June 1, 2025

The Truth is in the Data! | Sasquatch Data Project

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Season 1

In this episode, we sit down with Terrestrial, a key figure in the Sasquatch Data Project, who shares her fascinating journey into the world of Bigfoot research. Terrestrial, who started her journey at the age of five after watching the Patterson-Gimlin film, is now optimizing witness reports for statistical analysis. We explore her project that aims to compile Bigfoot sightings into a comprehensive open-source dataset, designed for in-depth analysis. Featuring intriguing findings such as increased sightings during full and new moon phases and regional differences in Sasquatch characteristics, this conversation delves into the potential to predict Bigfoot activity using data science. Terrestrial's expertise in data and her background with NASA's Dawn mission bring a unique perspective to Bigfoot research. This episode also touches on odd personal encounters and the broader implications of her work. Tune in to discover how data analysis might unlock new insights into the elusive world of Bigfoot.

Resources:

https://www.sasquatchdataproject.com

https://www.bfro.net

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slash the Big for Society and now let's get on

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with the show. At Bigfoot Society, get the privilege of

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talking to Terrestrial today. She is an individual with a

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Sasquatch data project that first was introduced to her work

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through TikTok and it is very very interesting stuff, indeed

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that I don't really hear a lot of people talk

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about in the way that she is doing. So welcome

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to the show, Terrestrial. How's it going.

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Thank you, I'm really excited to be here. I'm doing good.

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Hew are you?

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Yeah? I'm doing great and just having a good Sunday afternoon,

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hanging out getting to talk about Bigfoot. So I can't

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complain about that. Let's start at the beginning. What was

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it that first got you into Bigfoot? To begin with? Terrestrial?

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Yeah, so I have kind of a funny story of

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how this all unfolded. I was like five years old,

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and it was kind of weird because I was home

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from school that day, which never happens. My dad was

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also home from work because he was prepping for a

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colonoscopy the next day, so we were just like vegging

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out on the couch while he was doing that, and

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he's always been into like kind of like aliens and

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ghost stuff. So we were watching Discovery Channel and Doug

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Hicheck's Fastball to Legendnique Science came on the TV, and

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that's where I saw the Patterson give One film for

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the first time, and my brain just like exploded. I

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was like, this is the craziest thing I've ever seen,

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and kind of from there I was just hooked and

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I've been looking into it ever since.

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That's awesome. Yeah, Doug is such a cool guy to

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talk to, and he's really a pioneer in the field.

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I can't wait to see when his second documentary follow

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up to that comes out, maybe later this year or

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the year after. We'll see. I know they have a

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little bit of work left to do, but yeah, we'll

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all be waiting for that as well. So Terrestrial walk

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me through what the Sasquatch Data project is.

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Yeah, so, I guess in its essence, I'm trying to

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put Bigfoot witness reports into a format that's optimized for

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data analysis. So I'm essentially creating a giant spreadsheet that's

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you can, you know, run whatever statistical analysis you want

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to if you want to, you know, use it for

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any kind of code you can. And it's also open

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source too, so anyone can go and download it off

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of my website at the time. Let's see, right now,

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I'm about fifteen hundred reports deep into the BFRO website,

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and I'm parsing all of those reports into about one

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hundred and thirty six columns I think right now is

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what I'm up to. And those columns are composed of

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different traits to sasquatches, whether it's physical, environmental, behavioral, that

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kind of thing. So in its essence, that's what it is.

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But I'm also really interested in the mathematical side of things,

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so like actually using the data set forward statistical analysis

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and seeing what we can learn about sasquatches.

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It's interesting. Do you have a background with data or math?

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Then I do.

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Yeah. So honestly, when when I was an undergraduate, I

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kind of did like a PhD. As an undergrad, I

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had a really great opportunity to work on NASA's down mission,

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and I was very angry into the team, and I

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got to like first author a paper, I got to

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co author a number of papers regarding my research on

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this dwarf planet and our solar system called Series so

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and a lot of my research was having to maintain

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and create like a very large data set and then

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running statistical analysis on that data. So I feel like

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I'm kind of pulling those skills that I got from

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my research and you know, applying it to the sasquatch field.

