1 00:00:00,270 --> 00:00:03,510 You're listening to episode 767 of a very spatial podcast. 2 00:00:03,660 --> 00:00:05,670 August 17th, 2025. 3 00:00:16,560 --> 00:00:18,090 Hello and welcome to a very spatial podcast. 4 00:00:18,090 --> 00:00:18,630 I'm Jesse. 5 00:00:18,870 --> 00:00:19,320 I'm Sue. 6 00:00:20,115 --> 00:00:20,625 I am Barb 7 00:00:20,744 --> 00:00:21,494 and this is Frank. 8 00:00:21,705 --> 00:00:26,325 And this week we're going to talk to the folks over at World Pop about data 9 00:00:26,325 --> 00:00:29,025 sets that are coming out in the near future, and of course the work they've 10 00:00:29,025 --> 00:00:31,185 been doing for, uh, a while now already. 11 00:00:31,544 --> 00:00:33,945 Be sure to stick around after the interview for our regular 12 00:00:33,945 --> 00:00:35,370 news web corner and events. 13 00:00:36,360 --> 00:00:41,700 Happy to be joined today by Professor Andy Tatum, who is, , director of World Pop and 14 00:00:41,700 --> 00:00:46,030 Heather Chamberlain, senior Enterprise, fellow of World Pop, , which world Pop 15 00:00:46,030 --> 00:00:47,950 is at the University of Southampton. 16 00:00:47,950 --> 00:00:49,000 Thank you for joining us today. 17 00:00:49,060 --> 00:00:49,480 Thank you. 18 00:00:49,750 --> 00:00:50,320 Great to be here. 19 00:00:50,650 --> 00:00:52,600 Let's begin with what is World Pop? 20 00:00:52,605 --> 00:00:56,120 Well, , world Pop is a, an applied research group. 21 00:00:56,525 --> 00:01:02,825 Here at University of Southampton and we are focused on using 22 00:01:02,825 --> 00:01:08,465 geospatial data to fill gaps in, in small area population data. 23 00:01:08,465 --> 00:01:15,965 So working out methods co-developing them with, with data users from governments and 24 00:01:15,965 --> 00:01:18,875 UN agencies, and yeah, trying to improve. 25 00:01:19,580 --> 00:01:23,060 Our ability to estimate and map populations. 26 00:01:23,150 --> 00:01:26,690 Now, whenever we look at, at population, of course, we have a variety of 27 00:01:26,930 --> 00:01:29,150 sources that, uh, we'll talk about. 28 00:01:29,150 --> 00:01:32,960 I'm sure as we go through the conversation that are everything from 29 00:01:32,960 --> 00:01:36,485 global, uh, un, which of course you're helping with some of that as well. 30 00:01:37,185 --> 00:01:40,605 Uh, all the way down to subsections of of countries. 31 00:01:40,785 --> 00:01:44,145 Let's talk a little bit about your methodologies of bringing all these 32 00:01:44,145 --> 00:01:46,625 disparate sources, , together. 33 00:01:46,835 --> 00:01:49,025 I guess our methods already cover sort of. 34 00:01:49,480 --> 00:01:52,870 Two main, uh, broad scenarios. 35 00:01:52,990 --> 00:01:58,080 So we focus, , on a, a sort of subset of methods that we call top-down methods, 36 00:01:58,080 --> 00:02:02,820 where we're really looking at national or subnational, generally census-based 37 00:02:02,910 --> 00:02:07,620 data or projections and disaggregating those down to to smaller areas. 38 00:02:07,770 --> 00:02:12,429 So our focus really is on providing, , as granular and, , a granular small spatial. 39 00:02:12,549 --> 00:02:14,859 , Areas of estimates as possible. 40 00:02:15,369 --> 00:02:19,629 And so when we do, when we work on methods that are based on census data, that's 41 00:02:19,629 --> 00:02:23,799 disaggregated, that works if we have good census data, but we know that in 42 00:02:23,799 --> 00:02:27,399 a lot of countries that data may not be available or it may be highly outdated. 