WEBVTT

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<v ->Hey, folks.</v>

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Welcome to the FEMC Recreation Products webinar.

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We're giving people

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just maybe one extra minute to filter in.

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We definitely will be using

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most of the 90 minutes that we have,

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so we'll be starting very soon.

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Everyone, for folks who have just come in,

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we're just giving people one extra minute to get in

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and then we'll get started.

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All right, so welcome everybody.

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Thanks for joining us today.

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For those of you who don't know me,

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my name is Alison Adams,

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I'm the Director of the FEMC.

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And for those who may not be familiar with FEMC,

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stands for Forest Ecosystem Monitoring Cooperative.

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And we are a regional collaborative,

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we work in New England and New York.

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And we do long-term forest monitoring

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and data analysis and synthesis as well as data storage,

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for forest-related datasets.

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And a couple of quick housekeeping things before we start.

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This webinar is approved for 1 1/2 SAF credits,

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and we'll be just submitting the attendance record

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from today to SAF.

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So there's nothing additional you need to do

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to get those credits if you want them.

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And during the webinar,

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you should be able to ask any questions

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using the Q&amp;A feature.

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And I'll be facilitating question and answer.

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This is my first time facilitating a webinar

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in the Teams webinar interface,

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so if there are a few hiccups,

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please just bear with me and we'll sort it out.

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So today, we're gonna be sharing the products of a project

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FEMC has been working on for the past few years,

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looking at the interactions between recreation

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and forest health in the northeastern United States.

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This project started a few years ago

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with a literature review and expert interviews

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to identify needs related to the impacts

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of recreation on forests.

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And the main thing that came out of that effort

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was that there was a need for tools for managers

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and decision-makers to assess those impacts.

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So we decided to tackle that need in two complementary ways,

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and you'll be hearing about both of those today.

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The first one,

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and the one that we'll be talking about first,

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was the creation of a suite of geospatial products

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showing the intensity of hiking and mountain biking

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on land in the northeast,

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and then combining that with maps of soil health,

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vulnerability to erosion,

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as well as maps showing where different wildlife

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may be affected by recreation.

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And then the second thing that we did

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was an inventory of infield monitoring methods

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to assess how recreation affects many different variables

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related to forest health.

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And then creating an accompanying decision support tool

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to help you choose a method,

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if you wanted to do some on the ground monitoring

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of those impacts.

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So like I said, today we have two presentations for you,

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one on each of those pieces.

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Each will be maybe around 30 minutes long,

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with 10 or so minutes for questions.

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And then we'll take a very short break in between

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so people can come or go or do whatever they need to do,

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and then we'll jump into the second one.

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All right, and with that we'll start with Soren Donisvitch,

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he's FEMC's Data Engineer,

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and he'll be sharing the geospatial products.

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<v ->Thanks, Alison.</v>

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So let's get to it,

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we do have a lot of slides to cover,

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so wanna leave time for questions.

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So today, I'm gonna be covering over the geospatial products

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that we released in the past year

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and demonstrated in our yearly conference.

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This is the geospatial kind of leg

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of this recreational project.

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Today, we're gonna be diving a little bit more in depth

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than we did in the conference,

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to really look at how things layers were created,

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some of the nuances of their applications,

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and then really how you can download

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and access these layers.

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We really wanna make sure that our primary purpose

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from this project was to be able to provide you,

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the land managers and/or researchers,

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with the tools to be able to like answer

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and dive into these questions.

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So as a brief agenda and overview,

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we're gonna be going over

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kind of what the project goals were,

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really just creating those different layers,

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how to access and download those layers.

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Again, these are all hosted online and opened for download.

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Overview of the recreation data layers themselves,

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each one that we produced,

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how they were made.

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The methods individually

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on how they were created and synthesized.

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Using some use applications of these datasets as well.

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And finally, we'll try to have some time

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for at least like 10 minutes or so for Q&amp;A.

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So kind of back to project goals,

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Alison covered things quite nicely,

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but we tried to look at this overarching question

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within our entire region.

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This is a regional project

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that's supposed to cover geospatially

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all of the kind of northeast,

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those states that Alison said that we cover.

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What we found is that there was the little to no

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truly like regionally standardized recreation dataset

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that we could pull from.

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There were a lot of datasets that were small and like,

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you know, done by a park by park basis,

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or were targeted towards a specific research question

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or management question.

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But none of it was able to really be aggregated

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into one standardized dataset

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to tie to large regional geospatial products.

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We also found that recreational use measured

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was kind of inconsistency, so inconsistently,

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so one area would measure it one way

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and another and the other,

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which kind of led off to the branch

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that Alyssa will be going over to a little bit later.

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We also wanna make sure that we didn't find

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any really regionalized dataset

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that could tie to the ground forest health data

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and recreational intensity.

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So a lot of those forest plots,

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there's a wealth of inventory data within the northeast,

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but very rarely are those taken directly adjacent to a trail

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that looks at forest or tree health.

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And so we really came up with the project goals

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to try to create some regionalized geospatial layers

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to try to tackle these questions.

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And to make sure that whatever we made,

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it would all be public,

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we would all be able to share it,

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you'd be able to download,

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you'd be able to use it for any kind of analysis

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and/or products you would want to create.

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We also wanted to make sure that we provided the support

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and land managers to really be using these layers,

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and that this was truly a regionalized dataset

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to answer kind of regional questions.

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Once we had these layers,

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we also did a preliminary analysis

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where we looked at,

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can we see if there's a relationship between

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recreation and forest health?

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I would say reading through the technical report

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goes far more in depth about what our findings,

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but we did find a weak relationship.

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But functionally we really found that 30-meter resolution,

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which is what we used for our health proxy,

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we'll go into it a little bit later,

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is just a little bit too coarse of a scale

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to see what that relationship really is.

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And so definitely a lot more work to be done

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at maybe a finer resolution.

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So let's dive into it.

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So first off, how are you gonna download

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and access the layers that we published?

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There are two primary ways,

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you can go to our website,

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you can follow this link,

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you can download it in multiple different file types.

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Basic steps to downloading is going to the repository link,

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selecting this desired layer from the dropdown,

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and clicking the Download button.

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You can follow the QR code,

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but it takes us to this part of the website,

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and you can see, you can look at the overview of the layer.

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That link takes you to an overview of the dataset

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as well as the download.

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The other option is to go directly to our hub site,

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our GS Online Hub site,

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where you can download these layers as well.

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You would just go through,

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instead you would go through our search bar.

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So you'd go to these areas,

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you could click on them

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and they would take you to our downloader.

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From here you can see there's a little Cloud button,

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you can download any type of file format you would want.

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I would recommend for a lot of these data

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that are quite large,

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so I would use file geodatabase, KML,

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whatever you would prefer to do

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to work with all these data.

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It does take some time to download,

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so just be patient.

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So let's get into an overview of the data layers themselves.

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We created four primary kind of layers

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based on a bunch of different other layers.

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Again, all of them are available for download.

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You would be able to replicate

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like what we are doing here today with those data.

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But those four primary kind of data layers

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were recreational use intensity.

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So this is the one most people

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are gonna probably be interested in.

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It aggregated Strava data and iNaturalist data

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to vector line layer,

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so those are OSM-based, OpenStreetMap-based lines data.

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This is highly, I would say,

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at a really in detail layer

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to be able to look at individual trails

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and how much use was occurring.

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Again, this is all like limited to 2022

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is when we did the data analysis

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and creation of these different layers.

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But on the right, you can see this vector line layer.

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Another nuance is that all of these data,

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so for hiking and biking,

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as well as hiking with soil and biking with soil,

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are all z-score normalized

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as a part of what we needed to do

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to meet Strava's requirements

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for making these data public.

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So you don't get the actual use value,

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you get a z-score normalization of that value.

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So the next one would be our raster datasets.

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So for this is a hotspot analysis using kernel density.

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We'll dive into what that means

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and how that was created in a little bit.

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But this is really to be looking at regional questions.

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So what are the hotspots within the area

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of the northeast that are proportionally receiving

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a lot more recreation in a given space?

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You can pick out large geospatial patterns this way,

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and it's really to try to help land managers and researchers

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identify these hotspots

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and what that might mean for the research

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and/or recreational management.

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So the next thing that I'm sure everyone

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is also interested in,

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I've received a couple emails about this,

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is the soil suitability for trails.

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This comes directly from the NRCS Web Soil Survey data.

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You can go to their website, you can download it,

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it's an integral part of the data you can get there.

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What we did is we aggregated all of the polygons

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and applied those values to the entire northeast,

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and are providing that in easily downloadable fashion.

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This is what that looks like,

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and so this is a polygon layer.

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Again, very large dataset to download and work with,

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but has a multitude of applications and uses.

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The next thing we did

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was forest wildlife disturbance and fragmentation by trails.

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This was a vector polygon layer done for 60 feet,

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100 feet, and 400 feet

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largely replicating something

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that was done in New Hampshire.

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Took a look at those buffers

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to see how wildlife was impacted by recreation

268
00:12:10.020 --> 00:12:12.660
or based on different animal categories.

269
00:12:12.660 --> 00:12:14.910
We'll go a little bit more into that later.

270
00:12:14.910 --> 00:12:16.530
And that's what that looks like.

271
00:12:16.530 --> 00:12:18.480
We'll harken back to that in a little bit.

272
00:12:18.480 --> 00:12:21.030
So, and then also the forest canopy health.

273
00:12:21.030 --> 00:12:24.090
So we used NDVI deviance from norm,

274
00:12:24.090 --> 00:12:25.110
we used ForWarn,

275
00:12:25.110 --> 00:12:28.140
which is a product from the Forest Service,

276
00:12:28.140 --> 00:12:31.590
a really good product that looks at how green things are

277
00:12:31.590 --> 00:12:33.780
in a given window of time,

278
00:12:33.780 --> 00:12:36.720
compared to how normally green they were

279
00:12:36.720 --> 00:12:39.420
as a kind of metric or proxy

280
00:12:39.420 --> 00:12:42.420
we gave for forest health when we were doing those analyses.

