Algorithms & Deer Movement

You're listening to the Sportsman's Empire Podcast Network. You're home for real, relatable outdoor podcasts. Go Wild is a free social community created for and by hunters. This means that unlike mainstream social media, your trophy pictures won't be censored. They're encouraged. As you spend time on Go Wild, you will earn awesome rewards such as gift cards, free swag, and big discounts on brands like Garmin and Vortex. You will even earn $10 just for signing up. Visit downloadgowild.com and sign up today. Ladies and gentlemen, welcome to another episode of the Hunting Gear Podcast. I'm your host Dan Johnson. Today we're going to be talking with Bill Thompson of Spartan Forge. Now, if you're not familiar with Spartan Forge, it is a mobile hunting app that you can download to your phone. They also have desktop availability. But, man, we talk a lot about technology today. We talk about algorithms. We talk about collared deer studies. We talk about 3D map imagery. It's crazy where hunting technology has gone in the last several years. Because I can remember, before cell phones, before internet, before even trail cameras were out, I would just go into the woods and hunt. And now it seems like if you're not taking some type of device with you that I would use the word technology, that is technology, like a digital technology, like a computer program or something like that, then you're lost. I use hunt stand right now. And I just imagine what you take those mobile apps away from me and having to go back to the old printed maps or the old plat books and what that would be like. It would be absolutely crazy. And so the industry has changed. The technology has changed. And some say it's for the better. Some people don't like it. But today we get into a really detailed conversation with Bill about Spartan Forge, how he kind of came up with some of the stuff that he's come up with, the advancements that the app has made over the last couple of years, how all of that, all the data that he collects, translates into information given to the end user on what days, if you're not familiar, with Spartan Forge's predictive deer hunting, a predictive deer movement platform that tells you basically in your area what days are going to be the best to hunt, given, you know, call their deer study data, given how it impacts deer movement, historical data like that. And so it's a really interesting episode and I'm sure if you guys are deer nuts, you know, this isn't a hundred percent gear talk because we also get into things like what drives certain aspects like humidity and precipitation and how that affects deer in different parts of the country. So it's a fun episode. I always like talking with guys like Bill who are extremely intelligent. And even at the beginning of the episode, we get a little background into Bill and his time spent in the military doing whatever it is he was doing in the military. So it's a pretty interesting episode. I know you guys will like it. Before we get into today's episode though, I do have to send a shout out to the people who make this podcast possible. If you guys are looking for a saddle and I know that this year I'm going to be taking a couple more, what I would say, less spot and stock, more tree stand hunts. And so I'm getting ready to, I'm getting ready and I'm excited to use a saddle more this year. So if you're looking for a saddle, saddle hunting, accessories, platforms, climbing sticks, you name it, go check out tethered. If you are looking to document your hunts, then I strongly suggest you go check out Tacticam. The Tacticam has the new 6.0 version currently out. It has image stabilization. It has an LCD screen. It records in 4K. It can be mounted to your bow. It can be mounted to your gun. And that way you can record what you see out in the woods. And then you can come home, show the wife, show the kids, show your buddies. Even helps with shot placement. If you're unsure about where you shot a deer, let's say you can take it home, throw it in your computer, review the footage, and then that'll tell you if you need to wait or go after the deer. So hunt stand is the next one. Let's see, Tacticam and then hunt stand. If you're looking for, you know, today we talk a lot about mobile apps. So if you are looking for a mobile hunting app, if you're a hardcore deer hunter, go check out hunt stand. Hunt stand has a lot of functionality behind it. Hunt stand has, is the most, one of the most popular apps on the market for a reason. And it's just because it gives you a lot of options. And while you're at huntstand.com, read up on all that functionality and then also read up on their pro Whitetail platform that they've just, I guess, introduced over the last year. It's pretty intriguing. So go check that out, huntstand.com. And that's it. The other thing I want to say real quick is we do a lot of talking about gear on this podcast, very little talk about conservation. So if you are looking to give back in 2023, just, I'm not going to sit here and try to pitch it, go look at fish in wildlife.org in the conservation organization, 2% for conservation. Go check that out, see what they're all about. And if you want to get 2% for conservation certified, go check that website out. So we're done with the intro. Let's just get into today's episode with Bill Thompson of Thompson or Thomas. Now I messed it up. Either way, I apologize. Bill from Spartan Forge. All right. From Spartan Forge, I have Bill Thompson on the show today. Bill, what's up, man? Not much, Dan. How are you? Doing good. Hey, remind me again where you're located. Right now I'm out in West Virginia in Bridgeport. I retired from the military last year in November. And my wife got a job out here. So we've been out here in West Virginia. I can kind of work from anywhere, but I suppose it's been a couple of years since I've been going back and forth from out here. But yeah, in West Virginia now. Gotcha. And so let's see how many years in the military and what was your kind of background in the military? Yeah, so I did almost 21 years. My background in the military is complex, but I started as an enlisted guy doing like signals intelligence, which is, you know, just think like radios, cell phones, that type of stuff, signals intelligence, exploitation, and radars as well. And then I kind of pivoted from there to going into the human intelligence realm, which is like, you know, sources and that type of stuff, well deployed and doing