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That's fantastic. I would imagine that is probably not anything

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you're involved with currently, though.

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No, it's not.

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No, gotcha, Now, what are some things that you have

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come across when you start to jump into this data?

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Have there been any things that have jumped out that

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just have you know, gotten you really excited when you

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start looking through it.

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Yeah, I feel like every day I'm kind of thinking about,

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Oh I could go look into this, Oh I should

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go look into that. So I have a tendency to

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kind of bop around into you know what I'm looking at.

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You know. The question though that kind of got me

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to actually start working on my data set with the

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Sasquatch data project is you know, I heard someone say

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once that sasquatches are more active under full moods, and

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I was like, well, how do we know that, because

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there's not data to back that up. It's just something

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that was said. So that was actually my first kind

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of analysis that I did with the data set, and

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that was one of the most surprising so far, I think,

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where I did find that there is an increase in

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recorded sasquatch sightings under full moods, but also new moods.

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So when the moon is zero percent illuminated, and that

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difference in the reports during those times is statistically significant

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compared to those reports for the middle values of middle illumination.

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So that's really interesting because you go, well, why would

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that be the case, And to me, I think it

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points towards evidence of sasquatches partaking in this predator prey

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relationship that's very documented. Essentially, nocturnal predators engage in this

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under full moons. You know, prey have evolved to learn

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that nocturnal predators can see better, therefore they decrease their activities. So,

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you know, nocturnal predators will increase their activities because they're hunting,

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but also they know that prayer are going to be

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less active, so they're like staking out their territory and

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that kind of thing. And then under new moons, it's

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kind of the opposite where pray are more active because

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they know that predators can't see as well. Therefore predators

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are spending more time hunting, so their activities increase under

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these new and full moon conditions. And to see that

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in the data with sasquatches is just fascinating, honestly. Like

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I know, correlation does not necessarily mean causation, but it

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is really interesting to see the similar the similarities there.

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That is fascinating. Have you had any researchers reach out

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to you yet and been like, yeah, you're you're you're

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on the ball with this, or or maybe you're a

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little off with this.

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Oh no, you know, no one has really said anything

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about it yet, and I'm like hoping to get some feedback,

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like I've been thinking about that a lot. Actually, I'm

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like looking for feedback, like, you know what, to see

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what people think about it. But no, no, no one's

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really reached out to me yet.

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Well, one of the reasons I'm having you on is

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so hopefully you will get some feedback. Listeners might be like, yeah,

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this isn't the normal bigfoot society show. Let's hear about

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someone who sees bigfoot. Well, guys, I think this is

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pretty important that we are aware of this research that

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Terrestrial is doing. So this is kind of like a

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one off show, So I think we all can can

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learn a great deal from what she is doing in

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her research. But have you been able to use the

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data that you've been looking through to kind of get

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a better picture of what sasquatch might normally look like?

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Yes, I have. So lately I've been looking at the

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heights of sasquatches. That's been something I'm interested in, particularly

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if there are differences in the reported heights between regions.

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So lately I've been comparing physical traits between the southern

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and the western regions of the United States, and so

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far I have found that there is not really a

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difference between the heights that are reported, at least between

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the South and the West. They came back not statistically

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significant and very close in value. I think, like the

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West was seven point two eight was the average height,

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and then the South was seven point four feet. But

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I'm just pulling those numbers out. It's somewhere around there.

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But what I did find that was interesting is that

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there are differences in the hair colors that are reported

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between the southern and Western United States. Something that I

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was interested in looking at particularly was I feel like

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I hear a lot that there are more reports of

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like red brown sasquatches in the South. So I looked

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into that, and it turns out there are, and it

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is a statistically significant difference between the Western United States.