281
00:12:43.350 --> 00:12:44.550
That's what that looks like.

282
00:12:44.550 --> 00:12:46.800
And again, it's a 30-meter resolution,

283
00:12:46.800 --> 00:12:48.930
which was enough to be able to find some correlations

284
00:12:48.930 --> 00:12:52.530
between recreation and forest health,

285
00:12:52.530 --> 00:12:53.970
or our proxy for forest health,

286
00:12:53.970 --> 00:12:55.830
but really wasn't fine enough scale

287
00:12:55.830 --> 00:12:57.210
to be able to really determine

288
00:12:57.210 --> 00:12:58.650
what those relationships truly are,

289
00:12:58.650 --> 00:13:00.960
the magnitude of their impact.

290
00:13:00.960 --> 00:13:03.330
Again, we really wanna focus on all these datasets

291
00:13:03.330 --> 00:13:06.000
are open to download,

292
00:13:06.000 --> 00:13:07.110
and to be able to use.

293
00:13:07.110 --> 00:13:08.580
They're open source data,

294
00:13:08.580 --> 00:13:10.620
they're just aggregated and provided in a way

295
00:13:10.620 --> 00:13:13.440
that can be easily interacted with,

296
00:13:13.440 --> 00:13:15.990
except for Strava, which requires a Metro partnership,

297
00:13:15.990 --> 00:13:17.430
which is just an easy application,

298
00:13:17.430 --> 00:13:18.780
great company to work with.

299
00:13:20.070 --> 00:13:21.480
Recreational use intensity,

300
00:13:21.480 --> 00:13:22.890
so this is that line layer.

301
00:13:22.890 --> 00:13:24.450
Let's go over the methodology.

302
00:13:24.450 --> 00:13:27.480
The picture on the right shows those use intensities,

303
00:13:27.480 --> 00:13:30.210
so for here this is hiking,

304
00:13:30.210 --> 00:13:33.060
green is like the NLCD forests,

305
00:13:33.060 --> 00:13:36.120
which is what we use to clip to forested areas.

306
00:13:36.120 --> 00:13:38.430
All of these layers, it should be noted are clipped

307
00:13:38.430 --> 00:13:39.750
to NLCD forests,

308
00:13:39.750 --> 00:13:42.870
so those areas that are forested.

309
00:13:42.870 --> 00:13:45.330
And so you won't be receiving data from these downloads

310
00:13:45.330 --> 00:13:47.493
that are in non-forested areas.

311
00:13:48.810 --> 00:13:52.170
So let's go the process in which you actually integrate

312
00:13:52.170 --> 00:13:55.890
Strava and OS and iNaturalist data.

313
00:13:55.890 --> 00:13:58.500
So basic pre-processing is data acquisition

314
00:13:58.500 --> 00:14:00.840
where first we got OSM data,

315
00:14:00.840 --> 00:14:02.430
so that's OpenStreetMap data,

316
00:14:02.430 --> 00:14:05.163
line data for the entire northeast.

317
00:14:06.090 --> 00:14:08.040
This data had really useful things

318
00:14:08.040 --> 00:14:11.850
like what the trails were made of and/or class of trail.

319
00:14:11.850 --> 00:14:13.920
We really wanted to be looking at permeable trails

320
00:14:13.920 --> 00:14:17.070
because that's what the soil layer really is focused on.

321
00:14:17.070 --> 00:14:18.240
It's not really appropriate

322
00:14:18.240 --> 00:14:22.143
for non-permeable trails like concrete.

323
00:14:23.370 --> 00:14:26.700
Then we also got Strava data where we aggregated a multitude

324
00:14:26.700 --> 00:14:31.410
of all of the different states with Strava data from 2022.

325
00:14:31.410 --> 00:14:33.450
Should be noted that when you aggregate from Strava,

326
00:14:33.450 --> 00:14:36.480
they have a common column that's the OSM line,

327
00:14:36.480 --> 00:14:39.330
which is correlates to the Waze ID.

328
00:14:39.330 --> 00:14:40.920
And so when you correlate to,

329
00:14:40.920 --> 00:14:43.470
when you tie things and join based on the Waze ID,

330
00:14:43.470 --> 00:14:47.100
you would take the average or max of that line.

331
00:14:47.100 --> 00:14:48.780
So what you do is, is that one line

332
00:14:48.780 --> 00:14:50.430
would be tied to the use of,

333
00:14:50.430 --> 00:14:52.410
you say 100 were hiking there

334
00:14:52.410 --> 00:14:54.900
would be tied to a Waze ID,

335
00:14:54.900 --> 00:14:57.450
which is what when you download from Strava

336
00:14:57.450 --> 00:14:58.860
is what you receive.

337
00:14:58.860 --> 00:15:00.780
So once you join with Strava trip data

338
00:15:00.780 --> 00:15:01.900
to the OSM Waze ID,

339
00:15:01.900 --> 00:15:04.230
that's kind of you have one dataset,

340
00:15:04.230 --> 00:15:07.020
then there's the line feature layer

341
00:15:07.020 --> 00:15:09.150
that can be then joined with OSM.

342
00:15:09.150 --> 00:15:11.880
So once you then have,

343
00:15:11.880 --> 00:15:13.770
we created all these different data layers,

344
00:15:13.770 --> 00:15:15.120
but then we went back to our committee

345
00:15:15.120 --> 00:15:16.500
and we found that Strava

346
00:15:16.500 --> 00:15:19.080
was definitely a specific population

347
00:15:19.080 --> 00:15:20.010
and that we were advised

348
00:15:20.010 --> 00:15:21.600
to add in a different population,

349
00:15:21.600 --> 00:15:23.310
maybe we're missing out in certain demographic

350
00:15:23.310 --> 00:15:24.990
that Strava wasn't capturing.

351
00:15:24.990 --> 00:15:28.620
And we found one of the best ways to kind of get at accounts

352
00:15:28.620 --> 00:15:30.720
of maybe where people were using recreation

353
00:15:30.720 --> 00:15:31.950
was using iNaturalist.

354
00:15:31.950 --> 00:15:35.160
And what we did was we downloaded those public points,

355
00:15:35.160 --> 00:15:36.390
those x and y coordinates,

356
00:15:36.390 --> 00:15:40.203
and we tied them spatially to the nearest OSM line trail.

357
00:15:41.130 --> 00:15:42.720
One of the kind of nuances of this

358
00:15:42.720 --> 00:15:45.150
is that we weren't able to apply it to the entire line,

359
00:15:45.150 --> 00:15:46.440
just the nearest line

360
00:15:46.440 --> 00:15:48.180
'cause you don't know how that individual

361
00:15:48.180 --> 00:15:50.370
may have gotten to that point,

362
00:15:50.370 --> 00:15:53.730
but that is how we tie iNaturalist data in.

363
00:15:53.730 --> 00:15:55.710
Once things are kind of joined and combined,

364
00:15:55.710 --> 00:15:59.370
you can join everything again based on the Waze ID

365
00:15:59.370 --> 00:16:00.900
into one standard dataset

366
00:16:00.900 --> 00:16:03.240
where you aggregate all of the use values

367
00:16:03.240 --> 00:16:04.983
for hiking and biking.

368
00:16:05.910 --> 00:16:08.760
Then we also did filtered by permeable trails.

369
00:16:08.760 --> 00:16:11.580
So again we were really trying to focus on those areas

370
00:16:11.580 --> 00:16:13.290
that were on permeable trails.

371
00:16:13.290 --> 00:16:15.900
So we used that as we use highway classes

372
00:16:15.900 --> 00:16:18.450
from the OSM database,

373
00:16:18.450 --> 00:16:20.770
which is trails that are traditionally

374
00:16:23.010 --> 00:16:24.570
on permeable surfaces,

375
00:16:24.570 --> 00:16:26.340
bridleways, cycleways, footways

376
00:16:26.340 --> 00:16:28.050
as well as the surface class itself.

377
00:16:28.050 --> 00:16:30.930
So this tells you what those surfaces are made of,

378
00:16:30.930 --> 00:16:33.663
such as ground dirt, compacted soil, things like that.

379
00:16:34.920 --> 00:16:36.000
The other thing is in order

380
00:16:36.000 --> 00:16:38.370
to meet the publication requirements for Strava data,

381
00:16:38.370 --> 00:16:40.860
we also had to do z-score normalization

382
00:16:40.860 --> 00:16:43.230
of all of hiking and biking

383
00:16:43.230 --> 00:16:46.260
in order to be able to make those data public.

384
00:16:46.260 --> 00:16:47.520
Post-processing as we, again,

385
00:16:47.520 --> 00:16:49.140
we clip everything to the region

386
00:16:49.140 --> 00:16:52.473
as well as the NLCD forests.

387
00:16:54.060 --> 00:16:56.970
So some applications from this later are numerous,

388
00:16:56.970 --> 00:16:58.980
but a lot of times some high-level stuff

389
00:16:58.980 --> 00:17:01.410
as some identifying high use corridors,

390
00:17:01.410 --> 00:17:03.150
assessing recreational impacts,

391
00:17:03.150 --> 00:17:05.220
informing land management,

392
00:17:05.220 --> 00:17:06.810
and supporting recreational planning.

393
00:17:06.810 --> 00:17:09.180
So it's really a fine scale dataset

394
00:17:09.180 --> 00:17:10.710
to be able to see where trails

395
00:17:10.710 --> 00:17:14.040
are receiving more usage, less usage.

396
00:17:14.040 --> 00:17:15.990
We can really help planning out

397
00:17:15.990 --> 00:17:18.963
like maybe targeted allocation of resources.

398
00:17:20.070 --> 00:17:23.550
So onto the regional kind of kernel density hotspots.

399
00:17:23.550 --> 00:17:26.310
So we did this for mountain biking and hiking,

400
00:17:26.310 --> 00:17:27.900
and this is really to provide a product

401
00:17:27.900 --> 00:17:29.730
that gives you kind of regional,

402
00:17:29.730 --> 00:17:33.240
a regional look at the datasets themselves.