that type of work. But from a technical point of view, I became a war officer about 11 years ago. And the easiest way to think about war officers like a technician in the military just kind of advising office general officers and colonels on what types of technologies are good, which ones are not good, how can they can be integrated into the unit to be like a force multiplier for a commander while they're overseas or during their mission in Garrison or whatever. And the kind of war officers kind of sitting between, we're not soldiers. We're not well, we're soldiers, but we're not enlisted men and we're also not officers. We're kind of like in between. So if you think about the military as like a school, your NCOs are kind of like your teachers, whereas your lower enlisted guys are kind of your students. And then you have your officers, which are like your principals, your vice principals, administrators, that type of thing. War officers would be the guys who are like recommending the curriculum and what books to use and what, you know, that type of thing. The technical that's the military unit. Okay, I gotcha. And so in order for you to recommend those things, you have to be somewhat well versed in those things. So what kind of training did you guys have to go through? I mean, is it like drone training or is it more like, hey, here, here are a list of educational tactics that you can use to train your soldiers better. So for myself, the training that I went through was a lot of technical, well, there's technical and tactical training, but a lot of it was computer programming. Network, like network diagnostics, understanding how what networks are, what their vulnerabilities are, how they work. And then on that. And then there are other disciplines to where I learned other, you know, you know, how to go and meet a guy for a meeting in the middle of maybe, you know, in Pakistan somewhere and a ranging meeting where there's operational security. And there's, you know, your physical security and you're thinking about all the second and third over effects of those things. But I was, again, looking at those types of things from a technical perspective. So the training was vast. There was lots of different, you know, we could do a whole podcast on just the training and all of that stuff that go into that from the technical side to the operational side and the tactical side and all of that. But there was a lot of it there. And I guess if I had to summarize it all, it would be, you know, lots of learning, you know, coding, code bases, network architecture, and then kind of that, the tactical implementation of the exploitation of those types of networks and so on and so forth. So, you know, everybody, everybody talks about how big the United States military budget is, right? And we have the biggest, baddest military in the world for a reason. It's because we spend a lot of money on it. And so were you able to get your hands on some pretty, like, I don't know, whether that's maybe software or just things like that or just like Holy cow. This is mind-blowingly awesome. Yeah. So I'll say this about my experience of the US government. You know, doing on that side of the house, some of the stuff I did, you could consider it like ethical hacking. Yeah. And I think my last year, I was an advisor, I was an advisor for a general officer and for a colonel that were doing development in what was called the offensive cyber realm. So that's ethical hacking. I believe that year, the US budget for the DOD was something like $750 billion. And I would say that 60% of that budget is judiciously executed, whereas the other 40% is wasted. There's some reason it's necessary for waste sometimes, but a lot of times it's just the government is very resistant to updating and resistant to kind of cutting the fat. But so there are cool programs that I worked on and there are cool things that I built. But at the end of the day, it was the ability for me to do the kind of stuff that would be illegal in any other context. I got you. So breaking systems, breaking networks, you know, think, just, you know, let your mind wander. All of the things that you would want to do if you had, you know, a free rank of break as much as you wanted to. And that's kind of the role of the military is not to get too deep into it, but they have what's called, you know, title 10 authority. Yeah. And title 10 already just means you get to break shit. Yeah. And as long as it achieves the goals. So yes, there were cool tools and there were cool things that we got to use. But at the end of the day, it was more about the tactics and the operations that we got to engage in that I think really separates us from everyone else. Because anybody else who was doing the things that I was doing for my last 10 years, my military career would have been arrested and any other context. Yeah. So that was the best part. That's absolutely crazy. Now, you know, I've talked to some guys before who I don't know if I would say they were directly in the same type of platform that you were in or doing exactly what you were doing, but they did some pretty, some pretty cool things. Where was the balance between like stress and dude, this is awesome doing this type like fun. Yeah. So the stress was more on like the family side or not being around my kids when they were young. You know, I miss the birth of my son deploying to Iraq. I think for my second tour to Iraq. This for me at least, I feel like I'm an outlier in the military because I just benefited tremendously for my military service in a way that I think is exceptional and really a story that's only possible in the US. So, you know, I joined from a trailer home in the middle of nowhere, North Dakota, where I really had no prospects for my future and, you know, got into the military and kind of luck into some things, which is again, a whole nother podcast that I could do. But, you know, one of the reasons I got into intelligence work is just because I simply forgot my driver's license at home. So I was signing up to be an MP because I was 16, maybe 17 at the time when I was signing up. I wanted to be a military policeman and I just simply forgot my driver's license. And I was like, the recruiter is like, well, you can come back next month because we had to go to Minneapolis from where I was from in North Dakota in order to sign up. And I was like, look, I'm not going home for another month. Sign me up for something else and get me the hell out of