Regional Conservation Strategy Viewer
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Allensworth, Greg
Albo, Tommy
The Intertwine Alliance has formed a Regional Conservation Strategy (RCS) for the area surrounding Portland, Oregon. The goal of the RCS is to guide the development by providing a high-resolution model of habitat valuation and land cover.The RCS Viewer allows website visitors to access and visualize high-value habitat and land cover, without specialized GIS knowledge or tools. By selecting, uploading, or drawing an area of interest, the user can bring up a feature-rich summary of habitat valuation and land cover.This presentation covers some uses of the RCS Viewer of interest to conservation-concerned planners in the area, as well as some technical details of interest to developers.
 All right. Boise. Baltimore. Baltimore. North-East California. All right. Seattle. Seattle. Port. Idaho. Idaho. Back in the back. Madison. Madison. Madison. Yeah. Why do you want me to come? All right. Switzerland. Switzerland. Okay. San Francisco, Portland. Anyway, so the Intertwine Alliance is this, it's a member of the Metropolitan Green Space Alliance. There's seven of them in the country, regional sort of coalitions. It was formed in 2009. It's really to ensure that the region's network of parks, trails, and natural areas is completed and cared for, and to help the region's residents connect with nature and live active, healthy lives. I work for Metro Oregon across the street here. There's a number of people that are members of this alliance, so I'm just representing them today. I work with Green Info Network, where essentially a nonprofit who works with other nonprofits providing GIS services. So print maps for parks and rec services, interactive maps for social justice and conservation sort of thing. This definitely fits in that third category. Active maps for conservation. So going to discuss very briefly what the RCS itself is. It's a localized thing. You might not have heard of one. Then the viewer software itself, which helps them to, provides information for enabling decisions. And then a little bit on the tech stuff, depending on how interested we are in the technology, as opposed to the actual real world and human uses for it. And then discuss some of the real world use cases for it and the value they've gotten out of it. And I thought we were going to focus more on the tech stuff. Anyway, the regional conservation strategy is an initiative of the Intertwinal Alliance and it's focusing on biodiversity conservation. So why did we take this on? There's been a number of individuals in this region that have been working on this for decades, organizations that have been working on it for decades, knowing that the importance of conservation goes beyond political boundaries. And there's been a lot of efforts. The problem is there's been no effort that sort of comprised of the whole region. We've had statewide efforts. We've had citywide, great prioritization efforts. We've had ecoregional efforts by the Nature Conservancy. But again, nothing sort of comprised of the whole area. And that was a challenge when we went to funding and sort of tried to share our vision. So one of our main things, some of the reasons we did this was to create that shared vision for biodiversity conservation, to increase the impact of our work, to connect jurisdictions and existing planning efforts, and to increase funding opportunities. Which one's the arrow? The spacebar over there. All right. So it's really comprised of three main elements. The Regional Conservation Strategy, which is a 161-page document that really outlines a number of different chapters from biodiversity corridors, climate change, existing conditions. And it's a great resource that we have available for everybody. There's the Biodiversity Guide, which is a 360-page document, which is also, you know, this is the science behind the RCS. And so these are available online. And there's no small effort. It's a three-year project, 161 volunteers, writers for this, 75 different organizations. So it's a big effort. And then we got the Habitat Modeling and the data output. We also have a executive summary, which any of you guys can take one here. I got a few. And we have the RCS viewer. And we're stuck. Okay. There we go. So what is this, the region that we're... We're not up there. Did it just blank out? There you go. That's... Okay. Odd. All right. That's... Okay, then. That's interesting. I take blame for that. I hate the overclass. All right. There. Thank you. Then can we do just the screen change instead of all the notes? That's strange. I've never done that before. Yeah. All right. We'll see if we can move on from here. We can work from this. Yeah, I guess we'll just... Can we just move? How do we move forward? Use the arrows. All right. So here's... Hopefully the animations will work. Anyway, here's our region. It's a big region. It's 10... Parts of 10 counties, two states. I think it's like 33 sub-watersheds. And our first key decision was creating a prioritization effort. It was creating a better habitat model. Most of our previous conservation or prioritizations wasn't able to depict high priority areas in an urban setting. And you see this all over the country. If you don't have a good land cover, it's just really hard to model prioritization or high value habitats. So this was the results of our initial habitat model. And I'm kind of confused on what we're working here. But as you can see, the