403
00:17:33.240 --> 00:17:35.700
So looking at the line layers,

404
00:17:35.700 --> 00:17:37.050
it can be very difficult

405
00:17:37.050 --> 00:17:40.260
to actually pick out regional significance,

406
00:17:40.260 --> 00:17:44.490
and which is why we created the the hotspots themselves.

407
00:17:44.490 --> 00:17:46.770
As you can see from the picture on the right here,

408
00:17:46.770 --> 00:17:49.710
you can see how certain areas definitely pick out,

409
00:17:49.710 --> 00:17:50.910
are able to be seen regionally

410
00:17:50.910 --> 00:17:53.733
and provide maybe some types of regional hierarchies.

411
00:17:54.570 --> 00:17:57.420
So going over the methods for kernel density,

412
00:17:57.420 --> 00:17:59.070
what you do is you take those line,

413
00:17:59.070 --> 00:18:02.010
that line feature and then you apply points,

414
00:18:02.010 --> 00:18:04.710
turns them into points along the lines

415
00:18:04.710 --> 00:18:07.380
and we did it along a one-meter intervals

416
00:18:07.380 --> 00:18:09.390
and making sure that they're evenly extracted

417
00:18:09.390 --> 00:18:11.850
along all of the OSM lines.

418
00:18:11.850 --> 00:18:14.070
Then you wanna make sure they also retain that use value.

419
00:18:14.070 --> 00:18:16.860
So that point would have a value of the amount,

420
00:18:16.860 --> 00:18:18.690
the magnitude of hiking

421
00:18:18.690 --> 00:18:20.823
and the magnitude of mountain biking.

422
00:18:21.660 --> 00:18:24.780
Once there, you would then use those,

423
00:18:24.780 --> 00:18:27.607
if I'm gonna put up the actual,

424
00:18:27.607 --> 00:18:29.370
you know, formula here,

425
00:18:29.370 --> 00:18:30.720
if anyone's super interested,

426
00:18:30.720 --> 00:18:32.160
but it's really that p-value.

427
00:18:32.160 --> 00:18:34.380
And so what you do is under the hood it's just looking,

428
00:18:34.380 --> 00:18:37.080
it's taking that point and applying the 100 people

429
00:18:37.080 --> 00:18:38.760
that were on that trail,

430
00:18:38.760 --> 00:18:40.863
then it would count that point as 100.

431
00:18:41.850 --> 00:18:43.830
And then what kernel density is,

432
00:18:43.830 --> 00:18:45.180
it just kind of looks over a window

433
00:18:45.180 --> 00:18:47.460
and sees how many points are in a given area

434
00:18:47.460 --> 00:18:48.783
to give you a density.

435
00:18:50.490 --> 00:18:53.550
And then post-processing would be clipping to the states.

436
00:18:53.550 --> 00:18:54.810
Something that's really important about this

437
00:18:54.810 --> 00:18:57.090
is that the color scale really matters.

438
00:18:57.090 --> 00:18:59.910
It turns out that recreation kind of happens

439
00:18:59.910 --> 00:19:01.350
on an exponential scale

440
00:19:01.350 --> 00:19:02.610
where we really like to recreate,

441
00:19:02.610 --> 00:19:03.990
we recreate a lot.

442
00:19:03.990 --> 00:19:05.760
And so if you're downloading these layers,

443
00:19:05.760 --> 00:19:07.410
I would definitely highly recommend playing

444
00:19:07.410 --> 00:19:08.940
around with a color scale.

445
00:19:08.940 --> 00:19:11.040
If you use a log transformation

446
00:19:11.040 --> 00:19:14.100
or a standard deviation color scheme,

447
00:19:14.100 --> 00:19:15.480
it'll pick out those areas.

448
00:19:15.480 --> 00:19:16.560
That's what you see on the right here

449
00:19:16.560 --> 00:19:18.780
is the standard deviation color format

450
00:19:18.780 --> 00:19:20.010
and it picks out those areas

451
00:19:20.010 --> 00:19:23.520
that maybe aren't on those extremes

452
00:19:23.520 --> 00:19:25.680
and better, it allows you to visualize

453
00:19:25.680 --> 00:19:27.080
recreation in the northeast.

454
00:19:28.800 --> 00:19:31.340
So, what's really this good about this layer

455
00:19:31.340 --> 00:19:33.750
is it allows you to pick out regional significance.

456
00:19:33.750 --> 00:19:35.250
So you can immediately kind of your eye

457
00:19:35.250 --> 00:19:36.750
gravitates towards areas

458
00:19:36.750 --> 00:19:38.460
that we would traditionally consider hot spots

459
00:19:38.460 --> 00:19:39.930
in the northeast, places like that,

460
00:19:39.930 --> 00:19:42.450
Adirondacks, the White Mountains, Acadia.

461
00:19:42.450 --> 00:19:45.330
But it also because you change the scales

462
00:19:45.330 --> 00:19:46.590
and you are able to look at things,

463
00:19:46.590 --> 00:19:50.700
it can pick up on different aspects like the long trail.

464
00:19:50.700 --> 00:19:53.010
You can look also see the AT itself.

465
00:19:53.010 --> 00:19:55.230
Along that line, you're also able to see areas

466
00:19:55.230 --> 00:19:57.270
of high intensity and low intensity,

467
00:19:57.270 --> 00:19:59.610
which might inform like where you want to be doing

468
00:19:59.610 --> 00:20:03.120
or strategically considering putting in water bars

469
00:20:03.120 --> 00:20:06.360
or areas where you might want to be redoing flagging.

470
00:20:06.360 --> 00:20:08.010
This is really meant to be a tool

471
00:20:08.010 --> 00:20:11.040
to say regionally this area is of significance,

472
00:20:11.040 --> 00:20:13.500
which might be able to inform your allocation

473
00:20:13.500 --> 00:20:17.103
of resources based on a data driven kind of visual.

474
00:20:18.060 --> 00:20:20.613
So it's a lot more, again, like I said,

475
00:20:21.840 --> 00:20:24.480
easier way to assess broad patterns regionally

476
00:20:24.480 --> 00:20:27.960
and give regional significance to specific areas.

477
00:20:27.960 --> 00:20:31.470
So for instance, you can see along the AT,

478
00:20:31.470 --> 00:20:32.790
you know, areas like the Bigelows

479
00:20:32.790 --> 00:20:36.120
and the Hundred-Mile Wilderness are more popular,

480
00:20:36.120 --> 00:20:38.130
and therefore you might want to be allocating

481
00:20:38.130 --> 00:20:39.210
more or less resources

482
00:20:39.210 --> 00:20:42.990
depending on your specific objectives.

483
00:20:42.990 --> 00:20:44.730
So some other further application

484
00:20:44.730 --> 00:20:46.290
is really that kind of prioritize

485
00:20:46.290 --> 00:20:48.240
and justify trail maintenance,

486
00:20:48.240 --> 00:20:49.830
like I talked about developing

487
00:20:49.830 --> 00:20:51.930
sustainable recreational plans

488
00:20:51.930 --> 00:20:54.720
based on high-risk or low-risk areas

489
00:20:54.720 --> 00:20:57.450
and data-driven regional allocation of resources.

490
00:20:57.450 --> 00:21:00.030
So this hotspot map is really supposed to be a tool

491
00:21:00.030 --> 00:21:01.627
for kind of regionalizing

492
00:21:03.348 --> 00:21:05.403
the application of these data.

493
00:21:06.600 --> 00:21:08.820
Now onto the soil recreation suitability.

494
00:21:08.820 --> 00:21:10.290
So this map on the right you can see

495
00:21:10.290 --> 00:21:14.130
is soil recreation suitability for our area.

496
00:21:14.130 --> 00:21:15.120
Again, this is pulling directly

497
00:21:15.120 --> 00:21:17.490
from the NRC Web Soil Survey,

498
00:21:17.490 --> 00:21:19.080
but I'll dive a little bit more into

499
00:21:19.080 --> 00:21:21.690
like what was used in their model to create

500
00:21:21.690 --> 00:21:24.240
what they can consider to be susceptibility.

501
00:21:24.240 --> 00:21:26.370
They used slope, erosion factor,

502
00:21:26.370 --> 00:21:29.850
organic matter, ponding, depth to saturation zone,

503
00:21:29.850 --> 00:21:32.550
stoniness, sand and clay content.

504
00:21:32.550 --> 00:21:34.500
Something that comes out of this

505
00:21:34.500 --> 00:21:39.000
is a categorization of how suitable the soil is.

506
00:21:39.000 --> 00:21:41.970
Soil classifications are not limited, somewhat limited,

507
00:21:41.970 --> 00:21:45.240
limited based on their ability to support recreation.

508
00:21:45.240 --> 00:21:47.100
They also provide a numeric scale,

509
00:21:47.100 --> 00:21:48.900
which is a 0-1.

510
00:21:48.900 --> 00:21:51.360
0 being not limited, it's great,

511
00:21:51.360 --> 00:21:53.460
it's perfect for recreation.

512
00:21:53.460 --> 00:21:55.410
1 being limited.

513
00:21:55.410 --> 00:21:57.720
Some nuances when it comes to Web Soil Survey

514
00:21:57.720 --> 00:22:00.570
as we know that it can be fairly coarse.

515
00:22:00.570 --> 00:22:03.363
There's really isn't any substitute for it,

516
00:22:04.470 --> 00:22:06.330
but you might be on a trail

517
00:22:06.330 --> 00:22:08.400
that's perfectly suitable for soils

518
00:22:08.400 --> 00:22:10.170
but still be within a polygon

519
00:22:10.170 --> 00:22:13.080
that is considered wildly to be not suitable.

520
00:22:13.080 --> 00:22:14.790
And so that you have to be considering

521
00:22:14.790 --> 00:22:16.110
when using this layer,

522
00:22:16.110 --> 00:22:20.250
the scale in which you are trying to be applying it.