here. And so to kind of answer your question, my military service, I just benefited tremendously. I came out with degree. I came out with certifications. I came out speaking three languages. I came out with a wealth of knowledge. And it's kind of one of the reasons why I pushed so hard. You know, when I started the company and I was bringing employees on, I was like, look, everything that we're going to do, we're going to try to benefit military people in one way or, you know, we can talk about it later, but we do these veterans on that type of thing where we're raising money for people that didn't benefit from my military service. So for me, most of the suck involved in the military was being away from my kids, being away from family, missing things like death of grandparents and, you know, the type of stuff that you would want to be around for. But on the other hand, you know, I did, there's, there's, I, again, I can fill a whole podcast with what I thought were all the awesome things I got to do in the military, breaking things, you know, coming up with cool things. Coming up with cool technology to assist the special operations command, getting a lot of to do all of the kind of like cool stuff that you think for the first five or seven years I really didn't get to do a lot of cool stuff. It was a lot of motor pools. It was a lot of checking vehicles. It was being a grunt. It was rucksacking around and carrying stuff and PMCS and vehicles, preventative maintenance and checks on vehicles and kind of just being a grunt. And then I kind of fell into getting to do other stuff later after I guess I'd kind of proved myself, where I did get to do the stuff that I think most people dream about whenever they, you know, a lot of guys when I joined the intelligence discipline in the military. Dream about the stuff that I did for like the last 13 years, and again, just super beneficial and super fortunate to get all of that. So that's kind of that's kind of what it was to me, you know, I didn't mind deploying. I didn't mind doing operations. I lived for doing operations. I lived for supporting the battlefield commander. I lived for doing technical assistance for human intelligence operations for signals intelligence operations. I used to fly around in airplanes and and set up operations for ODA teams and special forces guys to go in and do stuff like really got to get all the benefits from it. And really the problem for me was just being away from my kids but I'm making up for that now. So there you go. There's always time. There's always time. Okay, so 21 years in the military, you step away from it. You're retired now. Now, how many years ago did you start Spartan Forge? I actually started Spartan Forge while I was deployed to Afghanistan in 2015. So I was still active duty. I didn't incorporate, you know, I incorporated the business in 2017. The first name I had come up for Spartan Forge was called Open Season. And I started collecting collared GPS data and dealing with academics and talking to people through my experience. I was working at the time. I had just gotten done advising on a development effort with DARPA, which is kind of like the mad scientists of the DoD. They just get to think of all kinds of crazy stuff like, hey, you know, let's put a rocket on Mars. They do that type of stuff. And so I was working with them and I met some academics and made some inroads to people and had built or assisted and supervised the building of other neural networks to benefit the government and the military. And it started occurring to me that I could probably do a lot of this stuff with GPS data from collared your studies. So I started collecting that I made my first phone calls to academics and talking to people just during downtime while I was in Afghanistan and in 2015 between operations. I would, you know, just reach out and say, hey, I'm here doing this type of stuff. And I think I could do the same thing. And a lot of academics were willing to share that data. And that's kind of when I started doing that. I've been collecting it ever since. It just got some more dear data the other day that we're working on integrating to our neural network. So pretty continuous since 2015. Yeah. And so when you first started Spartan Forge did or the company or what, you know, that you had before Spartan Forge before the name tag. Was it always about forecasting deer movement or had you planned into going into the high debt, the, you know, the high definition, very detailed mapping and all the other stuff that comes with Spartan Forge. Yeah, so no, I wanted to focus on neural networks. I always saw Spartan Forge as a machine learning company. There are other things I want to do. And I still am doing in my very little spare time in the neural network machine learning artificial intelligence realm that I do. And I always wanted to start the company as a machine learning company. I still call it a machine learning company. I was originally supposed to be working. I worked for about two years with another very prominent mapping company. You know, the most probably the most prominent mapping company. And had basically gotten to the point where I was going to sign a contract with them. But then, you know, things came up for me and I didn't, you know, the contract wasn't favorable because it kind of locked me down for many years. And I always thought, I want to focus on the machine learning side of stuff. I just need to integrate with an application that's already doing this and then I can help them do it better because a lot of these companies that are out there just dealing in commodity data. And it's just, you know, you can think of them as like, you know, British petroleum, Amco and, and, and another oil company like all three of them are just selling oil. They're not, they're not doing anything crazy or innovative with the oil. Mapping data is out there. None of these companies that launched these programs are collecting their own mapping data. They're paying other companies to go and do it. Or to get the information. So my focus was always, what do we do with this mapping data or how do we improve a lot of hunters by capitalizing on this data and then integrating it with other pieces of data that I'm creating a comprehensive planning and execution picture for hunters, a lot like I did with the military, a lot of the programs I advised on the military were multi intelligence multi source intelligence collection systems that presented information to