urban areas here were not highlighted either. Most of it's out towards the farther reaches up towards the headlands. And so we took a different take on it and we clipped this raster output. And what we found is that then we could re-ramp the scores. And these scores allowed us to sort of bring out the urban quality of habitat. This took a lot of GIS manipulation and whatnot. And it was just somewhat time consuming. These datasets were huge. So one of the key decisions was not to draw polygons for priority habitats. And that's kind of a unique decision that we made. The reason being is because you're dealing with five meter pixels of results, we would get clumps that were very high value habitats. But we'd have little circles all over the region. And so we decided to provide the raw data as an output. And this was great for a GIS specialist who could do some of the work and sort of look at their different areas. Do we miss? Yeah. Sorry about this, guys. I think we're on the wrong presentation. Well let me try to summarize this. So when we had this output, I had a few other slides in here. What did, is it provided you the opportunity to see that if you looked in the very urban area here, where we weren't seeing any high value habitat, we were, kind of last year, Greg. Well really the upshot of it is that within the urban areas, you still have lower and higher value habitat. And then the city of Beaverton, for example, looks like it's very poorly on a regional scale. However, if you zoom in on Beaverton and use that as your local focus, there are higher and lower value regions in Beaverton. And sometimes that's what you're really concerned about. Maybe the land in Beaverton doesn't have the highest value compared to the headlands up there, but there are still areas and opportunities within Beaverton where your environmental impact and your habitat value have their own variation. So the question was, how can we present the tools and visualizations and the ability to do these GIS operations without using ArcGIS and advanced GIS knowledge and having a GISP on hand? Yeah. Yeah. So then I'm going to let Greg talk about the viewer. The big challenge for us was really to empower the local user to define an area. Then this area that they defined, they would re-ramp the values of the model and the output that you would see, the high value habitats per relative area that they selected. And so it was really cool while we were working with this in GIS, but we needed a tool that would get this to the decision makers, to the policy makers who don't have GIS experience. And so that was the challenge we went to green info with and we're super happy with the product they came up with. I'll let him show you some of that and then we'll show you some examples. Sorry about the earlier glitch, we kind of lost it there. So at its core, it's a browser based interface over the RCS habitat valuation model, park lands and other such data sets. The goal was that you don't need any specialized GIS knowledge nor even software. In fact, even if you don't have the ability to make a shape file, if you can just hand draw on the map, it'll still be able to do the calculations and get your habitat valuations, stats and so forth. So you can define an area in a number of ways. You can select a bunch of prefab ones such as city boundaries and counties and so forth off of a menu. You can upload a shape file or just go to hand draw. What you end up with is something like this. This here is habitat value shown on the full regional spectrum, that is to say contained within the entire universe of the intertwined area. As you see, most of this is comparatively low value. However, over there near the grassy areas, it ends up being somewhat higher value as one would expect. But what about local focus? Within local focus, you see a whole lot more area that's indicated as being high value for this area. Switch back and forth here. And you can see regional perspective and local focus. It's one of the taglines. Likewise, if we want to show only the highest value habitat, that ranked in the top 10 percentile of all, there is some significant area here, significant opportunities and focus. And yet, if we look at a local focus, we discover even more opportunities for doing good locally. Having selected an area, you get back a fairly rich set of statistics, most notably land cover. So, again, forest land, riparian land, places that are already developed, places that are already conserved, habitat valuation and so forth, nice, rich set of pie charts. They find this immensely useful. Most of this data is baked enough that they can use it as is in their reports. Perhaps most importantly, however, into your local focus is the ability to make annotations on the map. Having already selected the area, they're able to define known backyard habitats, color code them, add text labels, even legends and so forth onto the map. Again, using simple drawing tools, you see the icons there up on the top. So not only do you get your local focus, but you really get to make your map speak by adding your own annotations. You can literally circle it and write, don't dig here. The ability to save and share the state of the viewer at any given time, including your annotations, your maps, the whiteout and everything, all stored in this relatively tiny, early idea. It loads back up, loads your previous state, which means you can pick up where you left off and save another version of it or pass this URL onto somebody else and they