523
00:22:20.250 --> 00:22:21.750
For more information, I'm sure you can,

524
00:22:21.750 --> 00:22:25.230
you know, going into some of the caveats

525
00:22:25.230 --> 00:22:27.080
of working with Web Soil Survey data.

526
00:22:28.230 --> 00:22:29.880
So then onto the wildlife disturbance

527
00:22:29.880 --> 00:22:31.650
and forest block sizes,

528
00:22:31.650 --> 00:22:33.843
this is fairly straightforward.

529
00:22:34.920 --> 00:22:36.030
You know, what it really shows

530
00:22:36.030 --> 00:22:38.700
is how trails impact contiguous forest blocks

531
00:22:38.700 --> 00:22:42.030
and specifically impacting certain species.

532
00:22:42.030 --> 00:22:43.650
It was a 60 feet for amphibians,

533
00:22:43.650 --> 00:22:45.300
150 feet for birds,

534
00:22:45.300 --> 00:22:47.520
and 400 feet for large mammals.

535
00:22:47.520 --> 00:22:49.410
This is based on,

536
00:22:49.410 --> 00:22:51.090
at least the buffering and extraction

537
00:22:51.090 --> 00:22:54.120
is based on New Hampshire trails for wildlife and people,

538
00:22:54.120 --> 00:22:57.070
that only expanded from New Hampshire to the entire region.

539
00:22:59.160 --> 00:23:01.860
what we did was we buffered all OSM trails,

540
00:23:01.860 --> 00:23:06.000
all OpenStreetMap trails regardless of what they were,

541
00:23:06.000 --> 00:23:08.820
everything from bike trails to highways.

542
00:23:08.820 --> 00:23:10.320
And so this segments the forest

543
00:23:10.320 --> 00:23:12.240
and it's really meant to be used in conjunction

544
00:23:12.240 --> 00:23:13.800
with our other recreation layers

545
00:23:13.800 --> 00:23:16.260
to see how recreation is impacting

546
00:23:16.260 --> 00:23:19.293
or maybe impacting these different wildlife categories.

547
00:23:20.520 --> 00:23:21.420
Some applications,

548
00:23:21.420 --> 00:23:24.030
it helps assess wildlife habitat connectivity

549
00:23:24.030 --> 00:23:24.863
to some extent.

550
00:23:24.863 --> 00:23:27.060
There are better layers that look at connectivity.

551
00:23:27.060 --> 00:23:29.790
This layer is really cement to be used in conjunction

552
00:23:29.790 --> 00:23:33.240
with our other recreational layers.

553
00:23:33.240 --> 00:23:35.340
It supports trail planning.

554
00:23:35.340 --> 00:23:39.420
So do you want to break up a contiguous forest block

555
00:23:39.420 --> 00:23:42.570
that is not being very impacted by recreation

556
00:23:42.570 --> 00:23:44.850
or do you want to go into an area that already is?

557
00:23:44.850 --> 00:23:46.770
And might help strategize

558
00:23:46.770 --> 00:23:50.520
undisturbed forest patches versus others.

559
00:23:50.520 --> 00:23:54.210
Also might help with conservation strategies as well.

560
00:23:54.210 --> 00:23:56.760
Now onto the NDVI deviance from norm layer.

561
00:23:56.760 --> 00:23:58.710
So this is what we use for our health proxy.

562
00:23:58.710 --> 00:24:02.070
Again, this is based off of a ForeWorn product.

563
00:24:02.070 --> 00:24:03.900
The basic methods is you get the data,

564
00:24:03.900 --> 00:24:06.570
you can pull it down from ForeWorn

565
00:24:06.570 --> 00:24:08.250
public data as well.

566
00:24:08.250 --> 00:24:09.083
Really what it is,

567
00:24:09.083 --> 00:24:12.840
is it takes how green it is in a given window

568
00:24:12.840 --> 00:24:15.570
and then ties it to how green things are normally

569
00:24:15.570 --> 00:24:17.103
based on the 30-year median.

570
00:24:18.210 --> 00:24:19.710
If you're interested in the actual formula.

571
00:24:19.710 --> 00:24:21.300
That's what it looks like.

572
00:24:21.300 --> 00:24:24.390
But the values above really are to show positive values

573
00:24:24.390 --> 00:24:28.380
and indicate that there's an improvement in NDVI.

574
00:24:28.380 --> 00:24:31.203
Negative would indicate the canopy stress.

575
00:24:32.160 --> 00:24:35.070
So cumulative deviance calculation itself

576
00:24:35.070 --> 00:24:36.600
was what we did is we took

577
00:24:36.600 --> 00:24:39.240
all of their publicly available eight-day windows,

578
00:24:39.240 --> 00:24:42.810
but again we're looking at 2022 as an entire year.

579
00:24:42.810 --> 00:24:44.250
So we aggregated them,

580
00:24:44.250 --> 00:24:47.523
making cumulative deviance from norm,

581
00:24:48.450 --> 00:24:51.600
and then we took the magnitude of that seasonal deviance

582
00:24:51.600 --> 00:24:54.030
to really create one layer that's supposed to capture

583
00:24:54.030 --> 00:24:58.200
how off or how maybe less healthy

584
00:24:58.200 --> 00:24:59.250
it was in that season

585
00:24:59.250 --> 00:25:02.250
and tried to correlate that to magnitude

586
00:25:02.250 --> 00:25:04.083
and presence of recreation.

587
00:25:06.180 --> 00:25:08.640
So some applications of this layer

588
00:25:08.640 --> 00:25:09.720
and this kind of methodology

589
00:25:09.720 --> 00:25:12.630
and using ForeWarn are there's a plethora of them,

590
00:25:12.630 --> 00:25:16.380
but you can access chronic canopy stress.

591
00:25:16.380 --> 00:25:17.940
Again, like what we did is try to link it

592
00:25:17.940 --> 00:25:19.890
to recreational use

593
00:25:19.890 --> 00:25:22.590
as well as climate and land degradation analysis.

594
00:25:22.590 --> 00:25:25.230
So this layer is kind of used for many different things.

595
00:25:25.230 --> 00:25:27.180
We used it specifically for trying

596
00:25:27.180 --> 00:25:29.180
to use it a health proxy for recreation.

597
00:25:30.420 --> 00:25:33.180
So now on to some examples.

598
00:25:33.180 --> 00:25:36.060
So I know I'd be going pretty fast,

599
00:25:36.060 --> 00:25:38.940
so now we'll kinda try to slow things down.

600
00:25:38.940 --> 00:25:42.030
So as an example of these layers themselves,

601
00:25:42.030 --> 00:25:44.820
right here we have Acadia Maine and Burke Mountain.

602
00:25:44.820 --> 00:25:47.040
These are both what we might consider areas

603
00:25:47.040 --> 00:25:49.050
of very high importance within the northeast

604
00:25:49.050 --> 00:25:51.090
or they receive a lot of recreation,

605
00:25:51.090 --> 00:25:53.440
proportionally more than a lot of other places.

606
00:25:54.390 --> 00:25:56.310
And you can see the color scale here

607
00:25:56.310 --> 00:26:00.180
is yellow is indicating a light amount of usage,

608
00:26:00.180 --> 00:26:02.700
red is indicating a high amount of usage,

609
00:26:02.700 --> 00:26:04.920
and it should be noted that these scales

610
00:26:04.920 --> 00:26:07.050
are meant to show the fact

611
00:26:07.050 --> 00:26:09.570
that they are on an exponential scale.

612
00:26:09.570 --> 00:26:13.200
The reds are experiencing, oranges are experiencing

613
00:26:13.200 --> 00:26:15.150
a lot of usage,

614
00:26:15.150 --> 00:26:16.650
but the way that we're coloring it

615
00:26:16.650 --> 00:26:18.750
allows you to actually see which trails

616
00:26:18.750 --> 00:26:22.950
are actually experiencing those extremes.

617
00:26:22.950 --> 00:26:24.780
I would say color scale really matters

618
00:26:24.780 --> 00:26:26.610
in looking in these data themselves

619
00:26:26.610 --> 00:26:28.680
and to be able to categorize

620
00:26:28.680 --> 00:26:31.833
areas of high or low use next to one another.

621
00:26:33.510 --> 00:26:36.600
In contrast, if you were to use just a continuous scale

622
00:26:36.600 --> 00:26:37.560
on the right here,

623
00:26:37.560 --> 00:26:39.330
when we're considering soils,

624
00:26:39.330 --> 00:26:40.770
I would say again to the back of the left,

625
00:26:40.770 --> 00:26:42.660
the left map indicates areas

626
00:26:42.660 --> 00:26:45.630
that's just raw hiking and biking count.

627
00:26:45.630 --> 00:26:48.330
On the right is where we took into consideration

628
00:26:48.330 --> 00:26:49.230
that soil risk,

629
00:26:49.230 --> 00:26:51.510
that 0-1 scale,

630
00:26:51.510 --> 00:26:53.430
and we'll go over how that's done.

631
00:26:53.430 --> 00:26:57.330
But you can see that when we're considering soil risk

632
00:26:57.330 --> 00:26:59.460
and/or these areas that are experiencing

633
00:26:59.460 --> 00:27:02.670
a ton of recreation,

634
00:27:02.670 --> 00:27:05.370
you're gonna, even if you're on really, really good soils,

635
00:27:05.370 --> 00:27:09.240
you are going to have a high amount of,

636
00:27:09.240 --> 00:27:12.270
you're gonna still have a high value.

637
00:27:12.270 --> 00:27:13.103
Really what it's showing

638
00:27:13.103 --> 00:27:15.030
is that the color scale really matters.

639
00:27:15.030 --> 00:27:16.410
You really have to contextualize

640
00:27:16.410 --> 00:27:19.740
what you're looking at based on the scale itself.

641
00:27:19.740 --> 00:27:20.573
The other thing is,

642
00:27:20.573 --> 00:27:22.140
is that how this is actually being calculated,

643
00:27:22.140 --> 00:27:23.880
what we do is we take that hike count.

