UrbanFootprint: Next-Gen Scenario Planning Tool
Likuski, Andy
UrbanFootprint is a new open source scenario planning tool that seeks to revolutionize the practice of planning, with the potential to allow for a closer integration with research, public involvement, and education. Within version 1.1 alpha now complete and the next version currently under development, UrbanFootprint is a new state-of-the-art model that uses open source geographic information system (GIS) technology to create and evaluate physical land use/transportation investment scenarios. It is designed to be deployed by government agencies, private entities and NGOs. The model translates disparate data describing the existing environment and future urban development plans into a common data language, and defines future scenarios through the application of a new common set of ÔPlace Types'. The model's suite of Place Types represents a complete range of development types and patterns, from higher density mixed-use centers, to separated-use residential and commercial areas, to institutional and industrial areas. The physical and demographic characteristics associated with the Place Types are used to calculate the impacts of each scenario. UrbanFootprint represents the new standard for scenario modeling tools intended for use by urban and regional planners at the local, county, regional, state or national level. Running on a backbone of PostGIS, PostgreSQL and Ubuntu Linux 64-bit, it takes full advantage of today's hardware processing capabilities to model the impacts of future urban growth scenarios on the base (existing) environment in future years to generate outcomes for a full list of metrics, including: Travel behavior (vehicle miles traveled, transit trips, walking trips, fuel consumed, fuel cost, criteria pollutant emissions, transportation electricity consumed and impacts); Energy &amp; Water consumption (for transportation &amp; buildings); Land Consumption by type; Infrastructure Cost (capital and operations &amp; maintenance); City revenue from residential development; Public Health Impacts (Obesity, Asthma, Rhinitis, Pedestrian-Vehicle Collisions, Respiratory &amp; Cardiovascular Health Incidences); and Greenhouse Gas (GHG) Emissions.
 Kita-sikai Hi, how are you doing? Hi, how are you doing? Hi, how are you doing? Hello, how are you doing? Hi, how are you? Hi, how are you doing? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Yeah, a little bit, but no discussion. I'm just telling you, I'm not sure what the name of the room is called. Very small apartment with a friend. We went to the four-altern. I'm going to get started. It's one o'clock. This is a presentation about an urban planning platform. How many of you are in the urban planning business? How many people are familiar with urban footprint? Urban footprint is an open-source scenario planning platform. It's a suite of tools that let cities and regions compare alternative land use futures. It's a project that allows cities and regions to create or take the existing future land use plans. They show them on a map, look at the future data, and run analysis to see what the future land use plans are. The base year, the study year, might be 2012, for instance, when they gathered their data in a future scenario year, such as 2050 or 2030. I'll get more into this, but the goal is to see how those different future scenarios compare. My name is Andy Likuski. I'm not Garland Woodsong who's listed in the program. I'm the primary software developer for Urban Footprint. Cal Thorpe Associates is an urban design and planning firm located in Berkeley, California. We're a small team with a big product, so hopefully there's something here of interest to you. This kind of program is often called a sketch tool because it literally lets you sketch futures on the map, which we'll get into on the interface. You're going to have a base parcel on the map that shows you what's on the ground and be able to sketch a future scenario and run analytics. The project is also exciting because it is fully open source. The stack is an open source stack and I'll get into details on that. Once I'm the software guy and less the presenter planner guy, feel free to ask me questions on anything, but I'm going to be able to give you a lot of information on the software. I might have to defer a little bit on some of the planning elements of it. Cal Thorpe Associates has been around for a long time. They have decades of experience with scenario planning. You can see some of the regions that we've worked in. Our focus is on transit oriented development and compact growth, trying to meet some of these global and regional climate environmental health goals that a lot of you are surely interested in. We've been leveraging geospatial software technology for a long time with the goal of improving the efficiency and the visibility of the planning process. We specialize in regional planning and we also work with individual cities and towns. What got Urban Footprint started was a project called Vision California which took place from 2008 to 2012 approximately. This was a project that was funded by the California High Speed Rail Authority along with the California Strategic Growth Council to basically show some of the differences, how these different future scenarios could have impacts on environmental, fiscal, and health. And really to tie in the interplay with transportation and land use investments. So rather than looking at them in a silo to show how where you build can impact your either positive or negative results. We modeled data in the five major population regions of California and when you're doing this kind of geographic analysis you tend to hit some. And also when you're dealing with this many features and doing analysis you tend to hit some limits of traditional modeling software. And we actually broke the proprietary software that we were working with. We were having 12 hour long runs of analysis that would go overnight and then end up with a very mysterious error message. And so the team was actually forced into an open source stack and we're very happy that we moved in that direction. So we started with, we moved into the post GIS, Django, and we were using open layers that was the original, the Vision California product. So here's just one of the many outputs of the Vision California project. This shows, we analyzed a number of different future scenarios but this shows the two extremes, business as usual, sprawl auto oriented, business park in the suburbs kind of growth versus growing smart which is really kind of a package of more aggressive green scenarios designed to concentrate growth around existing and future transit networks. So you can see on the left a lot of that pink area is near population centers but really sprawled out. And on the right you can see much more compact growth around the planned high speed rail corridor from San Francisco Sacramento to LA and San Diego. And very happily that high speed rail project is now finally being constructed. I'll also show some slides at the end of the presentation that demonstrates the impact of these different scenarios on important analysis like greenhouse gas emissions, public health, fiscal impacts, water and energy. And please let me know if you have questions. I know some of this stuff, especially for non planners, can go right over your head so stop me if any of this doesn't make sense. So here's a look at the two software products that came out of the vision California process. The one on the bottom is actually called Rapid Fire and that's actually a spreadsheet based model. It's for non-geospatial comparison of scenarios. This was developed for regions that either don't have access to geospatial data or don't need to do geospatial comparison. They might just have a few different future scenarios that they're looking at, compact, sprawl or something in the middle and they have some policy packages. They want to compare what the results are. So Rapid Fire was designed to work without geospatial analysis. Urban Footprint, the one on the top, is the one that this talk is going to focus on and that is the web-enabled open source and geospatially obviously aware platform. So Urban Footprint, like a lot of GIS applications, starts with you got to start with your data and you have to get your data together and organize it. And then the other two parts of Urban Footprint are taking that base data, developing scenarios and running analysis. So in terms of data development and organization, our clients, which are typically the regional entities or cities, typically provide us with feature data. It's often parcels but it could be something bigger like transportation areas. And that data at a minimum needs to contain information about employment categories on the parcels or other features and as well as dwelling unit categories and probably some kind of land use code so that we can categorize it and show it on the map. They might also need to supply us with additional data such as information about what are energy usage so that we can run certain types of analysis. Other data we can sometimes infer from census data. So what we get depends on what the client has available and what we can get from elsewhere. So once you have that base data load, it gets normalized into our system and at that point the client is able to visualize one or more layers on the map. Usually they're base data parcels we would say but perhaps also transportation networks that they've given us. So that's sort of a starting point where you can review the features that you have on the ground representing a year such as 2014. The next step is to actually create or import the future scenarios which are going to be your two or more alternative scenarios that you want to analyze. Oftentimes a client will already have future scenarios on the books. A lot of, especially in California, the regional entities are required by law to come up with future plans for every few years for a certain target year. So they might have a few different alternative plans for 2040 say already on the books and then it's our job to take those plans and translate them into urban footprint. And that gives us scenario development. Now it might