WEBVTT

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<v Speaker 1>Welcome to Big for Society. If you have BIGFOD activity

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<v Speaker 1>to report from the same areas discussed in this episode,

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<v Speaker 1>please reach out to me directly after this episode. And

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<v Speaker 1>if you'd like to be on the podcast to discuss

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<v Speaker 1>a personal Bigfoot encounter, please reach out to me directly

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<v Speaker 1>at Bigfoot Society at gmail dot com. Do you wish

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<v Speaker 1>there was more Big for Society to listen to you

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<v Speaker 1>every week? Well there is now. If you become a

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<v Speaker 1>supporting member over at Patreon, you get a special members

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<v Speaker 1>only episode every single week on Wednesdays, and sometimes even

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<v Speaker 1>more episodes. Head on over to Patreon dot com. Forward

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<v Speaker 1>slash the Big for Society and now let's get on

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<v Speaker 1>with the show. At Bigfoot Society, get the privilege of

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<v Speaker 1>talking to Terrestrial today. She is an individual with a

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<v Speaker 1>Sasquatch data project that first was introduced to her work

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<v Speaker 1>through TikTok and it is very very interesting stuff, indeed

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<v Speaker 1>that I don't really hear a lot of people talk

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<v Speaker 1>about in the way that she is doing. So welcome

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<v Speaker 1>to the show, Terrestrial. How's it going.

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<v Speaker 2>Thank you, I'm really excited to be here. I'm doing good.

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<v Speaker 2>Hew are you?

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<v Speaker 1>Yeah? I'm doing great and just having a good Sunday afternoon,

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<v Speaker 1>hanging out getting to talk about Bigfoot. So I can't

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<v Speaker 1>complain about that. Let's start at the beginning. What was

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<v Speaker 1>it that first got you into Bigfoot? To begin with? Terrestrial?

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<v Speaker 2>Yeah, so I have kind of a funny story of

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<v Speaker 2>how this all unfolded. I was like five years old,

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<v Speaker 2>and it was kind of weird because I was home

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<v Speaker 2>from school that day, which never happens. My dad was

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<v Speaker 2>also home from work because he was prepping for a

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<v Speaker 2>colonoscopy the next day, so we were just like vegging

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<v Speaker 2>out on the couch while he was doing that, and

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<v Speaker 2>he's always been into like kind of like aliens and

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<v Speaker 2>ghost stuff. So we were watching Discovery Channel and Doug

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<v Speaker 2>Hicheck's Fastball to Legendnique Science came on the TV, and

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<v Speaker 2>that's where I saw the Patterson give One film for

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<v Speaker 2>the first time, and my brain just like exploded. I

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<v Speaker 2>was like, this is the craziest thing I've ever seen,

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<v Speaker 2>and kind of from there I was just hooked and

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<v Speaker 2>I've been looking into it ever since.

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<v Speaker 1>That's awesome. Yeah, Doug is such a cool guy to

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<v Speaker 1>talk to, and he's really a pioneer in the field.

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<v Speaker 1>I can't wait to see when his second documentary follow

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<v Speaker 1>up to that comes out, maybe later this year or

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<v Speaker 1>the year after. We'll see. I know they have a

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<v Speaker 1>little bit of work left to do, but yeah, we'll

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<v Speaker 1>all be waiting for that as well. So Terrestrial walk

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<v Speaker 1>me through what the Sasquatch Data project is.

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<v Speaker 2>Yeah, so, I guess in its essence, I'm trying to

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<v Speaker 2>put Bigfoot witness reports into a format that's optimized for

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<v Speaker 2>data analysis. So I'm essentially creating a giant spreadsheet that's

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<v Speaker 2>you can, you know, run whatever statistical analysis you want

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<v Speaker 2>to if you want to, you know, use it for

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<v Speaker 2>any kind of code you can. And it's also open

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<v Speaker 2>source too, so anyone can go and download it off

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<v Speaker 2>of my website at the time. Let's see, right now,

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<v Speaker 2>I'm about fifteen hundred reports deep into the BFRO website,

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<v Speaker 2>and I'm parsing all of those reports into about one

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<v Speaker 2>hundred and thirty six columns I think right now is

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<v Speaker 2>what I'm up to. And those columns are composed of

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<v Speaker 2>different traits to sasquatches, whether it's physical, environmental, behavioral, that

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<v Speaker 2>kind of thing. So in its essence, that's what it is.

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<v Speaker 2>But I'm also really interested in the mathematical side of things,

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<v Speaker 2>so like actually using the data set forward statistical analysis

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<v Speaker 2>and seeing what we can learn about sasquatches.

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<v Speaker 1>It's interesting. Do you have a background with data or math?

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<v Speaker 1>Then I do.

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<v Speaker 2>Yeah. So honestly, when when I was an undergraduate, I

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<v Speaker 2>kind of did like a PhD. As an undergrad, I

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<v Speaker 2>had a really great opportunity to work on NASA's down mission,

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<v Speaker 2>and I was very angry into the team, and I

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<v Speaker 2>got to like first author a paper, I got to

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<v Speaker 2>co author a number of papers regarding my research on

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<v Speaker 2>this dwarf planet and our solar system called Series so

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<v Speaker 2>and a lot of my research was having to maintain

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<v Speaker 2>and create like a very large data set and then

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<v Speaker 2>running statistical analysis on that data. So I feel like

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<v Speaker 2>I'm kind of pulling those skills that I got from

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<v Speaker 2>my research and you know, applying it to the sasquatch field.

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<v Speaker 1>That's fantastic. I would imagine that is probably not anything

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<v Speaker 1>you're involved with currently, though.

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<v Speaker 2>No, it's not.

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<v Speaker 1>No, gotcha, Now, what are some things that you have

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<v Speaker 1>come across when you start to jump into this data?

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<v Speaker 1>Have there been any things that have jumped out that

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<v Speaker 1>just have you know, gotten you really excited when you

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<v Speaker 1>start looking through it.

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<v Speaker 2>Yeah, I feel like every day I'm kind of thinking about,

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<v Speaker 2>Oh I could go look into this, Oh I should

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<v Speaker 2>go look into that. So I have a tendency to

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<v Speaker 2>kind of bop around into you know what I'm looking at.

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<v Speaker 2>You know. The question though that kind of got me

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<v Speaker 2>to actually start working on my data set with the

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<v Speaker 2>Sasquatch data project is you know, I heard someone say

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<v Speaker 2>once that sasquatches are more active under full moods, and

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<v Speaker 2>I was like, well, how do we know that, because

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<v Speaker 2>there's not data to back that up. It's just something

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<v Speaker 2>that was said. So that was actually my first kind

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<v Speaker 2>of analysis that I did with the data set, and

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<v Speaker 2>that was one of the most surprising so far, I think,

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<v Speaker 2>where I did find that there is an increase in

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<v Speaker 2>recorded sasquatch sightings under full moods, but also new moods.

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<v Speaker 2>So when the moon is zero percent illuminated, and that

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<v Speaker 2>difference in the reports during those times is statistically significant

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<v Speaker 2>compared to those reports for the middle values of middle illumination.

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<v Speaker 2>So that's really interesting because you go, well, why would

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<v Speaker 2>that be the case, And to me, I think it

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<v Speaker 2>points towards evidence of sasquatches partaking in this predator prey

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<v Speaker 2>relationship that's very documented. Essentially, nocturnal predators engage in this

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<v Speaker 2>under full moons. You know, prey have evolved to learn

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<v Speaker 2>that nocturnal predators can see better, therefore they decrease their activities. So,

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<v Speaker 2>you know, nocturnal predators will increase their activities because they're hunting,

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<v Speaker 2>but also they know that prayer are going to be

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<v Speaker 2>less active, so they're like staking out their territory and

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<v Speaker 2>that kind of thing. And then under new moons, it's

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<v Speaker 2>kind of the opposite where pray are more active because

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<v Speaker 2>they know that predators can't see as well. Therefore predators

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<v Speaker 2>are spending more time hunting, so their activities increase under

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<v Speaker 2>these new and full moon conditions. And to see that

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<v Speaker 2>in the data with sasquatches is just fascinating, honestly. Like

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<v Speaker 2>I know, correlation does not necessarily mean causation, but it

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<v Speaker 2>is really interesting to see the similar the similarities there.

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<v Speaker 1>That is fascinating. Have you had any researchers reach out

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<v Speaker 1>to you yet and been like, yeah, you're you're you're

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<v Speaker 1>on the ball with this, or or maybe you're a

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<v Speaker 1>little off with this.

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<v Speaker 2>Oh no, you know, no one has really said anything

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<v Speaker 2>about it yet, and I'm like hoping to get some feedback,

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<v Speaker 2>like I've been thinking about that a lot. Actually, I'm

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<v Speaker 2>like looking for feedback, like, you know what, to see

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<v Speaker 2>what people think about it. But no, no, no one's

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<v Speaker 2>really reached out to me yet.