644
00:27:23.880 --> 00:27:26.380
So say you have 100 people on a trail

645
00:27:27.300 --> 00:27:29.610
and then you take that soil suitability value,

646
00:27:29.610 --> 00:27:31.020
which is a 0-1,

647
00:27:31.020 --> 00:27:32.010
you multiply it.

648
00:27:32.010 --> 00:27:35.130
So a 1 would maintain that value at 100,

649
00:27:35.130 --> 00:27:38.550
which means it's not really suited, it's a high value.

650
00:27:38.550 --> 00:27:41.640
And then if you multiply it by a decimal, like a .1,

651
00:27:41.640 --> 00:27:42.473
that value would be 10,

652
00:27:42.473 --> 00:27:45.270
100 times .1 is 10, right?

653
00:27:45.270 --> 00:27:48.450
So it reduces kind of like that impact.

654
00:27:48.450 --> 00:27:52.980
So that value is not a direct

655
00:27:52.980 --> 00:27:54.810
isn't directly used,

656
00:27:54.810 --> 00:27:58.020
it's just meant to be a scalarization of soil suitability

657
00:27:58.020 --> 00:28:00.660
and how much use is actually occurring on that.

658
00:28:00.660 --> 00:28:02.040
But if you also have,

659
00:28:02.040 --> 00:28:03.480
and the reason why I pick out these two areas

660
00:28:03.480 --> 00:28:04.980
is because they're extremes.

661
00:28:04.980 --> 00:28:06.210
Within the region,

662
00:28:06.210 --> 00:28:10.980
Acadia and Burke Mountain receive a ton of usage

663
00:28:10.980 --> 00:28:13.200
exponentially more than a lot of other places.

664
00:28:13.200 --> 00:28:17.460
And even if you are on places like that actually do have,

665
00:28:17.460 --> 00:28:19.050
and you can see from the red areas

666
00:28:19.050 --> 00:28:21.720
where you have trails that are on suitable soils,

667
00:28:21.720 --> 00:28:24.570
you still have those bright reds,

668
00:28:24.570 --> 00:28:26.670
and that's just indicative of the fact

669
00:28:26.670 --> 00:28:28.140
that you have a ton of usage

670
00:28:28.140 --> 00:28:29.730
that even if you are on good soils

671
00:28:29.730 --> 00:28:31.590
isn't going to mitigate that.

672
00:28:31.590 --> 00:28:34.560
So you have to think about where you are looking at

673
00:28:34.560 --> 00:28:36.030
contextually within the region,

674
00:28:36.030 --> 00:28:37.680
especially when using these data.

675
00:28:39.180 --> 00:28:41.400
So let's go on

676
00:28:41.400 --> 00:28:44.820
to using multi-layer analysis for wildlife.

677
00:28:44.820 --> 00:28:47.070
It's another useful example, I think.

678
00:28:47.070 --> 00:28:48.090
The same areas,

679
00:28:48.090 --> 00:28:50.490
but this time we're actually using a bunch

680
00:28:50.490 --> 00:28:52.080
of different layers stacked together.

681
00:28:52.080 --> 00:28:55.230
As you know, most people who work with GIS a lot of times,

682
00:28:55.230 --> 00:28:57.300
you know, the human eye can really help

683
00:28:57.300 --> 00:28:58.920
visualize things really well.

684
00:28:58.920 --> 00:28:59.910
And so what we're doing here

685
00:28:59.910 --> 00:29:02.670
is we're taking into consideration the 400-foot buffers,

686
00:29:02.670 --> 00:29:04.500
so large mammalian species.

687
00:29:04.500 --> 00:29:06.420
We have the line layer itself

688
00:29:06.420 --> 00:29:10.920
that's looking at the counts of biking in the area

689
00:29:10.920 --> 00:29:13.230
and then also that raster,

690
00:29:13.230 --> 00:29:15.750
which is kind of giving regional significance

691
00:29:15.750 --> 00:29:17.820
to those lines themselves.

692
00:29:17.820 --> 00:29:19.410
So it helps you to better kind of see

693
00:29:19.410 --> 00:29:21.060
maybe you have a really high use trail,

694
00:29:21.060 --> 00:29:23.400
but it's not regionally of like receiving those,

695
00:29:23.400 --> 00:29:25.470
that amount of usage.

696
00:29:25.470 --> 00:29:27.660
So for instance, in Acadia, Maine,

697
00:29:27.660 --> 00:29:28.816
you can see what's traditionally,

698
00:29:28.816 --> 00:29:31.950
everybody likes to go to that one side of the island.

699
00:29:31.950 --> 00:29:33.600
And so you can see those areas

700
00:29:33.600 --> 00:29:38.460
that are based on that wildlife buffers,

701
00:29:38.460 --> 00:29:40.380
those contiguous forest blocks

702
00:29:40.380 --> 00:29:43.500
and where the human-animal interactions might be occurring.

703
00:29:43.500 --> 00:29:46.060
That might be able to inform strategic

704
00:29:47.250 --> 00:29:49.350
how you wanna allocate maybe areas

705
00:29:49.350 --> 00:29:51.540
where you want to be doing different types of management

706
00:29:51.540 --> 00:29:52.440
based on wildlife

707
00:29:52.440 --> 00:29:56.160
and/or prioritizing certain contiguous forest blocks

708
00:29:56.160 --> 00:29:57.840
based on recreational patterns.

709
00:29:57.840 --> 00:30:01.890
Same thing could be said for Burke Mountain.

710
00:30:01.890 --> 00:30:03.270
Again, I'm kind of just talking here,

711
00:30:03.270 --> 00:30:06.360
but you can use these data

712
00:30:06.360 --> 00:30:07.650
in conjunction with,

713
00:30:07.650 --> 00:30:09.480
and rather to look at trail management.

714
00:30:09.480 --> 00:30:12.180
So adjusting or rerouting trails

715
00:30:12.180 --> 00:30:14.400
based on kind of informed decisions

716
00:30:14.400 --> 00:30:15.840
about like where people are recreating

717
00:30:15.840 --> 00:30:18.573
with regards to different types of animals.

718
00:30:19.620 --> 00:30:20.760
Conservation planning,

719
00:30:20.760 --> 00:30:22.770
so maybe you have certain contiguous forest blocks,

720
00:30:22.770 --> 00:30:25.013
like if you're looking at Burke Mountain here,

721
00:30:26.100 --> 00:30:27.840
you might be able to see in the bottom right

722
00:30:27.840 --> 00:30:30.270
you have that area that's high amounts of usage

723
00:30:30.270 --> 00:30:31.680
in a very small area.

724
00:30:31.680 --> 00:30:33.570
Do you want to be creating a new trail

725
00:30:33.570 --> 00:30:36.360
that breaks off into a contiguous forest block

726
00:30:36.360 --> 00:30:40.650
that isn't really being disturbed by recreation?

727
00:30:40.650 --> 00:30:43.953
How do you wanna mitigate the human-animal interactions?

728
00:30:45.450 --> 00:30:48.090
It helps you plan about where trails are

729
00:30:48.090 --> 00:30:50.040
versus where trails might be,

730
00:30:50.040 --> 00:30:51.540
areas where you might want to cut back

731
00:30:51.540 --> 00:30:53.493
on where those trails are, et cetera.

732
00:30:54.600 --> 00:30:57.000
So this is really meant all of these different layers

733
00:30:57.000 --> 00:30:59.190
are really meant for you

734
00:30:59.190 --> 00:31:02.460
to be able to download them

735
00:31:02.460 --> 00:31:05.010
and use them for informed decision-making

736
00:31:05.010 --> 00:31:09.390
based on where are areas of high impact or low impact,

737
00:31:09.390 --> 00:31:13.290
prioritizing areas of trail management,

738
00:31:13.290 --> 00:31:15.300
supporting data-driven discussions

739
00:31:15.300 --> 00:31:17.373
based on land use for land use planning.

740
00:31:18.330 --> 00:31:19.920
These data in conjunction

741
00:31:19.920 --> 00:31:22.440
are really supposed to allow you

742
00:31:22.440 --> 00:31:27.440
to look at regionalization of these kind of uses.

743
00:31:27.480 --> 00:31:31.800
For instance, like you go back to the heat maps themselves.

744
00:31:31.800 --> 00:31:34.630
If you're trying to think about prioritization of trail

745
00:31:36.060 --> 00:31:39.580
resources and considering

746
00:31:40.830 --> 00:31:42.270
trail maintenance, right?

747
00:31:42.270 --> 00:31:44.070
How are you gonna be able to justify to,

748
00:31:44.070 --> 00:31:45.270
you know, if you're applying to a grant

749
00:31:45.270 --> 00:31:49.290
or you're trying to strategically allocate resources

750
00:31:49.290 --> 00:31:50.850
appropriately using visuals,

751
00:31:50.850 --> 00:31:54.660
this might be a very useful tool for you to display.

752
00:31:54.660 --> 00:31:58.200
You might know exactly where recreation needs to be managed,

753
00:31:58.200 --> 00:32:00.300
but if you're trying to display this to the outer public,

754
00:32:00.300 --> 00:32:02.550
these are some tools that might be able to aid you

755
00:32:02.550 --> 00:32:05.253
in justifying those projects.

756
00:32:06.810 --> 00:32:09.930
I really want to acknowledge

757
00:32:09.930 --> 00:32:10.950
all the people that were part of this.

758
00:32:10.950 --> 00:32:14.220
A lot of this was driven by our committee members.

759
00:32:14.220 --> 00:32:17.400
They provided a wealth of resources

760
00:32:17.400 --> 00:32:20.553
and direction for this study as well as our primary funders.

761
00:32:22.020 --> 00:32:25.830
And I think we have time for a bunch of questions,

762
00:32:25.830 --> 00:32:26.790
which is kind of like the point

763
00:32:26.790 --> 00:32:28.020
we wanna make the point of the webinar

764
00:32:28.020 --> 00:32:30.480
as I'm sure have received some emails with some questions.