can see exactly what you're seeing, up to and including that red circle that says, don't dig here. If you're really into paper takeaways, which a lot of us are, you know, I can give you the instructions to follow this on an iPad, but, you know, sometimes you just want a PDF that you can print it out, copy, then you can just hand away 100 of them. Well, the PDF is extremely rich. They're very happy with this. And again, it fits a lot of their reporting formats and they find they can use a lot of this stuff almost as is. Use your local map, regional scaling, color ramping, all the same statistics you saw before. So a quick overview of the technology stuff. It's underlying. We use good old open layers, map server, map box and so forth. Now this, if anyone's more interested in the tech stuff, just... What? Yeah, the thing seems to have these little screws sticking out of it that's making it not seat properly. Come on, guys. All righty. So if anyone's really interested in the tech stuff, just go ahead and ask and we can chat about it later on. Again, map server, open layers. A good old is what we use for the raster calculations. This is a NumPy library which uses the GDAL tools to do queries on rasters. There's other technology for doing this. At the time, PostGIS raster was not considered production ready, so we went with a Google at the time. These days, if I were to do it again, I'd probably use PostGIS raster. It's very performant and has much lower memory usage than doing it in a Google. Why do we use open layers? Just because I had boilerplate code for a lot of these uploading and editing functions. We get in leaflet, again, equally valid, but we had boilerplate code. Map box, wonderful choice for our base maps. Simply because they're a remotely hosted tile service, very performant, very reasonably priced and very attractive base maps without having to generate them all ourselves. There was some talk of using Bing and Google base maps, but again, they have APIs as opposed to simple XYZ services. The programming's a little tougher and, well, frankly, they change their APIs in their terms so often, better to just steer clear of it and go someplace where the terms are really clear and static. Map servers, what we use for the server side rendering of these rasters. There's other technologies for it. Map servers is the one that we chose. No strong reasons, but most notably, variable interpolation is very valuable here. Having selected an area, the min and max can be passed back and then used to set the color ramp with less programming and overhead than, say, doing it with SLDs and GeoServer. The PDF printing uses a toolkit called WK HTML to PDF. This mouthful, the WK means WebKit. Somebody actually ported the WebKit rendering engine into a bunch of command line utilities and shared object libraries for Linux, which effectively means that you can take an HTML document, feed it to this puppy, and get back a PDF. Use your PDFs in HTML, and it even supports JavaScript, D3, Google Maps, Open Layers. It's absolutely wonderful. You can start with copying and pasting your browser side code and be 50% there. D3 is what we use for the pie charts here. High charts is what we'd probably use next time. It's also a really good one. D3, though, was fairly expedient for making these pie charts that we did need. Now, the RCS viewer wanted to discuss some of the real-world uses and impacts so far at, and this is where the real value comes in, of course. One of them, we're going to see this one, the Simple Land Cover Survey. This is the city of Tualatin. Yeah, so this is a great tool, not just for non-GIS, but even for GIS folks. You'll get calls and say, you know, to get quick statistics of a city's land cover, it's 33% tree cover is great. You can compare it to the city of Portland's, what they've done in the past, and, you know, they're very precise about their percentage. This tool came within a percentage of what they are claiming their tree cover is. So when you compare two different cities, or even what the nationwide goal for urban tree canopy would be, this is a great tool. Not only does it do this, I don't know if there's another example, but that same area of the statistics, say, how many parks do you have in there, and give you how many federal, federally owned, state-owned, locally owned parks, and that's great. So we have some pre-canned geographies in here, all the city jurisdictions, all the watersheds, but you can also just draw in any shape, or you can upload a shape file, and it will clip that. And the results are within a couple minutes. I mean, it's pretty fast. And it's just great to have that statistics for whatever you need. For a big complex area, calculation time can run of 60 to 100 seconds. For this one, since it's canned, and was pre-calculated last time, someone asked for a to wallet, and results are in about three seconds. That beats ARCMAP's time. Let's see, decisions, like this one, for example, a bunch of grantees looking to do improvements to, I forget what the program was specifically, but. Yeah, so we have a program in Metro, and people, we get a lot of grant applicants for some habitat restoration. So we just uploaded these shape files into the program. We labeled these, and during the grant-making decision process, they were able to zoom into one of these areas. Since it's labeled, it's number 23, and you can say, oh, you know, what's the habitat around here? Not as the answer, but certainly as sort of some background reference