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I actually found that there is a meaningful difference between

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the number of reports of red brown, white and gray

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sasquatches in the South versus the West. So that's really

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interesting because I think the thing is too, especially with

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the statistical significance testing, is it essentially tells us, you know,

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is this difference that we're seeing in the data meaningful

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or could it have happened due to just random chants,

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Like that's just how the data decided to fall. And

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when you do find statistical significance, it's essentially telling you, Okay,

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something is going on to cause this to happen. What

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that is is not clear right now, but you know,

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the chances of this just randomly occurring are very small, like,

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you know, less than five percent. So those those are

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two things that I've been looking at recently that I

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think are pretty interesting.

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Have you been able to find a baseline or maybe

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information about normal behavior of the sasquatch due to all

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these reports that you've been looking at.

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Yeah, so that's a really interesting question because I would

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say the majority of the reports that I go through

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are extremely short. So typically the witness, either the witnesses

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the sasquatch or the sasquatch sees the witness first and

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then they make eye contact or they just you know,

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become aware of each other, and then one of them

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leaves the encounter. Typically it's not like a prolonged encounter

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where the witness is actually watching them maybe like eat

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or drink or I don't know, just exist in the

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environment do whatever they do. So I haven't pulled stats

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on like who's leaving the encounter first, is it the

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witness or the sasquatch, But I am keeping up with

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it in the spreadsheets, so that would be it, you know,

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that would tell us something about their behaviors. I do

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know that intimidation encounters or like aggressive encounters are not

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that at least they're not very commonly reported. Currently in

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the data set, about five percent of reports involve like

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aggressive behaviors from sasquatches. So right now, it's you know,

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I can't make any certain you know, I can't like,

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for say, or for certain say that there's like a

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certain way they should behave because the encounters are typically

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very short, like less than a minute.

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But yeah, that's interesting. In the community, there's seems to

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be these different things that are always passed around verbally

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as kind of like community knowledge, and it's very cool

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to see some of these things maybe be proven or

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disproven due to analysis of data. Have you found after

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looking through your data that a class A sightings happen

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more towards certain times of year.

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So this is the interesting thing. And I want to

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touch on what you just said a little bit because

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That's also part of the reason that I'm working on

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the Sasquatch Data project is because I feel like there

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is a lot of you know, there are a lot

216
00:12:52.039 --> 00:12:54.639
of ideas in the community, but there's not a lot

217
00:12:54.679 --> 00:12:57.960
of data to back up the ideas. So part of

218
00:12:58.200 --> 00:13:01.000
what I'm trying to do is actually take these ideas

219
00:13:01.000 --> 00:13:04.440
that are passed around and say, well, you know, based

220
00:13:04.480 --> 00:13:07.720
on at least this subset of reports, that doesn't seem

221
00:13:07.759 --> 00:13:09.799
to be true, or this does seem to be true.

222
00:13:10.200 --> 00:13:12.480
So yeah, that's definitely a big component of what I'm

223
00:13:12.480 --> 00:13:15.320
trying to do as well. I'm sorry, I got off

224
00:13:15.360 --> 00:13:16.799
on a different train of thought.

225
00:13:17.039 --> 00:13:18.759
Yeah, that happens to be a lot too. Have you

226
00:13:18.840 --> 00:13:23.080
found that Class A sightings happen more towards a certain

227
00:13:23.120 --> 00:13:27.840
time of year after analyzing the data from the sighting reports.

228
00:13:27.960 --> 00:13:31.879
Yeah, so, at least how they are reported, Class A

229
00:13:32.000 --> 00:13:37.080
sightings do tend to cluster among certain months. I can

230
00:13:37.120 --> 00:13:41.919
actually pull up at least my most recent data because

231
00:13:41.960 --> 00:13:44.879
I just looked at this, so it looks like, yeah,

232
00:13:45.039 --> 00:13:49.240
so for Class A sightings. Actually I didn't do it

233
00:13:49.240 --> 00:13:51.440
by class, so I can't quite tell you, but at

234
00:13:51.519 --> 00:13:55.399
least for sightings in general not designated by Class A

235
00:13:55.559 --> 00:14:00.960
or B. Right now, July has the most, followed by October,