765
00:32:30.480 --> 00:32:32.373
So here's some time for,

766
00:32:33.390 --> 00:32:35.190
to kind of dive into the layers themselves

767
00:32:35.190 --> 00:32:36.900
and/or nuances or any questions you have.

768
00:32:36.900 --> 00:32:40.920
Here are the slide titles and numbers

769
00:32:40.920 --> 00:32:42.483
if you wanna kick that started.

770
00:32:44.310 --> 00:32:47.280
<v ->Great, so we have about 10 minutes for questions</v>

771
00:32:47.280 --> 00:32:49.110
on this section,

772
00:32:49.110 --> 00:32:51.180
and you can raise your hand and I'll call on you

773
00:32:51.180 --> 00:32:53.250
or if you wanna type a question in the Q&amp;A,

774
00:32:53.250 --> 00:32:55.143
I'm happy to read that as well.

775
00:33:22.890 --> 00:33:24.440
<v ->Bradford, I saw your hand up.</v>

776
00:33:35.850 --> 00:33:37.523
All right, I wonder if I have to unmute you.

777
00:33:41.880 --> 00:33:43.980
Bradford, are you able to unmute yourself?

778
00:33:54.180 --> 00:33:56.910
Well, for now maybe we'll go to Dave Wilcox

779
00:33:56.910 --> 00:33:57.750
whose hand I see up

780
00:33:57.750 --> 00:34:00.420
and then Bradford, we'll try and figure out what's going on.

781
00:34:00.420 --> 00:34:02.070
<v Dave>Hi, can you hear me?</v>

782
00:34:02.070 --> 00:34:02.940
<v ->Yes.</v>

783
00:34:02.940 --> 00:34:05.730
<v Dave>Okay, thanks.</v>

784
00:34:05.730 --> 00:34:08.400
That's a very interesting presentation.

785
00:34:08.400 --> 00:34:13.320
My question is about permeability.

786
00:34:13.320 --> 00:34:14.160
How are you,

787
00:34:14.160 --> 00:34:17.560
what are your definitions of trail permeability

788
00:34:18.480 --> 00:34:22.680
and impermeability I guess would be the opposite of that?

789
00:34:22.680 --> 00:34:23.523
Just curious.

790
00:34:24.450 --> 00:34:27.690
<v ->Yeah, so it was based off of OpenStreetMaps</v>

791
00:34:27.690 --> 00:34:30.450
provides kind of a wealth of data.

792
00:34:30.450 --> 00:34:32.520
Two of the columns there are based on

793
00:34:32.520 --> 00:34:36.880
categorization of line.
<v ->Hello.</v>

794
00:34:36.880 --> 00:34:40.680
<v ->Oh, just one second, we'll get back to you.</v>

795
00:34:40.680 --> 00:34:42.570
So you have to go there is-
<v ->Hello.</v>

796
00:34:42.570 --> 00:34:44.760
How do I get the mics to work?

797
00:34:44.760 --> 00:34:46.060
<v ->Oh, your mic is working.</v>

798
00:34:47.340 --> 00:34:51.750
I'll just respond to the question about trail permeability.

799
00:34:51.750 --> 00:34:54.300
So that is based on OpenStreetMaps' data.

800
00:34:54.300 --> 00:34:55.380
So what you do is,

801
00:34:55.380 --> 00:34:57.210
is you can look at the two-
<v ->How do you get?</v>

802
00:34:57.210 --> 00:34:58.680
<v ->Columns and what ends up happening</v>

803
00:34:58.680 --> 00:35:02.490
is you look at trail type categorization.

804
00:35:02.490 --> 00:35:03.900
So they get things like highways.

805
00:35:03.900 --> 00:35:06.630
So highways are usually paved

806
00:35:06.630 --> 00:35:08.430
versus things that are classified

807
00:35:08.430 --> 00:35:10.620
as like paths are usually not.

808
00:35:10.620 --> 00:35:14.130
And this is all within like when you are,

809
00:35:14.130 --> 00:35:15.690
OpenStreetMaps' data is collected,

810
00:35:15.690 --> 00:35:18.387
it bases things based off those categorizations.

811
00:35:18.387 --> 00:35:20.220
And so that's one way.

812
00:35:20.220 --> 00:35:23.440
The other way is they actually have a column that uses

813
00:35:26.340 --> 00:35:27.900
what the surface is itself.

814
00:35:27.900 --> 00:35:30.210
So they would say whether paved concrete

815
00:35:30.210 --> 00:35:31.980
versus what we considered permeable,

816
00:35:31.980 --> 00:35:34.680
meaning you can have compacted dirt,

817
00:35:34.680 --> 00:35:36.870
which is how permeable is that?

818
00:35:36.870 --> 00:35:37.800
Well, we use the idea

819
00:35:37.800 --> 00:35:39.870
of what might be considered traditionally permeable,

820
00:35:39.870 --> 00:35:42.600
so like we're using hard like concrete surfaces

821
00:35:42.600 --> 00:35:44.370
that we are considering not permeable

822
00:35:44.370 --> 00:35:46.320
versus what might be permeable.

823
00:35:46.320 --> 00:35:47.400
So compacted soil,

824
00:35:47.400 --> 00:35:50.700
even though probably not very permeable,

825
00:35:50.700 --> 00:35:53.520
could be and what maybe was or is,

826
00:35:53.520 --> 00:35:55.503
you know, can be eroded.

827
00:35:56.730 --> 00:35:58.500
And so those are what we used

828
00:35:58.500 --> 00:36:01.590
for kind of like filtering down to those trails

829
00:36:01.590 --> 00:36:03.360
that we considered permeable.

830
00:36:03.360 --> 00:36:05.280
However, the range of permeability

831
00:36:05.280 --> 00:36:06.960
really depends on the line itself,

832
00:36:06.960 --> 00:36:08.310
all of which if you,

833
00:36:08.310 --> 00:36:10.050
when you download the data you can filter out,

834
00:36:10.050 --> 00:36:13.440
for instance, compacted soil out of those data.

835
00:36:13.440 --> 00:36:16.380
But we did consider some of those maybe non-traditional,

836
00:36:16.380 --> 00:36:20.613
it is still what's considered dirt or soil.

837
00:36:22.380 --> 00:36:25.980
So I don't know if that fully answers your question.

838
00:36:25.980 --> 00:36:27.189
<v ->Yeah, yeah.</v>
<v ->I think the easiest...</v>

839
00:36:27.189 --> 00:36:29.490
Yeah.
<v ->That's helpful.</v>

840
00:36:29.490 --> 00:36:32.460
That's without, yeah,

841
00:36:32.460 --> 00:36:34.290
that's kind of what I assume

842
00:36:34.290 --> 00:36:35.200
and as we know,

843
00:36:35.200 --> 00:36:39.460
a lot of trails that are on

844
00:36:40.320 --> 00:36:45.320
dirt are so compacted that they aren't as permeable

845
00:36:45.780 --> 00:36:48.090
as they once were, but they're not.

846
00:36:48.090 --> 00:36:50.850
Yeah, but that's helpful.

847
00:36:50.850 --> 00:36:51.840
<v ->Yeah, and something that's,</v>

848
00:36:51.840 --> 00:36:53.580
you know, even thinking about that question

849
00:36:53.580 --> 00:36:54.600
in the data itself

850
00:36:54.600 --> 00:36:56.880
is you could filter to trails that you might consider

851
00:36:56.880 --> 00:37:00.210
to be highly permeable like gravel or sand

852
00:37:00.210 --> 00:37:03.450
and also trails that are compacted soil

853
00:37:03.450 --> 00:37:05.490
and see and go to those locations

854
00:37:05.490 --> 00:37:07.710
and see how they're impacting that area.

855
00:37:07.710 --> 00:37:12.710
So again, this data provides like a wealth of opportunities

856
00:37:12.870 --> 00:37:14.280
to be able to strategize.

857
00:37:14.280 --> 00:37:16.140
Like let's go to that area that has compact,

858
00:37:16.140 --> 00:37:19.200
all the lines and all the trails that have compacted soil

859
00:37:19.200 --> 00:37:21.780
and all those areas that might be considered

860
00:37:21.780 --> 00:37:23.250
to have really well drained soil

861
00:37:23.250 --> 00:37:26.150
and try to actually see if there's a difference in impact.

862
00:37:28.110 --> 00:37:31.380
Great question.
<v ->Great, thank you</v>

863
00:37:31.380 --> 00:37:33.510
<v ->Bradford Elliot, I wanted to give you an opportunity</v>

864
00:37:33.510 --> 00:37:37.350
to ask your question if you can unmute yourself.

865
00:37:37.350 --> 00:37:40.225
<v Bradford>Yeah, I think I finally figured that out.</v>

866
00:37:40.225 --> 00:37:43.110
<v ->Okay.</v>
<v ->I was wondering if we</v>

867
00:37:43.110 --> 00:37:44.920
could go back to slide 13

868
00:37:46.050 --> 00:37:49.650
and just, I was questioning about the scales there

869
00:37:49.650 --> 00:37:51.319
or the legend rather.

870
00:37:51.319 --> 00:37:52.619
(clears throat) Excuse me.

871
00:37:53.670 --> 00:37:54.750
Just walk me through.

872
00:37:54.750 --> 00:37:56.583
Yeah, excellent.
<v ->This one?</v>

873
00:37:58.020 --> 00:37:59.490
The next or this one?
<v ->The first one.</v>

874
00:37:59.490 --> 00:38:02.580
Yeah, that one, that's the wildlife one.

875
00:38:02.580 --> 00:38:04.533
So say looking at Acadia,

876
00:38:07.068 --> 00:38:09.660
the colors that I see,

877
00:38:09.660 --> 00:38:13.680
you know, raising from sort of a pale green to white,

878
00:38:13.680 --> 00:38:16.830
that's acreage of those

879
00:38:16.830 --> 00:38:20.070
particular blocks?

