1 00:00:00,200 --> 00:00:04,560 Speaker 1: Now here's a highlight from Coast to Coast AM on iHeartRadio. 2 00:00:04,760 --> 00:00:08,400 Speaker 2: Brett Shanning is the founder of modern Geo and we're 3 00:00:08,440 --> 00:00:14,920 Speaker 2: talking about geospatial intelligence analysis modern hyphen geo dot net, 4 00:00:14,920 --> 00:00:22,360 Speaker 2: the website modern hyphen geo dot net. Before the break, 5 00:00:22,760 --> 00:00:29,000 Speaker 2: we were talking about geo spatial intelligence and its applications 6 00:00:29,000 --> 00:00:31,200 Speaker 2: in the business world. I think you were about to 7 00:00:31,280 --> 00:00:36,920 Speaker 2: discuss or we're talking about, for example, compiling mailing lists 8 00:00:36,960 --> 00:00:37,720 Speaker 2: for businesses. 9 00:00:38,880 --> 00:00:44,680 Speaker 3: Yes, so one of our largest services to our clients 10 00:00:45,760 --> 00:00:53,159 Speaker 3: is providing targeted mailing lists. Now, the marketers in the 11 00:00:53,200 --> 00:00:57,520 Speaker 3: audience are probably given a good goofa right about now 12 00:00:57,640 --> 00:01:01,720 Speaker 3: saying we've been doing that for sixty seventy years, But 13 00:01:02,080 --> 00:01:07,399 Speaker 3: our clients say otherways. What they're doing is I have 14 00:01:07,520 --> 00:01:09,680 Speaker 3: one client that's over the moon about it. They have 15 00:01:09,760 --> 00:01:14,000 Speaker 3: a business that does a lot of advertising and it's 16 00:01:14,000 --> 00:01:17,920 Speaker 3: still very successful with mailing campaigns. And she told me 17 00:01:18,000 --> 00:01:21,880 Speaker 3: last week, she said, Brett, I've been using a shotgun 18 00:01:22,520 --> 00:01:26,080 Speaker 3: for the last ten years and you handed me a scalpel, 19 00:01:27,520 --> 00:01:30,639 Speaker 3: and that has made all the difference in the world. Now, 20 00:01:31,720 --> 00:01:34,720 Speaker 3: how do we make a targeted mailing list? So the 21 00:01:34,760 --> 00:01:36,520 Speaker 3: first thing we do is we sit down with a client, 22 00:01:36,600 --> 00:01:41,000 Speaker 3: we say, what do you need to know about your environment? 23 00:01:41,120 --> 00:01:46,039 Speaker 3: We have to understand your business. Then we understand what 24 00:01:46,080 --> 00:01:49,120 Speaker 3: your project is or what your goal is for getting 25 00:01:49,120 --> 00:01:54,240 Speaker 3: the word out about your business. We then develop indicators 26 00:01:55,360 --> 00:02:00,240 Speaker 3: and we turn to geospatial science to start classifying the 27 00:02:00,440 --> 00:02:05,960 Speaker 3: area of interest that you want to start marketing in. Now, 28 00:02:06,280 --> 00:02:09,720 Speaker 3: what we have done a lot of is we take 29 00:02:09,880 --> 00:02:13,680 Speaker 3: county data. We overlay the county data with the property 30 00:02:13,760 --> 00:02:19,720 Speaker 3: lines onto a just a regular Google map, it's just 31 00:02:19,800 --> 00:02:23,519 Speaker 3: a satellite image, and then we do what's called image classification. 