236
00:14:01.320 --> 00:14:04.919
which is interesting. So they definitely and reports do cluster

237
00:14:05.039 --> 00:14:08.440
around the summer and fall months in general. Whether that's

238
00:14:08.480 --> 00:14:12.440
more so due to human activity or sasquatch activity, it's

239
00:14:12.519 --> 00:14:17.120
unclear at this point, but I do know that the

240
00:14:17.159 --> 00:14:20.080
majority of class acieties do happen during like the summer

241
00:14:20.120 --> 00:14:22.759
and the fall. And an idea that I've had that

242
00:14:22.799 --> 00:14:24.960
we can kind of, you know, that I've been thinking

243
00:14:25.039 --> 00:14:28.440
about at least for a while, is potentially looking at

244
00:14:29.039 --> 00:14:33.320
like the National Parks publish the amount of permits that

245
00:14:33.399 --> 00:14:37.399
they give out every year, particularly to or for like

246
00:14:37.559 --> 00:14:40.279
backpacking permits. So I'm like, well, if we could look

247
00:14:40.320 --> 00:14:42.080
at that and see when people are at least going

248
00:14:42.200 --> 00:14:45.159
backpacking or like into these more remote parts of the

249
00:14:45.200 --> 00:14:49.840
parks and stuff. I wonder if we could somehow use

250
00:14:49.919 --> 00:14:53.440
that data to determine if you know, more people are

251
00:14:53.480 --> 00:14:57.440
going out into these more remote parts of the continent

252
00:14:57.519 --> 00:15:00.480
during these months, and maybe that's why we're seeing this increase,

253
00:15:00.679 --> 00:15:02.679
or is it more of a sasquatch thing, like they're

254
00:15:02.679 --> 00:15:05.639
more active during these times of year. I haven't I

255
00:15:05.759 --> 00:15:09.679
haven't quite figured out how to make that jump.

256
00:15:09.480 --> 00:15:14.000
Yet big, so society will be right back after these messages.

257
00:15:29.960 --> 00:15:33.159
Yeah, at least for reports. There are more during the

258
00:15:33.159 --> 00:15:34.000
summer in the fall.

259
00:15:35.559 --> 00:15:39.279
That was really interesting because you know, if you go

260
00:15:39.360 --> 00:15:41.799
by what you hear, or at least what I hear

261
00:15:41.879 --> 00:15:44.559
from the community, it's like you gotta go out in October,

262
00:15:44.679 --> 00:15:47.639
like October is when crazy stuff happens, but it's like

263
00:15:48.240 --> 00:15:51.919
July wouldn't really wouldn't really think so? I mean, or

264
00:15:52.080 --> 00:15:53.879
maybe there's some listeners that are like, well, maybe you

265
00:15:53.879 --> 00:15:56.360
should really think so you're not talking to the right people.

266
00:15:57.000 --> 00:16:01.679
But now that's that's extremely interesting. Is there data that

267
00:16:01.759 --> 00:16:05.919
you wish you had from these encounter reports that you're reading.

268
00:16:06.519 --> 00:16:10.080
Yes, a lot of the time, like I, like I said,

269
00:16:10.120 --> 00:16:12.600
with my data set, I have over one hundred and

270
00:16:12.679 --> 00:16:16.519
thirty five different traits or different you know, variables that

271
00:16:16.559 --> 00:16:19.840
I'm looking at, and most of the time, most of

272
00:16:19.879 --> 00:16:23.799
the columns remain blink because either the witness didn't offer

273
00:16:23.879 --> 00:16:26.759
up the information which is you know, totally valid, like

274
00:16:27.039 --> 00:16:29.759
they've had an encounter that a lot of them don't

275
00:16:29.799 --> 00:16:31.759
know how to process it, so they don't even know

276
00:16:31.840 --> 00:16:35.039
like what the researcher would be interested in knowing, but

277
00:16:35.120 --> 00:16:38.279
then the follow up from the researcher, you know, I

278
00:16:38.279 --> 00:16:42.080
feel like there's missed opportunities there to extract more information,