also be the case that you have no future plans or you want to modify one in which case you might start with the base scenario and create a future scenario and literally paint futures on the map. You select certain parcels and say well this used to be this, I want it in 2040 to be this. So one might do a query and say I want all the low density single family homes that are within this instance of a transit stop and we want to upgrade them to mixed use development for the target year. And that way you get a certain increase of employment, certain employment categories and dwelling unit categories increase whereas other ones might decrease. So either way you do it you're going to get future scenarios whether imported or made from scratch or a combination of the two. While you're making these future scenarios you have the opportunity to run analysis on them and some of the analysis is very simple. It's just saying well what's the delta from the base year to the future year? How much more employment of certain types do we have? How many more dwelling units do we have? Other analysis, all the modules shown here are somewhat more complex or very complex. So we have things like what are the local fiscal impacts? What are the public health impacts? Transportation you can do VMT analysis, vehicle miles traveled and that's a much more complicated one that requires analysis of travel networks. So I'm going to, the rest of the presentation is going to focus on these three different parts of scenario development analysis. So let's start with the software stack for those that are interested in this. Starting from the top going down the bottom on the website of things we're currently using polymaps for the maps that's transitioning to leaflets since polymaps is no longer supported. We use a JavaScript framework called Sproutcore which enables us to do a single page web app. It's very powerful model view controller framework for those of you who know what that is. Let's us do a single page app with a lot of complex functionality embedded into the system. On the server side we've got Django running, Python, Postgres, PostGIS on top of Ubuntu, usual suspects in this world. We also work with D3 for our charting and we do some Socket.io code to send messages back and forth to the client. And we also use tiles to render our tiles on the server side. Let me know if you have more questions on this I can talk forever about that. So here's a first look at urban footprint. This is a map of the city of Irvine in the Los Angeles region. It shows their parcels and it's colored by the land use codes that they gave us. So we focus of course on the map is the center of attention. We want to make sure that most of the interaction involves looking at the map. We support a limited set of features on the map such as navigation of course, but also selecting features and very targeted editing of those features. So we don't allow users to just edit any feature they want. We usually create specific editors that's tailored to their workflow. In the case of Irvine they need to be able to review their land use codes, edit them, and then comment on the edits they made. So this little edit interface on the right is doing just that. The rest of the app here you've got your various layers on the left which is unlimited number of layers they can bring in the system. We typically pre-configure layers for them at this point because of the complexity of it, but we also have the ability to import certain types of layers and export layers to various formats. We don't yet have a styling tool or a map legend tool, but that's on our short-term roadmap. So we're really hoping to have a pretty full-featured input and output and editing process for the layers in the near term. There's also a layer organized. You can drag and drop layers to get the order on the map that you want. On the top is a query editing interface. This is one of the neatest parts of the app. This is very powerful SQL-based querying of the map features and you can do all the typical SQL style querying. You can do filters. You can join any two-layer tables together geographically or by attribute. You can also do aggregation so you can sum your dwelling units or employments or average them and then you can group by other attributes. So you might, for instance, have several jurisdictional codes that represent different cities in your region. You might want to group by those jurisdictional codes and say, well, I want to know what my average employment is or break it down by certain employment categories and get the selections on the map. So these are query results we're seeing here for individual features but you can also get aggregate results and show them there and export them to CSV and other formats. And then finally on the right, I showed that before. That's the pop-out editor area but that also is the area where you can run analysis. So we do a lot of different projects for different clients based on their needs. In the case of, we just did a project for the Southern California Association of Governors, also known as SCAG, and SCAG is the largest metropolitan planning organization. In the United States, they have 190 cities in six counties so whenever you do work with SCAG, you're dealing with immense amounts of data. So for this project, it was a data review pilot and what they wanted to do was take the data that they had at the regional level and expose it to the various cities in the region. So we picked several cities out from Orange County and LA County to take a look at the data that SCAG had and check over the land use codes, make sure things were correct and if not, make changes to those codes and add comments in. So this was a neat project because it forced us to, on the fly, implement some new features like a complete user permission system. So now we have user permissions that limit what region a certain client sees. So if you log in as a city of Irvine, you're only going to see the Irvine city parcels even though it's just one server serving up everything. And we also have the ability to limit what is editable and who has administrative access. The other neat thing about this project is because it was designed to have low-level people at a certain jurisdiction making edits and then managers in that jurisdiction or the regional entity review the parcels, we implemented a data review system so that when somebody edits a land use code and comments on it, then the manager can go and take a look at everything that's been edited and decide whether they want to approve that and merge it into the master copy, which we would call the, say, the master scenario versus the draft scenario. So our focus has really been to implement features based on what our current clients need and those features, of course, because it's open source, everything is fully available to the other clients. We also implemented a really primitive data versioning system for this release which allows whenever any feature is edited, a revision is created and you can go back and take a look at your revision history. So in some ways we're trying to get a little bit toward a Git-style repository system for the feature data where we can support, we can show versions of the data and allow merging from a draft scenario to a master scenario. And this kind of thing is really important for government agencies that need a lot of, that have a lot of auditing and want to keep track of what's happening with a large number of jurisdictions. Very similar slide, I'm sorry, this is the next slide here, is actually sketching futures. This is sort of the second part of those, the three tiers I showed you. So first we had the analysis of what was on the ground and this is actually a project for the San Diego region, the San Diego Regional Planning Agency. They already had a number of 2050, year 2050 future scenarios on the books outside of urban footprint. So you can see on the top left about six different scenarios that we brought into the system and showing on the map is one of those future scenarios along, so you can see the parcels are colored along with the transit networks there. And here's a first view at some of the D3 charts we create. And these D3 charts are great because as the users update their parcels, as they paint them with new land uses, we constantly run analysis on the back end and update the charts. So each time that you paint one or more parcels, you'll see these charts updated and you can see, well, now I have a different delta between my base year and my future year in terms of employment. And we also have charts that allow you to compare one or more scenarios side by side so you can see which scenario is performing better or worse in a certain category. And then finally, you know, managing each one of these scenarios typically contains a unique set of layer features. It's a lot of data to manage. Some of the data is shared. Some of the data that doesn't change can be shared. The goal of this is to allow clients to rapidly experiment with new scenarios. So take one scenario, clone it a couple times, make some changes and see how it performs. So we're constantly trying to make the system faster and more distributed on the back end. Clients have the ability to choose land use codes when they're painting their future parcels. This is something that Cal Thorpe Associates really specializes in, a hierarchy of land use and building types. So what we do is we actually take sample buildings that exist in the real world. We model them in the system. You can see the editor in back there for a certain building. And then we combine similar type buildings into what's called a building type. And that is what a client will typically use to paint their future scenarios. We also have, when clients are painting bigger areas than parcels, we have something called a place type which allows them to combine building information with other urban forms such as streets and parks into what's called a place type, a combination of different assets on the ground. And we can, on the front image here, you can see a visualization of one of those place types. So we have some aggregate attribute information as long as well as some D3 charts based on the information. So that was