information. Is there parks around there? Is it a key linkage between two protected areas already? So it was very helpful for them, just in the discussion of these grant applicants. This is beautiful. Trail planning, again, with the annotations. Here's the planned trail. Here's an existing trail. Well, the pink trails are proposed, I believe, and then the grain are existing, or vice versa. But yeah, to be able to annotate this and then send this across to another jurisdiction, and they'll open this URL that generates, and all this annotation, the view, the opacity, is all right there. So now you can have a conversation on the phone looking at the exact same thing. And that's been really helpful. And then you can save this if that person doesn't answer the phone or whatever. You can just say this, say, I'm going to call them next week. When you open up that URL next week, it's still there, again, with all the annotations and everything you were looking at. Not like a GIS whiteboard. Yeah. That's what we were trying for. Another example, backyard habitats. Do you have your locations? Do you have your stats? There's more to it than the stats. And this was for Audubon. They just were curious of what's this Laurelhurst neighborhood look like? We do backyard habitat. Let us upload our shapefile or KML and see what the existing, where our existing backyard habitats certifications are, where our, I don't know, I can't read them. But anyway, just the different variables of what their habitats were. They could also get the statistics. And it's just interesting to see the neighborhood versus neighborhood. What's the tree canopy here versus this one? And then to see, if you were looking at that from the regional, you would not see any high value habitat. This is probably taking the tool a little too far. I mean, that's just parkland. That's why it's green. It's pretty easy to tell. And the rest are trees versus buildings. But if you zoom out a little bit more, you will see connectivity and sort of some trends. Yeah, so that's it for example. But it really has helped form and leverage partnerships. It's very attractive to funders. When you go to Washington and you take some of these and you pass these out, it looks official. I mean, it is official. But it really does say that we are working together. We're all working on the same thing. And I didn't mention this earlier, but this process, this regional conservation strategy, was never meant to sort of take place of any previous efforts. It's really to complement their efforts. So if you're a city of Portland, you probably have better data than we were able to use in this program for this modeling just for the capacity. But this will also show it sort of validates or complements what they've already decided. These are our high priority areas. And if there is differences, you go check it on the field and say, well, why did they say this is high value? This is going to make mistakes for sure. We don't know where oak habitat is. We're going to pick up golf courses. So one thing we'll do is we'll shade out golf courses or cemeteries and we'll take those back. But we have that in here as a layer so you can just make those opaque. And so that high value habitat underneath those isn't showing up. And I think probably the biggest benefit of this whole effort is just the partnerships and the collaboration of all these groups and organizations that have worked together. And to all be working with these same documents has been very valuable. Having a tool has really helped mostly just to give people access to this information. If there's only 10 or 20 GIS people using this, they can do great sophisticated stuff. It's not reaching the audiences that we want it to reach. So I think that's kind of it. If anyone has any questions on the technology, it would be just the conservation strategy here. Hey guys, as you know I've used this data on about three different projects in the Portland metro area and I think it's great. It's really, really powerful stuff. One question I have for you is when you set the local focus, you said you guys were rescaling it. So setting the denominator based on the highest local score in the RCS. And the other question I have, this is getting fairly technical with the data, but I've been dying to ask you guys this, how do you integrate the riparian data set with the upland data set because they overlap? The first one, yeah, we take the min and max within the area, then do a cumulative area graph to find the percentile breaks. So within the local focus, that really is basically how it works. It's not simple quartiles and stuff. It's a cumulative area chart. Second one, how do you integrate the riparian data? The second one, the riparian is integrated now into the upland. So it's kind of like, I don't know if it says upland on there. Yeah, because originally we were going that way. It was going to be how the birds fly, the fish swim, and the critters crawl. But we ended up having some issues with defining those. And so the riparian is really riparian habitat and is a great layer on its own. But it is also integrated into the high value habitats. Okay, so you can just use the high value habitat. You can, but I mean it's, so then your riparian values are going to be only a portion of what it is. And if you go out to Washington County in the flood plains, you're going to see very, I mean, Brian Shepard Clean Water Services, I'm sure he would say I use both, because that's not going to pull it up high enough. You'll see slightly variations in the riparian. Anyone else? All right. All right, well thanks guys. Sorry about the glitches on the presentation. We kind of missed those.