279
00:16:42.320 --> 00:16:45.159
especially from these Class A close range sightings where the

280
00:16:45.159 --> 00:16:48.720
witness got a really good look at the sasquatch, particularly

281
00:16:48.960 --> 00:16:54.440
particularly like facial features like eye size, eye color, nose shape,

282
00:16:54.799 --> 00:16:59.759
different like cranial structure features, or in some cases, like

283
00:16:59.799 --> 00:17:03.080
the reports don't even include like a hair color from

284
00:17:03.120 --> 00:17:07.039
Class A sightings, which confuses me. So a lot of

285
00:17:07.079 --> 00:17:09.640
the time there is there are times when I'm like

286
00:17:10.400 --> 00:17:14.599
wishing I had more information, especially latitude longitude information for

287
00:17:14.680 --> 00:17:17.799
where the sighting happened. There's this thing you can do

288
00:17:18.000 --> 00:17:22.640
called maximum entropy, where essentially you can by using like

289
00:17:22.799 --> 00:17:26.160
latitude longitude locations of where an animal was that you

290
00:17:26.200 --> 00:17:31.519
can essentially stack that with different environmental layers I guess,

291
00:17:31.960 --> 00:17:35.160
and basically you run it through an algorithm and it'll

292
00:17:35.200 --> 00:17:38.640
tell you where you're likely to see that animal based

293
00:17:38.680 --> 00:17:42.359
on the features surrounding that location of where it was seen.

294
00:17:42.839 --> 00:17:46.400
So like, that's something that I'm really interested in doing too,

295
00:17:46.480 --> 00:17:49.799
but I don't have enough latitude longitude data to actually

296
00:17:49.839 --> 00:17:52.319
do that because you need quite a few points based

297
00:17:52.359 --> 00:17:54.559
on the region that you're looking at. So that way

298
00:17:54.720 --> 00:17:58.440
we could actually like predict where, you know, you might

299
00:17:58.519 --> 00:18:00.680
have an encounter with a sasquatch, where they might be

300
00:18:01.200 --> 00:18:06.119
based on other sightings. So yeah, I'm I'm always looking

301
00:18:06.160 --> 00:18:09.759
for more information. I love getting into the details of things.

302
00:18:10.200 --> 00:18:13.839
But I would say, like the physical traits and the

303
00:18:13.920 --> 00:18:16.480
latitude longitude data, I'm always looking.

304
00:18:16.240 --> 00:18:19.480
For that seems like a really big deal. So I

305
00:18:19.480 --> 00:18:21.720
want to make sure that I get that correct. So

306
00:18:22.559 --> 00:18:26.559
let's say someday you get enough of that data, you

307
00:18:26.680 --> 00:18:31.319
could have the computer program make a map where it's like,

308
00:18:31.880 --> 00:18:35.799
instead of us just going off of oh, well, this

309
00:18:35.839 --> 00:18:38.880
person tells me, yeah, you should check out this state

310
00:18:38.920 --> 00:18:41.720
park or National Force, you could have a map where

311
00:18:41.839 --> 00:18:46.039
it would like show you really good ideas of where

312
00:18:46.079 --> 00:18:49.400
to go and check out. Is that correct essentially?

313
00:18:49.640 --> 00:18:53.279
Yeah, So biologists use this to predict where other species

314
00:18:53.359 --> 00:18:57.000
might be, like what they're interested in a particular species.

315
00:18:57.440 --> 00:18:59.400
And the thing about it is, though, you have to

316
00:18:59.400 --> 00:19:03.599
be pretty because you know, the capacity of what computers

317
00:19:03.599 --> 00:19:05.839
can run is only so much, and also you don't

318
00:19:05.839 --> 00:19:07.839
want to make it too broad because then it will

319
00:19:08.200 --> 00:19:13.599
kind of overestimate an area. So the Yeah, the problem

320
00:19:13.680 --> 00:19:18.400
is getting enough reports from a pretty small area, even

321
00:19:18.559 --> 00:19:22.200
like statewide is a little too large, you can't do it.