43 00:02:27,739 --> 00:02:31,489 It may be many decades since the census was conducted, and so that's when we 44 00:02:31,489 --> 00:02:35,179 also work on methods that we call bottom up methods, which are more focused 45 00:02:35,179 --> 00:02:39,349 on looking at alternative sources of population data and working out if 46 00:02:39,349 --> 00:02:43,249 we can extrapolate or model those in some way to try and fill in data gaps 47 00:02:43,339 --> 00:02:44,659 to go ahead and continue with that. 48 00:02:44,659 --> 00:02:46,209 Can you give some examples of some of the. 49 00:02:46,640 --> 00:02:49,189 Bottom ups, uh, sources that you've been using. 50 00:02:49,429 --> 00:02:49,909 Sure. 51 00:02:49,909 --> 00:02:54,590 So, , some of the really early work we did in this context was with, , Afghanistan, 52 00:02:54,929 --> 00:02:56,609 , where the last census was conducted. 53 00:02:56,670 --> 00:02:59,489 The last national census was conducted in 1979. 54 00:02:59,849 --> 00:03:03,899 So there's lots of uncertainty there in terms of population numbers and obviously 55 00:03:03,899 --> 00:03:08,679 a lot of population movement and conflict, , have occurred since that, since that time. 56 00:03:08,950 --> 00:03:09,579 So. 57 00:03:09,910 --> 00:03:13,510 That was one of the early instances in which some of these methods 58 00:03:13,510 --> 00:03:16,939 were developed, and that was making use of, , survey data for specific 59 00:03:16,939 --> 00:03:20,509 provinces and then using modeling to try and fill in some of the gaps. 60 00:03:20,869 --> 00:03:25,849 Now as we see this combination of the bottom up and the top down examples 61 00:03:25,849 --> 00:03:29,329 that you're, or data sets that you're utilizing, how do you deal with 62 00:03:29,329 --> 00:03:32,089 some of the border issues as well? 63 00:03:32,179 --> 00:03:36,739 So you have the ability to look at a country and, and I think 64 00:03:36,739 --> 00:03:38,899 everybody can understand how we. 65 00:03:39,454 --> 00:03:42,574 Can conflate different parts of a country through various surveys 66 00:03:42,574 --> 00:03:43,989 and trying to understand that. 67 00:03:44,624 --> 00:03:47,385 , But how do you deal with some of the border issues whenever you have somewhere 68 00:03:47,385 --> 00:03:51,644 like, you know, Pakistan and Afghanistan where you do have more recent, actual 69 00:03:51,644 --> 00:03:55,665 national census in one country versus dealing with the bottom up survey 70 00:03:55,665 --> 00:03:57,734 based on the other side of the border? 71 00:03:57,855 --> 00:03:57,945 Okay. 72 00:03:57,945 --> 00:04:00,254 Yeah, it's a great, great question. 73 00:04:00,254 --> 00:04:06,285 And it very much depends, I think, on on who, who and how these types 74 00:04:06,285 --> 00:04:08,204 of data are going to be used. 75 00:04:08,535 --> 00:04:11,595 So we're, we are generally producing. 76 00:04:11,984 --> 00:04:13,664 Two types of population data. 77 00:04:13,664 --> 00:04:18,375 One where we're trying to produce a consistent set of estimates across the 78 00:04:18,375 --> 00:04:22,184 entire world for multiple time periods and trying to do the best we can. 79 00:04:22,184 --> 00:04:28,034 And there we are making use of projections and estimates for one country, and then 80 00:04:28,034 --> 00:04:30,554 next door we may be using a census on. 81 00:04:30,554 --> 00:04:35,490 What we're trying to do is turn all of those into a. Grided set of estimates, 82 00:04:35,490 --> 00:04:40,260 so numbers of people per one kilometer or 100 by a hundred BTA grid square. 83 00:04:40,439 --> 00:04:45,120 But in other situations, we may be working directly with a government who 84 00:04:45,120 --> 00:04:50,159 are interested in producing more recent estimates for a very specific time 85 00:04:50,159 --> 00:04:52,020 period and a very specific purpose. 