kind of phase two, the scenario development. And then finally we have to test the impacts using this, using the various analysis. So again, you see public health, fiscal impacts, et cetera. These are analysis modules that are developed in-house at Cal Thorper outside, and they are all peer reviewed by the academic and scientific community and continually upgraded as new information becomes available. Since it's an open source project, our goal is to make these as transparent as possible to provide feedback when these analysis modules run so that users of the system and the public can understand how the numbers are being generated and have a reasonable amount of confidence in the results. Here's an example of one of the analysis modules. This is the vehicle miles traveled module running. This is a big one that takes a long time. We do run distributed processes on the back end to speed it up. So when you're developing scenarios, you can run this analysis at any time and get the aggregate results here. So this is showing some total VMT for the region in aggregate form. And then we also have the ability to, of course, show the results on the map as in this case, green to red going from green is least VMT near the city centers and red is the most VMT away from the city centers. And we can, of course, also represent it with specific custom VMT charts up on top there. So these analysis modules are pretty exciting because they can be updated, extended, and we're always looking to add new ones to make the system more powerful and useful to our clients. So we're also working on APIs to better expose the modules to the front end and to allow contribution and collaboration. And then finally, I'm just going to quickly show some of the results of the Vision California project. This is actually from the spreadsheet based model, the rapid fire model, just to give you an idea of how persuasive some of these results can be to decision makers. So here's, this is for California in 2050 showing the amount of land that can be saved by adopting a smart growth policy instead of a business as usual policy. Enough land saved to match Delaware and Rhode Island combined. And then similarly, here's one for VMT showing just a tremendous number of miles traveled reduced by adopting the smart growth policy. Here's one, here's a, this is a vision or this is an urban footprint result from Vision California, again, showing another VMT map, showing how in the LA region how much of the VMT, how much VMT is increased in the outlying areas versus in nearer the city centers. Here's a couple more, one showing how much water can be saved by 2050, 50 times, Hetch Hetchy. Similarly, building energy, enough power for all homes in California for eight years. Energy savings, household energy, household savings from energy and water and auto fuel and ownership savings by concentrating land use. Greenhouse gas emissions, significant savings by building more compact building, buildings closer to transit and reducing passenger vehicle miles. And there's a lot more information on these results, if anybody's interested. Then real quick, a couple next steps for urban footprint. We're really working on scaling this up for multiple users as we did for the LA region, providing customer support is a big goal of ours when you have a lot of different municipal area, municipal workers like planners and other people using the software you need to have basically 24 hour support. And then also constantly user interface enhancements. So if people give us suggestions all the time and we're always trying to make the product better. And then some other, always working to improve the analytic modules. We're working on social equity indicators, for instance, climate adaption and resilience and conservation ecosystem services. And we have a few other ones that are in the work as well. And then the last, last thing is we're really working on bringing this more to the local planning process. So when community reviews of plans, we can get the community engaged by showing the software to them and allowing them to see how their decisions are decision makers decisions can impact their quality of life. We're also hoping to help out with the general planning process for cities and regions, as well as assessing health impacts and helping with climate action planning. So there's a lot going on here. That's a super quick overview. Let me know if you have specific questions either about what we do in terms of planning or in terms of software. Yeah. So is it working? Yeah. So the land use is associated to land parcel. What if the land parts of the associates are dealing with future scenarios? There could be a huge land parcel which is now agricultural land and you would like to, and I think that the urban partner would like just to split debt into different sections. Do you deal with that? We don't support editing the parcels right now in the software that will probably be supported in the future. There are, I think, some ways to do it on the back end. So even if they can't split it up, we can bring it into the system so that it's different than the base year. So I mean, currently what the software does is just, when you were talking about painting, it means that there is a swath of land which is associated with given land use, right? All the parcels within that bounding box or whatever that they are selected, right? So whole parcels. Whole parcels, although the way the analysis works, if you were to take an agricultural parcel and turn it into mixed use development, the system is smart enough to know that that's not going to be a single parcel. It's going to analyze it on the assumption that it would be broken up into many parcels. Okay. So one more technical question. Since of course the software is multi-user, do you deal with people applying different, designing different scenarios to the same data set? We typically clone the feature sets that are going to be edited. That's where really the data review and merging process, sort of the Git-like versioning becomes really important. But each scenario, we clone the minimum. We don't want to clone anything more than we have to, but obviously if they're editing, we have to make a new copy. So we're talking about cloning the, including geometry or just the language? We, yeah, we clone the geometry too. Is that a bit expensive? It is, yeah. All these things can be optimized and it would be nice. In the future, the goal would be to have the geometry normalized and just reference it separately. We do a little bit of that and it's going to keep getting better as time goes on. Thanks. So is this, as an open source project, is it something that we, that somebody would need to go through you as a client? Or is it something that we can just, that somebody could download and implement at their city? At this point, you have to go through us. Our goal is to make it more and more friendly. I mean, it's a complicated process and it's probably going to usually need some amount of help, but the goal is, as an open source process, to make it more and more accessible. So there is more plug and play to it that we start publishing the data standards so you know how to set it up and get the data in your system. But it does run on, it runs on the Amazon Cloud. That's the way we typically run it and it's becoming, it's getting the point where it's multi. It's very distributed and so typically it requires some amount of assistance to get set up and running. Okay, well I think my time is up so thank you very much. Music
 Kita-sikai  Hi, how are you doing?  Hi, how are you doing?  Hi, how are you doing?  Hello, how are you doing?  Hi, how are you?  Hi, how are you doing?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Hi, how are you?  Yeah, a little bit, but no discussion.  I'm just telling you, I'm not sure what the name of the room is called.  Very small apartment with a friend.  We went to the four-altern.  I'm going to get started. It's one o'clock.  This is a presentation about an urban planning platform.  How many of you are in the urban planning business?  How many people are familiar with urban footprint?  Urban footprint is an open-source scenario planning platform.  It's a suite of tools that let cities and regions compare alternative land use futures.  It's a project that allows cities and regions to create or take the existing future land use plans.  They show them on a map, look at the future data, and run analysis to see what the future land use plans are.  The base year, the study year, might be 2012, for instance, when they gathered their data in a future scenario year, such as 2050 or 2030.  I'll get more into this, but the goal is to see how those different future scenarios compare.  My name is Andy Likuski. I'm not Garland Woodsong who's listed in the program.  I'm the primary software developer for Urban Footprint.  Cal Thorpe Associates is an urban design and planning firm located in Berkeley, California.  We're a small team with a big product, so hopefully there's something here of interest to you.  This kind of program is often called a sketch tool because it literally lets you sketch futures on the map, which we'll get into on the interface.  You're going to have a base parcel on the map that shows you what's on the ground and be able to sketch a future scenario and run analytics.  The project is also exciting because it is fully open source. The stack is an open source stack and I'll get into details on that.  Once I'm the software guy and less the presenter planner guy, feel free to ask me questions on anything, but I'm going to be able to give you a lot of information on the software.  I might have to defer a little bit on some of the planning elements of it.  Cal Thorpe Associates has been around for a long time. They have decades of experience with scenario planning.  You can see some of the regions that we've worked in. Our focus is on transit oriented development and compact growth, trying to meet some of these global and regional climate environmental health goals that a lot of you are surely interested in.  We've been leveraging geospatial software technology for a long time with the goal of improving the efficiency and the visibility of the planning process.  