might be able to kind of work with it yourself to say, well, I know this location has higher X or Y, or I've got some proprietary or internal data that I can apply to the problem, and then the model will answer more the rest of the question for you. So you might kind of blend the analyses.

Sandra Loi: Okay. Great. Thank you. So we are at the top of the hour. I know, Jim, you said you have – you would be able to hang on, so maybe we can go through a few more, and then we can wrap up, if that still works for you.

Jim Kuiper: Sure.

Sandra Loi: Okay. Great. Is there a mechanism to recommend which layers to use based off project needs?

Jim Kuiper: Well, I guess maybe just emailing the EZMT.ANL.gov, and we've got a lot of colleagues here at Argonne, and really interested and excited about helping people use the tool for these types of analyses. So I guess would only just suggest at that level that we might be able to discuss it with you and provide some ideas and also listen to your needs. That might lead to refinements in the tool that make it more useful for you as well. So that would be my suggestion for that.

Sandra Loi: Okay. Great. Are there layers available that identify or to identify businesses associated with potential charger installation locations?

Jim Kuiper: Yes. That's another to do. That's a really important one, especially along the corridors. You're not going to just site these things in the middle of a field. So truck stops, fueling stations, convenience stores, libraries, all kinds of facilities like that, there's some great use cases for partnering with folks to get these chargers installed. So we're looking for data for some of those, and hoping and planning to add it as time goes on.

Currently, we have one about the density of mass transit stations as another potential use case where you've got first or last mile commuters that are wanting to use ride sharing services, for example. And it might be advantageous to site chargers there. So that's one of those types of layers that we do have. So far, we don't have data for those amenities and those locations separate. What we typically do for that is zoom the map in, pair it with Google Maps, and look at some of the businesses in the area, and just kind of manually prospect for some ideas, which is a pretty crude way of doing it. But it's where we are today.

Sandra Loi: Okay. Great. Is there an ability to create printable map layouts with legends for display outside of the tool?

Jim Kuiper: That, we're not – it's not real great for that. You're welcome to take screen snapshots and use the graphics. There used to be a print tool, and we had some problems with it, and it was just not very reliable or working that well. So it's not real good for publishing maps external to the tool. If you're a GIS user, downloading the data, you could readily use the data and create maps with that software. But for the most part, it's just an online display tool. Yeah.

Sandra Loi: Okay. Great. So we have two more questions. This next one, when we were looking at the model with TNC drivers and where chargers would be a good location, did it take into consideration TNC drivers with EVs already, or just TNC drivers in general?

Jim Kuiper: It's sort of a blend of both. There have been some studies that have correlated kind of multi-family housing with increased ridership as well as increased numbers of drivers that live there, so they might want to charge their vehicle overnight, or they might need to top off their charge between rides. So there's some correlation there, and that's about as far as we have it currently.

Sandra Loi: Great. The last question here, or maybe one more, is it possible to model proximity to public transit locations or park and ride locations?

Jim Kuiper: It's possible – well, we have data for park and ride that's more limited to the project area. That could be added as a modeling layer. It isn't there now. And what was the first one?

Sandra Loi: Public transit locations and park and ride locations.

Jim Kuiper: Yeah. Public transit, there is – let's see. What's the quickest way here? As long as I categorized it. Distance to mass transit hubs is part – is about as close as we get to that, so that would be kind of one of those proximity layers, and where the mass transit hubs are defined as airports and train stations. It doesn’t get at bus stations. And then there's actually a mapping layer – we found a data set related to that, so there's a different layer in the mapping library that gets at some of that, but not specifically park and ride. Let's see. Yeah. Public transit stop density. Yeah, so that's – and then if you use the info tool, some – these only have one variable that they're showing on the map. Sometimes they have – here it has two metrics, stops per square mile and stops per population. So those are two data sets we do have that kind of get at part of that question.

Sandra Loi: Okay. Great. Well, we're nearly ten past, so we'll go ahead and wrap things up. So thank you, Jim, for hanging in a little bit longer, and thanks to you and your colleague Sydney, again, for the presentation today. And thank you, Margaret Smith, for your comments at the beginning of today's webinar.

So a reminder, this is being recorded, and both the recording and the slides will be posted on the Clean Cities Coalition Network website within the next seven business days. So thank you all for being here with us today, and this concludes today's webinar. Have a great week, and thank you so much.

Jim Kuiper: yeah. Thanks, everyone.

Sydney Wu: Thank you.