86 00:04:52,049 --> 00:04:54,030 And in some of those cases, it can be around. 87 00:04:54,400 --> 00:04:58,900 Border issues where populations have changed substantially because of conflict, 88 00:04:59,289 --> 00:05:01,210 because of, uh, natural disaster. 89 00:05:01,600 --> 00:05:05,650 And that means populations have changed a lot since the last census. 90 00:05:06,010 --> 00:05:09,370 And so they're interested in using things like satellite imagery and 91 00:05:09,370 --> 00:05:13,809 mapping buildings to get a better idea of how many people that are likely 92 00:05:13,809 --> 00:05:15,520 to be and where they're likely to be. 93 00:05:15,580 --> 00:05:19,064 That brings up whole other questions for areas that are depopulating and, 94 00:05:19,210 --> 00:05:21,730 and number of structures, but that's a separate set of conversations. 95 00:05:21,730 --> 00:05:22,944 I think whenever we look at. 96 00:05:23,554 --> 00:05:26,974 This, and you did mention that you're looking at one kilometer pixel size. 97 00:05:27,244 --> 00:05:30,304 What's the scale that you kind of consider this to be at? 98 00:05:30,364 --> 00:05:32,135 'cause of course, we have a resolution, but that doesn't 99 00:05:32,135 --> 00:05:36,974 necessarily, , infer the scale that you consider this to be appropriate at. 100 00:05:37,185 --> 00:05:37,995 Do you wanna start with 101 00:05:38,115 --> 00:05:42,015 Yeah, I mean, I think so The, the finest resolution that we pro we 102 00:05:42,015 --> 00:05:45,344 produce estimates at is three arc seconds, about a hundred meters at the 103 00:05:45,344 --> 00:05:47,414 equator, and we tend to recommend that. 104 00:05:47,740 --> 00:05:51,340 You shouldn't use a single grid cell to estimate just the population in that 105 00:05:51,340 --> 00:05:55,420 grid cell and consider it as an actual representative boundary of that being 106 00:05:55,420 --> 00:05:57,010 an exact number in that grid cell. 107 00:05:57,010 --> 00:06:01,700 But as you aggregate these grid cells up to larger areas, , say to one kilometer 108 00:06:01,700 --> 00:06:05,210 or, or larger areas, then we're gonna have more confidence in those numbers 109 00:06:05,210 --> 00:06:08,390 being reflective of the, of the actual spatial distribution of population. 110 00:06:08,690 --> 00:06:12,920 I don't think we have a hard and fast rule of exactly the minimum size of a unit, but 111 00:06:13,160 --> 00:06:16,490 yeah, the, the advantage of this gradient data sets is. 112 00:06:17,100 --> 00:06:17,370 Sure. 113 00:06:17,370 --> 00:06:22,860 You know, the flexibility to be able to summarize it by different administrative 114 00:06:22,860 --> 00:06:28,680 units or different decision making units or to delineate the outside outskirts of a 115 00:06:28,680 --> 00:06:33,360 city and work out an estimate of how many people are in that city, or to integrate 116 00:06:33,360 --> 00:06:37,140 it with other types of data like the location of a health facility, and try and 117 00:06:37,140 --> 00:06:41,370 work out how many people are living within five kilometers of that health facility. 118 00:06:41,850 --> 00:06:45,405 Very difficult to do if you are using administrative count. 119 00:06:45,745 --> 00:06:46,825 It's within. 120 00:06:47,135 --> 00:06:50,465 Boundaries that are all the different shapes and sizes, but the gridded 121 00:06:50,465 --> 00:06:54,435 data offers that, , opportunity to, to summarize and aggregate. 