We specialize in regional planning and we also work with individual cities and towns.  What got Urban Footprint started was a project called Vision California which took place from 2008 to 2012 approximately.  This was a project that was funded by the California High Speed Rail Authority along with the California Strategic Growth Council to basically show some of the differences,  how these different future scenarios could have impacts on environmental, fiscal, and health.  And really to tie in the interplay with transportation and land use investments.  So rather than looking at them in a silo to show how where you build can impact your either positive or negative results.  We modeled data in the five major population regions of California and when you're doing this kind of geographic analysis you tend to hit some.  And also when you're dealing with this many features and doing analysis you tend to hit some limits of traditional modeling software.  And we actually broke the proprietary software that we were working with. We were having 12 hour long runs of analysis that would go overnight and then end up with a very mysterious error message.  And so the team was actually forced into an open source stack and we're very happy that we moved in that direction.  So we started with, we moved into the post GIS, Django, and we were using open layers that was the original, the Vision California product.  So here's just one of the many outputs of the Vision California project.  This shows, we analyzed a number of different future scenarios but this shows the two extremes, business as usual, sprawl auto oriented,  business park in the suburbs kind of growth versus growing smart which is really kind of a package of more aggressive green scenarios designed to concentrate growth around existing and future transit networks.  So you can see on the left a lot of that pink area is near population centers but really sprawled out.  And on the right you can see much more compact growth around the planned high speed rail corridor from San Francisco Sacramento to LA and San Diego.  And very happily that high speed rail project is now finally being constructed.  I'll also show some slides at the end of the presentation that demonstrates the impact of these different scenarios on important analysis like greenhouse gas emissions, public health, fiscal impacts, water and energy.  And please let me know if you have questions.  I know some of this stuff, especially for non planners, can go right over your head so stop me if any of this doesn't make sense.  So here's a look at the two software products that came out of the vision California process.  The one on the bottom is actually called Rapid Fire and that's actually a spreadsheet based model.  It's for non-geospatial comparison of scenarios.  This was developed for regions that either don't have access to geospatial data or don't need to do geospatial comparison.  They might just have a few different future scenarios that they're looking at, compact, sprawl or something in the middle and they have some policy packages.  They want to compare what the results are.  So Rapid Fire was designed to work without geospatial analysis.  Urban Footprint, the one on the top, is the one that this talk is going to focus on and that is the web-enabled open source and geospatially obviously aware platform.  So Urban Footprint, like a lot of GIS applications, starts with you got to start with your data and you have to get your data together and organize it.  And then the other two parts of Urban Footprint are taking that base data, developing scenarios and running analysis.  So in terms of data development and organization, our clients, which are typically the regional entities or cities, typically provide us with feature data.  It's often parcels but it could be something bigger like transportation areas.  And that data at a minimum needs to contain information about employment categories on the parcels or other features and as well as dwelling unit categories and probably some kind of land use code so that we can categorize it  and show it on the map.  They might also need to supply us with additional data such as information about what are energy usage so that we can run certain types of analysis.  Other data we can sometimes infer from census data.  So what we get depends on what the client has available and what we can get from elsewhere.  So once you have that base data load, it gets normalized into our system and at that point the client is able to visualize one or more layers on the map.  Usually they're base data parcels we would say but perhaps also transportation networks that they've given us.  So that's sort of a starting point where you can review the features that you have on the ground representing a year such as 2014.  The next step is to actually create or import the future scenarios which are going to be your two or more alternative scenarios that you want to analyze.  Oftentimes a client will already have future scenarios on the books.  A lot of, especially in California, the regional entities are required by law to come up with future plans for every few years for a certain target year.  So they might have a few different alternative plans for 2040 say already on the books and then it's our job to take those plans and translate them into urban footprint.  And that gives us scenario development.  Now it might also be the case that you have no future plans or you want to modify one in which case you might start with the base scenario and create a future scenario and literally paint futures on the map.  You select certain parcels and say well this used to be this, I want it in 2040 to be this.  So one might do a query and say I want all the low density single family homes that are within this instance of a transit stop and we want to upgrade them to mixed use development for the target year.  And that way you get a certain increase of employment, certain employment categories and dwelling unit categories increase whereas other ones might decrease.  So either way you do it you're going to get future scenarios whether imported or made from scratch or a combination of the two.  While you're making these future scenarios you have the opportunity to run analysis on them and some of the analysis is very simple.  It's just saying well what's the delta from the base year to the future year?  How much more employment of certain types do we have?  How many more dwelling units do we have?  Other analysis, all the modules shown here are somewhat more complex or very complex.  So we have things like what are the local fiscal impacts?  What are the public health impacts?  Transportation you can do VMT analysis, vehicle miles traveled and that's a much more complicated one that requires analysis of travel networks.  So I'm going to, the rest of the presentation is going to focus on these three different parts of scenario development analysis.  So let's start with the software stack for those that are interested in this.  Starting from the top going down the bottom on the website of things we're currently using polymaps for the maps that's transitioning to leaflets since polymaps is no longer supported.  We use a JavaScript framework called Sproutcore which enables us to do a single page web app.  It's very powerful model view controller framework for those of you who know what that is.  Let's us do a single page app with a lot of complex functionality embedded into the system.  On the server side we've got Django running, Python, Postgres, PostGIS on top of Ubuntu, usual suspects in this world.  We also work with D3 for our charting and we do some Socket.io code to send messages back and forth to the client.  And we also use tiles to render our tiles on the server side.  Let me know if you have more questions on this I can talk forever about that.  So here's a first look at urban footprint.  This is a map of the city of Irvine in the Los Angeles region.  It shows their parcels and it's colored by the land use codes that they gave us.  So we focus of course on the map is the center of attention.  We want to make sure that most of the interaction involves looking at the map.  We support a limited set of features on the map such as navigation of course,  but also selecting features and very targeted editing of those features.  So we don't allow users to just edit any feature they want.  We usually create specific editors that's tailored to their workflow.  In the case of Irvine they need to be able to review their land use codes, edit them,  and then comment on the edits they made.  So this little edit interface on the right is doing just that.  The rest of the app here you've got your various layers on the left  which is unlimited number of layers they can bring in the system.  We typically pre-configure layers for them at this point because of the complexity of it,  but we also have the ability to import certain types of layers  and export layers to various formats.  We don't yet have a styling tool or a map legend tool,  but that's on our short-term roadmap.  So we're really hoping to have a pretty full-featured input and output  and editing process for the layers in the near term.  There's also a layer organized.  You can drag and drop layers to get the order on the map that you want.  On the top is a query editing interface.  This is one of the neatest parts of the app.  This is very powerful SQL-based querying of the map features  and you can do all the typical SQL style querying.  You can do filters.  You can join any two-layer tables together geographically or by attribute.  You can also do aggregation so you can sum your dwelling units or employments  or average them and then you can group by other attributes.  So you might, for instance, have several jurisdictional codes that represent different cities in your region.  You might want to group by those jurisdictional codes and say,  well, I want to know what my average employment is  or break it down by certain employment categories and get the selections on the map.  So these are query results we're seeing here for individual features  but you can also get aggregate results and show them there  and export them to CSV and other formats.  