commanders and what's called like a common operating picture. This common operating picture is kind of like from a military perspective it's kind of like a snapshot of what the battlefield looks like and all of the amplifying information that's collected and updated. So, Commander can look at it anytime and have a pretty good picture of what's going on in their area of responsibility. My goal with anything I was going to do in Spartan Forge was to integrate with another company. And then kind of show them what else was the art of possible and put it all together and this, you know, common operating picture. And Spartan Forge itself now is moving towards that I'd say we're brought 40 or 50% of the way there. And so yeah, my goal was to integrate with someone else but once I, you know, I did a lot of investigation on some of these other companies. And I don't want to speak out of school on here I encourage people to do their own. Look into these companies but I didn't like some of the investors that they had I didn't like the core tenants or principles of the investors that they had, or the leadership that they had, whether it was because they're anti first amendment or anti second or the investors in some of these companies. And then, but I always said to myself I'll still join with these companies because I don't want to do this all on my own. But then the contracts were just so litigious and so that you know they want to do a year long contract with you then lock you down for not doing anything else with anyone else for five years. And I just wasn't willing to do that so then I then I basically was on the call one day with a guy who was in charge of one of these companies he was the president I believe. And he just kept telling me like look just come with us do them do the map do the neural network stuff. Let us worry about mapping you don't want to worry about mapping mapping is really difficult. And he said that like three times to me on the column by the end of the call I was like you know what I'm going to do mapping anybody who says that. That's not difficult that much must be lying. Yeah. So I looked into it and I was like this isn't difficult at all the difficult parts the machine learning part so that kind of led me to where I am today. Yeah. All right so you've used terms like machine learning and neural neural network. Yes. So for a double deer hunter like myself explain what that is. So a neural that the simplest way to think of a neural network is that it's a computer system that recognizes patterns in data and then can make predictions on those patterns. Gotcha. So the reason it's called a neural network is because it's inspired by the structure and the function of the human brain. So in a neural network there's like layers of neurons and each one of those neurons become weighted based on the data that you show it so that the simplest way to think about this as is you know and I'm going to kind of jack this up but I'm trying to make the analogy work. If you have like three ways that you travel to work from your home to work. You have like three routes that you take. You can think about that like a neuron like a structure and say over the last year, your time in the car is measured every time you drive a certain route or a different route. So if you have a machine look at that and say well over the past year what was the most efficient route when it was raining or was the most efficient route when it was sunny. What was the most efficient route during this time because there was traffic or this time when there wasn't traffic. And then that neuron gets weighted. And then, and then in the future you can look at all of those conditions then make an informed prediction on what route you should take to work based on past circumstances. So a neural network is just millions and millions and millions of those neurons informing a decision based on patterns. And it's the same way that like the human brain works the human brain. We're just patterned we're just really sophisticated pattern recognition machines. We recognize colors like RGB and we recognize personalities and we recognize patterns of other animals and our environment and what things look like. And those patterns are comprises the structure of the brain. So in neural networks, you are just making new neurons based on other data. So in this case, the first network that I made was myself and my founders made was based on color GPS data to inform those neurons. So the during the training the neural networks given a set of input and corresponding desired outputs or you ask it for what you what you're getting from it and then you can measure it and make success based on other data sets. So say I get a bunch of color data from Ohio deer and I make a neural network for Ohio or I've made it for other states. And then I'll get data from somewhere else. And then I'll wait and see how similar those deer move or how dissimilar those deer are and how they move. And then I'll make another network based on the new data. But I can test the old networks based on the new data. So it's, that's the, I'm, I hope I explained that well. So it's kind of difficult. Is this, would this, would this be an accurate statement. Basically what you have is just a ton of data and you're organizing it in a way that people can can find. They can see it and find patterns in it. Yes, exactly. Well, the machines recognize the patterns and then they try to prescribe the patterns to people. The most difficult part of any neural network is the data collection. Having enough data to do it like I could teach you how to make a convolutional neural network in an afternoon. The algorithms have been around for a long time. It doesn't take a ton of coding experience to actually do this part. Google's made a lot of it very easy through a program called TensorFlow. So you can do it on your own. The difficult part is getting their requisite amount of data to train it. Gotcha. All right. So, um, when, when we first talked, you know, as a deer hunter, I live in Iowa, you, and just for conversation purposes, you just mentioned Ohio. Right. And so I live in, in Iowa. How does deer movement in Ohio or any other state? Like, what does that have to do with me here in Iowa? And how, how can I look at that data from different states and go, Hey, you know, and this is, this is talking about predictive deer movement at this point. But why does deer movement in Ohio matter to me in Iowa? Sure. So there, there's a few