122 00:06:54,465 --> 00:06:58,365 And it's at those kind of aggregate levels that we we're more confident in 123 00:06:58,365 --> 00:07:02,415 those estimates and that that tends to be how our data are typically used, rather 124 00:07:02,415 --> 00:07:07,635 than someone going to one grid cell and saying, there's nine people here. 125 00:07:07,635 --> 00:07:09,805 Definitely, , which we don't really recommend. 126 00:07:10,195 --> 00:07:10,495 It is. 127 00:07:10,495 --> 00:07:11,275 It is one of the. 128 00:07:11,695 --> 00:07:16,885 Positives and negatives of RAs is that they provide a sense of fuzziness as 129 00:07:16,885 --> 00:07:21,655 long as you understand fuzziness, but a lot of the population sees it as 130 00:07:22,015 --> 00:07:24,535 like a, an image pixel where this. 131 00:07:25,015 --> 00:07:27,355 Color means it must be this in this area. 132 00:07:27,355 --> 00:07:31,285 Whereas a lot of our, our raster data is whether we're talking 133 00:07:31,285 --> 00:07:36,265 about soils or population, it's a representation of what we think it is. 134 00:07:36,625 --> 00:07:40,255 So yeah, there's this whole other conversation about fuzziness that exists 135 00:07:40,255 --> 00:07:42,715 with, with these type of data sets. 136 00:07:42,715 --> 00:07:45,925 But again, they're important to have, and I think the fact that we have 137 00:07:45,925 --> 00:07:50,575 been moving away from some of our traditional vector hard boundaries. 138 00:07:51,490 --> 00:07:57,340 Two Raster has helped to hopefully confer a little bit more of the, 139 00:07:57,400 --> 00:07:59,830 the idea of continuous boundaries. 140 00:08:00,430 --> 00:08:03,340 Compared to what we did back in the, the nineties and early two thousands. 141 00:08:03,400 --> 00:08:03,640 Yeah. 142 00:08:03,640 --> 00:08:08,590 And it's, I guess a big focus of our statistical modeling teams 143 00:08:09,010 --> 00:08:14,320 is to try and not just produce a single estimate, um, because we know 144 00:08:14,320 --> 00:08:15,610 there's a lot of uncertainties in. 145 00:08:16,225 --> 00:08:20,605 Population data, but to, uh, they're developing a lot of different types of 146 00:08:20,605 --> 00:08:26,935 Bayesian models so that we are producing for each grid cell a full posterior 147 00:08:27,055 --> 00:08:31,855 distribution of population estimates so that those can be summarized in different 148 00:08:31,855 --> 00:08:33,660 ways to give you, uh, not only a. 149 00:08:34,220 --> 00:08:38,569 Best estimate, but a confidence interval to say, we're, we're pretty sure there's 150 00:08:38,569 --> 00:08:43,395 not more than this many people in this, this area that you're looking at or, or 151 00:08:43,400 --> 00:08:45,530 should not be less than this many people. 152 00:08:45,530 --> 00:08:48,110 But, but when you're looking at an individual grid cell, those 153 00:08:48,110 --> 00:08:50,180 confidence intervals can be huge. 154 00:08:50,510 --> 00:08:54,020 But when you start to aggregate up, you yeah, you're, you're 155 00:08:54,020 --> 00:08:56,300 getting actually perhaps useful. 156 00:08:56,540 --> 00:08:59,390 Information in terms of an estimate and its confidence interval, 157 00:08:59,510 --> 00:09:01,490 but there's always also the challenges with trying to 158 00:09:01,490 --> 00:09:03,200 communicate that uncertainty Yes. 159 00:09:03,200 --> 00:09:04,160 Associated with that data. 160 00:09:04,370 --> 00:09:05,150 Yeah, definitely. 161 00:09:05,240 --> 00:09:07,880 Whenever we say population, we kind of assume we're just talking about. 162 00:09:08,710 --> 00:09:13,329 Density perhaps, or sheer numbers, but there's a lot of different data that you 163 00:09:13,329 --> 00:09:15,339 do actually provide through World Pop. 164 00:09:15,339 --> 00:09:18,700 Can you go through a few of the different aspects besides just general 165 00:09:18,700 --> 00:09:19,960 population that you're providing? 