 All right.  Boise.  Baltimore.  Baltimore.  North-East California.  All right.  Seattle.  Seattle.  Port.  Idaho.  Idaho.  Back in the back.  Madison.  Madison.  Madison.  Yeah.  Why do you want me to come?  All right.  Switzerland.  Switzerland.  Okay.  San Francisco, Portland.  Anyway, so the Intertwine Alliance is this, it's a member of the Metropolitan Green Space  Alliance.  There's seven of them in the country, regional sort of coalitions.  It was formed in 2009.  It's really to ensure that the region's network of parks, trails, and natural areas is completed  and cared for, and to help the region's residents connect with nature and live active, healthy  lives.  I work for Metro Oregon across the street here.  There's a number of people that are members of this alliance, so I'm just representing  them today.  I work with Green Info Network, where essentially a nonprofit who works with other nonprofits  providing GIS services.  So print maps for parks and rec services, interactive maps for social justice and conservation  sort of thing.  This definitely fits in that third category.  Active maps for conservation.  So going to discuss very briefly what the RCS itself is.  It's a localized thing.  You might not have heard of one.  Then the viewer software itself, which helps them to, provides information for enabling  decisions.  And then a little bit on the tech stuff, depending on how interested we are in the technology,  as opposed to the actual real world and human uses for it.  And then discuss some of the real world use cases for it and the value they've gotten  out of it.  And I thought we were going to focus more on the tech stuff.  Anyway, the regional conservation strategy is an initiative of the Intertwinal Alliance  and it's focusing on biodiversity conservation.  So why did we take this on?  There's been a number of individuals in this region that have been working on this for  decades, organizations that have been working on it for decades, knowing that the importance  of conservation goes beyond political boundaries.  And there's been a lot of efforts.  The problem is there's been no effort that sort of comprised of the whole region.  We've had statewide efforts.  We've had citywide, great prioritization efforts.  We've had ecoregional efforts by the Nature Conservancy.  But again, nothing sort of comprised of the whole area.  And that was a challenge when we went to funding and sort of tried to share our vision.  So one of our main things, some of the reasons we did this was to create that shared vision  for biodiversity conservation, to increase the impact of our work, to connect jurisdictions  and existing planning efforts, and to increase funding opportunities.  Which one's the arrow?  The spacebar over there.  All right.  So it's really comprised of three main elements.  The Regional Conservation Strategy, which is a 161-page document that really outlines  a number of different chapters from biodiversity corridors, climate change, existing conditions.  And it's a great resource that we have available for everybody.  There's the Biodiversity Guide, which is a 360-page document, which is also, you know,  this is the science behind the RCS.  And so these are available online.  And there's no small effort.  It's a three-year project, 161 volunteers, writers for this, 75 different organizations.  So it's a big effort.  And then we got the Habitat Modeling and the data output.  We also have a executive summary, which any of you guys can take one here.  I got a few.  And we have the RCS viewer.  And we're stuck.  Okay.  There we go.  So what is this, the region that we're...  We're not up there.  Did it just blank out?  There you go.  That's...  Okay.  Odd.  All right.  That's...  Okay, then.  That's interesting.  I take blame for that.  I hate the overclass.  All right.  There.  Thank you.  Then can we do just the screen change instead of all the notes?  That's strange.  I've never done that before.  Yeah.  All right.  We'll see if we can move on from here.  We can work from this.  Yeah, I guess we'll just...  Can we just move?  How do we move forward?  Use the arrows.  All right.  So here's...  Hopefully the animations will work.  Anyway, here's our region.  It's a big region.  It's 10...  Parts of 10 counties, two states.  I think it's like 33 sub-watersheds.  And our first key decision was creating a prioritization effort.  It was creating a better habitat model.  Most of our previous conservation or prioritizations wasn't able to depict high priority areas  in an urban setting.  And you see this all over the country.  If you don't have a good land cover, it's just really hard to model prioritization or  high value habitats.  So this was the results of our initial habitat model.  And I'm kind of confused on what we're working here.  But as you can see, the urban areas here were not highlighted either.  Most of it's out towards the farther reaches up towards the headlands.  And so we took a different take on it and we clipped this raster output.  And what we found is that then we could re-ramp the scores.  And these scores allowed us to sort of bring out the urban quality of habitat.  This took a lot of GIS manipulation and whatnot.  And it was just somewhat time consuming.  These datasets were huge.  So one of the key decisions was not to draw polygons for priority habitats.  