880
00:38:20.070 --> 00:38:21.930
<v ->Yes, sir. Yes.</v>
<v ->Okay.</v>

881
00:38:21.930 --> 00:38:26.090
And then the bike count goes up.

882
00:38:26.090 --> 00:38:27.183
So the 400,

883
00:38:28.440 --> 00:38:29.370
I guess the question is,

884
00:38:29.370 --> 00:38:33.150
where's the 400 foot come in here?

885
00:38:33.150 --> 00:38:36.030
Are these areas that are not within

886
00:38:36.030 --> 00:38:37.710
400 feet of a trail maybe?

887
00:38:37.710 --> 00:38:38.730
<v Soren>Yeah, exactly.</v>

888
00:38:38.730 --> 00:38:41.640
So these are areas that are not within 400 feet.

889
00:38:41.640 --> 00:38:43.770
So we buffered the trails out 400 feet

890
00:38:43.770 --> 00:38:47.520
and then we subtracted it from NLCD forests.

891
00:38:47.520 --> 00:38:50.640
So all of the those pale white

892
00:38:50.640 --> 00:38:53.400
and to green polygons

893
00:38:53.400 --> 00:38:57.360
are the areas that based on the wildlife

894
00:38:57.360 --> 00:38:58.860
that kind of 400 foot buffer

895
00:38:58.860 --> 00:39:00.780
aren't being disturbed by wildlife.

896
00:39:00.780 --> 00:39:03.960
While it's kind of nice to have those red lines

897
00:39:03.960 --> 00:39:08.220
and the heat map

898
00:39:08.220 --> 00:39:12.900
underneath kind of contextualizes within that buffer

899
00:39:12.900 --> 00:39:14.790
how much interaction there might be

900
00:39:14.790 --> 00:39:19.610
having between wildlife and/or human recreation.

901
00:39:21.843 --> 00:39:23.820
And so you really have to almost kind of like do that

902
00:39:23.820 --> 00:39:26.070
kind of human analysis of like, "Oh,

903
00:39:26.070 --> 00:39:27.450
bottom left hand corner,

904
00:39:27.450 --> 00:39:28.680
you see that there are some trails

905
00:39:28.680 --> 00:39:31.347
that have like a lot of usage."

906
00:39:32.970 --> 00:39:36.060
Maybe you want to prioritize those areas,

907
00:39:36.060 --> 00:39:39.240
you know, making sure that trails don't occur in those areas

908
00:39:39.240 --> 00:39:40.860
that are not currently experiencing

909
00:39:40.860 --> 00:39:44.168
that kind of recreational interference, I guess.

910
00:39:44.168 --> 00:39:45.883
<v Bradford>Got you.</v>

911
00:39:45.883 --> 00:39:47.130
Okay, good.

912
00:39:47.130 --> 00:39:48.540
All right, thank you.

913
00:39:48.540 --> 00:39:49.823
<v Soren>Yeah, great question.</v>

914
00:40:04.139 --> 00:40:06.960
<v ->Alison, you're muted.</v>
<v ->Oh, Alison. Yeah.</v>

915
00:40:06.960 --> 00:40:08.460
<v ->My apologies. (laughs)</v>

916
00:40:08.460 --> 00:40:09.390
Thank you.

917
00:40:09.390 --> 00:40:12.930
I was saying we have two questions in the Q&amp;A.

918
00:40:12.930 --> 00:40:13.860
We'll answer those,

919
00:40:13.860 --> 00:40:15.600
and then if we have time we'll get to Aaron

920
00:40:15.600 --> 00:40:19.800
and then there's a question in the chat as well.

921
00:40:19.800 --> 00:40:21.360
<v Rick>Is ordering some blood labs</v>

922
00:40:21.360 --> 00:40:22.683
that looks like-
<v ->Oh.</v>

923
00:40:23.790 --> 00:40:25.350
And I think somebody is-
<v ->And cover my insurance</v>

924
00:40:25.350 --> 00:40:26.400
if I got a referral

925
00:40:26.400 --> 00:40:28.133
or got an order from your-
<v ->Not muted.</v>

926
00:40:30.822 --> 00:40:31.655
There we go.

927
00:40:32.670 --> 00:40:36.030
All right, so in the Q&amp;A,

928
00:40:36.030 --> 00:40:39.240
we have two questions from Robert Bryan in Maine.

929
00:40:39.240 --> 00:40:41.760
One is iNaturalist has specialized data too,

930
00:40:41.760 --> 00:40:42.960
most hikers do not use it.

931
00:40:42.960 --> 00:40:44.940
How were the trail use counts estimated

932
00:40:44.940 --> 00:40:47.040
and how reliable are they?

933
00:40:47.040 --> 00:40:50.670
Soren?
<v ->Yeah, so iNaturalist,</v>

934
00:40:50.670 --> 00:40:52.590
so we had a worry about Strava

935
00:40:52.590 --> 00:40:57.090
being a very specific set of recreationists.

936
00:40:57.090 --> 00:40:59.280
They probably have a certain demographic

937
00:40:59.280 --> 00:41:02.100
and so yeah, iNaturalist is not what we might be considered

938
00:41:02.100 --> 00:41:03.630
to be hikers maybe,

939
00:41:03.630 --> 00:41:06.480
but we were trying to get at a completely different

940
00:41:06.480 --> 00:41:09.903
population that still is likely using trails.

941
00:41:11.130 --> 00:41:15.060
And so what we did was within a range of the trails,

942
00:41:15.060 --> 00:41:19.950
what we do is we take that x and y lat and long point

943
00:41:19.950 --> 00:41:21.090
for iNaturalist

944
00:41:21.090 --> 00:41:23.520
and associate is like if there's one lat and long point

945
00:41:23.520 --> 00:41:24.353
for one person,

946
00:41:24.353 --> 00:41:27.475
we associate that to the nearest trail,

947
00:41:27.475 --> 00:41:30.150
and so that nearest like OSM line segment.

948
00:41:30.150 --> 00:41:33.270
And so it is a certain amount

949
00:41:33.270 --> 00:41:36.330
of assumption that you make that

950
00:41:36.330 --> 00:41:39.520
they used that nearest trail with

951
00:41:41.310 --> 00:41:42.900
to get there.

952
00:41:42.900 --> 00:41:45.300
You know, a lot of people might be orienteering.

953
00:41:45.300 --> 00:41:46.560
We tried to filter out those areas

954
00:41:46.560 --> 00:41:50.160
to within the range of the line themselves.

955
00:41:50.160 --> 00:41:52.860
And so we weren't taking into consideration

956
00:41:52.860 --> 00:41:54.510
points that were really, really, really, really far

957
00:41:54.510 --> 00:41:57.360
from the trail to like to the nearest trail.

958
00:41:57.360 --> 00:41:59.370
It was only within the iNaturalist points

959
00:41:59.370 --> 00:42:02.160
that were closer to the trails themselves,

960
00:42:02.160 --> 00:42:03.630
but it's a one-to-one.

961
00:42:03.630 --> 00:42:06.030
So like we don't know how,

962
00:42:06.030 --> 00:42:09.070
you know, that person might have gotten to the trail

963
00:42:09.960 --> 00:42:11.220
so we could only associate it

964
00:42:11.220 --> 00:42:13.080
to the nearest OSM line segment

965
00:42:13.080 --> 00:42:14.223
and count it as one.

966
00:42:16.170 --> 00:42:18.810
So yeah.
<v ->And I was gonna</v>

967
00:42:18.810 --> 00:42:19.710
jump in, Soren,

968
00:42:19.710 --> 00:42:22.080
and say the other piece of that question,

969
00:42:22.080 --> 00:42:24.630
you know, how reliable are these estimates?

970
00:42:24.630 --> 00:42:27.930
So we're not providing straight up counts

971
00:42:27.930 --> 00:42:30.390
of the number of folks who are using a trail,

972
00:42:30.390 --> 00:42:31.770
they're relative estimate,

973
00:42:31.770 --> 00:42:33.510
they're relative use estimates.

974
00:42:33.510 --> 00:42:35.730
So yes, we don't think

975
00:42:35.730 --> 00:42:38.850
that the counts estimate are exactly accurate,

976
00:42:38.850 --> 00:42:42.000
and that's partly why we're not providing them to you.

977
00:42:42.000 --> 00:42:45.420
And that's because we are only able to get these pieces

978
00:42:45.420 --> 00:42:47.340
of populations who are using the trails

979
00:42:47.340 --> 00:42:48.930
and glue them together

980
00:42:48.930 --> 00:42:53.130
to try and get a sense of how much each trail is being used.

981
00:42:53.130 --> 00:42:55.590
But we are more confident in the relative usage

982
00:42:55.590 --> 00:42:57.690
that trails that are showing up

983
00:42:57.690 --> 00:42:59.400
as heavily used are probably the trails

984
00:42:59.400 --> 00:43:00.750
that are heavily used compared

985
00:43:00.750 --> 00:43:02.910
to the trails that are less used.

986
00:43:02.910 --> 00:43:06.180
And so that's kind of the answer to that question.

987
00:43:06.180 --> 00:43:07.410
We don't have straight up counts

988
00:43:07.410 --> 00:43:08.943
that we're providing you.

989
00:43:10.620 --> 00:43:14.070
So Robert had another question in the Q&amp;A

990
00:43:14.070 --> 00:43:15.000
that I wanted to answer

991
00:43:15.000 --> 00:43:16.500
'cause it's the order they came in on

992
00:43:16.500 --> 00:43:19.110
and that was that the OSM trail layer

993
00:43:19.110 --> 00:43:20.700
misses many local trails.

994
00:43:20.700 --> 00:43:23.280
Will there be a way for a land manager,

995
00:43:23.280 --> 00:43:25.140
say like a local land trust

996
00:43:25.140 --> 00:43:25.973
to put in layers

997
00:43:25.973 --> 00:43:27.630
so that they can look at their specific properties

998
00:43:27.630 --> 00:43:28.830
that are not on OSM,

999
00:43:28.830 --> 00:43:31.320
and are there any plans for a web map version of this?