And then finally on the right, I showed that before.  That's the pop-out editor area but that also is the area where you can run analysis.  So we do a lot of different projects for different clients based on their needs.  In the case of, we just did a project for the Southern California Association of Governors,  also known as SCAG, and SCAG is the largest metropolitan planning organization.  In the United States, they have 190 cities in six counties  so whenever you do work with SCAG, you're dealing with immense amounts of data.  So for this project, it was a data review pilot and what they wanted to do was  take the data that they had at the regional level and expose it to the various cities in the region.  So we picked several cities out from Orange County and LA County  to take a look at the data that SCAG had and check over the land use codes,  make sure things were correct and if not, make changes to those codes and add comments in.  So this was a neat project because it forced us to, on the fly, implement some new features  like a complete user permission system.  So now we have user permissions that limit what region a certain client sees.  So if you log in as a city of Irvine, you're only going to see the Irvine city parcels  even though it's just one server serving up everything.  And we also have the ability to limit what is editable and who has administrative access.  The other neat thing about this project is because it was designed to have low-level people  at a certain jurisdiction making edits and then managers in that jurisdiction  or the regional entity review the parcels, we implemented a data review system  so that when somebody edits a land use code and comments on it, then the manager can go  and take a look at everything that's been edited and decide whether they want to approve that  and merge it into the master copy, which we would call the, say, the master scenario versus the draft scenario.  So our focus has really been to implement features based on what our current clients need  and those features, of course, because it's open source, everything is fully available to the other clients.  We also implemented a really primitive data versioning system for this release  which allows whenever any feature is edited, a revision is created  and you can go back and take a look at your revision history.  So in some ways we're trying to get a little bit toward a Git-style repository system for the feature data  where we can support, we can show versions of the data and allow merging from a draft scenario to a master scenario.  And this kind of thing is really important for government agencies that need a lot of, that have a lot of auditing  and want to keep track of what's happening with a large number of jurisdictions.  Very similar slide, I'm sorry, this is the next slide here, is actually sketching futures.  This is sort of the second part of those, the three tiers I showed you.  So first we had the analysis of what was on the ground  and this is actually a project for the San Diego region, the San Diego Regional Planning Agency.  They already had a number of 2050, year 2050 future scenarios on the books outside of urban footprint.  So you can see on the top left about six different scenarios that we brought into the system  and showing on the map is one of those future scenarios along,  so you can see the parcels are colored along with the transit networks there.  And here's a first view at some of the D3 charts we create.  And these D3 charts are great because as the users update their parcels,  as they paint them with new land uses, we constantly run analysis on the back end and update the charts.  So each time that you paint one or more parcels, you'll see these charts updated  and you can see, well, now I have a different delta between my base year and my future year in terms of employment.  And we also have charts that allow you to compare one or more scenarios side by side  so you can see which scenario is performing better or worse in a certain category.  And then finally, you know, managing each one of these scenarios  typically contains a unique set of layer features.  It's a lot of data to manage. Some of the data is shared.  Some of the data that doesn't change can be shared.  The goal of this is to allow clients to rapidly experiment with new scenarios.  So take one scenario, clone it a couple times, make some changes and see how it performs.  So we're constantly trying to make the system faster and more distributed on the back end.  Clients have the ability to choose land use codes when they're painting their future parcels.  This is something that Cal Thorpe Associates really specializes in, a hierarchy of land use and building types.  So what we do is we actually take sample buildings that exist in the real world.  We model them in the system. You can see the editor in back there for a certain building.  And then we combine similar type buildings into what's called a building type.  And that is what a client will typically use to paint their future scenarios.  We also have, when clients are painting bigger areas than parcels,  we have something called a place type which allows them to combine building information with other urban forms such as streets and parks into what's called a place type,  a combination of different assets on the ground.  And we can, on the front image here, you can see a visualization of one of those place types.  So we have some aggregate attribute information as long as well as some D3 charts based on the information.  So that was kind of phase two, the scenario development.  And then finally we have to test the impacts using this, using the various analysis.  So again, you see public health, fiscal impacts, et cetera.  These are analysis modules that are developed in-house at Cal Thorper outside,  and they are all peer reviewed by the academic and scientific community and continually upgraded as new information becomes available.  Since it's an open source project, our goal is to make these as transparent as possible to provide feedback when these analysis modules run  so that users of the system and the public can understand how the numbers are being generated and have a reasonable amount of confidence in the results.  Here's an example of one of the analysis modules.  This is the vehicle miles traveled module running.  This is a big one that takes a long time.  We do run distributed processes on the back end to speed it up.  So when you're developing scenarios, you can run this analysis at any time and get the aggregate results here.  So this is showing some total VMT for the region in aggregate form.  And then we also have the ability to, of course, show the results on the map as in this case, green to red going from green is least VMT near the city centers  and red is the most VMT away from the city centers.  And we can, of course, also represent it with specific custom VMT charts up on top there.  So these analysis modules are pretty exciting because they can be updated, extended, and we're always looking to add new ones to make the system more powerful  and useful to our clients.  So we're also working on APIs to better expose the modules to the front end and to allow contribution and collaboration.  And then finally, I'm just going to quickly show some of the results of the Vision California project.  This is actually from the spreadsheet based model, the rapid fire model, just to give you an idea of how persuasive some of these results can be to decision makers.  So here's, this is for California in 2050 showing the amount of land that can be saved by adopting a smart growth policy instead of a business as usual policy.  Enough land saved to match Delaware and Rhode Island combined.  And then similarly, here's one for VMT showing just a tremendous number of miles traveled reduced by adopting the smart growth policy.  Here's one, here's a, this is a vision or this is an urban footprint result from Vision California, again, showing another VMT map, showing how in the LA region how much of the VMT, how much VMT is increased in the outlying areas versus in nearer the city centers.  Here's a couple more, one showing how much water can be saved by 2050, 50 times, Hetch Hetchy.  Similarly, building energy, enough power for all homes in California for eight years.  Energy savings, household energy, household savings from energy and water and auto fuel and ownership savings by concentrating land use.  Greenhouse gas emissions, significant savings by building more compact building, buildings closer to transit and reducing passenger vehicle miles.  And there's a lot more information on these results, if anybody's interested.  Then real quick, a couple next steps for urban footprint.  We're really working on scaling this up for multiple users as we did for the LA region, providing customer support is a big goal of ours when you have a lot of different municipal area, municipal workers like planners and other people using the software you need to have basically 24 hour support.  And then also constantly user interface enhancements.  So if people give us suggestions all the time and we're always trying to make the product better.  And then some other, always working to improve the analytic modules.  We're working on social equity indicators, for instance, climate adaption and resilience and conservation ecosystem services.  And we have a few other ones that are in the work as well.  And then the last, last thing is we're really working on bringing this more to the local planning process.  So when community reviews of plans, we can get the community engaged by showing the software to them and allowing them to see how their decisions are decision makers decisions can impact their quality of life.  We're also hoping to help out with the general planning process for cities and regions, as well as assessing health impacts and helping with climate action planning.  So there's a lot going on here. That's a super quick overview.  Let me know if you have specific questions either about what we do in terms of planning or in terms of software.  Yeah.  So is it working?  Yeah.  So the land use is associated to land parcel.  What if the land parts of the associates are dealing with future scenarios?  