And that's kind of a unique decision that we made.  The reason being is because you're dealing with five meter pixels of results, we would  get clumps that were very high value habitats.  But we'd have little circles all over the region.  And so we decided to provide the raw data as an output.  And this was great for a GIS specialist who could do some of the work and sort of look  at their different areas.  Do we miss?  Yeah.  Sorry about this, guys.  I think we're on the wrong presentation.  Well let me try to summarize this.  So when we had this output, I had a few other slides in here.  What did, is it provided you the opportunity to see that if you looked in the very urban  area here, where we weren't seeing any high value habitat, we were, kind of last year,  Greg.  Well really the upshot of it is that within the urban areas, you still have lower and  higher value habitat.  And then the city of Beaverton, for example, looks like it's very poorly on a regional  scale.  However, if you zoom in on Beaverton and use that as your local focus, there are higher  and lower value regions in Beaverton.  And sometimes that's what you're really concerned about.  Maybe the land in Beaverton doesn't have the highest value compared to the headlands up  there, but there are still areas and opportunities within Beaverton where your environmental  impact and your habitat value have their own variation.  So the question was, how can we present the tools and visualizations and the ability to  do these GIS operations without using ArcGIS and advanced GIS knowledge and having a GISP  on hand?  Yeah.  Yeah.  So then I'm going to let Greg talk about the viewer.  The big challenge for us was really to empower the local user to define an area.  Then this area that they defined, they would re-ramp the values of the model and the output  that you would see, the high value habitats per relative area that they selected.  And so it was really cool while we were working with this in GIS, but we needed a tool that  would get this to the decision makers, to the policy makers who don't have GIS experience.  And so that was the challenge we went to green info with and we're super happy with the product  they came up with.  I'll let him show you some of that and then we'll show you some examples.  Sorry about the earlier glitch, we kind of lost it there.  So at its core, it's a browser based interface over the RCS habitat valuation model, park  lands and other such data sets.  The goal was that you don't need any specialized GIS knowledge nor even software.  In fact, even if you don't have the ability to make a shape file, if you can just hand  draw on the map, it'll still be able to do the calculations and get your habitat valuations,  stats and so forth.  So you can define an area in a number of ways.  You can select a bunch of prefab ones such as city boundaries and counties and so forth  off of a menu.  You can upload a shape file or just go to hand draw.  What you end up with is something like this.  This here is habitat value shown on the full regional spectrum, that is to say contained  within the entire universe of the intertwined area.  As you see, most of this is comparatively low value.  However, over there near the grassy areas, it ends up being somewhat higher value as  one would expect.  But what about local focus?  Within local focus, you see a whole lot more area that's indicated as being high value  for this area.  Switch back and forth here.  And you can see regional perspective and local focus.  It's one of the taglines.  Likewise, if we want to show only the highest value habitat, that ranked in the top 10 percentile  of all, there is some significant area here, significant opportunities and focus.  And yet, if we look at a local focus, we discover even more opportunities for doing good locally.  Having selected an area, you get back a fairly rich set of statistics, most notably land  cover.  So, again, forest land, riparian land, places that are already developed, places that are  already conserved, habitat valuation and so forth, nice, rich set of pie charts.  They find this immensely useful.  Most of this data is baked enough that they can use it as is in their reports.  Perhaps most importantly, however, into your local focus is the ability to make annotations  on the map.  Having already selected the area, they're able to define known backyard habitats, color  code them, add text labels, even legends and so forth onto the map.  Again, using simple drawing tools, you see the icons there up on the top.  So not only do you get your local focus, but you really get to make your map speak by adding  your own annotations.  You can literally circle it and write, don't dig here.  The ability to save and share the state of the viewer at any given time, including your  annotations, your maps, the whiteout and everything, all stored in this relatively tiny, early  idea.  It loads back up, loads your previous state, which means you can pick up where you left  off and save another version of it or pass this URL onto somebody else and they can see  exactly what you're seeing, up to and including that red circle that says, don't dig here.  If you're really into paper takeaways, which a lot of us are, you know, I can give you  the instructions to follow this on an iPad, but, you know, sometimes you just want a PDF  that you can print it out, copy, then you can just hand away 100 of them.  Well, the PDF is extremely rich.  They're very happy with this.  