322
00:19:22.240 --> 00:19:24.960
But you have to have a high enough density of

323
00:19:25.039 --> 00:19:29.880
sightings to be able to properly run the algorithm basically.

324
00:19:30.160 --> 00:19:32.519
So yeah, so it could be. It potentially could be

325
00:19:32.519 --> 00:19:35.039
a very powerful tool. It's just you know, having enough

326
00:19:35.119 --> 00:19:35.759
data to do it.

327
00:19:36.359 --> 00:19:40.319
Powerful in the right or wrong hands, for sure.

328
00:19:41.160 --> 00:19:41.599
Yeah.

329
00:19:41.920 --> 00:19:46.160
Interesting, Approximately how close are you to being able to

330
00:19:46.200 --> 00:19:47.599
run a program like that?

331
00:19:48.160 --> 00:19:50.480
I started working on it actually last month. I had

332
00:19:50.480 --> 00:19:53.319
this idea of like, oh, we could probably do that, Like,

333
00:19:53.599 --> 00:19:55.759
you know, let me see if I have enough data

334
00:19:55.799 --> 00:19:58.880
points for a latitude longitude. I am pretty far away,

335
00:19:59.519 --> 00:20:03.440
at least from my reports that I'm getting off of

336
00:20:03.480 --> 00:20:06.880
the BFRO because I am going through reports by hand.

337
00:20:07.759 --> 00:20:10.720
It's taken me a while to get through everything, and

338
00:20:11.680 --> 00:20:14.559
I'm about fifteen hundred reports deep. I've got about five

339
00:20:14.599 --> 00:20:19.000
thousand more to go, and that's just one data set

340
00:20:19.039 --> 00:20:23.000
of reports. So it really just depends on the state

341
00:20:23.119 --> 00:20:28.359
and the area of how many data points would be enough. Yeah,

342
00:20:28.440 --> 00:20:30.920
it really just depends. I mean, the more data the better, right,

343
00:20:31.039 --> 00:20:34.680
So as much latitude and longitude data I can get.

344
00:20:35.160 --> 00:20:39.200
The better the but the better the program will essentially

345
00:20:39.720 --> 00:20:42.759
predict I definitely don't have enough right now.

346
00:20:44.200 --> 00:20:46.119
Man, if you can get access to the flats, that

347
00:20:46.160 --> 00:20:50.920
would be awesome. That is allegedly the name of the

348
00:20:51.720 --> 00:20:55.759
back door database for the BFRO, and I'm sure there's

349
00:20:55.799 --> 00:21:00.680
way more information on there. That's just me hypothesizer because

350
00:21:00.720 --> 00:21:05.279
I'm not in there. But so you have to manually

351
00:21:05.319 --> 00:21:07.599
go through all these reports to pretty much see if

352
00:21:07.640 --> 00:21:10.759
they have a latitude and longitude in them, right.

353
00:21:11.359 --> 00:21:13.920
Yeah, So there's a couple of things, and this is

354
00:21:14.240 --> 00:21:16.920
kind of what I've run into, you know, extracting any

355
00:21:16.960 --> 00:21:20.720
information from the reports. Is the witness there. There aren't

356
00:21:20.720 --> 00:21:25.519
always a latitude longitudes just blatantly on the report. Sometimes

357
00:21:25.799 --> 00:21:29.519
the researcher will give us it'll say like GPS coordinates

358
00:21:29.680 --> 00:21:32.119
or you know whatever, or the witness will actually provide

359
00:21:32.119 --> 00:21:35.000
a lot of tude longitude, but that's not very common.

360
00:21:35.480 --> 00:21:37.759
A lot of the times the witness will give an

361
00:21:37.839 --> 00:21:45.920
extremely detailed location, be you there, and then walk twenty

362
00:21:45.920 --> 00:21:48.119
more feet, and that's where this happens. So I'll go

363
00:21:48.200 --> 00:21:49.000
on Google Maps.