ways to answer that question. The first way to answer that question is they all are all the same species. So when you have the same species, there are evolutionary underpinnings that kind of are in every animal that are the same, at least from birth. And the only way that you can get rid of them or change them is through a very harsh conditioning. You can think about it like you, like, what does Dan Johnson have in common with a guy from Africa? Well, if I throw a baseball at either of them, even if the other guys never seen a baseball, they will generally flinch and throw their hands in their face as the first reaction. And that comes from like a very deep part of the lizard brain that every human has. So reactions to stimulus are going to be the same across a species, regardless of where the species lives. So with deer in the same way, the reaction to weather events, or to what weather events and how it influences deer and deer movement, or the responses to things like drought or different types of factors are going to be the same at some level, or at least we know it's going to influence the animal at some level in the same. But the difference has to become, and the reason why neural networks important to use them this is because the neural network can look at 50 pieces of input data and say, well, a deer in Iowa and Ohio are pretty similar. But when I'm looking at this from North Carolina, you know, what, you know, the amount of time that that adverse weather affects deer in North Carolina versus Ohio are not the same for different types of the year. So for a three day rainstorm is going to affect the deer in North Carolina differently than it would affect the deer from Ohio. But what the neural network knows is that it affects both. So it's just the easiest way to answer that is the predictions differ and basically based on where you are latitude and longitude wise in the US, based on stressors in the environment differently, but affects them all just differently. And that's the role, the proper role of a neural network, you would never be able to get some a person to recognize all of those nuances, you'd have to hunt your whole life first just to understand deer in an area accurately. But then to understand deer in different areas accurately in different places and what gets them going or what doesn't get them going or moving or all of those things would be an impossible data task for a human to carry out. So that's the proper way for a neural network to kind of separate the wheat from the chaff and find out what what in a weather forecast is actually going to impact your movement and how. Okay. I hope that answers that question. Yeah. Also, by the way, I have no short answers. I apologize to everybody in the area, but you know, this stuff is very, there's a lot going on here. And when I ask her questions, I'm trying to be as accurate and is data science oriented as possible, but still presenting a palatable product for a podcast. Yeah. So I hope I can manage that tension. Yeah. Okay. And so, you know, positioning off that then. All right, we're still talking about predicted deer movement. How do I, the hunter, use everything that you've just said. I download your app. I start to use it. How do I use it? And how do I use it properly. So there are a variety of. Different ways to use all of the features in the application, but just focusing on the neural network. You should think about it in general deer movement. You shouldn't think about it in specific. It's not going to help you. It'll help you, but it's not good. It's not something I would use biblically against like a five, six or seven year old buck. Because the one thing I've learned from looking at all of the GPS data and I've looked at tons and more deer data than I think probably everyone's ever looked at. I'm not sure I've met somebody who's looked at more individual deer and their movements and what affects them. One thing I can say empirically about it is bucks just none of them are the same. And all of them are different for different reasons. So when it comes to like using my neural network for a buck, especially a mature one. You're going to have to kind of. Allow it to or try to understand how it influences that deer and when it's correct and when it's not correct and then try to use it. But more use it for just general deer movement. So if you're just out trying to schwack a dough or you're just out trying to see deer or you're taking a kid out with you. Or you're trying to correlate movement with deer cameras and other things. Then the neural network's super handy. But there will never be a network and it's why I kind of laughed to myself whenever I see these other predictive networks where it's like, hey, I'm Joe so and so hunter. I've developed a neural network for a year that's going to get you the biggest buck. No matter where you are in the US. It's like none of them are the same and there's no way anybody one person could develop a neural network that's going to help you kill a buck. Or that's going to accurately say every deer in the woods is going to stand up. But you know you look at some of these other options like what you need to be in the woods at 1 45 p.m. today. And so you think about that and a lot of people get excited and they're like, all right, I'm running into the woods at 1 45. But then you think about the data, the proposition of the data. And it's like, so wait, this thing is saying that every deer in the woods is just going to be moving at 1 45. That's not possible. Right. So, so what the hunter needs to understand with a neural network like mine is is that it's got thousands of years of deer data. And what I mean by that is if one deer wears one collar for eight years, that's eight years of deer data. If three deer wear three collars for eight years, that's 24 years of deer data. I've looked at, you know, thousands of years of this deer data. And so that what the network has sussed out from that is you basically have three types of movement that categorized deer movement. And that is during the daylight hours, they're going to be staying in their bedding areas. They may move out to transition areas or they could be anywhere in their range. And those are kind of the three buckets. Anybody saying that they have a network that predicts any better than that is not being honest with you. And the reason is because after all of this deer data, that's about as good as it gets. So you can think about that as it the value proposition