And again, it fits a lot of their reporting formats and they find they can use a lot of  this stuff almost as is.  Use your local map, regional scaling, color ramping, all the same statistics you saw before.  So a quick overview of the technology stuff.  It's underlying.  We use good old open layers, map server, map box and so forth.  Now this, if anyone's more interested in the tech stuff, just...  What?  Yeah, the thing seems to have these little screws sticking out of it that's making it  not seat properly.  Come on, guys.  All righty.  So if anyone's really interested in the tech stuff, just go ahead and ask and we can chat  about it later on.  Again, map server, open layers.  A good old is what we use for the raster calculations.  This is a NumPy library which uses the GDAL tools to do queries on rasters.  There's other technology for doing this.  At the time, PostGIS raster was not considered production ready, so we went with a Google  at the time.  These days, if I were to do it again, I'd probably use PostGIS raster.  It's very performant and has much lower memory usage than doing it in a Google.  Why do we use open layers?  Just because I had boilerplate code for a lot of these uploading and editing functions.  We get in leaflet, again, equally valid, but we had boilerplate code.  Map box, wonderful choice for our base maps.  Simply because they're a remotely hosted tile service, very performant, very reasonably  priced and very attractive base maps without having to generate them all ourselves.  There was some talk of using Bing and Google base maps, but again, they have APIs as opposed  to simple XYZ services.  The programming's a little tougher and, well, frankly, they change their APIs in their terms  so often, better to just steer clear of it and go someplace where the terms are really  clear and static.  Map servers, what we use for the server side rendering of these rasters.  There's other technologies for it.  Map servers is the one that we chose.  No strong reasons, but most notably, variable interpolation is very valuable here.  Having selected an area, the min and max can be passed back and then used to set the color  ramp with less programming and overhead than, say, doing it with SLDs and GeoServer.  The PDF printing uses a toolkit called WK HTML to PDF.  This mouthful, the WK means WebKit.  Somebody actually ported the WebKit rendering engine into a bunch of command line utilities  and shared object libraries for Linux, which effectively means that you can take an HTML  document, feed it to this puppy, and get back a PDF.  Use your PDFs in HTML, and it even supports JavaScript, D3, Google Maps, Open Layers.  It's absolutely wonderful.  You can start with copying and pasting your browser side code and be 50% there.  D3 is what we use for the pie charts here.  High charts is what we'd probably use next time.  It's also a really good one.  D3, though, was fairly expedient for making these pie charts that we did need.  Now, the RCS viewer wanted to discuss some of the real-world uses and impacts so far  at, and this is where the real value comes in, of course.  One of them, we're going to see this one, the Simple Land Cover Survey.  This is the city of Tualatin.  Yeah, so this is a great tool, not just for non-GIS, but even for GIS folks.  You'll get calls and say, you know, to get quick statistics of a city's land cover, it's  33% tree cover is great.  You can compare it to the city of Portland's, what they've done in the past, and, you know,  they're very precise about their percentage.  This tool came within a percentage of what they are claiming their tree cover is.  So when you compare two different cities, or even what the nationwide goal for urban  tree canopy would be, this is a great tool.  Not only does it do this, I don't know if there's another example, but that same area  of the statistics, say, how many parks do you have in there, and give you how many federal,  federally owned, state-owned, locally owned parks, and that's great.  So we have some pre-canned geographies in here, all the city jurisdictions, all the  watersheds, but you can also just draw in any shape, or you can upload a shape file,  and it will clip that.  And the results are within a couple minutes.  I mean, it's pretty fast.  And it's just great to have that statistics for whatever you need.  For a big complex area, calculation time can run of 60 to 100 seconds.  For this one, since it's canned, and was pre-calculated last time, someone asked for  a to wallet, and results are in about three seconds.  That beats ARCMAP's time.  Let's see, decisions, like this one, for example, a bunch of grantees looking to do improvements  to, I forget what the program was specifically, but.  Yeah, so we have a program in Metro, and people, we get a lot of grant applicants for some  habitat restoration.  So we just uploaded these shape files into the program.  We labeled these, and during the grant-making decision process, they were able to zoom  into one of these areas.  Since it's labeled, it's number 23, and you can say, oh, you know, what's the habitat  around here?  Not as the answer, but certainly as sort of some background reference information.  Is there parks around there?  Is it a key linkage between two protected areas already?  So it was very helpful for them, just in the discussion of these grant applicants.  This is beautiful.  Trail planning, again, with the annotations.  Here's the planned trail.  Here's an existing trail.  