364
00:21:49.400 --> 00:21:52.480
Sorry, I get a lot of weird stuff happening in

365
00:21:52.480 --> 00:21:55.519
my phone calls. Do you mind repeating that just a

366
00:21:55.559 --> 00:21:57.400
little bit your your phone went out entirely?

367
00:21:57.960 --> 00:22:04.319
Oh sorry, Yeah. So with the reports on the BFUR website,

368
00:22:04.799 --> 00:22:08.880
they do not always give a latitude longitude. Sometimes they do.

369
00:22:09.039 --> 00:22:12.640
Sometimes either the witness or the researcher who follows up

370
00:22:12.680 --> 00:22:16.240
will give a latitude longitude. But most of the time

371
00:22:16.400 --> 00:22:19.799
it's the witness giving like a really detailed description of

372
00:22:19.839 --> 00:22:23.039
where their encounter happened. So they'll say, like, you know,

373
00:22:23.160 --> 00:22:26.039
turn onto this road, go to this mile marker, and

374
00:22:26.119 --> 00:22:29.759
then like drive twenty more feet and that's where the

375
00:22:29.880 --> 00:22:33.119
encounter happened. So I'll go on like Google Maps or something,

376
00:22:33.920 --> 00:22:36.839
and I will go down the street view and find

377
00:22:37.079 --> 00:22:39.920
the spot and then extract the latitude longitude that way.

378
00:22:40.279 --> 00:22:44.640
So like some people have suggested, like you know, webscraping

379
00:22:44.839 --> 00:22:48.880
or something to extract the latitude longitude. But honestly, most

380
00:22:48.920 --> 00:22:51.839
of the time I'm having to go and manually find it,

381
00:22:51.960 --> 00:22:54.599
like in Google Maps or Google Earth or whatever.

382
00:22:54.720 --> 00:22:59.000
Man, it feels like there should be some other I

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

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

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

386
00:23:08.960 --> 00:23:12.240
I like it. It's a challenge, but yeah, I'm like,

387
00:23:12.319 --> 00:23:16.200
if I could, just if I could have more more data, have.

388
00:23:16.240 --> 00:23:20.119
You found any you know, well, I would imagine you're

389
00:23:20.200 --> 00:23:23.720
looking at a lot of these places manually through Google Maps.

390
00:23:24.440 --> 00:23:28.480
Have you noticed any similarities of the places that are

391
00:23:28.519 --> 00:23:31.839
starting to come out or any commonalities?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

406
00:24:25.519 --> 00:24:26.440
reports too much.

407
00:24:27.240 --> 00:24:31.400
Absolutely. You know, Let's say someday you are able to

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

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

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

411
00:24:42.519 --> 00:24:45.519
it's the best idea for me to look for bigfoot.

412
00:24:45.720 --> 00:24:49.599
What is your hope that that would be used for

413
00:24:50.039 --> 00:24:50.480
the good.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

434
00:26:04.319 --> 00:26:07.279
be used for purely research. ADS.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

500
00:30:31.759 --> 00:30:32.119
a lot.

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

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

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

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

505
00:30:50.240 --> 00:30:52.279
not so scared or extremely scared?

506
00:30:52.319 --> 00:30:52.359
Like?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

538
00:32:47.319 --> 00:32:48.799
features or anything like that.

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

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

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

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

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

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

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

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

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

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

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

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

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

552
00:33:36.880 --> 00:33:38.799
regions or states exactly.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

635
00:38:49.320 --> 00:38:52.840
in other other data other databases of witness.

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

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

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

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

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

641
00:39:09.280 --> 00:39:10.639
one before.

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

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

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

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

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

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

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

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

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

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

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

653
00:39:53.199 --> 00:39:53.960
things yourself.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

743
00:45:03.480 --> 00:45:04.239
anything like that.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

758
00:45:50.840 --> 00:45:51.000
with me.

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

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

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

762
00:45:59.480 --> 00:46:00.000
was so much fun.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

803
00:48:27.280 --> 00:48:28.239
your tickets today.