32 00:02:23,639 --> 00:02:28,560 Speaker 3: Now you've mentioned AI. I thought that that news article 33 00:02:28,720 --> 00:02:33,760 Speaker 3: is just an absolutely awesome segue into this. Let me 34 00:02:34,000 --> 00:02:36,239 Speaker 3: reassure everyone out there that's worried AI is going to 35 00:02:36,280 --> 00:02:39,840 Speaker 3: take your job. It's not. Someone has to program the AI. 36 00:02:40,000 --> 00:02:43,600 Speaker 3: Someone has to work with the AI. And what we 37 00:02:43,680 --> 00:02:48,640 Speaker 3: do is we have programmed AI to look for very 38 00:02:48,680 --> 00:02:54,240 Speaker 3: specific things in a specific designated area, the area of 39 00:02:54,280 --> 00:02:58,280 Speaker 3: interest to the AOI, and in that AOI will tell 40 00:02:58,320 --> 00:03:01,519 Speaker 3: it to look for houses with four or more peaks, 41 00:03:02,320 --> 00:03:07,960 Speaker 3: in ground pools, a lot of pavements, circular driveways, landscaping, 42 00:03:08,040 --> 00:03:15,359 Speaker 3: whatever indicators are indicative of the type of client that 43 00:03:15,440 --> 00:03:16,359 Speaker 3: our client. 44 00:03:16,120 --> 00:03:19,880 Speaker 2: Needs to find now people with money. People with money. 45 00:03:19,880 --> 00:03:24,720 Speaker 4: In other words, for the most part, yes, and what 46 00:03:24,760 --> 00:03:27,520 Speaker 4: we like to say, and this is something that business 47 00:03:27,520 --> 00:03:32,240 Speaker 4: owners are really interested right now, recession proof clients. 48 00:03:32,960 --> 00:03:35,520 Speaker 3: Folks that when you have another two thousand and eight, 49 00:03:35,880 --> 00:03:41,960 Speaker 3: they go, I guess I'll get one seven dollars latte 50 00:03:42,080 --> 00:03:48,040 Speaker 3: today instead of two. So what we have done is 51 00:03:49,200 --> 00:03:52,720 Speaker 3: we ran a test because we're scientists. That's really what 52 00:03:52,760 --> 00:03:57,360 Speaker 3: we are is we're scientists first who happen to be 53 00:03:58,200 --> 00:04:05,840 Speaker 3: cartographic specialists. And so my client the shotgun to scalpel client, 54 00:04:06,640 --> 00:04:11,920 Speaker 3: she said, you know, I bought these sixty five hundred 55 00:04:11,920 --> 00:04:17,080 Speaker 3: addresses from the USPS in their market, their direct mail program. 56 00:04:17,240 --> 00:04:19,880 Speaker 3: That was about five cents an address. When I added 57 00:04:19,920 --> 00:04:26,440 Speaker 3: in the cost of printing, mailing, and then replying to everything, 58 00:04:27,000 --> 00:04:31,960 Speaker 3: it turns into about a dollar eighty per address. So 59 00:04:32,240 --> 00:04:35,560 Speaker 3: a dollar eighty out the door now sixty five hundred 60 00:04:35,800 --> 00:04:38,640 Speaker 3: times a dollar eighty is a pretty good chunk of change. 61 00:04:38,880 --> 00:04:41,799 Speaker 3: I think it came out to around eleven seven hundred dollars. 62 00:04:42,120 --> 00:04:46,000 Speaker 2: But they makes it makes sense, right, And what's the 63 00:04:46,080 --> 00:04:49,640 Speaker 2: conversion on that? Typically? What what? What? How many customers 64 00:04:49,640 --> 00:04:50,520 Speaker 2: would they get out of that? 65 00:04:51,520 --> 00:04:54,960 Speaker 3: So they had a one point nine eight conversion rate, 66 00:04:56,200 --> 00:05:01,440 Speaker 3: So one of sixty five hundred converted to say, now 67 00:05:01,520 --> 00:05:04,800 Speaker 3: in that exact same area, we identified one hundred and 68 00:05:04,920 --> 00:05:11,400 Speaker 3: eleven addresses. That total campaign came to under seven hundred 69 00:05:11,440 --> 00:05:15,640 Speaker 3: dollars and they have currently climbing. They're going to get 70 00:05:15,640 --> 00:05:18,680 Speaker 3: me numbers later on this week, but currently climbing, they 71 00:05:18,680 --> 00:05:22,240 Speaker 3: have an eight point one percent conversion rate in one month. 72 00:05:23,279 --> 00:05:25,120 Speaker 3: You can't argue with science. 73 00:05:27,000 --> 00:05:32,160 Speaker 2: That's what geospatial intel analysis can do for small businesses. 