166 00:09:20,079 --> 00:09:24,175 Yeah, so the, the our, our main focus is, I guess primarily population counts. 167 00:09:24,755 --> 00:09:29,075 But also broken down by age and sex classes. 168 00:09:29,285 --> 00:09:33,215 That's the kind of second thing that people tend to want to know in terms of 169 00:09:33,215 --> 00:09:37,535 who, who is there in terms of children, women of childbearing, age, the elderly. 170 00:09:37,955 --> 00:09:43,555 But we also do work, , making use of household survey data to try and estimate. 171 00:09:44,165 --> 00:09:49,635 Things like, , rates of poverty, vaccination coverage, literacy, access to 172 00:09:49,635 --> 00:09:52,665 sanitation, travel times to healthcare. 173 00:09:52,995 --> 00:09:57,795 So again, it very much depends on who is the end user and the kind of 174 00:09:57,975 --> 00:10:01,425 information they want to know and the kind of decisions they want to make. 175 00:10:01,725 --> 00:10:05,685 And yeah, there were a variety of projects going on at at World 176 00:10:05,685 --> 00:10:07,635 Pop in terms of producing these. 177 00:10:07,875 --> 00:10:12,675 These types of outputs with an ultimate aim of trying to build up, I guess, 178 00:10:12,675 --> 00:10:17,835 some kind of demographic atlas so that we are trying, trying to understand who 179 00:10:17,835 --> 00:10:22,635 those populations are for, making sure that nobody's is left behind and that 180 00:10:22,995 --> 00:10:27,015 those, uh, decisions can be tailored to those populations most at need. 181 00:10:27,165 --> 00:10:30,615 You also mentioned that you're looking at different times, so in also 182 00:10:30,615 --> 00:10:34,155 included in this is things like urban change, global settlement growth. 183 00:10:34,720 --> 00:10:37,900 So can you talk a little bit about how you're dealing with this as a temporal 184 00:10:37,900 --> 00:10:39,520 issue as well as a spatial issue? 185 00:10:39,760 --> 00:10:44,709 Yeah, so, , I mean this is something where the data that's available on settlements 186 00:10:44,709 --> 00:10:48,910 and down to the level of individual buildings has really rapidly progressed 187 00:10:48,910 --> 00:10:51,579 in the last five plus five to 10 years. 188 00:10:51,870 --> 00:10:55,665 , And so we're constantly sort of trying to adapt and integrate that. 189 00:10:56,410 --> 00:10:58,000 Data into our workflows. 190 00:10:58,180 --> 00:11:02,540 And increasingly, , as you said, it, data that is, , temporarily explicit 191 00:11:02,540 --> 00:11:06,500 that's allowing us to see these changes in settlements, , allow us to 192 00:11:06,500 --> 00:11:10,950 integrate this into, , where we're, . Distributing population across the 193 00:11:10,950 --> 00:11:14,790 surface, but also look at potentially projecting this into the future as well. 194 00:11:15,270 --> 00:11:19,680 So some of the, the, the new, , data that we're having that, that we've, , recently 195 00:11:19,680 --> 00:11:24,530 developed that will be, , published and made openly available launched, , soon is 196 00:11:24,530 --> 00:11:29,840 integrating that data at an an annual time step for every year from 2015 up to 2030. 197 00:11:30,270 --> 00:11:33,465 , And so that is integrating the settlement growth for. 198 00:11:34,145 --> 00:11:35,525 , Every country globally. 199 00:11:35,835 --> 00:11:39,035 , And then using, , estimates of population, , down to the 200 00:11:39,035 --> 00:11:40,655 individual grid cell level as well. 201 00:11:41,015 --> 00:11:43,805 And of course, people wanna be able to utilize this in their existing 202 00:11:44,165 --> 00:11:47,165 workflows, um, and technologies. 