There could be a huge land parcel which is now agricultural land and you would like to, and I think that the urban partner would like just to split debt into different sections.  Do you deal with that?  We don't support editing the parcels right now in the software that will probably be supported in the future.  There are, I think, some ways to do it on the back end.  So even if they can't split it up, we can bring it into the system so that it's different than the base year.  So I mean, currently what the software does is just, when you were talking about painting, it means that there is a swath of land which is associated with given land use, right?  All the parcels within that bounding box or whatever that they are selected, right?  So whole parcels.  Whole parcels, although the way the analysis works, if you were to take an agricultural parcel and turn it into mixed use development,  the system is smart enough to know that that's not going to be a single parcel.  It's going to analyze it on the assumption that it would be broken up into many parcels.  Okay.  So one more technical question.  Since of course the software is multi-user, do you deal with people applying different, designing different scenarios to the same data set?  We typically clone the feature sets that are going to be edited.  That's where really the data review and merging process, sort of the Git-like versioning becomes really important.  But each scenario, we clone the minimum.  We don't want to clone anything more than we have to, but obviously if they're editing, we have to make a new copy.  So we're talking about cloning the, including geometry or just the language?  We, yeah, we clone the geometry too.  Is that a bit expensive?  It is, yeah.  All these things can be optimized and it would be nice.  In the future, the goal would be to have the geometry normalized and just reference it separately.  We do a little bit of that and it's going to keep getting better as time goes on.  Thanks.  So is this, as an open source project, is it something that we, that somebody would need to go through you as a client?  Or is it something that we can just, that somebody could download and implement at their city?  At this point, you have to go through us.  Our goal is to make it more and more friendly.  I mean, it's a complicated process and it's probably going to usually need some amount of help,  but the goal is, as an open source process, to make it more and more accessible.  So there is more plug and play to it that we start publishing the data standards so you know how to set it up and get the data in your system.  But it does run on, it runs on the Amazon Cloud.  That's the way we typically run it and it's becoming, it's getting the point where it's multi.  It's very distributed and so typically it requires some amount of assistance to get set up and running.  Okay, well I think my time is up so thank you very much.  Music
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Kita-sikai Hi, how are you doing? Hi, how are you doing? Hi, how are you doing? Hello, how are you doing? Hi, how are you? Hi, how are you doing? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Hi, how are you? Yeah, a little bit, but no discussion. I'm just telling you, I'm not sure what the name of the room is called. Very small apartment with a friend. We went to the four-altern. I'm going to get started. It's one o'clock. This is a presentation about an urban planning platform. How many of you are in the urban planning business? How many people are familiar with urban footprint? Urban footprint is an open-source scenario planning platform. It's a suite of tools that let cities and regions compare alternative land use futures. It's a project that allows cities and regions to create or take the existing future land use plans. They show them on a map, look at the future data, and run analysis to see what the future land use plans are. The base year, the study year, might be 2012, for instance, when they gathered their data in a future scenario year, such as 2050 or 2030. I'll get more into this, but the goal is to see how those different future scenarios compare. My name is Andy Likuski. I'm not Garland Woodsong who's listed in the program. I'm the primary software developer for Urban Footprint. Cal Thorpe Associates is an urban design and planning firm located in Berkeley, California. We're a small team with a big product, so hopefully there's something here of interest to you. This kind of program is often called a sketch tool because it literally lets you sketch futures on the map, which we'll get into on the interface. You're going to have a base parcel on the map that shows you what's on the ground and be able to sketch a future scenario and run analytics. The project is also exciting because it is fully open source. The stack is an open source stack and I'll get into details on that. Once I'm the software guy and less the presenter planner guy, feel free to ask me questions on anything, but I'm going to be able to give you a lot of information on the software. I might have to defer a little bit on some of the planning elements of it. Cal Thorpe Associates has been around for a long time. They have decades of experience with scenario planning. You can see some of the regions that we've worked in. Our focus is on transit oriented development and compact growth, trying to meet some of these global and regional climate environmental health goals that a lot of you are surely interested in. We've been leveraging geospatial software technology for a long time with the goal of improving the efficiency and the visibility of the planning process. We specialize in regional planning and we also work with individual cities and towns. What got Urban Footprint started was a project called Vision California which took place from 2008 to 2012 approximately. This was a project that was funded by the California High Speed Rail Authority along with the California Strategic Growth Council to basically show some of the differences, how these different future scenarios could have impacts on environmental, fiscal, and health. And really to tie in the interplay with transportation and land use investments. So rather than looking at them in a silo to show how where you build can impact your either positive or negative results. We modeled data in the five major population regions of California and when you're doing this kind of geographic analysis you tend to hit some. And also when you're dealing with this many features and doing analysis you tend to hit some limits of traditional modeling software. And we actually broke the proprietary software that we were working with. We were having 12 hour long runs of analysis that would go overnight and then end up with a very mysterious error message. And so the team was actually forced into an open source stack and we're very happy that we moved in that direction. So we started with, we moved into the post GIS, Django, and we were using open layers that was the original, the Vision California product. So here's just one of the many outputs of the Vision California project. This shows, we analyzed a number of different future scenarios but this shows the two extremes, business as usual, sprawl auto oriented, business park in the suburbs kind of growth versus growing smart which is really kind of a package of more aggressive green scenarios designed to concentrate growth around existing and future transit networks. So you can see on the left a lot of that pink area is near population centers but really sprawled out. And on the right you can see much more compact growth around the planned high speed rail corridor from San Francisco Sacramento to LA and San Diego. And very happily that high speed rail project is now finally being constructed. I'll also show some slides at the end of the presentation that demonstrates the impact of these different scenarios on important analysis like greenhouse gas emissions, public health, fiscal impacts, water and energy. And please let me know if you have questions. I know some of this stuff, especially for non planners, can go right over your head so stop me if any of this doesn't make sense. So here's a look at the two software products that came out of the vision California process. The one on the bottom is actually called Rapid Fire and that's actually a spreadsheet based model. It's for non-geospatial comparison of scenarios. This was developed for regions that either don't have access to geospatial data or don't need to do geospatial comparison. They might just have a few different future scenarios that they're looking at, compact, sprawl or something in the middle and they have some policy packages. They want to compare what the results are. So Rapid Fire was designed to work without geospatial analysis. Urban Footprint, the one on the top, is the one that this talk is going to focus on and that is the web-enabled open source and geospatially obviously aware platform. So Urban Footprint, like a lot of GIS applications, starts with you got to start with your data and you have to get your data together and organize it. And then the other two parts of Urban Footprint are taking that base data, developing scenarios and running analysis. So in terms of data development and organization, our clients, which are typically the regional entities or cities, typically provide us with feature data. It's often parcels but it could be something bigger like transportation areas. And that data at a minimum needs to contain information about employment categories on the parcels or other features and as well as dwelling unit categories and probably some kind of land use code so that we can categorize it and show it on the map. They might also need to supply us with additional data such as information about what are energy usage so that we can run certain types of analysis. Other data we can sometimes infer from census data. So what we get depends on what the client has available and what we can get from elsewhere. So once you have that base data load, it gets normalized into our system and at that point the client is able to visualize one or more layers on the map. Usually they're base data parcels we would say but perhaps also transportation