74 00:05:33,000 --> 00:05:38,760 Speaker 3: That's it positioning your business for the global to the local, 75 00:05:40,320 --> 00:05:45,880 Speaker 3: developing data flows workflows to make you more efficient. We 76 00:05:46,160 --> 00:05:50,840 Speaker 3: find the efficiencies and we answer the questions that you've 77 00:05:50,920 --> 00:05:53,800 Speaker 3: never been able to answer before because you haven't had 78 00:05:53,800 --> 00:05:57,200 Speaker 3: the tools. The tools have literally not existed. 79 00:05:59,040 --> 00:06:04,920 Speaker 2: So you were talking about star Trek Tech and I 80 00:06:04,960 --> 00:06:09,080 Speaker 2: received this email from Nicholas, I won't give his last name. 81 00:06:09,960 --> 00:06:15,960 Speaker 2: Nicholas emails asks ask your guests about some more local 82 00:06:16,160 --> 00:06:20,920 Speaker 2: sensing called Tara Hurts scanners and what they can do. 83 00:06:22,240 --> 00:06:24,640 Speaker 2: Are you familiar with Tara Hurts scanners? 84 00:06:26,200 --> 00:06:30,440 Speaker 3: Unfortunately I am not. Okay, I have heard of them, 85 00:06:31,000 --> 00:06:35,159 Speaker 3: but I can't speak professionally. I'm not familiar beyond just 86 00:06:35,520 --> 00:06:36,440 Speaker 3: table parlance. 87 00:06:37,120 --> 00:06:41,280 Speaker 2: Okay, these are apparently are being used in airports, Tara 88 00:06:41,360 --> 00:06:46,080 Speaker 2: hurts laser scanners. It can be used to spy on 89 00:06:46,120 --> 00:06:46,960 Speaker 2: people in airports. 90 00:06:47,080 --> 00:06:52,960 Speaker 3: Okay, I can from my degree, I can speak a 91 00:06:53,120 --> 00:06:57,160 Speaker 3: little bit to some of the security measures. If if 92 00:06:57,240 --> 00:07:00,160 Speaker 3: they're the laser scanners I'm thinking of in the at 93 00:07:00,160 --> 00:07:06,480 Speaker 3: the airports, they're basically lidar that's internal and it's used 94 00:07:06,520 --> 00:07:13,040 Speaker 3: to scan to scan people. And what it's doing is 95 00:07:13,080 --> 00:07:15,600 Speaker 3: instead of looking at the floor of a jungle, it's 96 00:07:15,680 --> 00:07:18,960 Speaker 3: looking at the body and trying to pick out right 97 00:07:19,040 --> 00:07:22,080 Speaker 3: angles or angles that are indicative of a weapon or 98 00:07:22,080 --> 00:07:26,040 Speaker 3: an explosive. I did have some experience with that, but 99 00:07:26,120 --> 00:07:29,880 Speaker 3: again I don't use that technology in modern geo, so 100 00:07:30,000 --> 00:07:33,080 Speaker 3: I can't really speak to it beyond just what I've 101 00:07:33,120 --> 00:07:35,560 Speaker 3: told you. So sorry, Nick, I wish I could tell 102 00:07:35,600 --> 00:07:36,320 Speaker 3: you something different. 103 00:07:37,440 --> 00:07:43,960 Speaker 2: That's okay. I began the show or the segment with 104 00:07:44,040 --> 00:07:51,720 Speaker 2: you mentioning this whistleblower, David Grush, who was formerly with 105 00:07:51,880 --> 00:08:00,000 Speaker 2: the National Geospatial Intelligence Agency, and you said that while 106 00:08:00,040 --> 00:08:06,040 Speaker 2: you hadn't followed Grush's I don't know story that closely. 