203 00:11:47,165 --> 00:11:50,495 So can you talk a little bit about the integrations that you have? 204 00:11:50,495 --> 00:11:54,875 Everything from, of course, the API all the way through to some of the, 205 00:11:54,935 --> 00:11:59,705 uh, extensions that you have for ArcGIS, uh, QGIS, those type of things. 206 00:12:00,500 --> 00:12:05,150 A very large group of things that I've thrown out for you to talk about now. 207 00:12:06,860 --> 00:12:08,030 Uh, yeah, yeah. 208 00:12:08,090 --> 00:12:12,830 This is, this is where we should bring in our more spatial data tech people. 209 00:12:12,830 --> 00:12:15,510 But, , but yeah, we, we trying to make. 210 00:12:16,325 --> 00:12:20,975 The, the types of data we produce as accessible as possible to all 211 00:12:20,975 --> 00:12:22,595 kinds of different users out there. 212 00:12:22,655 --> 00:12:29,045 , And because it's population data, it underpins so many, so many bits of work 213 00:12:29,045 --> 00:12:33,545 that academics, commercial companies, governments, UN agencies are doing. 214 00:12:33,545 --> 00:12:38,465 So we have the API, we have, uh, a QGIS plugin, as you 215 00:12:38,465 --> 00:12:41,265 mentioned, that enables users to. 216 00:12:41,885 --> 00:12:46,685 To automatically download and access those data and bring them into QGIS. 217 00:12:47,045 --> 00:12:51,035 Our data are available within Esri's Living Atlas. 218 00:12:51,485 --> 00:12:57,875 They are part of the humanitarian data exchange that un Cher lead a part of 219 00:12:57,875 --> 00:13:00,065 the un NFPAs population data portal. 220 00:13:00,485 --> 00:13:03,425 Uh, and they're also within tools like health. 221 00:13:03,965 --> 00:13:08,345 Information systems that about half the world's government uses. 222 00:13:08,345 --> 00:13:14,465 So that DHIS two system, they're, they're part of those to, to enable access 223 00:13:14,465 --> 00:13:16,745 to small area population estimates 224 00:13:17,195 --> 00:13:19,805 and integrated into to Google Earth engine as well. 225 00:13:20,015 --> 00:13:20,615 Yes. 226 00:13:20,675 --> 00:13:20,915 Yep. 227 00:13:21,215 --> 00:13:25,445 So hosted on that system and accessible to that, uh, data community as well. 228 00:13:25,775 --> 00:13:27,035 I think the list is probably quite long. 229 00:13:27,035 --> 00:13:28,025 That's a small flavor. 230 00:13:28,895 --> 00:13:28,955 Yeah. 231 00:13:29,015 --> 00:13:31,625 Whenever we look at this, you also mentioned, uh, some of 232 00:13:31,625 --> 00:13:32,885 the UN organizations that. 233 00:13:33,665 --> 00:13:36,065 You're partnering with as well as other countries. 234 00:13:36,065 --> 00:13:39,335 Can you talk a little bit more about how, uh, especially at the UN 235 00:13:39,335 --> 00:13:44,435 level, different portions of the UN are, are utilizing these data sets? 236 00:13:44,555 --> 00:13:47,795 Yeah, so it very much depends on the agency, of course, 237 00:13:47,795 --> 00:13:49,595 but for instance, whenever. 238 00:13:49,970 --> 00:13:55,610 There is any kind of earthquake, flood, other, other type of natural disaster. 239 00:13:56,090 --> 00:14:03,440 Then, , UNOCHA unsat, , make use of our data sets to overlay them with, 240 00:14:03,530 --> 00:14:07,280 with things like the extent of a flood, the track of a hurricane. 241 00:14:07,565 --> 00:14:11,465 To estimate numbers of people likely to be exposed to those. 242 00:14:11,465 --> 00:14:14,825 So it enables that kind of rapid assessment and more precise assessment 243 00:14:14,825 --> 00:14:18,095 than if they were using census data and match the boundaries. 