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<v Speaker 1>Well, one of the reasons I'm having you on is

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<v Speaker 1>so hopefully you will get some feedback. Listeners might be like, yeah,

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<v Speaker 1>this isn't the normal bigfoot society show. Let's hear about

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<v Speaker 1>someone who sees bigfoot. Well, guys, I think this is

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<v Speaker 1>pretty important that we are aware of this research that

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<v Speaker 1>Terrestrial is doing. So this is kind of like a

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<v Speaker 1>one off show, So I think we all can can

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<v Speaker 1>learn a great deal from what she is doing in

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<v Speaker 1>her research. But have you been able to use the

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<v Speaker 1>data that you've been looking through to kind of get

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<v Speaker 1>a better picture of what sasquatch might normally look like?

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<v Speaker 2>Yes, I have. So lately I've been looking at the

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<v Speaker 2>heights of sasquatches. That's been something I'm interested in, particularly

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<v Speaker 2>if there are differences in the reported heights between regions.

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<v Speaker 2>So lately I've been comparing physical traits between the southern

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<v Speaker 2>and the western regions of the United States, and so

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<v Speaker 2>far I have found that there is not really a

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<v Speaker 2>difference between the heights that are reported, at least between

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<v Speaker 2>the South and the West. They came back not statistically

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<v Speaker 2>significant and very close in value. I think, like the

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<v Speaker 2>West was seven point two eight was the average height,

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<v Speaker 2>and then the South was seven point four feet. But

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<v Speaker 2>I'm just pulling those numbers out. It's somewhere around there.

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<v Speaker 2>But what I did find that was interesting is that

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<v Speaker 2>there are differences in the hair colors that are reported

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<v Speaker 2>between the southern and Western United States. Something that I

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<v Speaker 2>was interested in looking at particularly was I feel like

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<v Speaker 2>I hear a lot that there are more reports of

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<v Speaker 2>like red brown sasquatches in the South. So I looked

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<v Speaker 2>into that, and it turns out there are, and it

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<v Speaker 2>is a statistically significant difference between the Western United States.

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<v Speaker 2>I actually found that there is a meaningful difference between

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<v Speaker 2>the number of reports of red brown, white and gray

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<v Speaker 2>sasquatches in the South versus the West. So that's really

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<v Speaker 2>interesting because I think the thing is too, especially with

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<v Speaker 2>the statistical significance testing, is it essentially tells us, you know,

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<v Speaker 2>is this difference that we're seeing in the data meaningful

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<v Speaker 2>or could it have happened due to just random chants,

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<v Speaker 2>Like that's just how the data decided to fall. And

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<v Speaker 2>when you do find statistical significance, it's essentially telling you, Okay,

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<v Speaker 2>something is going on to cause this to happen. What

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<v Speaker 2>that is is not clear right now, but you know,

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<v Speaker 2>the chances of this just randomly occurring are very small, like,

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<v Speaker 2>you know, less than five percent. So those those are

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<v Speaker 2>two things that I've been looking at recently that I

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<v Speaker 2>think are pretty interesting.

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<v Speaker 1>Have you been able to find a baseline or maybe

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<v Speaker 1>information about normal behavior of the sasquatch due to all

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<v Speaker 1>these reports that you've been looking at.

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<v Speaker 2>Yeah, so that's a really interesting question because I would

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<v Speaker 2>say the majority of the reports that I go through

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<v Speaker 2>are extremely short. So typically the witness, either the witnesses

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<v Speaker 2>the sasquatch or the sasquatch sees the witness first and

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<v Speaker 2>then they make eye contact or they just you know,

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<v Speaker 2>become aware of each other, and then one of them

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<v Speaker 2>leaves the encounter. Typically it's not like a prolonged encounter

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<v Speaker 2>where the witness is actually watching them maybe like eat

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<v Speaker 2>or drink or I don't know, just exist in the

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<v Speaker 2>environment do whatever they do. So I haven't pulled stats

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<v Speaker 2>on like who's leaving the encounter first, is it the

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<v Speaker 2>witness or the sasquatch, But I am keeping up with

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<v Speaker 2>it in the spreadsheets, so that would be it, you know,

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<v Speaker 2>that would tell us something about their behaviors. I do

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<v Speaker 2>know that intimidation encounters or like aggressive encounters are not

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<v Speaker 2>that at least they're not very commonly reported. Currently in

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<v Speaker 2>the data set, about five percent of reports involve like

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<v Speaker 2>aggressive behaviors from sasquatches. So right now, it's you know,

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<v Speaker 2>I can't make any certain you know, I can't like,

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<v Speaker 2>for say, or for certain say that there's like a

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<v Speaker 2>certain way they should behave because the encounters are typically

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<v Speaker 2>very short, like less than a minute.

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<v Speaker 1>But yeah, that's interesting. In the community, there's seems to

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<v Speaker 1>be these different things that are always passed around verbally

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<v Speaker 1>as kind of like community knowledge, and it's very cool

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<v Speaker 1>to see some of these things maybe be proven or

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<v Speaker 1>disproven due to analysis of data. Have you found after

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<v Speaker 1>looking through your data that a class A sightings happen

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<v Speaker 1>more towards certain times of year.

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<v Speaker 2>So this is the interesting thing. And I want to

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<v Speaker 2>touch on what you just said a little bit because

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<v Speaker 2>That's also part of the reason that I'm working on

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<v Speaker 2>the Sasquatch Data project is because I feel like there

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<v Speaker 2>is a lot of you know, there are a lot

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<v Speaker 2>of ideas in the community, but there's not a lot

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<v Speaker 2>of data to back up the ideas. So part of

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<v Speaker 2>what I'm trying to do is actually take these ideas

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<v Speaker 2>that are passed around and say, well, you know, based

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<v Speaker 2>on at least this subset of reports, that doesn't seem

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<v Speaker 2>to be true, or this does seem to be true.

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<v Speaker 2>So yeah, that's definitely a big component of what I'm

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<v Speaker 2>trying to do as well. I'm sorry, I got off

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<v Speaker 2>on a different train of thought.

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<v Speaker 1>Yeah, that happens to be a lot too. Have you

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<v Speaker 1>found that Class A sightings happen more towards a certain

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<v Speaker 1>time of year after analyzing the data from the sighting reports.

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<v Speaker 2>Yeah, so, at least how they are reported, Class A

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<v Speaker 2>sightings do tend to cluster among certain months. I can

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<v Speaker 2>actually pull up at least my most recent data because

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<v Speaker 2>I just looked at this, so it looks like, yeah,

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<v Speaker 2>so for Class A sightings. Actually I didn't do it

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<v Speaker 2>by class, so I can't quite tell you, but at

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<v Speaker 2>least for sightings in general not designated by Class A

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<v Speaker 2>or B. Right now, July has the most, followed by October,

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<v Speaker 2>which is interesting. So they definitely and reports do cluster

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<v Speaker 2>around the summer and fall months in general. Whether that's

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<v Speaker 2>more so due to human activity or sasquatch activity, it's

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<v Speaker 2>unclear at this point, but I do know that the

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<v Speaker 2>majority of class acieties do happen during like the summer

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<v Speaker 2>and the fall. And an idea that I've had that

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<v Speaker 2>we can kind of, you know, that I've been thinking

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<v Speaker 2>about at least for a while, is potentially looking at

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<v Speaker 2>like the National Parks publish the amount of permits that

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<v Speaker 2>they give out every year, particularly to or for like

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<v Speaker 2>backpacking permits. So I'm like, well, if we could look

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<v Speaker 2>at that and see when people are at least going

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<v Speaker 2>backpacking or like into these more remote parts of the

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<v Speaker 2>parks and stuff. I wonder if we could somehow use

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<v Speaker 2>that data to determine if you know, more people are

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<v Speaker 2>going out into these more remote parts of the continent

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<v Speaker 2>during these months, and maybe that's why we're seeing this increase,

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<v Speaker 2>or is it more of a sasquatch thing, like they're

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<v Speaker 2>more active during these times of year. I haven't I

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<v Speaker 2>haven't quite figured out how to make that jump.

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<v Speaker 1>Yet big, so society will be right back after these messages.

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<v Speaker 2>Yeah, at least for reports. There are more during the

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<v Speaker 2>summer in the fall.

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<v Speaker 1>That was really interesting because you know, if you go

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<v Speaker 1>by what you hear, or at least what I hear

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<v Speaker 1>from the community, it's like you gotta go out in October,

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<v Speaker 1>like October is when crazy stuff happens, but it's like

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<v Speaker 1>July wouldn't really wouldn't really think so? I mean, or

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<v Speaker 1>maybe there's some listeners that are like, well, maybe you

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<v Speaker 1>should really think so you're not talking to the right people.

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<v Speaker 1>But now that's that's extremely interesting. Is there data that

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<v Speaker 1>you wish you had from these encounter reports that you're reading.