107 00:08:06,400 --> 00:08:10,520 Speaker 2: You said something about how geospatial intelligence would be a 108 00:08:10,560 --> 00:08:18,520 Speaker 2: great way to approach the whole UFO issue. Can we 109 00:08:18,920 --> 00:08:20,320 Speaker 2: maybe delve into that a little bit? 110 00:08:21,200 --> 00:08:25,880 Speaker 3: Oh? Absolutely, So this is a topic near and dear 111 00:08:25,960 --> 00:08:30,280 Speaker 3: to my heart. If somebody came by and handed me 112 00:08:30,680 --> 00:08:35,520 Speaker 3: a security clearance and a cubicle, I would do what 113 00:08:35,640 --> 00:08:42,480 Speaker 3: is called exogeography in a heartbeat. Now, I have some 114 00:08:42,600 --> 00:08:45,880 Speaker 3: friends that work in the Space Force. Of course, they're 115 00:08:45,960 --> 00:08:49,480 Speaker 3: not allowed to tell me what they do. All they 116 00:08:49,520 --> 00:08:54,520 Speaker 3: have told me is that they watch space. That's literally 117 00:08:54,559 --> 00:08:59,880 Speaker 3: all they've said. Now, knowing that we have an age 118 00:08:59,880 --> 00:09:04,880 Speaker 3: and see that is doing nothing but tracking space junk, satellites, 119 00:09:05,880 --> 00:09:10,079 Speaker 3: orbital bodies. And that's on their website, right, that's not classified. 120 00:09:10,120 --> 00:09:12,240 Speaker 3: That's on their website. They'll tell you they're doing that. 121 00:09:13,679 --> 00:09:18,240 Speaker 3: There is a new field opening up in which we 122 00:09:18,360 --> 00:09:23,120 Speaker 3: are now quite literally three D mapping the Solar System. 123 00:09:24,440 --> 00:09:31,000 Speaker 3: We're mapping asteroids, comets, anything that is trackable. We are 124 00:09:32,320 --> 00:09:40,840 Speaker 3: basically using GIS geographic information systems in an outer space 125 00:09:41,440 --> 00:09:46,120 Speaker 3: method Because it doesn't really matter if you're on the Earth, 126 00:09:46,160 --> 00:09:48,199 Speaker 3: as long as you have a fixed point you can 127 00:09:48,320 --> 00:09:52,400 Speaker 3: navigate from that point, and so a geographic information system, 128 00:09:52,400 --> 00:09:54,400 Speaker 3: as long as you have a fixed reference point, you 129 00:09:54,400 --> 00:09:57,600 Speaker 3: can map anything you want. You can map the entire universe. 130 00:09:58,760 --> 00:10:07,840 Speaker 3: So my opinion of applying geographic science to the the 131 00:10:07,960 --> 00:10:13,679 Speaker 3: UFO question. We can track flight paths, we can you 132 00:10:13,720 --> 00:10:16,439 Speaker 3: can go and and this is actually a product that 133 00:10:16,480 --> 00:10:23,240 Speaker 3: we use for our business is we actually provide an 134 00:10:23,320 --> 00:10:26,920 Speaker 3: app to our clients, and most of it is contractors 135 00:10:26,960 --> 00:10:30,400 Speaker 3: they can log their work, but someone like a movefon 136 00:10:30,559 --> 00:10:34,319 Speaker 3: could use this where you can take photos, you can 137 00:10:34,360 --> 00:10:38,160 Speaker 3: take audio, you can take video, you can take notes. 138 00:10:39,120 --> 00:10:43,040 Speaker 3: You can also when you take that information on the 139 00:10:43,160 --> 00:10:46,960 Speaker 3: handheld device, when we build it on the desktop and 140 00:10:47,000 --> 00:10:50,480 Speaker 3: push it to the cloud, that end user cannot alter 141 00:10:50,640 --> 00:10:54,120 Speaker 3: that data once it's collected. So you had now have 142 00:10:54,880 --> 00:10:58,559 Speaker 3: data that is basically locked down according to a time 143 00:10:58,720 --> 00:11:00,559 Speaker 3: date in geotag stamp. 144 00:11:01,400 --> 00:11:01,559 Speaker 1: Right. 145 00:11:01,640 --> 00:11:05,000 Speaker 2: You can't manipulate the photo. You can't run it through photoshop, 146 00:11:05,400 --> 00:11:06,360 Speaker 2: et cetera exactly. 