for a hunter could be when I'm looking at the neural network. The neural networks going to tell me generally deer are going to be staying close to bedding during day hours. They or they may move out into bedding in the staging areas or into a scrape line or they may be just outside of bedding areas. Or it could be there what we call full range and the way that the neural network looks at full range is if you are driving by a field every day to work where you never see deer. Then all of a sudden there are 21 deer in that field, the neural network would call that a full range day. In other words, deer are out there moving much more aggressively than they normally would. And there's litne factors that influence that. And that's kind of the best that you can hope to get from what would take. You wouldn't have enough humans observing deer for a month enough time to get that anymore clear on that data. So that network that we've developed predicts accurately still only at like 66, 67% of the time. So, and I'm very clear about that because I'm not trying to oversell somebody or give somebody snake oil on something, but you're getting two thirds of the time, you're getting an accurate prediction on what most of the deer population will be doing. When it comes down to patterning individual bucks or going after any individual bucks, you might find some, it might inform the neural networks, the neural network might inform you some and saying, hey, generally, whenever it says core area day, my bucks not leaving bedding. And so that's something I would tell people to look at a one on one instance because again I'm not going to sell them snake oil and tell them that this thing's far better than it is. It might not be great marketing, but I can sleep at night. Yeah. And so, you know, I've talked with just about every predictive deer hunting model app, whatever you want to say, that's currently out there. And for the most part, there are, there are some, some commonalities. There are some similarities between yours and some of the other ones that are out there. But, and you kind of there just mentioned the, you know, there's like a handful of things that are really that go into the equation that predicts deer movement. The word algorithm pops up in some of this and how, how the out, how certain apps pop out what days to go hunting and what times to go hunting and things like that. If I said to you, I have 40 years of hunting experience and I kind of go in and break down what my hunting experience has been in certain conditions in certain times a year. And I implement that into my algorithm. Does that benefit the output of data at all? I would say in that area where that person has learned and influenced and be able to control that environment and set up, you know, they're obviously very, you know, people that make those types of that work probably know a lot about their deer on their property. But again, what moves deer on their property where they've learned or where they've looked at these things is not necessarily what's going to get moved deer moving in Saskatchewan or upper New York or in Florida, which is why we collect this data from all over the US because the factors that deer moving in Florida are not the factors. I'll give you an example in Mississippi relative humidity and humidity in general is bears a factor on deer movement. It has almost no factor on North Dakota deer or Minnesota deer. It doesn't change the, it doesn't affect one way or the other really that I can see in the data on what gets deer moving. Another one is interesting. Yeah, it's just, it depends on the state that you're in and when I'm looking at the data, but then also when you start getting into areas, you know, one of the, one of the things that affect deer movement the most is rainfall in Alabama, for instance, I've been saying this for a couple of years now if there's a light rain, I would be hunting all the time. It seems to really get deer moving down in those areas. And I, you know, I've talked to a lot of academics about this and I think one of the reasons is because they have flash flood scenarios. And I think it helps the deer if they're dynamic during rainfall to make sure that they're not getting washed away or something else. And it may be that they just have an evolutionary mechanism that kind of keeps them moving during the rain because you never know what's going to happen. Whereas again, that were in flatter areas or in the Midwest where flooding isn't the problem in a specific area outside of like the Red River Valley. The deer don't react to rain like they do in someplace like Alabama. But then again, in a place like the mountain country in the northeast PA, North Carolina, the Blue Ridge Mountains and out West. You'll see factors like cloud cover and thermal generation affect deer movement. Much more deer in the northeast need reliable thermal thermal generation. And in order to send check areas, they need reliable cloud cover in order to understand how the wind is going to shift or be different as a result of movement, especially in pressured areas. Those things really change how nothing will get a deer moving in Pennsylvania like a drastic wind shift. A drastic wind shift, especially among mature deer. They might not move far, but they will move when wind shifts because they want to be in an area, especially when they're betting, where they can take advantage of the wind direction and thermal generation in the sun's placement in the sky so that they can smell everything around them. If you have a consistent wind, that's the normalized wind for that year. You're not going to see a lot of daytime deer movement in pre-rut or mid-October in Pennsylvania. Whereas wind direction in really tight agricultural country doesn't affect the ton. The deer don't have to move as far and they're kind of in their shelter belt or their fence row, and they're not going to walk across that beat field or that soybean field and expose themselves just because the wind has changed. So again, that's kind of what I'm getting at with the neural network is I'm able to take the data from all over the country, and all of those factors I just talked about are fed into these algorithms, and then the network can predict the most accurately based on where it is in the U.S. Gotcha. All right. And so... You got the predictive deer movement portion of this, right? And it sounds to me like that's just continuous, always learning, always improving model that there's