Well, the pink trails are proposed, I believe, and then the grain are existing, or vice versa.  But yeah, to be able to annotate this and then send this across to another jurisdiction,  and they'll open this URL that generates, and all this annotation, the view, the opacity,  is all right there.  So now you can have a conversation on the phone looking at the exact same thing.  And that's been really helpful.  And then you can save this if that person doesn't answer the phone or whatever.  You can just say this, say, I'm going to call them next week.  When you open up that URL next week, it's still there, again, with all the annotations  and everything you were looking at.  Not like a GIS whiteboard.  Yeah.  That's what we were trying for.  Another example, backyard habitats.  Do you have your locations?  Do you have your stats?  There's more to it than the stats.  And this was for Audubon.  They just were curious of what's this Laurelhurst neighborhood look like?  We do backyard habitat.  Let us upload our shapefile or KML and see what the existing, where our existing backyard  habitats certifications are, where our, I don't know, I can't read them.  But anyway, just the different variables of what their habitats were.  They could also get the statistics.  And it's just interesting to see the neighborhood versus neighborhood.  What's the tree canopy here versus this one?  And then to see, if you were looking at that from the regional, you would not see any high  value habitat.  This is probably taking the tool a little too far.  I mean, that's just parkland.  That's why it's green.  It's pretty easy to tell.  And the rest are trees versus buildings.  But if you zoom out a little bit more, you will see connectivity and sort of some trends.  Yeah, so that's it for example.  But it really has helped form and leverage partnerships.  It's very attractive to funders.  When you go to Washington and you take some of these and you pass these out, it looks  official.  I mean, it is official.  But it really does say that we are working together.  We're all working on the same thing.  And I didn't mention this earlier, but this process, this regional conservation strategy,  was never meant to sort of take place of any previous efforts.  It's really to complement their efforts.  So if you're a city of Portland, you probably have better data than we were able to use  in this program for this modeling just for the capacity.  But this will also show it sort of validates or complements what they've already decided.  These are our high priority areas.  And if there is differences, you go check it on the field and say, well, why did they  say this is high value?  This is going to make mistakes for sure.  We don't know where oak habitat is.  We're going to pick up golf courses.  So one thing we'll do is we'll shade out golf courses or cemeteries and we'll take those  back.  But we have that in here as a layer so you can just make those opaque.  And so that high value habitat underneath those isn't showing up.  And I think probably the biggest benefit of this whole effort is just the partnerships  and the collaboration of all these groups and organizations that have worked together.  And to all be working with these same documents has been very valuable.  Having a tool has really helped mostly just to give people access to this information.  If there's only 10 or 20 GIS people using this, they can do great sophisticated stuff.  It's not reaching the audiences that we want it to reach.  So I think that's kind of it.  If anyone has any questions on the technology, it would be just the conservation strategy  here.  Hey guys, as you know I've used this data on about three different projects in the Portland  metro area and I think it's great.  It's really, really powerful stuff.  One question I have for you is when you set the local focus, you said you guys were rescaling  it.  So setting the denominator based on the highest local score in the RCS.  And the other question I have, this is getting fairly technical with the data, but I've been  dying to ask you guys this, how do you integrate the riparian data set with the upland data  set because they overlap?  The first one, yeah, we take the min and max within the area, then do a cumulative area  graph to find the percentile breaks.  So within the local focus, that really is basically how it works.  It's not simple quartiles and stuff.  It's a cumulative area chart.  Second one, how do you integrate the riparian data?  The second one, the riparian is integrated now into the upland.  So it's kind of like, I don't know if it says upland on there.  Yeah, because originally we were going that way.  It was going to be how the birds fly, the fish swim, and the critters crawl.  But we ended up having some issues with defining those.  And so the riparian is really riparian habitat and is a great layer on its own.  But it is also integrated into the high value habitats.  Okay, so you can just use the high value habitat.  You can, but I mean it's, so then your riparian values are going to be only a portion of what  it is.  And if you go out to Washington County in the flood plains, you're going to see very,  I mean, Brian Shepard Clean Water Services, I'm sure he would say I use both, because  that's not going to pull it up high enough.  You'll see slightly variations in the riparian.  Anyone else?  All right.  All right, well thanks guys.  Sorry about the glitches on the presentation.  We kind of missed those.
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