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<v Speaker 2>Yes, a lot of the time, like I, like I said,

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<v Speaker 2>with my data set, I have over one hundred and

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<v Speaker 2>thirty five different traits or different you know, variables that

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<v Speaker 2>I'm looking at, and most of the time, most of

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<v Speaker 2>the columns remain blink because either the witness didn't offer

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<v Speaker 2>up the information which is you know, totally valid, like

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<v Speaker 2>they've had an encounter that a lot of them don't

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<v Speaker 2>know how to process it, so they don't even know

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<v Speaker 2>like what the researcher would be interested in knowing, but

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<v Speaker 2>then the follow up from the researcher, you know, I

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<v Speaker 2>feel like there's missed opportunities there to extract more information,

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<v Speaker 2>especially from these Class A close range sightings where the

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<v Speaker 2>witness got a really good look at the sasquatch, particularly

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<v Speaker 2>particularly like facial features like eye size, eye color, nose shape,

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<v Speaker 2>different like cranial structure features, or in some cases, like

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<v Speaker 2>the reports don't even include like a hair color from

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<v Speaker 2>Class A sightings, which confuses me. So a lot of

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<v Speaker 2>the time there is there are times when I'm like

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<v Speaker 2>wishing I had more information, especially latitude longitude information for

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<v Speaker 2>where the sighting happened. There's this thing you can do

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<v Speaker 2>called maximum entropy, where essentially you can by using like

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<v Speaker 2>latitude longitude locations of where an animal was that you

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<v Speaker 2>can essentially stack that with different environmental layers I guess,

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<v Speaker 2>and basically you run it through an algorithm and it'll

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<v Speaker 2>tell you where you're likely to see that animal based

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<v Speaker 2>on the features surrounding that location of where it was seen.

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<v Speaker 2>So like, that's something that I'm really interested in doing too,

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<v Speaker 2>but I don't have enough latitude longitude data to actually

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<v Speaker 2>do that because you need quite a few points based

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<v Speaker 2>on the region that you're looking at. So that way

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<v Speaker 2>we could actually like predict where, you know, you might

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<v Speaker 2>have an encounter with a sasquatch, where they might be

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<v Speaker 2>based on other sightings. So yeah, I'm I'm always looking

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<v Speaker 2>for more information. I love getting into the details of things.

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<v Speaker 2>But I would say, like the physical traits and the

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<v Speaker 2>latitude longitude data, I'm always looking.

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<v Speaker 1>For that seems like a really big deal. So I

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<v Speaker 1>want to make sure that I get that correct. So

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<v Speaker 1>let's say someday you get enough of that data, you

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<v Speaker 1>could have the computer program make a map where it's like,

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<v Speaker 1>instead of us just going off of oh, well, this

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<v Speaker 1>person tells me, yeah, you should check out this state

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<v Speaker 1>park or National Force, you could have a map where

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<v Speaker 1>it would like show you really good ideas of where

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<v Speaker 1>to go and check out. Is that correct essentially?

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<v Speaker 2>Yeah, So biologists use this to predict where other species

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<v Speaker 2>might be, like what they're interested in a particular species.

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<v Speaker 2>And the thing about it is, though, you have to

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<v Speaker 2>be pretty because you know, the capacity of what computers

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<v Speaker 2>can run is only so much, and also you don't

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<v Speaker 2>want to make it too broad because then it will

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<v Speaker 2>kind of overestimate an area. So the Yeah, the problem

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<v Speaker 2>is getting enough reports from a pretty small area, even

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<v Speaker 2>like statewide is a little too large, you can't do it.

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<v Speaker 2>But you have to have a high enough density of

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<v Speaker 2>sightings to be able to properly run the algorithm basically.

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<v Speaker 2>So yeah, so it could be. It potentially could be

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<v Speaker 2>a very powerful tool. It's just you know, having enough

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<v Speaker 2>data to do it.

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<v Speaker 1>Powerful in the right or wrong hands, for sure.

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<v Speaker 2>Yeah.

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<v Speaker 1>Interesting, Approximately how close are you to being able to

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<v Speaker 1>run a program like that?

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<v Speaker 2>I started working on it actually last month. I had

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<v Speaker 2>this idea of like, oh, we could probably do that, Like,

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<v Speaker 2>you know, let me see if I have enough data

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<v Speaker 2>points for a latitude longitude. I am pretty far away,

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<v Speaker 2>at least from my reports that I'm getting off of

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<v Speaker 2>the BFRO because I am going through reports by hand.

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<v Speaker 2>It's taken me a while to get through everything, and

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<v Speaker 2>I'm about fifteen hundred reports deep. I've got about five

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<v Speaker 2>thousand more to go, and that's just one data set

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<v Speaker 2>of reports. So it really just depends on the state

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<v Speaker 2>and the area of how many data points would be enough. Yeah,

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<v Speaker 2>it really just depends. I mean, the more data the better, right,

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<v Speaker 2>So as much latitude and longitude data I can get.

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<v Speaker 2>The better the but the better the program will essentially

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<v Speaker 2>predict I definitely don't have enough right now.

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<v Speaker 1>Man, if you can get access to the flats, that

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00:20:46.160 --> 00:20:50.920
<v Speaker 1>would be awesome. That is allegedly the name of the

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<v Speaker 1>back door database for the BFRO, and I'm sure there's

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<v Speaker 1>way more information on there. That's just me hypothesizer because

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<v Speaker 1>I'm not in there. But so you have to manually

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<v Speaker 1>go through all these reports to pretty much see if

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<v Speaker 1>they have a latitude and longitude in them, right.

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<v Speaker 2>Yeah, So there's a couple of things, and this is

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<v Speaker 2>kind of what I've run into, you know, extracting any

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<v Speaker 2>information from the reports. Is the witness there. There aren't

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<v Speaker 2>always a latitude longitudes just blatantly on the report. Sometimes

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<v Speaker 2>the researcher will give us it'll say like GPS coordinates

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<v Speaker 2>or you know whatever, or the witness will actually provide

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<v Speaker 2>a lot of tude longitude, but that's not very common.

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<v Speaker 2>A lot of the times the witness will give an

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<v Speaker 2>extremely detailed location, be you there, and then walk twenty

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<v Speaker 2>more feet, and that's where this happens. So I'll go

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<v Speaker 2>on Google Maps.

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<v Speaker 1>Sorry, I get a lot of weird stuff happening in

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<v Speaker 1>my phone calls. Do you mind repeating that just a

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<v Speaker 1>little bit your your phone went out entirely?

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<v Speaker 2>Oh sorry, Yeah. So with the reports on the BFUR website,

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<v Speaker 2>they do not always give a latitude longitude. Sometimes they do.

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<v Speaker 2>Sometimes either the witness or the researcher who follows up

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<v Speaker 2>will give a latitude longitude. But most of the time

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<v Speaker 2>it's the witness giving like a really detailed description of

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<v Speaker 2>where their encounter happened. So they'll say, like, you know,

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<v Speaker 2>turn onto this road, go to this mile marker, and

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<v Speaker 2>then like drive twenty more feet and that's where the

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<v Speaker 2>encounter happened. So I'll go on like Google Maps or something,

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<v Speaker 2>and I will go down the street view and find

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<v Speaker 2>the spot and then extract the latitude longitude that way.

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<v Speaker 2>So like some people have suggested, like you know, webscraping

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<v Speaker 2>or something to extract the latitude longitude. But honestly, most

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<v Speaker 2>of the time I'm having to go and manually find it,

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<v Speaker 2>like in Google Maps or Google Earth or whatever.

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<v Speaker 1>Man, it feels like there should be some other I

383
00:22:59.079 --> 00:23:01.200
<v Speaker 1>get it, though. I mean, like sometimes you just have

384
00:23:01.279 --> 00:23:04.319
<v Speaker 1>to put in the work. But that's a lot of work.

385
00:23:05.240 --> 00:23:08.839
<v Speaker 2>Wow, It is, but it's worth it. Like it's really fun.

386
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<v Speaker 2>I like it. It's a challenge, but yeah, I'm like,

387
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<v Speaker 2>if I could, just if I could have more more data, have.

388
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<v Speaker 1>You found any you know, well, I would imagine you're

389
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<v Speaker 1>looking at a lot of these places manually through Google Maps.

390
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<v Speaker 1>Have you noticed any similarities of the places that are

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<v Speaker 1>starting to come out or any commonalities?

392
00:23:32.400 --> 00:23:36.839
<v Speaker 2>You know? This is something that is pretty interesting because

393
00:23:36.920 --> 00:23:42.839
<v Speaker 2>I haven't really noticed any any kind of clustering or

394
00:23:42.920 --> 00:23:46.799
<v Speaker 2>anything of like environmental traits. But to be fair, I

395
00:23:46.839 --> 00:23:49.119
<v Speaker 2>haven't really looked into that too much. I've been more

396
00:23:49.160 --> 00:23:52.920
<v Speaker 2>focused on like the physical traits of stasquatches. You know,

397
00:23:53.000 --> 00:23:56.960
<v Speaker 2>the majority of reports do happen in more rural areas,

398
00:23:57.559 --> 00:24:02.079
<v Speaker 2>and quite a few happen at private, you know, residences,

399
00:24:02.160 --> 00:24:06.079
<v Speaker 2>people's houses. There's quite a few road crossings, and those

400
00:24:06.119 --> 00:24:08.920
<v Speaker 2>typically happen on more rural roads, though you do have

401
00:24:09.039 --> 00:24:11.440
<v Speaker 2>like the highway every once in a while. It's like,

402
00:24:11.839 --> 00:24:13.640
<v Speaker 2>you know, you go on Google Earth and there's plenty

403
00:24:13.640 --> 00:24:16.640
<v Speaker 2>of cars traveling on this highway. It's not like an

404
00:24:16.680 --> 00:24:20.119
<v Speaker 2>obscure road or anything. But I haven't really looked into

405
00:24:20.279 --> 00:24:25.400
<v Speaker 2>the more environmental aspect of or locational aspects of the

406
00:24:25.519 --> 00:24:26.440
<v Speaker 2>reports too much.