networks that they've given us. So that's sort of a starting point where you can review the features that you have on the ground representing a year such as 2014. The next step is to actually create or import the future scenarios which are going to be your two or more alternative scenarios that you want to analyze. Oftentimes a client will already have future scenarios on the books. A lot of, especially in California, the regional entities are required by law to come up with future plans for every few years for a certain target year. So they might have a few different alternative plans for 2040 say already on the books and then it's our job to take those plans and translate them into urban footprint. And that gives us scenario development. Now it might also be the case that you have no future plans or you want to modify one in which case you might start with the base scenario and create a future scenario and literally paint futures on the map. You select certain parcels and say well this used to be this, I want it in 2040 to be this. So one might do a query and say I want all the low density single family homes that are within this instance of a transit stop and we want to upgrade them to mixed use development for the target year. And that way you get a certain increase of employment, certain employment categories and dwelling unit categories increase whereas other ones might decrease. So either way you do it you're going to get future scenarios whether imported or made from scratch or a combination of the two. While you're making these future scenarios you have the opportunity to run analysis on them and some of the analysis is very simple. It's just saying well what's the delta from the base year to the future year? How much more employment of certain types do we have? How many more dwelling units do we have? Other analysis, all the modules shown here are somewhat more complex or very complex. So we have things like what are the local fiscal impacts? What are the public health impacts? Transportation you can do VMT analysis, vehicle miles traveled and that's a much more complicated one that requires analysis of travel networks. So I'm going to, the rest of the presentation is going to focus on these three different parts of scenario development analysis. So let's start with the software stack for those that are interested in this. Starting from the top going down the bottom on the website of things we're currently using polymaps for the maps that's transitioning to leaflets since polymaps is no longer supported. We use a JavaScript framework called Sproutcore which enables us to do a single page web app. It's very powerful model view controller framework for those of you who know what that is. Let's us do a single page app with a lot of complex functionality embedded into the system. On the server side we've got Django running, Python, Postgres, PostGIS on top of Ubuntu, usual suspects in this world. We also work with D3 for our charting and we do some Socket.io code to send messages back and forth to the client. And we also use tiles to render our tiles on the server side. Let me know if you have more questions on this I can talk forever about that. So here's a first look at urban footprint. This is a map of the city of Irvine in the Los Angeles region. It shows their parcels and it's colored by the land use codes that they gave us. So we focus of course on the map is the center of attention. We want to make sure that most of the interaction involves looking at the map. We support a limited set of features on the map such as navigation of course, but also selecting features and very targeted editing of those features. So we don't allow users to just edit any feature they want. We usually create specific editors that's tailored to their workflow. In the case of Irvine they need to be able to review their land use codes, edit them, and then comment on the edits they made. So this little edit interface on the right is doing just that. The rest of the app here you've got your various layers on the left which is unlimited number of layers they can bring in the system. We typically pre-configure layers for them at this point because of the complexity of it, but we also have the ability to import certain types of layers and export layers to various formats. We don't yet have a styling tool or a map legend tool, but that's on our short-term roadmap. So we're really hoping to have a pretty full-featured input and output and editing process for the layers in the near term. There's also a layer organized. You can drag and drop layers to get the order on the map that you want. On the top is a query editing interface. This is one of the neatest parts of the app. This is very powerful SQL-based querying of the map features and you can do all the typical SQL style querying. You can do filters. You can join any two-layer tables together geographically or by attribute. You can also do aggregation so you can sum your dwelling units or employments or average them and then you can group by other attributes. So you might, for instance, have several jurisdictional codes that represent different cities in your region. You might want to group by those jurisdictional codes and say, well, I want to know what my average employment is or break it down by certain employment categories and get the selections on the map. So these are query results we're seeing here for individual features but you can also get aggregate results and show them there and export them to CSV and other formats. And then finally on the right, I showed that before. That's the pop-out editor area but that also is the area where you can run analysis. So we do a lot of different projects for different clients based on their needs. In the case of, we just did a project for the Southern California Association of Governors, also known as SCAG, and SCAG is the largest metropolitan planning organization. In the United States, they have 190 cities in six counties so whenever you do work with SCAG, you're dealing with immense amounts of data. So for this project, it was a data review pilot and what they wanted to do was take the data that they had at the regional level and expose it to the various cities in the region. So we picked several cities out from Orange County and LA County to take a look at the data that SCAG had and check over the land use codes, make sure things were correct and if not, make changes to those codes and add comments in. So this was a neat project because it forced us to, on the fly, implement some new features like a complete user permission system. So now we have user permissions that limit what region a certain client sees. So if you log in as a city of Irvine, you're only going to see the Irvine city parcels even though it's just one server serving up everything. And we also have the ability to limit what is editable and who has administrative access. The other neat thing about this project is because it was designed to have low-level people at a certain jurisdiction making edits and then managers in that jurisdiction or the regional entity review the parcels, we implemented a data review system so that when somebody edits a land use code and comments on it, then the manager can go and take a look at everything that's been edited and decide whether they want to approve that and merge it into the master copy, which we would call the, say, the master scenario versus the draft scenario. So our focus has really been to implement features based on what our current clients need and those features, of course, because it's open source, everything is fully available to the other clients. We also implemented a really primitive data versioning system for this release which allows whenever any feature is edited, a revision is created and you can go back and take a look at your revision history. So in some ways we're trying to get a little bit toward a Git-style repository system for the feature data where we can support, we can show versions of the data and allow merging from a draft scenario to a master scenario. And this kind of thing is really important for government agencies that need a lot of, that have a lot of auditing and want to keep track of what's happening with a large number of jurisdictions. Very similar slide, I'm sorry, this is the next slide here, is actually sketching futures. This is sort of the second part of those, the three tiers I showed you. So first we had the analysis of what was on the ground and this is actually a project for the San Diego region, the San Diego Regional Planning Agency. They already had a number of 2050, year 2050 future scenarios on the books outside of urban footprint. So you can see on the top left about six different scenarios that we brought into the system and showing on the map is one of those future scenarios along, so you can see the parcels are colored along with the transit networks there. And here's a first view at some of the D3 charts we create. And these D3 charts are great because as the users update their parcels, as they paint them with new land uses, we constantly run analysis on the back end and update the charts. So each time that you paint one or more parcels, you'll see these charts updated and you can see, well, now I have a different delta between my base year and my future year in terms of employment. And we also have charts that allow you to compare one or more scenarios side by side so you can see which scenario is performing better or worse in a certain category. And then finally, you know, managing each one of these scenarios typically contains a unique set of layer features. It's a lot of data to manage. Some of the data is shared. Some of the data that doesn't change can be shared. The goal of this is to allow clients to rapidly experiment with new scenarios. So take one scenario, clone it a couple times, make some changes and see how it performs. So we're constantly trying to make the system faster and more distributed on the back end. Clients have the ability to choose land use codes when they're painting their future parcels. This is something that Cal Thorpe Associates really specializes in, a hierarchy of land use and building types. So what we do is we actually take sample buildings that exist in