244 00:14:18,545 --> 00:14:22,985 We work with UNICEF who are interested obviously in the, the 245 00:14:23,255 --> 00:14:24,845 health and wellbeing of children. 246 00:14:25,175 --> 00:14:27,485 So in that case it's things like. 247 00:14:27,875 --> 00:14:32,775 Estimating numbers of children who are unvaccinated, , so vaccination coverage 248 00:14:32,775 --> 00:14:36,025 rates, but also where those children are, , to be able to reach them. 249 00:14:36,625 --> 00:14:42,415 We work with U-N-F-P-A who have a mandate to support countries produce, 250 00:14:42,515 --> 00:14:44,815 , a robust and rigorous census, , and. 251 00:14:45,280 --> 00:14:47,860 To produce population data itself. 252 00:14:47,950 --> 00:14:52,570 And so we've worked with them for a long time to co-develop methods to, 253 00:14:52,600 --> 00:14:57,940 to fill gaps where countries cannot do a census or they can do one but 254 00:14:57,940 --> 00:14:59,650 cannot reach everywhere in the country. 255 00:15:00,130 --> 00:15:03,790 And we work with World Health Organization for, for things like. 256 00:15:04,125 --> 00:15:08,745 Access to, to health services and again, for, for vaccination campaigns, 257 00:15:08,985 --> 00:15:10,275 and there may be others as well. 258 00:15:10,395 --> 00:15:14,505 I think the, the Food and Agriculture organization, I think we use our data 259 00:15:14,505 --> 00:15:18,135 in their, in their portals as well for looking at vulnerability, for 260 00:15:18,135 --> 00:15:20,790 looking at, um, uh, access to, to. 261 00:15:21,460 --> 00:15:23,230 Agricultural, uh, produce. 262 00:15:23,350 --> 00:15:26,740 And of course it's not just, uh, un that's, that's working on projects. 263 00:15:26,740 --> 00:15:30,400 If you go to your website, of course, world pop.org, uh, there's 264 00:15:30,400 --> 00:15:34,840 a whole list of current past, uh, projects as well as other materials 265 00:15:34,840 --> 00:15:36,700 including course access to the data. 266 00:15:36,700 --> 00:15:41,140 So whenever people are going to the site to look at the data, um, 267 00:15:41,140 --> 00:15:43,510 what are some of the things they should keep in mind in terms of. 268 00:15:44,485 --> 00:15:47,455 Questions they should ask themselves before they start looking at the data? 269 00:15:47,515 --> 00:15:50,725 Or are there tools to help them find data on your site? 270 00:15:51,115 --> 00:15:55,525 There are, I mean, there are basic tools to help you find, find the data. 271 00:15:55,525 --> 00:15:59,875 There's a, there's a page we put up to, to ask people, basically to get 272 00:15:59,875 --> 00:16:02,930 people thinking about why they are. 273 00:16:03,940 --> 00:16:06,430 Why they want the data, , what are their applications? 274 00:16:06,430 --> 00:16:10,360 Because as you say, we produce population data sets for different 275 00:16:10,360 --> 00:16:15,060 purposes, , for different time periods, , and for different spatial extents. 276 00:16:15,370 --> 00:16:19,180 , And so there's, yeah, there's a, there's a need for people to think about. 277 00:16:20,155 --> 00:16:24,545 Why, , what, why are they needing population data and what, 278 00:16:24,815 --> 00:16:26,015 what they want to use it for? 279 00:16:26,015 --> 00:16:30,525 Is it for a single recent time period for a single country, , or is it 280 00:16:30,705 --> 00:16:36,705 to look at changes over time across multiple countries and that as that. 281 00:16:36,925 --> 00:16:41,485 Those kind of things are, are things to think about, but also we try as much as 282 00:16:41,485 --> 00:16:45,835 possible to put documentation on there, on the website so that people can understand 283 00:16:45,835 --> 00:16:51,505 what goes in, what the types of modeling that's done involve and what comes out. 284 00:16:51,635 --> 00:16:54,665 , Because that's pretty important to understand. 