407
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<v Speaker 1>Absolutely. You know, Let's say someday you are able to

408
00:24:31.480 --> 00:24:36.400
<v Speaker 1>get you know, we'll call it like this ultimate Bigfoot Algorithm,

409
00:24:36.440 --> 00:24:38.960
<v Speaker 1>where you can run this program and you can see, like,

410
00:24:39.039 --> 00:24:42.519
<v Speaker 1>go show me the areas and in Washington State where

411
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<v Speaker 1>it's the best idea for me to look for bigfoot.

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00:24:45.720 --> 00:24:49.599
<v Speaker 1>What is your hope that that would be used for

413
00:24:50.039 --> 00:24:50.480
<v Speaker 1>the good.

414
00:24:51.160 --> 00:24:54.440
<v Speaker 2>Yeah, that's a really great question because I've thought about this,

415
00:24:54.920 --> 00:24:59.400
<v Speaker 2>and you know that the maccent modeling is not you know,

416
00:24:59.680 --> 00:25:02.839
<v Speaker 2>one hundred percent going to be correct. It's essentially looking

417
00:25:02.960 --> 00:25:07.240
<v Speaker 2>for features either in the topography or in the vegetation

418
00:25:07.759 --> 00:25:12.359
<v Speaker 2>or proximity to major roadways and things like that. Like,

419
00:25:12.400 --> 00:25:16.079
<v Speaker 2>it's looking for features that aren't going to be super

420
00:25:16.119 --> 00:25:19.200
<v Speaker 2>apparent to humans. So it's not going to be like

421
00:25:20.039 --> 00:25:23.839
<v Speaker 2>spot on every time for sure. It's more of a

422
00:25:23.880 --> 00:25:26.640
<v Speaker 2>tool to at least get an idea of Okay, well,

423
00:25:26.720 --> 00:25:30.200
<v Speaker 2>this area has a lot of the same features of

424
00:25:30.240 --> 00:25:34.240
<v Speaker 2>where you know these sightings occurred. So I do want

425
00:25:34.279 --> 00:25:36.359
<v Speaker 2>to make that like clear that it's not going to

426
00:25:36.480 --> 00:25:39.160
<v Speaker 2>tell us exactly where they are. At least, you know,

427
00:25:39.319 --> 00:25:42.480
<v Speaker 2>help us choose better spots to go research and I

428
00:25:42.480 --> 00:25:44.759
<v Speaker 2>would hope that it's used for research. I would hope that,

429
00:25:45.119 --> 00:25:49.440
<v Speaker 2>you know, it's purely used for research. And you know,

430
00:25:49.599 --> 00:25:55.079
<v Speaker 2>I'm I hope for the best for the sasquatch species.

431
00:25:55.119 --> 00:25:58.720
<v Speaker 2>I guess, like I definitely don't want to help people

432
00:25:58.799 --> 00:26:02.119
<v Speaker 2>like hunt them or something. I'm definitely like wanting to

433
00:26:02.200 --> 00:26:04.319
<v Speaker 2>conserve the species. So I do hope that it would

434
00:26:04.319 --> 00:26:07.279
<v Speaker 2>be used for purely research. ADS.

435
00:26:07.799 --> 00:26:11.759
<v Speaker 1>Yeah, no, I definitely agree with you. I mean, at

436
00:26:11.799 --> 00:26:16.519
<v Speaker 1>the end of the day, if there is species of

437
00:26:17.039 --> 00:26:19.880
<v Speaker 1>sasquatch or bigfoot in the United States, which I think

438
00:26:19.920 --> 00:26:23.440
<v Speaker 1>we both believe that one of these days there's going

439
00:26:23.519 --> 00:26:26.440
<v Speaker 1>to be a whole lot of information put out there

440
00:26:27.119 --> 00:26:29.559
<v Speaker 1>and the right people, which should be all of us,

441
00:26:29.720 --> 00:26:33.400
<v Speaker 1>need to stand up really quick and start probably a

442
00:26:33.440 --> 00:26:38.079
<v Speaker 1>conservation effort. We've had that chat a few times on

443
00:26:38.119 --> 00:26:40.599
<v Speaker 1>the show in the past, and it's an interesting one.

444
00:26:40.640 --> 00:26:45.680
<v Speaker 1>How do you start a conservation effort before creatures actually

445
00:26:45.720 --> 00:26:50.039
<v Speaker 1>even discovered. Yeah, it's an interesting conversation to have. Are

446
00:26:50.079 --> 00:26:55.920
<v Speaker 1>there any other ideas that the community might throw around

447
00:26:56.319 --> 00:27:00.119
<v Speaker 1>that you are trying to I wouldn't say challenge, but

448
00:27:00.279 --> 00:27:02.839
<v Speaker 1>look into if they're actually valid with your data.

449
00:27:03.519 --> 00:27:10.200
<v Speaker 2>Yeah, So, currently I've been looking into and I, at

450
00:27:10.279 --> 00:27:13.079
<v Speaker 2>least on TikTok, I get a lot of pushback on

451
00:27:13.119 --> 00:27:16.880
<v Speaker 2>this one, but I'm really interested in learning more about

452
00:27:17.079 --> 00:27:23.759
<v Speaker 2>the actual heights of sasquatches because just based on the data,

453
00:27:23.880 --> 00:27:27.279
<v Speaker 2>I'm not totally convinced, and a lot of like my

454
00:27:27.440 --> 00:27:31.319
<v Speaker 2>own personal thoughts and beliefs are purely based off what

455
00:27:31.359 --> 00:27:34.119
<v Speaker 2>I'm seeing in the data. You know, I'm not totally

456
00:27:34.160 --> 00:27:38.480
<v Speaker 2>convinced that they are truly reaching these really extreme heights.

457
00:27:38.480 --> 00:27:41.519
<v Speaker 2>So it's like ten plus feet even like the really

458
00:27:42.519 --> 00:27:45.799
<v Speaker 2>you know high nine point eight feet and stuff like

459
00:27:46.079 --> 00:27:50.559
<v Speaker 2>these really extreme heights, I'm not totally sure if that's,

460
00:27:51.319 --> 00:27:54.680
<v Speaker 2>you know, if that's a product of the witnesses fear.

461
00:27:55.000 --> 00:27:57.960
<v Speaker 2>There's this thing that happens when you're scared where you

462
00:27:58.400 --> 00:28:01.480
<v Speaker 2>perceive the stimulus as much as larger, sometimes up to

463
00:28:01.519 --> 00:28:04.480
<v Speaker 2>thirty percent larger than what it actually is. It's a

464
00:28:04.519 --> 00:28:07.599
<v Speaker 2>pretty documented phenomenon. It has a lot of different names.

465
00:28:07.720 --> 00:28:10.880
<v Speaker 2>I call it fear driven magnification. It's just one of them.

466
00:28:11.240 --> 00:28:13.799
<v Speaker 2>But yeah, basically, when you're scared, you perceive the stimulus

467
00:28:13.839 --> 00:28:17.119
<v Speaker 2>as either or anywhere between like seventeen to thirty percent

468
00:28:17.240 --> 00:28:20.000
<v Speaker 2>larger than what it actually is. And so in my

469
00:28:20.200 --> 00:28:23.160
<v Speaker 2>latest investigation that I've done, an analysis that I've done,

470
00:28:23.440 --> 00:28:29.640
<v Speaker 2>I found that as witness fear levels increase, so does

471
00:28:29.680 --> 00:28:33.279
<v Speaker 2>the average height of the reported sasquatch. And this increase

472
00:28:33.359 --> 00:28:37.640
<v Speaker 2>is statistically significant between like the elevated and extreme witness

473
00:28:37.680 --> 00:28:42.480
<v Speaker 2>fear groups versus the mild fear group, so often something

474
00:28:42.480 --> 00:28:46.000
<v Speaker 2>I've been pretty interested in. I also found that while

475
00:28:46.279 --> 00:28:50.400
<v Speaker 2>every wittness fear group has these reports of like ten

476
00:28:50.400 --> 00:28:54.839
<v Speaker 2>plus foot tall sasquatches, seventy seven percent of those reports

477
00:28:55.920 --> 00:29:00.640
<v Speaker 2>fall into these elevated and extreme fear groups. And you

478
00:29:00.680 --> 00:29:02.480
<v Speaker 2>can kind of look at that two ways. You can

479
00:29:02.519 --> 00:29:05.000
<v Speaker 2>either look at that as well it could be evidence

480
00:29:05.119 --> 00:29:08.960
<v Speaker 2>of this, you know, fear driven magnification, or you can

481
00:29:09.000 --> 00:29:11.720
<v Speaker 2>look at it as you know, people are going to

482
00:29:11.799 --> 00:29:15.079
<v Speaker 2>be more scared of a ten foot tall sasquatch versus

483
00:29:15.119 --> 00:29:18.039
<v Speaker 2>a seven foot tall sasquatch. But in my mind, we

484
00:29:18.119 --> 00:29:20.920
<v Speaker 2>know this happens. This is like a thing, it's both

485
00:29:21.039 --> 00:29:23.519
<v Speaker 2>mental and a physical thing that happens in your brain.