the real world. We model them in the system. You can see the editor in back there for a certain building. And then we combine similar type buildings into what's called a building type. And that is what a client will typically use to paint their future scenarios. We also have, when clients are painting bigger areas than parcels, we have something called a place type which allows them to combine building information with other urban forms such as streets and parks into what's called a place type, a combination of different assets on the ground. And we can, on the front image here, you can see a visualization of one of those place types. So we have some aggregate attribute information as long as well as some D3 charts based on the information. So that was kind of phase two, the scenario development. And then finally we have to test the impacts using this, using the various analysis. So again, you see public health, fiscal impacts, et cetera. These are analysis modules that are developed in-house at Cal Thorper outside, and they are all peer reviewed by the academic and scientific community and continually upgraded as new information becomes available. Since it's an open source project, our goal is to make these as transparent as possible to provide feedback when these analysis modules run so that users of the system and the public can understand how the numbers are being generated and have a reasonable amount of confidence in the results. Here's an example of one of the analysis modules. This is the vehicle miles traveled module running. This is a big one that takes a long time. We do run distributed processes on the back end to speed it up. So when you're developing scenarios, you can run this analysis at any time and get the aggregate results here. So this is showing some total VMT for the region in aggregate form. And then we also have the ability to, of course, show the results on the map as in this case, green to red going from green is least VMT near the city centers and red is the most VMT away from the city centers. And we can, of course, also represent it with specific custom VMT charts up on top there. So these analysis modules are pretty exciting because they can be updated, extended, and we're always looking to add new ones to make the system more powerful and useful to our clients. So we're also working on APIs to better expose the modules to the front end and to allow contribution and collaboration. And then finally, I'm just going to quickly show some of the results of the Vision California project. This is actually from the spreadsheet based model, the rapid fire model, just to give you an idea of how persuasive some of these results can be to decision makers. So here's, this is for California in 2050 showing the amount of land that can be saved by adopting a smart growth policy instead of a business as usual policy. Enough land saved to match Delaware and Rhode Island combined. And then similarly, here's one for VMT showing just a tremendous number of miles traveled reduced by adopting the smart growth policy. Here's one, here's a, this is a vision or this is an urban footprint result from Vision California, again, showing another VMT map, showing how in the LA region how much of the VMT, how much VMT is increased in the outlying areas versus in nearer the city centers. Here's a couple more, one showing how much water can be saved by 2050, 50 times, Hetch Hetchy. Similarly, building energy, enough power for all homes in California for eight years. Energy savings, household energy, household savings from energy and water and auto fuel and ownership savings by concentrating land use. Greenhouse gas emissions, significant savings by building more compact building, buildings closer to transit and reducing passenger vehicle miles. And there's a lot more information on these results, if anybody's interested. Then real quick, a couple next steps for urban footprint. We're really working on scaling this up for multiple users as we did for the LA region, providing customer support is a big goal of ours when you have a lot of different municipal area, municipal workers like planners and other people using the software you need to have basically 24 hour support. And then also constantly user interface enhancements. So if people give us suggestions all the time and we're always trying to make the product better. And then some other, always working to improve the analytic modules. We're working on social equity indicators, for instance, climate adaption and resilience and conservation ecosystem services. And we have a few other ones that are in the work as well. And then the last, last thing is we're really working on bringing this more to the local planning process. So when community reviews of plans, we can get the community engaged by showing the software to them and allowing them to see how their decisions are decision makers decisions can impact their quality of life. We're also hoping to help out with the general planning process for cities and regions, as well as assessing health impacts and helping with climate action planning. So there's a lot going on here. That's a super quick overview. Let me know if you have specific questions either about what we do in terms of planning or in terms of software. Yeah. So is it working? Yeah. So the land use is associated to land parcel. What if the land parts of the associates are dealing with future scenarios? There could be a huge land parcel which is now agricultural land and you would like to, and I think that the urban partner would like just to split debt into different sections. Do you deal with that? We don't support editing the parcels right now in the software that will probably be supported in the future. There are, I think, some ways to do it on the back end. So even if they can't split it up, we can bring it into the system so that it's different than the base year. So I mean, currently what the software does is just, when you were talking about painting, it means that there is a swath of land which is associated with given land use, right? All the parcels within that bounding box or whatever that they are selected, right? So whole parcels. Whole parcels, although the way the analysis works, if you were to take an agricultural parcel and turn it into mixed use development, the system is smart enough to know that that's not going to be a single parcel. It's going to analyze it on the assumption that it would be broken up into many parcels. Okay. So one more technical question. Since of course the software is multi-user, do you deal with people applying different, designing different scenarios to the same data set? We typically clone the feature sets that are going to be edited. That's where really the data review and merging process, sort of the Git-like versioning becomes really important. But each scenario, we clone the minimum. We don't want to clone anything more than we have to, but obviously if they're editing, we have to make a new copy. So we're talking about cloning the, including geometry or just the language? We, yeah, we clone the geometry too. Is that a bit expensive? It is, yeah. All these things can be optimized and it would be nice. In the future, the goal would be to have the geometry normalized and just reference it separately. We do a little bit of that and it's going to keep getting better as time goes on. Thanks. So is this, as an open source project, is it something that we, that somebody would need to go through you as a client? Or is it something that we can just, that somebody could download and implement at their city? At this point, you have to go through us. Our goal is to make it more and more friendly. I mean, it's a complicated process and it's probably going to usually need some amount of help, but the goal is, as an open source process, to make it more and more accessible. So there is more plug and play to it that we start publishing the data standards so you know how to set it up and get the data in your system. But it does run on, it runs on the Amazon Cloud. That's the way we typically run it and it's becoming, it's getting the point where it's multi. It's very distributed and so typically it requires some amount of assistance to get set up and running. Okay, well I think my time is up so thank you very much. Music
footprint is an open-source scenario planning platform. It's a suite of tools that let cities create or take the existing future land use exciting because it is fully open source. The able to give you a lot of information on the some of these global and regional climate geographic analysis you tend to hit some. And traditional modeling software. And we actually green scenarios designed to concentrate growth around existing and future transit networks. So like a lot of GIS applications, starts with you the regional entities or cities, typically transportation areas. And that data at a minimum with future plans for every few years for a might start with the base scenario and create a this used to be this, I want it in 2040 to be like what are the local fiscal impacts? What are do VMT analysis, vehicle miles traveled and Python, Postgres, PostGIS on top of Ubuntu, usual they had at the regional level and expose it to implement some new features like a complete user important for government agencies that need a lot this is the next slide here, is actually 2050 future scenarios on the books outside of managing each one of these scenarios typically contains a unique set of layer features. It's a be shared. The goal of this is to allow clients a hierarchy of land use and building types. So building type. And that is what a client will and the public can understand how the numbers are model, the rapid fire model, just to give you an this is an urban footprint result from Vision California for eight years. Energy savings, and water and auto fuel and ownership savings by concentrating land use. Greenhouse gas emissions, building, buildings closer to transit and right now in the software that will probably be So I mean, currently what the software does is associated with given land use, right? All the it into mixed use development, the system is including geometry or just the language? We, expensive? It is, yeah. All these things can be