1000
00:43:31.320 --> 00:43:32.700
And I'll take this

1001
00:43:32.700 --> 00:43:34.470
and then, Soren, you can try to answer

1002
00:43:34.470 --> 00:43:37.020
if I've missed anything else.

1003
00:43:37.020 --> 00:43:39.150
So there isn't currently.

1004
00:43:39.150 --> 00:43:42.270
You could download our maps

1005
00:43:42.270 --> 00:43:44.490
and add in your own layers of any kind,

1006
00:43:44.490 --> 00:43:46.530
but you're not necessarily going to be able to see

1007
00:43:46.530 --> 00:43:48.900
what the recreation usage is on those trails

1008
00:43:48.900 --> 00:43:50.100
if it wasn't one of the trails

1009
00:43:50.100 --> 00:43:52.140
that was reflected in our layer.

1010
00:43:52.140 --> 00:43:54.930
We do recognize that OpenStreetMaps is not perfect,

1011
00:43:54.930 --> 00:43:56.550
but when we're doing this at a regional scale,

1012
00:43:56.550 --> 00:43:59.070
it just wasn't feasible with the resources

1013
00:43:59.070 --> 00:44:01.080
and time that we had to go to every single park

1014
00:44:01.080 --> 00:44:02.700
and ask for a specific trail map

1015
00:44:02.700 --> 00:44:04.380
and then integrate those all together.

1016
00:44:04.380 --> 00:44:06.750
So you certainly can add your own maps

1017
00:44:06.750 --> 00:44:07.800
after you download these

1018
00:44:07.800 --> 00:44:10.230
and you have this uploaded in your own ArcGIS

1019
00:44:10.230 --> 00:44:12.900
or other sort of web mapping platform,

1020
00:44:12.900 --> 00:44:14.760
but you're not gonna be able to see recreational use

1021
00:44:14.760 --> 00:44:18.003
on those unless they match up with our trail maps.

1022
00:44:19.020 --> 00:44:21.540
Soren, do you have anything to add to that?

1023
00:44:21.540 --> 00:44:23.550
<v Soren>Yeah, I would second everything you say</v>

1024
00:44:23.550 --> 00:44:26.200
that these layers are really meant to be used

1025
00:44:27.870 --> 00:44:29.910
with whatever other data you have as well.

1026
00:44:29.910 --> 00:44:32.340
But no, you wouldn't really able to see the use counts

1027
00:44:32.340 --> 00:44:34.360
for the lines that you have

1028
00:44:36.270 --> 00:44:38.910
and all of the hosting is easily pulled

1029
00:44:38.910 --> 00:44:40.710
into a web map of your own

1030
00:44:40.710 --> 00:44:42.900
so you can visualize like all of the rasters

1031
00:44:42.900 --> 00:44:45.060
are Web Tiling Services

1032
00:44:45.060 --> 00:44:47.250
and you can visualize things quite easily

1033
00:44:47.250 --> 00:44:49.890
by pulling it into your Arc or QGIS space

1034
00:44:49.890 --> 00:44:51.390
and layer them really quickly

1035
00:44:51.390 --> 00:44:54.423
with any other kind of data you want or have.

1036
00:44:55.830 --> 00:44:58.500
But no, it wouldn't be able to tie use counts

1037
00:44:58.500 --> 00:45:00.720
to those trails that aren't in OSM.

1038
00:45:00.720 --> 00:45:02.550
It is a hole that's there,

1039
00:45:02.550 --> 00:45:04.200
but we just didn't have time to aggregate

1040
00:45:04.200 --> 00:45:07.263
all of the disparate trail data that are out there.

1041
00:45:08.820 --> 00:45:09.720
<v ->All right, I think we have time</v>

1042
00:45:09.720 --> 00:45:11.430
for one last question from Aaron,

1043
00:45:11.430 --> 00:45:14.130
and then Charles, I saw that you have a question in the chat

1044
00:45:14.130 --> 00:45:16.743
and we will try to answer that via email for you.

1045
00:45:18.450 --> 00:45:20.100
Aaron, go ahead.

1046
00:45:20.100 --> 00:45:21.810
<v ->Hi. Good afternoon.</v>

1047
00:45:21.810 --> 00:45:23.138
Thanks for the overview of this,

1048
00:45:23.138 --> 00:45:24.870
the data looks really interesting

1049
00:45:24.870 --> 00:45:27.810
and I'm looking forward to playing around with it,

1050
00:45:27.810 --> 00:45:30.450
but I was wondering if you could talk a little bit about

1051
00:45:30.450 --> 00:45:34.350
what scales you see this

1052
00:45:34.350 --> 00:45:37.113
as being most effective in using?

1053
00:45:38.040 --> 00:45:42.330
We frequently are at scales about 1:10,000 or less.

1054
00:45:42.330 --> 00:45:45.450
And I'm wondering if you think that your data

1055
00:45:45.450 --> 00:45:50.040
will be effective in those ranges?

1056
00:45:50.040 --> 00:45:50.873
Thanks.

1057
00:45:53.280 --> 00:45:54.600
<v Soren>I think, again,</v>

1058
00:45:54.600 --> 00:45:56.430
you know, I'm glad you're really gonna be able

1059
00:45:56.430 --> 00:45:57.720
to play around with the data,

1060
00:45:57.720 --> 00:45:58.680
and I think it really depends

1061
00:45:58.680 --> 00:46:02.253
on what the question you're trying to ask.

1062
00:46:04.560 --> 00:46:07.830
I would say that at really, really fine,

1063
00:46:07.830 --> 00:46:10.170
like the specific relationship

1064
00:46:10.170 --> 00:46:13.620
between forest canopy impact

1065
00:46:13.620 --> 00:46:17.520
and the magnitude of usage at a 30 x 30 meter resolution

1066
00:46:17.520 --> 00:46:21.960
is too coarse to really scientifically look

1067
00:46:21.960 --> 00:46:23.910
at that relationship.

1068
00:46:23.910 --> 00:46:27.270
But if you're trying to look at which specific trails

1069
00:46:27.270 --> 00:46:28.620
within your park,

1070
00:46:28.620 --> 00:46:32.400
especially 1:1,000 kind of is within that range,

1071
00:46:32.400 --> 00:46:33.900
you're able to like pick up on areas

1072
00:46:33.900 --> 00:46:35.100
where you're able to see

1073
00:46:36.780 --> 00:46:41.040
what specific trails might be receiving more or less usage.

1074
00:46:41.040 --> 00:46:43.980
I also think that this data is really useful

1075
00:46:43.980 --> 00:46:45.783
with a regional context.

1076
00:46:46.760 --> 00:46:50.880
So you can use especially the line layer

1077
00:46:50.880 --> 00:46:52.683
at really fine resolutions.

1078
00:46:53.760 --> 00:46:56.100
But if you are,

1079
00:46:56.100 --> 00:46:57.780
you know, a lot of this is designed,

1080
00:46:57.780 --> 00:47:00.000
a lot you can look at a single park with the data

1081
00:47:00.000 --> 00:47:01.590
that you currently already have.

1082
00:47:01.590 --> 00:47:04.320
The use of these layers is that it is regionalized,

1083
00:47:04.320 --> 00:47:06.690
it does have all of the northeast,

1084
00:47:06.690 --> 00:47:09.090
you can contextualize your park

1085
00:47:09.090 --> 00:47:12.940
or your state or your individual trail

1086
00:47:13.890 --> 00:47:16.500
to the entire region.

1087
00:47:16.500 --> 00:47:19.830
It's supposed to give both local

1088
00:47:19.830 --> 00:47:21.510
and regional context to it.

1089
00:47:21.510 --> 00:47:23.700
I think to answer your question,

1090
00:47:23.700 --> 00:47:25.361
yes, 1:1,000,

1091
00:47:25.361 --> 00:47:26.910
1 to I think 10,000 or 1,000, you said,

1092
00:47:26.910 --> 00:47:28.387
I think 10,000 you said

1093
00:47:28.387 --> 00:47:31.920
is appropriate for certain questions but not others.

1094
00:47:31.920 --> 00:47:33.450
And if you want to go into,

1095
00:47:33.450 --> 00:47:35.010
like feel free to reach out, we can work,

1096
00:47:35.010 --> 00:47:37.620
you know, and whatever questions you're asking

1097
00:47:37.620 --> 00:47:39.840
and we can talk about how appropriate they are.

1098
00:47:39.840 --> 00:47:41.610
But it really, really depends on what questions

1099
00:47:41.610 --> 00:47:43.413
you're using them for.

1100
00:47:44.400 --> 00:47:45.600
<v Aaron>Great, thanks.</v>

1101
00:47:46.890 --> 00:47:48.450
<v ->All right, thank you everybody.</v>

1102
00:47:48.450 --> 00:47:50.490
So we're gonna take just like a two-minute break,

1103
00:47:50.490 --> 00:47:52.500
we'll come back together at 12:50

1104
00:47:52.500 --> 00:47:54.030
for the second half of the presentation,

1105
00:47:54.030 --> 00:47:55.980
which is on field-based methods.

1106
00:47:55.980 --> 00:47:57.720
And I'm gonna, in the meantime,

1107
00:47:57.720 --> 00:47:59.100
in that couple of minute break,

1108
00:47:59.100 --> 00:48:00.960
I'm gonna share a few links

1109
00:48:00.960 --> 00:48:02.580
for things that Soren talked about,

1110
00:48:02.580 --> 00:48:04.080
so where you can download the data,

1111
00:48:04.080 --> 00:48:05.940
the link to the technical report, things like that.

1112
00:48:05.940 --> 00:48:07.390
So look to the chat for that.

1113
00:48:08.280 --> 00:48:09.570
And then we'll see-
<v ->Thanks, everybody.</v>

1114
00:48:09.570 --> 00:48:11.223
<v ->Folks back here at 12:50.</v>