486
00:29:23.559 --> 00:29:27.400
<v Speaker 2>You're amygdala basically lights up, and that's the structure in

487
00:29:27.440 --> 00:29:32.480
<v Speaker 2>your brain that processes emotion, particularly fear, and then it

488
00:29:32.519 --> 00:29:36.160
<v Speaker 2>does send feedback back to visual courtesies that you know,

489
00:29:36.720 --> 00:29:41.799
<v Speaker 2>then influence further perceptual processing. So like, we're going to

490
00:29:41.839 --> 00:29:46.160
<v Speaker 2>see this happen at some point in these witness reports,

491
00:29:46.480 --> 00:29:49.640
<v Speaker 2>because in you know, most of these reports, the witnesses

492
00:29:49.640 --> 00:29:52.359
<v Speaker 2>are really freaked out. I mean, they're having a potentially

493
00:29:52.440 --> 00:29:54.880
<v Speaker 2>life altering encounter. They're very scared.

494
00:29:55.720 --> 00:29:58.480
<v Speaker 1>Big for society will be right back after these messages.

495
00:30:14.640 --> 00:30:16.920
<v Speaker 2>So I think that's something we really need to keep

496
00:30:16.920 --> 00:30:20.640
<v Speaker 2>in mind when we're thinking about the size of sasquatches

497
00:30:21.319 --> 00:30:25.119
<v Speaker 2>is keeping in mind these psychological things that do happen

498
00:30:25.160 --> 00:30:28.160
<v Speaker 2>when you were scared. And that's been one of my

499
00:30:28.839 --> 00:30:31.720
<v Speaker 2>recent things that I've been looking into and thinking about

500
00:30:31.759 --> 00:30:32.119
<v Speaker 2>a lot.

501
00:30:32.640 --> 00:30:35.920
<v Speaker 1>That's extremely interesting. I'm sure this conversation would be totally

502
00:30:35.920 --> 00:30:39.559
<v Speaker 1>different if I had actually had a visual sighting prior

503
00:30:39.640 --> 00:30:44.759
<v Speaker 1>to this interview, which I haven't. How do you figure

504
00:30:44.839 --> 00:30:50.200
<v Speaker 1>out that a report falls under being really scared or

505
00:30:50.240 --> 00:30:52.279
<v Speaker 1>not so scared or extremely scared?

506
00:30:52.319 --> 00:30:52.359
<v Speaker 2>Like?

507
00:30:52.400 --> 00:30:55.720
<v Speaker 1>How do you make that back and forth in every report?

508
00:30:55.759 --> 00:31:00.599
<v Speaker 2>I can't always determine the fear level isn't always explicitly stated,

509
00:31:00.920 --> 00:31:03.240
<v Speaker 2>So in those cases I just put like a a

510
00:31:03.480 --> 00:31:08.559
<v Speaker 2>like that's my placeholder not applicable. So I basically created

511
00:31:08.640 --> 00:31:11.440
<v Speaker 2>five different fear groups. There is the no fear group,

512
00:31:11.599 --> 00:31:15.039
<v Speaker 2>where the witness explicitly states they were not scared during

513
00:31:15.079 --> 00:31:18.839
<v Speaker 2>the encounter at all. There's mild, where you know, the

514
00:31:18.880 --> 00:31:21.640
<v Speaker 2>witness was basically like, I was a little freaked out,

515
00:31:21.680 --> 00:31:26.240
<v Speaker 2>but really it wasn't, you know, anything crazy. Moderate the

516
00:31:26.279 --> 00:31:28.799
<v Speaker 2>witness may or may not have some kind of physical

517
00:31:28.839 --> 00:31:32.839
<v Speaker 2>reaction the hair stands up on their skin, or you know,

518
00:31:32.880 --> 00:31:38.119
<v Speaker 2>they just basically say I was scared. That's what I designated. Moderate.

519
00:31:38.200 --> 00:31:43.920
<v Speaker 2>As elevated is the witness expressing, you know, more elevated fear.

520
00:31:44.119 --> 00:31:47.559
<v Speaker 2>Maybe they start sweating, they start to get goose bumps,

521
00:31:47.880 --> 00:31:51.319
<v Speaker 2>they say they were very scared, like even just as

522
00:31:51.319 --> 00:31:54.559
<v Speaker 2>simple as that. And then extreme is when the witness

523
00:31:54.680 --> 00:31:58.079
<v Speaker 2>explicitly states like I'm never going into the woods again,

524
00:31:58.240 --> 00:32:00.359
<v Speaker 2>I actually just read a report this morning, Or this

525
00:32:00.400 --> 00:32:05.160
<v Speaker 2>woman will never drive after dark anymore because her you know,

526
00:32:05.240 --> 00:32:08.079
<v Speaker 2>experience was so terrifying, or they say like I was

527
00:32:08.240 --> 00:32:11.079
<v Speaker 2>scared to death. I was terrified. I've never been that

528
00:32:11.119 --> 00:32:13.680
<v Speaker 2>scared before in my life. I would say the majority

529
00:32:13.799 --> 00:32:17.799
<v Speaker 2>of reports are in that moderate and elevated fear group.

530
00:32:18.240 --> 00:32:20.279
<v Speaker 2>But yeah, I basically just have come up with like

531
00:32:20.319 --> 00:32:24.160
<v Speaker 2>a classification classifications for the different groups.

532
00:32:24.680 --> 00:32:26.880
<v Speaker 1>It's extremely interesting how you had to do that, and

533
00:32:27.279 --> 00:32:32.279
<v Speaker 1>it's it's pretty cool too. There's a left field question,

534
00:32:32.759 --> 00:32:35.720
<v Speaker 1>just because I think this is kind of interesting how

535
00:32:35.720 --> 00:32:39.160
<v Speaker 1>it comes up in some reports that I have that

536
00:32:39.200 --> 00:32:42.920
<v Speaker 1>I've received over the years. Do you find that a

537
00:32:42.960 --> 00:32:47.000
<v Speaker 1>lot of the reports that you're analyzing talk about orangutan

538
00:32:47.319 --> 00:32:48.799
<v Speaker 1>features or anything like that.

539
00:32:49.240 --> 00:32:51.920
<v Speaker 2>Yes, I don't know about a lot, but it has

540
00:32:51.960 --> 00:32:55.640
<v Speaker 2>been brought up, like, it does stand out that people

541
00:32:56.279 --> 00:33:01.160
<v Speaker 2>sometimes do describe especially the hair, especially the hair on

542
00:33:01.200 --> 00:33:07.039
<v Speaker 2>the arms as orangutan like. I would say that I've

543
00:33:07.079 --> 00:33:10.920
<v Speaker 2>read more reports where they describe the facial features as

544
00:33:11.440 --> 00:33:15.000
<v Speaker 2>more gorilla like if they are going to attach it

545
00:33:15.039 --> 00:33:17.480
<v Speaker 2>to one of the great ape species, if it's you know,

546
00:33:17.559 --> 00:33:20.720
<v Speaker 2>if it doesn't resemble a human, I typically see gorilla

547
00:33:20.799 --> 00:33:24.359
<v Speaker 2>more or the body stature more gorilla like. But on

548
00:33:24.440 --> 00:33:27.480
<v Speaker 2>occasion I do get or I have read those reports

549
00:33:27.519 --> 00:33:30.559
<v Speaker 2>where they say it was more like an orangutan, And

550
00:33:30.759 --> 00:33:32.759
<v Speaker 2>it would be interesting to see if that's like a

551
00:33:32.799 --> 00:33:36.480
<v Speaker 2>regional thing, if that seems to be clustered in particular

552
00:33:36.880 --> 00:33:38.799
<v Speaker 2>regions or states exactly.

553
00:33:39.000 --> 00:33:42.000
<v Speaker 1>Yeah, that's that. I was just thinking that as well.

554
00:33:42.079 --> 00:33:44.720
<v Speaker 1>I mean, this is an off to the side, but man,

555
00:33:44.759 --> 00:33:46.960
<v Speaker 1>I just love going to the Omahazu I'm on in

556
00:33:47.000 --> 00:33:50.680
<v Speaker 1>the Midwest and like there's a really great orangutan exhibit

557
00:33:50.720 --> 00:33:54.920
<v Speaker 1>there and just watching them. They're so smart you watching them,

558
00:33:55.000 --> 00:33:58.480
<v Speaker 1>and like imagine, man, can you imagine being the specific

559
00:33:58.599 --> 00:34:01.160
<v Speaker 1>Northwest and you see something that kind of looks like

560
00:34:01.240 --> 00:34:03.440
<v Speaker 1>that but it's a lot bigger. That would just be

561
00:34:03.599 --> 00:34:09.519
<v Speaker 1>like mind blowing for sure. In your analyzing of all

562
00:34:09.559 --> 00:34:14.119
<v Speaker 1>this data, does anything ever come up with like track size?