no end to it. It's just a continuous trying to make everything better. Now, when it comes to mapping, right? And you said the guy said three times in a row, so you thought, well, he's wrong. I'm going to do it myself. And so you started working with mapping, and it sounds to me like from what I understand, getting things like landowner data or satellite imagery and things like that is fairly easy to do. What makes then Spartan forges maps stand apart from maybe the other apps that are currently on the market? There's a couple of things there. The first, I think, is just the presentation and the user interface and the user experience. We are trying to allow hunters, you know, we have five to 15 centimeter imagery for about 45% of the U.S. What that means is if you have a five, the easiest way to think about it is if you have a five centimeter object on the ground and you fall into that coverage area, you should be able to see it through our mapping. That's, you know, hundreds of times better than what is currently on the market with like one to three meter imagery. And we just made a value proposition and investment from the beginning to invest in that type of stuff and work with these companies from the very beginning, where it might be cost prohibitive for a very large company to enter into that agreement, especially up front. Because of the amount of user base that they have is really what these companies are interested in. But also, we provide them data services back in other ways that are kind of proprietary. But essentially we have these relationships with these companies, but then we also present different maps from different times of the year. And we also go historical. So on that data, we go, we can go back 14 years on most of it. Some of it is 10 and then a very small subset of it is seven years. So you can go back and look at how the landscape has changed over time again in that high, high resolution form factor. But then we present other three other data layers that are one to three imagery through one to three meter imagery layers throughout many times of the year. So no matter where you are in the US, you're going to have some good maps. And it's not just going to be one map and you get what you get. There's going to be these different types of ones, but then also the way that people can interact with them and set up their maps. And for instance, inside of our mapping application. If you want to look at if you want to set your map up so that you have slope angle shading on LIDAR with property over it, you can make that custom map and what we call our lambda layer. But if you want to switch right back to an aerial, it's just one swipe of the thumb. Whereas in other applications, it's going to require between five and seven clicks and in some instances, 15 clicks. So you're spending a lot of time when you're in the field changing things up and doing things, whereas in our application, it's a very quick movement. And that really comes from my military background and understanding that from a design perspective, I want people to have to physically interact with the application as little as possible in order to get what they need. So they can focus on what's around them and not having the nose and the phone the whole time they're in the field. So that usability and the way that people interact with maps and set them up. But then also what we're gleaning and learning from the mapping. And for instance, here shortly we'll have out this one meter LIDAR imagery for most of the US. And it's very high quality LIDAR imagery where you can see in some instances, you can see cow trails on the ground, or you can see benches on the side of the map. You can see benches on the side of hills where you'd never be able to see it with top or anything else to really inform scouting or even where there's like a seep on the side of a hill. This is stuff that people generally don't have access to or haven't seen in the past. And we've made a large investment in getting that out there. So again, it's not just the data or the access to data. It's how it's presented and then how we show the hunter that this benefits them from a targeting and hunting perspective. Gotcha. Alright, so my next question then revolves around the data that you actually collect from the user that on how they're using it. So let's just compare me and you, for example. You may use an app and you may do all the predictive deer movement. You may check your weather and you may still drop pins and then look at the landowner data and do all that stuff. Whereas I may go into it and use it and just go, hey, I'm only using this just to drop pins and that's it. How do you use that information on to take the next step into like the best usability for the end user or to change or update the app based off that information. So we don't take a lot of user data and look at that. So when I'm looking at aggregated data to understand like where I might pay for more updated imagery because like the imagery that I talked about before is very expensive. So I'll look at user distribution and see where my largest user bases are and then add imagery for those user bases first. That's the really high quality stuff. I'll still give all of the other rest of the United States, all the other imagery repositories. But then I'm also looking at what pieces are being used the most or they use the least and then interacting with the pro staff. The guys that we work with understand, hey, you know, I see people aren't using journal, the journal and the application is often they're using historical imagery or vice versa and then try to pull the string on that and understand why. But again, the way that we architected this application is if you're just the guy that just wants to see where your property lines are and you just want to drop pins. Like that's all there and the weather is there and you can just use it in that really simple fashion. But then if people go on like our YouTube and watch some of the videos, which I encourage people to view when they sign up for the application. You can get really deep and really, you know, into the details of the data where you can understand, you know, what's the predominant win during the second week of November in Kansas in this part of Kansas. You get really deep into it, but we try to build the application so it can be as simple as you need it to be or it can be as complicated as you need it to be. You can dive as deep into the data as you need to be. But