147 00:11:07,160 --> 00:11:11,079 Speaker 3: And so now when you have an investigator going out 148 00:11:11,080 --> 00:11:15,200 Speaker 3: and talking to five, six, seven witnesses, they can go 149 00:11:15,320 --> 00:11:18,199 Speaker 3: to the exact spot and the witness can say, well, here, 150 00:11:18,280 --> 00:11:21,800 Speaker 3: hold your phone up and let's look at it. Over Okay, 151 00:11:21,400 --> 00:11:23,720 Speaker 3: that's that's right where it was, that's the angle I 152 00:11:23,800 --> 00:11:27,840 Speaker 3: was looking at. You can collect that point. You can 153 00:11:28,559 --> 00:11:31,800 Speaker 3: then go to the next person and do the same thing. Now, 154 00:11:31,960 --> 00:11:34,960 Speaker 3: once we get that back into the desktop where we 155 00:11:35,040 --> 00:11:39,600 Speaker 3: have computing power, because the phone pushes to the cloud 156 00:11:39,640 --> 00:11:42,280 Speaker 3: a database, and then we pull from that database to 157 00:11:42,320 --> 00:11:50,080 Speaker 3: make analysis. You can then run tangents between those. You 158 00:11:50,120 --> 00:11:54,880 Speaker 3: can you can essentially triangulate, so you can figure out 159 00:11:54,920 --> 00:11:59,840 Speaker 3: exactly how high the object was that they saw. And 160 00:12:00,040 --> 00:12:03,000 Speaker 3: with the notes that you've collected and the information, you 161 00:12:03,160 --> 00:12:09,320 Speaker 3: can't understand how the object or the phenomenon behaved, and 162 00:12:09,520 --> 00:12:14,440 Speaker 3: you can draw a much deeper contextual picture than what 163 00:12:14,559 --> 00:12:19,679 Speaker 3: you ever could with drawings and writings in the way 164 00:12:19,720 --> 00:12:23,640 Speaker 3: that most investigation is done. Your most investigation is sitting 165 00:12:23,679 --> 00:12:29,320 Speaker 3: down with a notepad and talking and taking notes. Well, 166 00:12:30,000 --> 00:12:35,600 Speaker 3: now we're able to bring geographic science to bear in this, 167 00:12:36,360 --> 00:12:42,400 Speaker 3: and I really think that when we start getting the 168 00:12:42,440 --> 00:12:47,720 Speaker 3: ability to apply exogeography so outside the atmosphere, to start 169 00:12:47,760 --> 00:12:51,839 Speaker 3: mapping the solar system, to start mapping things out there, 170 00:12:52,400 --> 00:12:56,600 Speaker 3: and when we can start pulling together investigations here on 171 00:12:56,679 --> 00:12:59,360 Speaker 3: the face of the Earth, I think that we're going 172 00:12:59,440 --> 00:13:01,400 Speaker 3: to be able to bring those two data sets together, 173 00:13:01,480 --> 00:13:03,680 Speaker 3: and we're going to be able to develop insights to 174 00:13:03,840 --> 00:13:07,960 Speaker 3: figure out what's really going on. And that excites me 175 00:13:08,120 --> 00:13:09,360 Speaker 3: because that is exciting. 176 00:13:09,520 --> 00:13:10,320 Speaker 2: That is exciting. 177 00:13:10,920 --> 00:13:14,720 Speaker 3: I don't buy into the idea that all aliens are evil, 178 00:13:15,280 --> 00:13:17,160 Speaker 3: and I don't buy into the idea that all aliens 179 00:13:17,160 --> 00:13:19,720 Speaker 3: are good. I think that there's a light and dark 180 00:13:19,720 --> 00:13:24,199 Speaker 3: in the universe, and there's a balance, and it fascinates 181 00:13:24,240 --> 00:13:29,600 Speaker 3: me to investigate that paradigm. 182 00:13:29,920 --> 00:13:35,320 Speaker 2: We should really get you involved with somebody's move on 183 00:13:35,480 --> 00:13:38,840 Speaker 2: field investigators and if they had some training in this, 184 00:13:39,600 --> 00:13:42,400 Speaker 2: I mean, that would really I think push the needle 185 00:13:42,440 --> 00:13:45,320 Speaker 2: in terms of UFO research. 