563
00:34:14.679 --> 00:34:20.199
<v Speaker 2>Yes, yeah, I've analyzed quite a few footprint reports. I

564
00:34:20.239 --> 00:34:23.119
<v Speaker 2>think at this point I'm at a round one hundred

565
00:34:23.199 --> 00:34:29.960
<v Speaker 2>and twin reports that I've analyzed that are footprint reports specifically. Now,

566
00:34:30.000 --> 00:34:35.119
<v Speaker 2>I will say that most of the reports that have

567
00:34:35.320 --> 00:34:38.159
<v Speaker 2>a footprint size like a length of ball with heel

568
00:34:38.199 --> 00:34:42.599
<v Speaker 2>with a depth the witness, it's not clear if the

569
00:34:42.639 --> 00:34:47.519
<v Speaker 2>witness actually measured the footprint with like a ruler or

570
00:34:47.960 --> 00:34:52.000
<v Speaker 2>anything like that. Typically what I see is they have

571
00:34:52.159 --> 00:34:55.280
<v Speaker 2>like either a picture of their boot next to the

572
00:34:55.320 --> 00:34:59.840
<v Speaker 2>footprint or they get like a general size estimate. So

573
00:35:00.000 --> 00:35:02.480
<v Speaker 2>you have to be really careful with how I present

574
00:35:02.559 --> 00:35:06.400
<v Speaker 2>the footprint data because it's not always Usually it's like

575
00:35:06.480 --> 00:35:10.800
<v Speaker 2>an estimate or like they're going purely based off of

576
00:35:10.920 --> 00:35:14.559
<v Speaker 2>their shoe size, which is not always super accurate either,

577
00:35:15.039 --> 00:35:17.639
<v Speaker 2>But I do. I have gone through about I think

578
00:35:17.639 --> 00:35:20.239
<v Speaker 2>about one hundred and twenty footprint reports so far.

579
00:35:20.880 --> 00:35:25.880
<v Speaker 1>That is really cool. You're doing everything by hand or

580
00:35:26.199 --> 00:35:31.000
<v Speaker 1>you know, just by yourself. Have you ever considered using

581
00:35:31.119 --> 00:35:35.679
<v Speaker 1>AI at all to analyze huge amounts of data or

582
00:35:35.760 --> 00:35:39.800
<v Speaker 1>is there maybe a certain way you feel about using that.

583
00:35:40.400 --> 00:35:43.840
<v Speaker 2>I use AI all the time, so yeah, when I'm

584
00:35:43.880 --> 00:35:48.599
<v Speaker 2>going through reports. In the beginning, I was purely manually

585
00:35:48.639 --> 00:35:52.239
<v Speaker 2>going through reports, like I was reading everything, extracting information,

586
00:35:52.480 --> 00:35:55.079
<v Speaker 2>and it was taking me forever to get through reports.

587
00:35:55.360 --> 00:35:59.960
<v Speaker 2>I use CHATGBT and Claude to help me get through reports. Basically,

588
00:36:00.119 --> 00:36:02.679
<v Speaker 2>I feed at my column names, I feed at the report,

589
00:36:02.760 --> 00:36:05.920
<v Speaker 2>and I have it, you know, extract information for me.

590
00:36:06.079 --> 00:36:09.639
<v Speaker 2>But there's some problems with that. So if you ask

591
00:36:10.400 --> 00:36:15.360
<v Speaker 2>these large language models a question twice, they don't always

592
00:36:15.360 --> 00:36:18.480
<v Speaker 2>give you the same response and especially in the same format.

593
00:36:18.719 --> 00:36:22.679
<v Speaker 2>So that creates an issue when you're trying to extract

594
00:36:22.679 --> 00:36:25.360
<v Speaker 2>information for something like what I'm trying to do with

595
00:36:25.360 --> 00:36:28.159
<v Speaker 2>the Sasquatch data project, where I'm trying to optimize it

596
00:36:28.199 --> 00:36:32.480
<v Speaker 2>for coding, I'm trying to optimize it for data analysis,

597
00:36:32.519 --> 00:36:35.039
<v Speaker 2>so it needs to be very structured and in a

598
00:36:35.079 --> 00:36:40.119
<v Speaker 2>particular format. Well, AI doesn't always follow the format, and

599
00:36:40.199 --> 00:36:44.400
<v Speaker 2>it also doesn't always extract the information correctly, especially in

600
00:36:44.440 --> 00:36:49.480
<v Speaker 2>these reports where maybe multiple encounters were reported in like

601
00:36:49.559 --> 00:36:55.199
<v Speaker 2>one report. It'll get confused. It'll start like basically putting

602
00:36:55.239 --> 00:36:58.280
<v Speaker 2>the wrong information with the wrong report. So I do

603
00:36:58.559 --> 00:37:02.280
<v Speaker 2>have it help me to get like the quick information out,

604
00:37:02.559 --> 00:37:06.840
<v Speaker 2>but I do still have to manually like read everything, extract,

605
00:37:07.039 --> 00:37:12.119
<v Speaker 2>fix issues and that kind of thing. So yeah, I

606
00:37:12.159 --> 00:37:15.480
<v Speaker 2>do use AI a lot, but I definitely don't lean

607
00:37:15.519 --> 00:37:18.360
<v Speaker 2>on it, and I don't I don't trust it to

608
00:37:18.559 --> 00:37:22.440
<v Speaker 2>do all of my parcing of big reports for sure,

609
00:37:23.000 --> 00:37:25.119
<v Speaker 2>but it is very helpful and it has sped up

610
00:37:25.119 --> 00:37:27.639
<v Speaker 2>the process for me a lot. It also helps me,

611
00:37:27.800 --> 00:37:30.920
<v Speaker 2>like with my code, because how I do my analysis

612
00:37:30.960 --> 00:37:34.239
<v Speaker 2>is I write everything in Python, and that's how I'm

613
00:37:34.280 --> 00:37:37.880
<v Speaker 2>doing like my statistical significance testing and my statistical analysis

614
00:37:37.920 --> 00:37:40.440
<v Speaker 2>is all through Python. So it's definitely helpful with the

615
00:37:40.480 --> 00:37:44.320
<v Speaker 2>coding part. But for parsing the reports, there's some issues

616
00:37:44.320 --> 00:37:47.079
<v Speaker 2>with it, but it does speed up things quite a bit.

617
00:37:47.679 --> 00:37:51.320
<v Speaker 1>Do you think that at any point you might start

618
00:37:51.760 --> 00:37:57.119
<v Speaker 1>bringing in other data sources besides the BFRO or you

619
00:37:57.360 --> 00:38:01.159
<v Speaker 1>probably will be focusing on just this for a Oh.

620
00:38:01.039 --> 00:38:03.440
<v Speaker 2>I would love to, like my ultimate goal with this.

621
00:38:03.599 --> 00:38:07.159
<v Speaker 2>I so with data analysis, you want to pull from

622
00:38:07.199 --> 00:38:09.920
<v Speaker 2>as many sources as possible. You want to have like

623
00:38:10.719 --> 00:38:12.800
<v Speaker 2>you know, you don't want to basically put all your

624
00:38:12.800 --> 00:38:15.079
<v Speaker 2>eggs in one basket. So I would really love to

625
00:38:15.119 --> 00:38:19.000
<v Speaker 2>start pulling from other data sets or other databases, especially

626
00:38:19.079 --> 00:38:21.840
<v Speaker 2>ones where they do, like you know, it has to

627
00:38:22.599 --> 00:38:24.920
<v Speaker 2>the reports have to go through some check like they've

628
00:38:24.920 --> 00:38:28.199
<v Speaker 2>got to either to follow up reports with witnesses or

629
00:38:28.480 --> 00:38:31.039
<v Speaker 2>something like that. But I would love to include other

630
00:38:31.639 --> 00:38:34.800
<v Speaker 2>databases in the in the data set. It would it

631
00:38:34.800 --> 00:38:38.599
<v Speaker 2>would really help strengthen these results too that I get

632
00:38:38.599 --> 00:38:44.159
<v Speaker 2>with you know, the statistical analysis. But for now, you know,

633
00:38:44.280 --> 00:38:46.079
<v Speaker 2>I guess for now, I'm just going to keep going

634
00:38:46.119 --> 00:38:49.320
<v Speaker 2>with the BFRO. But I would love to start pulling

635
00:38:49.320 --> 00:38:52.840
<v Speaker 2>in other other data other databases of witness.

636
00:38:52.519 --> 00:38:56.159
<v Speaker 1>Reports, absolutely. I mean the main one that would come

637
00:38:56.199 --> 00:38:58.400
<v Speaker 1>to my mind right now would be, you know, the

638
00:38:58.400 --> 00:39:02.559
<v Speaker 1>big Foot Mapping project. There quite a few unique witness

639
00:39:02.599 --> 00:39:07.199
<v Speaker 1>reports that come into that one that might be something

640
00:39:07.239 --> 00:39:09.239
<v Speaker 1>to I don't know if you've ever looked into that

641
00:39:09.280 --> 00:39:10.639
<v Speaker 1>one before.

642
00:39:10.320 --> 00:39:13.920
<v Speaker 2>But oh yeah, yeah I have. And Yeah, like I said,

643
00:39:14.159 --> 00:39:18.639
<v Speaker 2>I'm super open to pulling in other databases for sure,

644
00:39:18.719 --> 00:39:22.880
<v Speaker 2>because essentially it just strengthens like the results that I'm

645
00:39:22.920 --> 00:39:26.400
<v Speaker 2>getting through the analysis that I'm doing. When you have

646
00:39:26.599 --> 00:39:29.480
<v Speaker 2>when you do pull like data from different sources and

647
00:39:29.760 --> 00:39:32.280
<v Speaker 2>you know it's been checked and it's credible.