we're constantly looking at that to see what's being interacted with, what's being used, and then understanding from the user where they want to see improvement. And I think that's another thing that separates us from the from other companies is that if you message us on social media or ask a question about dear data or about a feature or a feature you like or you don't like or whatever. My marketing guys will be the first ones to see it, but then they'll direct it to me and I'll be the ones who are answering those. So I spend the most two hours of morning just interacting with users to kind of pull the string on the second order of that data where people are interacting with me and I'm trying to understand exactly what it is they want to see for the user, whereas I don't think you see that in a lot of other companies where you know the CEO and the lead architect of the solution is interacting with guys at the tactical level to really understand what they need. So that's just a kind of a cross section of how we do all of that. Yeah, awesome. All right. So, you guys, let's just do this. Why don't you tell us what's new on the app. Maybe over the last six months, what you guys have introduced any changes or updates to that. Yeah, so we introduced a web app. And that's going to be updated here shortly. I've shot away from doing dates now because there are things that I don't control like the app store and approval and that type of stuff. So we're trying to release it here very soon, but we've released a web app. You can transfer your points on there from other applications if you want. But then you can also just have a different display to look at the mapping to look at the areas to look at, you know, to do all of the planning and stuff. So if you're at work or whatever and you've got a lunch break and you want to hop on and look at the maps you can do that and drop points and that things up with your application. But then secondly, we've released some, you know, slope angle shading, which again, we were very meticulous and I just encourage people to look at it and kind of like our competitors, the way that we built that architected it makes it extremely accurate in a way that I don't think other people have thought about. And we've updated our five to 15 centimeter imagery. We have on our custom map layer. We've updated the way that people interact with that and can set custom maps. And then be able to, as I said before, reference those more simplified maps. So it's like you can either click the maps button or you can swipe through, but they're basically four maps or four layers. There's a top layer, a lighter area layer, a hybrid layer, and then your custom layer. And you can switch between those very quickly. And we have our Intel tab that we're constantly updating. That gives you, you know, pallet ability for forage that deer feed on the area, the buck to do ratios for county by county, the state draw odds, the state. Hunter population, the distribution, popular tracks of land that's all stuff that we added in the last year. And then going into this off season we're adding that LIDAR data that I talked about before, which is for about 65% of the US has one meter resolution. We added a functionality called Blue Force Tracker that will also be out this spring, which is essentially you and your son are hunting the same land together. You can him and yourself download this part and four jab. And then you click your property and then you say add a Blue Force tracker team, you put your son's email in there. And now anytime you're on the property together, you're auto sharing location in your auto sharing points. And you can do that with other buddies. If you're scouting large tracks of public land together, or if you're granting access to someone else to your property, you can tell them, hey, when you're on the property, I'm going to invite you to this Blue Force tracker team. So I know when you're in my backyard hunting so I don't send my kids back there. That will be out this spring. And then we've also partnered with Eastman's and their tag hub data. It's going to be coming out in the application here very soon, where you'll be able to look at state by state draw odds and count down to the county level and understand your likelihood of where and when you'll you can draw a tag. And then we're going to be coming out with some neural networks and algorithms in the future that will help optimize for people that are trying to do tag draws throughout the US. And then we're finally trying to put out some of our core where features. We were trying to get those out late last year, but we just realized some problems with the data that we've been fixing over the spring in the summer. So essentially what that'll be is just new ways to look at topography and things to enter that influence deer movement. It starts with this lighter layer that I'm talking about. But then it'll eventually get to the point where you can just highlight a piece of gun and the neural network will recommend places that you should scout. So, those things are all coming out here and we're hoping to have, or no we will have, we will button the product down by probably fixed in August. So the application that you have going into the hunting season will be the application that you have throughout the year. You know we've only been out for a year so last year we had to do in season updates but we're going to stay away from that this year. And then we're going to have a couple of other things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. So, we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to have a couple of things that we're going to do in the next year. And then we're going to have a couple of things that we're going to have a couple of things that we're going to do in the next year. You know, I would be consulting four or five six apps while I was doing my planning. My goal is to put that all into a one-stop shop and put all that data in the same place. And kind of push the technological edge and capacity of what data can do for hunters and put it all into place that looks nice is easily usable and highly customizable. Awesome. Awesome. Well, I tell you what, Bill, man, I really appreciate you taking time out of your day to hop on and be us with us today and give us an update on Spartan Forge. It sounds like not only are you busy, but you got a good thing going over there. So thanks again, man. Yeah, thank you for your time, Dan. Have a good one. ♪