186 00:13:46,240 --> 00:13:50,439 Speaker 3: Oh, it would if for someone for a move on 187 00:13:50,600 --> 00:13:57,960 Speaker 3: investigator or a sasquatch investigator, for someone like that that's 188 00:13:58,000 --> 00:14:02,800 Speaker 3: never been exposed to this, it would be the equivalent 189 00:14:03,559 --> 00:14:10,000 Speaker 3: of going from a chariot too like a blue angel. 190 00:14:11,640 --> 00:14:13,840 Speaker 3: That's that's the difference that we're dealing with here. 191 00:14:14,760 --> 00:14:16,400 Speaker 2: I was going to ask you about big foot, because 192 00:14:16,440 --> 00:14:19,760 Speaker 2: you mentioned it, and then you mentioned it rather well, 193 00:14:21,040 --> 00:14:27,960 Speaker 2: how how could we use geospatial data collection analysis for 194 00:14:27,960 --> 00:14:28,600 Speaker 2: for bigfoot? 195 00:14:29,920 --> 00:14:36,520 Speaker 3: Exactly the same way you can you can even in 196 00:14:36,640 --> 00:14:41,080 Speaker 3: the in a in a g I s you can 197 00:14:42,040 --> 00:14:47,120 Speaker 3: look at floodplains, weather patterns. I've actually developed a model 198 00:14:47,280 --> 00:14:51,360 Speaker 3: for roofers to figure out where they need to be 199 00:14:51,360 --> 00:14:56,480 Speaker 3: putting up yard signs according to traditional weather patterns and 200 00:14:58,080 --> 00:15:01,520 Speaker 3: form of certain indicators comes through, or when we get 201 00:15:01,520 --> 00:15:06,880 Speaker 3: a certain type of a climactic pattern, I can give 202 00:15:06,920 --> 00:15:10,200 Speaker 3: them about a week's notice and say, hey, you need 203 00:15:10,240 --> 00:15:12,360 Speaker 3: to go put your yard signs out here because this 204 00:15:12,440 --> 00:15:15,200 Speaker 3: is the area you're likely to see damage in. So 205 00:15:15,360 --> 00:15:18,680 Speaker 3: their name is in the forefront of people's minds when 206 00:15:18,800 --> 00:15:22,160 Speaker 3: that big storm comes through. Well, it's not a very 207 00:15:22,200 --> 00:15:27,440 Speaker 3: big stretch to extrapolate that into the hunt for Cryptis. 208 00:15:28,360 --> 00:15:33,240 Speaker 3: If we look at I think Gary, not Gary. David 209 00:15:33,280 --> 00:15:39,880 Speaker 3: Politis I one of my absolute all time favorites. What 210 00:15:40,040 --> 00:15:45,480 Speaker 3: if we were to start using something like our product. 211 00:15:47,120 --> 00:15:49,560 Speaker 3: I don't want to be too shameless and self promoting here, 212 00:15:49,600 --> 00:15:52,800 Speaker 3: but what if we were to use something like our 213 00:15:52,840 --> 00:15:58,080 Speaker 3: product to start collecting geospatial data on these disappearances. Now 214 00:15:58,160 --> 00:16:02,720 Speaker 3: we can even do this after the fact that most 215 00:16:02,720 --> 00:16:07,160 Speaker 3: of these disappearances were highly traumatic for the people involved, 216 00:16:08,320 --> 00:16:13,040 Speaker 3: And when you have a mother and father whose child 217 00:16:13,440 --> 00:16:19,200 Speaker 3: up and vanished and then they were blamed. They remember 218 00:16:19,400 --> 00:16:21,880 Speaker 3: every single detail about that. 219 00:16:22,560 --> 00:16:25,440 Speaker 1: Listen to more at Coast to Coast AM every weeknight 220 00:16:25,640 --> 00:16:28,800 Speaker 1: at one am Eastern, and go to Coast to coastam 221 00:16:28,880 --> 00:16:29,920 Speaker 1: dot com for more