648
00:39:33.000 --> 00:39:37.519
<v Speaker 1>Absolutely, maybe to switch gears for a little bit here

649
00:39:37.639 --> 00:39:43.119
<v Speaker 1>at the end. Have you ever experienced anything that might

650
00:39:43.199 --> 00:39:47.400
<v Speaker 1>be considered related to bigfoot out in the woods, or

651
00:39:47.440 --> 00:39:51.239
<v Speaker 1>had any any weird encounters yourself, or has this made

652
00:39:51.239 --> 00:39:53.119
<v Speaker 1>you want to go out and kind of look for

653
00:39:53.199 --> 00:39:53.960
<v Speaker 1>things yourself.

654
00:39:54.679 --> 00:39:59.840
<v Speaker 2>I've had two really weird things happen that I can't

655
00:40:00.119 --> 00:40:04.960
<v Speaker 2>attribute to anything that I at least, you know, nothing,

656
00:40:05.239 --> 00:40:07.519
<v Speaker 2>I just can't attribute it to anything else. I grew

657
00:40:07.639 --> 00:40:11.719
<v Speaker 2>up on a farm in Northeast Georgia and a pretty

658
00:40:11.760 --> 00:40:15.920
<v Speaker 2>rural part of Northeast Georgia. And the first one, the

659
00:40:15.960 --> 00:40:20.199
<v Speaker 2>first weird thing I guess that happened was basically how

660
00:40:20.239 --> 00:40:23.000
<v Speaker 2>the house is set up. It backs up to about

661
00:40:23.039 --> 00:40:26.559
<v Speaker 2>fifteen acres of woods at least on our property, and

662
00:40:26.599 --> 00:40:29.559
<v Speaker 2>then that property or our property backs up to another

663
00:40:29.559 --> 00:40:31.519
<v Speaker 2>one hundred and fifty acres of woods, and then there's

664
00:40:31.599 --> 00:40:34.360
<v Speaker 2>like some farms scattered around. But my dad was like

665
00:40:34.440 --> 00:40:36.199
<v Speaker 2>on the back porch and he comes in and he's like,

666
00:40:36.920 --> 00:40:40.760
<v Speaker 2>there's some people walking in our woods back there, and

667
00:40:41.679 --> 00:40:44.079
<v Speaker 2>we were my mom and I were really confused because,

668
00:40:44.159 --> 00:40:48.719
<v Speaker 2>you know, rural Georgia people trespassing. It's not a good combo, like,

669
00:40:48.800 --> 00:40:50.920
<v Speaker 2>most people aren't going to do that. So we go

670
00:40:51.000 --> 00:40:53.800
<v Speaker 2>out on the back porch and you could distinctly hear

671
00:40:53.880 --> 00:40:57.519
<v Speaker 2>two sets of bipedal footsteps. It definitely was not a

672
00:40:57.559 --> 00:41:01.119
<v Speaker 2>quadrupedal animal. It was like bipedal steps and definitely two

673
00:41:01.199 --> 00:41:04.760
<v Speaker 2>of them, and you could hear like a woman's voice.

674
00:41:05.280 --> 00:41:11.199
<v Speaker 2>But the weirdest thing about it was that they were

675
00:41:11.320 --> 00:41:14.000
<v Speaker 2>close enough to where we should have been able to

676
00:41:14.119 --> 00:41:19.800
<v Speaker 2>understand what they were saying, but you couldn't understand them.

677
00:41:19.840 --> 00:41:22.840
<v Speaker 2>And it also, at the same time sounded like it

678
00:41:22.880 --> 00:41:24.800
<v Speaker 2>was on the edge of hearing. And I know it

679
00:41:24.840 --> 00:41:28.280
<v Speaker 2>doesn't really make sense, but I've heard other people talk

680
00:41:28.320 --> 00:41:31.559
<v Speaker 2>about this when they say that they've you know, heard

681
00:41:31.679 --> 00:41:35.079
<v Speaker 2>sasquatches communicating like that's the only way to describe it.

682
00:41:35.119 --> 00:41:37.880
<v Speaker 2>And I totally understand what that means, because that's exactly

683
00:41:37.880 --> 00:41:40.800
<v Speaker 2>what I heard that day. Was like, it's like it's

684
00:41:40.840 --> 00:41:43.400
<v Speaker 2>close enough to where you should be able to understand them,

685
00:41:43.440 --> 00:41:47.239
<v Speaker 2>but it also sounds just bizarre and it's like on

686
00:41:47.280 --> 00:41:49.199
<v Speaker 2>the edge of hearing. But I you know, I can't

687
00:41:49.280 --> 00:41:54.400
<v Speaker 2>definitely say that those are sasquatches we were hearing, but

688
00:41:54.519 --> 00:41:58.559
<v Speaker 2>it was just like really weird. The second thing was

689
00:41:59.360 --> 00:42:03.599
<v Speaker 2>weirder my basically in that house, my bedroom was against

690
00:42:03.719 --> 00:42:05.920
<v Speaker 2>the back wall of the house, so again right next

691
00:42:05.920 --> 00:42:10.000
<v Speaker 2>to the woods, and I woke up one night and

692
00:42:10.079 --> 00:42:13.360
<v Speaker 2>I was hearing this noise like right outside and the

693
00:42:13.360 --> 00:42:19.159
<v Speaker 2>woodline of what sounded like a really demented turkey like gobbling,

694
00:42:19.239 --> 00:42:21.480
<v Speaker 2>and then there was like this horse snort at the end.

695
00:42:21.599 --> 00:42:24.119
<v Speaker 2>I know that sounds so weird, but that's what That's

696
00:42:24.159 --> 00:42:26.119
<v Speaker 2>the best way I know how to describe it. And

697
00:42:26.159 --> 00:42:29.599
<v Speaker 2>then you could very distinctly hear bipedal running back and

698
00:42:29.639 --> 00:42:32.039
<v Speaker 2>forth in the woods, just running back and forth and

699
00:42:32.079 --> 00:42:35.079
<v Speaker 2>doing this like weird turkey gobble thing with a horse snort,

700
00:42:35.559 --> 00:42:38.599
<v Speaker 2>And at first I thought like, Okay, this is like

701
00:42:38.639 --> 00:42:42.280
<v Speaker 2>the most messed up turkey I've ever heard, like something

702
00:42:42.440 --> 00:42:45.280
<v Speaker 2>is wrong. But then I realized, like, turkeys don't snort,

703
00:42:45.679 --> 00:42:48.719
<v Speaker 2>and also, there's no way that I would be able

704
00:42:48.719 --> 00:42:51.000
<v Speaker 2>to hear it running in the woods because it wouldn't

705
00:42:51.039 --> 00:42:53.079
<v Speaker 2>be large to us. We had horses on the farm

706
00:42:53.599 --> 00:42:56.039
<v Speaker 2>and we would let them kind of like free room

707
00:42:56.079 --> 00:42:59.639
<v Speaker 2>on the property, and I could barely hear them in

708
00:42:59.679 --> 00:43:02.559
<v Speaker 2>the world's from my room, like you could barely hear

709
00:43:02.599 --> 00:43:04.880
<v Speaker 2>their footsteps, and you know, they're like eight to twelve

710
00:43:04.960 --> 00:43:08.400
<v Speaker 2>hundred pound horses. So I'm like listening to this thing.

711
00:43:08.480 --> 00:43:11.079
<v Speaker 2>I'm sitting there, and at one point it did I'm like,

712
00:43:11.239 --> 00:43:13.079
<v Speaker 2>did the horses get out? But then I was like, no,

713
00:43:13.159 --> 00:43:15.079
<v Speaker 2>they don't. You know, that doesn't sound like a horse.

714
00:43:15.400 --> 00:43:17.960
<v Speaker 2>But the other weird thing was we had dogs that

715
00:43:18.199 --> 00:43:21.480
<v Speaker 2>lived outside and they were totally silent, and they usually

716
00:43:21.480 --> 00:43:25.000
<v Speaker 2>barked at everything. Like it was just weird that they

717
00:43:25.079 --> 00:43:29.199
<v Speaker 2>weren't barking or anything. So I'm listening to this for

718
00:43:29.239 --> 00:43:33.360
<v Speaker 2>at least six or seven minutes of this weird turkey

719
00:43:33.639 --> 00:43:37.719
<v Speaker 2>gobble horse snort, and then it just stops, and that

720
00:43:37.880 --> 00:43:40.119
<v Speaker 2>was it. That was the whole thing. Nothing else ever

721
00:43:40.199 --> 00:43:45.039
<v Speaker 2>happened it was just a really bizarre experience.

722
00:43:44.920 --> 00:43:49.000
<v Speaker 1>That is very very strange. I agree, anything weird ever

723
00:43:49.079 --> 00:43:51.880
<v Speaker 1>done to the horses themselves, No.

724
00:43:51.840 --> 00:43:55.079
<v Speaker 2>We never, No, we've never they never had anything happen

725
00:43:55.159 --> 00:43:59.960
<v Speaker 2>to them. I've heard reports of like sasquatches, like brain

726
00:44:00.000 --> 00:44:03.960
<v Speaker 2>eating horse hair and stuff, but we never had anything

727
00:44:04.079 --> 00:44:06.760
<v Speaker 2>like that happen at all. You know, sometimes they'd be

728
00:44:07.360 --> 00:44:10.880
<v Speaker 2>more spooky, you know at times, and other times we

729
00:44:10.920 --> 00:44:15.119
<v Speaker 2>always thought, okay, the bear's back or something. But nothing

730
00:44:15.199 --> 00:44:18.360
<v Speaker 2>weird ever happened like that. It was more just like

731
00:44:19.280 --> 00:44:23.000
<v Speaker 2>auditory stuff that was weird. And again I can't for

732
00:44:23.079 --> 00:44:25.679
<v Speaker 2>sure say that they were sasquatches, but also like I

733
00:44:25.679 --> 00:44:28.440
<v Speaker 2>don't know what the heck that would have been.

734
00:44:29.000 --> 00:44:33.159
<v Speaker 1>But yeah, it's very interesting. I've never heard anything like

735
00:44:33.199 --> 00:44:35.559
<v Speaker 1>that myself. I do get a lot of reports where

736
00:44:35.559 --> 00:44:41.079
<v Speaker 1>it's like weird combinations of animals doesn't really make sense,

737
00:44:41.840 --> 00:44:45.880
<v Speaker 1>and it's just it's very very strange, is what it

738
00:44:45.920 --> 00:44:49.480
<v Speaker 1>comes down to. But a terrestrial. It's been such a

739
00:44:49.519 --> 00:44:52.519
<v Speaker 1>pleasure having you on the show today, and I feel

740
00:44:52.519 --> 00:44:55.280
<v Speaker 1>like we've learned a lot through what you are doing

741
00:44:55.400 --> 00:44:58.559
<v Speaker 1>with your work currently. Is there a way that my

742
00:44:58.800 --> 00:45:03.440
<v Speaker 1>listeners can to help you maybe by sending data or

743
00:45:03.480 --> 00:45:04.239
<v Speaker 1>anything like that.

744
00:45:04.519 --> 00:45:07.679
<v Speaker 2>Yeah, if you have any kind of reports, especially if

745
00:45:07.679 --> 00:45:11.639
<v Speaker 2>you have like an exact glatitude or longitude associated with

746
00:45:11.719 --> 00:45:14.360
<v Speaker 2>the report, you can send them my way on my website,

747
00:45:14.400 --> 00:45:17.519
<v Speaker 2>Sasquatch Data Project dot com. I also have a resource

748
00:45:17.519 --> 00:45:20.880
<v Speaker 2>on there that you know, either witnesses can use or

749
00:45:20.960 --> 00:45:23.719
<v Speaker 2>researchers can use. It's basically like a worksheet with a

750
00:45:23.719 --> 00:45:26.159
<v Speaker 2>bunch of different questions on it that you can fill

751
00:45:26.199 --> 00:45:29.719
<v Speaker 2>out and send back to me and I can start

752
00:45:29.719 --> 00:45:34.159
<v Speaker 2>compiling those reports into my data set. But yeah, you

753
00:45:34.199 --> 00:45:38.559
<v Speaker 2>can contact me through email. It's just contact at Sasquatch

754
00:45:38.639 --> 00:45:41.679
<v Speaker 2>Data Project dot com. Or you can message me on

755
00:45:41.719 --> 00:45:45.480
<v Speaker 2>like Instagram or YouTube or TikTok any social media that

756
00:45:45.519 --> 00:45:48.719
<v Speaker 2>I'm on. The handle is at Sasquatch Data. But yeah,

757
00:45:49.039 --> 00:45:50.800
<v Speaker 2>those there a couple of ways to get in touch

758
00:45:50.840 --> 00:45:51.000
<v Speaker 2>with me.

759
00:45:51.599 --> 00:45:54.719
<v Speaker 1>Fantastic. Well, thank you for coming on the show today

760
00:45:54.760 --> 00:45:57.199
<v Speaker 1>and best of luck with your future research.

761
00:45:57.800 --> 00:45:59.320
<v Speaker 2>Thank you, thank you so much for having me. This

762
00:45:59.480 --> 00:46:00.000
<v Speaker 2>was so much fun.

763
00:46:00.920 --> 00:46:02.719
<v Speaker 1>Just wanted to take a few minutes to say thank

764
00:46:02.760 --> 00:46:06.800
<v Speaker 1>you to you all my listeners for listening to the podcast.

765
00:46:07.280 --> 00:46:10.039
<v Speaker 1>Please take a minute to help out the show by

766
00:46:10.199 --> 00:46:13.679
<v Speaker 1>subscribing on YouTube, making sure you hit the bell so

767
00:46:13.719 --> 00:46:17.239
<v Speaker 1>you don't miss any notifications, and share the episode on

768
00:46:17.280 --> 00:46:20.280
<v Speaker 1>YouTube with a friend. Also, if you're listening to us

769
00:46:20.320 --> 00:46:23.559
<v Speaker 1>on a podcast, thank you so much, make sure that

770
00:46:23.559 --> 00:46:27.800
<v Speaker 1>you're subscribed share the show with a friend. Really, it's

771
00:46:27.840 --> 00:46:31.039
<v Speaker 1>all about sharing the show wherever you can. If you've

772
00:46:31.039 --> 00:46:33.920
<v Speaker 1>had a bigfoot encounter related to the following or know

773
00:46:34.079 --> 00:46:38.159
<v Speaker 1>someone who has, please reach out to me at Bigfoot

774
00:46:38.199 --> 00:46:41.679
<v Speaker 1>Society at gmail dot com or pass on my email.

775
00:46:42.440 --> 00:46:47.280
<v Speaker 1>Here's the list. Number one encounters from Franklin County, Texas.

776
00:46:47.559 --> 00:46:51.119
<v Speaker 1>Number two encounters from the entire state of Iowa. Number

777
00:46:51.159 --> 00:46:55.119
<v Speaker 1>three encounters from Oakridge, Oregon or the surrounding area. Number

778
00:46:55.159 --> 00:46:58.280
<v Speaker 1>four any individuals that know about bigfoot being flown off

779
00:46:58.280 --> 00:47:01.960
<v Speaker 1>after the Mount Saint Helens eruption. Number five. Individuals that

780
00:47:02.039 --> 00:47:05.239
<v Speaker 1>have had a bigfoot encounter well in the military. Number six.

781
00:47:06.000 --> 00:47:08.320
<v Speaker 1>Those that have had a bigfoot encounter in the southern

782
00:47:08.400 --> 00:47:13.639
<v Speaker 1>New Hampshire or north central Massachusetts area, including Franklin County, Massachusetts.

783
00:47:14.079 --> 00:47:16.519
<v Speaker 1>Number seven. Individuals that have had a bigfoot encounter in

784
00:47:16.559 --> 00:47:20.000
<v Speaker 1>a Bible camp or boy Scout camp setting. Number eight

785
00:47:20.039 --> 00:47:22.360
<v Speaker 1>individuals that have had bigfoot try to enter their house

786
00:47:22.440 --> 00:47:26.119
<v Speaker 1>forcibly while they were living inside. Number nine individuals that

787
00:47:26.159 --> 00:47:30.920
<v Speaker 1>have actively have a bigfoot living on their property. And lastly,

788
00:47:31.400 --> 00:47:34.880
<v Speaker 1>any sightings that are in the Watchitaw National Forest Area

789
00:47:34.960 --> 00:47:39.519
<v Speaker 1>of Oklahoma or Arkansas. A special thank you to all

790
00:47:39.639 --> 00:47:43.679
<v Speaker 1>the Bigfoot Society, Patreon and YouTube channel members. It's your

791
00:47:43.719 --> 00:47:46.920
<v Speaker 1>support that helps keep the show going and I extremely

792
00:47:46.920 --> 00:47:50.840
<v Speaker 1>appreciate it. I'll see you back next time. Listeners. Saswath

793
00:47:50.840 --> 00:47:54.320
<v Speaker 1>Summerfest this year July eleventh through the twelfth. It's going

794
00:47:54.360 --> 00:47:58.960
<v Speaker 1>to be fantastic July eleventh through twelfth in Greenwaters Park

795
00:47:59.000 --> 00:48:02.960
<v Speaker 1>and Oakridge or again, and listeners, if you're going to go,

796
00:48:03.199 --> 00:48:05.960
<v Speaker 1>you can get a two day ticket for the cost

797
00:48:05.960 --> 00:48:10.599
<v Speaker 1>of one if you use the code b f S

798
00:48:11.159 --> 00:48:15.039
<v Speaker 1>like Bigfoot Society, but BFS and I'll get used some

799
00:48:15.199 --> 00:48:18.840
<v Speaker 1>off your cost. Priscilla wasn't nice enough to provide that

800
00:48:19.440 --> 00:48:21.800
<v Speaker 1>for my listeners. So there you go. I look forward

801
00:48:21.800 --> 00:48:23.440
<v Speaker 1>to seeing you there, so make sure you head over

802
00:48:23.480 --> 00:48:27.280
<v Speaker 1>to www. Dot Sasquatch Summerfest dot com and pick up

803
00:48:27.280 --> 00:48:28.239
<v Speaker 1>your tickets today.