285 00:16:55,385 --> 00:16:58,325 Uh, in, in some countries there hasn't been a census for 20 or 286 00:16:58,325 --> 00:17:02,885 30 years, and therefore we are producing estimates that are. 287 00:17:03,805 --> 00:17:04,555 Uncertain. 288 00:17:04,945 --> 00:17:07,645 , There may be, there may be better than anything else that's out 289 00:17:07,645 --> 00:17:11,725 there at the moment, but they still can be incredibly uncertain. 290 00:17:11,725 --> 00:17:14,905 And so people should keep in mind what's, what's going in. 291 00:17:15,415 --> 00:17:17,485 And it varies from country to country. 292 00:17:17,485 --> 00:17:22,855 So while we make available global estimates of each person in each 293 00:17:22,855 --> 00:17:24,360 grid square across the entire world. 294 00:17:25,120 --> 00:17:29,920 Those, uh, outputs have different levels of uncertainty because of 295 00:17:29,950 --> 00:17:33,280 the different types of inputs that are going in in some countries. 296 00:17:33,280 --> 00:17:39,130 It involves just taking a very recent, very well, uh, undertaken census and. 297 00:17:39,760 --> 00:17:41,320 Desegregating it to grid squares. 298 00:17:41,320 --> 00:17:45,430 In other cases, it involves a lot of estimation when there hasn't been 299 00:17:45,430 --> 00:17:48,280 a census since the 1980s or 1970s. 300 00:17:48,770 --> 00:17:52,250 , And though they may look the same on the grid, they of course are 301 00:17:52,310 --> 00:17:55,610 very different in terms of their, their levels of uncertainty. 302 00:17:56,450 --> 00:18:00,230 I think that, I, I hope, but I know is not the case that people 303 00:18:00,560 --> 00:18:04,340 think of with any kind of census, is that the results of this census 304 00:18:04,340 --> 00:18:06,200 is still a statistical product. 305 00:18:06,320 --> 00:18:08,750 Uh, we, we can't literally count every person. 306 00:18:09,125 --> 00:18:12,425 Um, so we take what we do get so we have more confidence in those that 307 00:18:12,425 --> 00:18:17,945 are better prepared and more recent, but it's still a statistical product. 308 00:18:17,945 --> 00:18:21,095 So while, yeah, I I it is different. 309 00:18:21,095 --> 00:18:25,355 I think it's the confidence as much as anything else, as I'm sure the, the 310 00:18:25,355 --> 00:18:27,305 statistics in the dataset would show. 311 00:18:27,785 --> 00:18:32,105 Um, and of course that's the choosing the right world pop population data for you 312 00:18:32,255 --> 00:18:36,455 page that's linked right off your front page, that, that helps get people, um, 313 00:18:36,515 --> 00:18:38,105 to that point of understanding the data. 314 00:18:38,495 --> 00:18:42,125 Is there anything else you would like to highlight about World Pop and especially 315 00:18:42,125 --> 00:18:43,505 the product that's coming out soon? 316 00:18:44,105 --> 00:18:45,455 I mean, we can Yeah. 317 00:18:45,485 --> 00:18:50,375 Highlight the, the new global data and the, the old data that 318 00:18:50,375 --> 00:18:54,575 we're calling Global One, uh, was a, a, a data set that covered. 319 00:18:54,920 --> 00:18:57,740 The year each year from 2020 20. 320 00:18:58,070 --> 00:19:03,500 It was, we finished producing it in 2018, and, and it has since found its 321 00:19:03,500 --> 00:19:09,120 way into hundreds of different uses, uh, and applications, , all across the world. 322 00:19:09,480 --> 00:19:12,480 But it's, , it's become quite outdated. 323 00:19:12,510 --> 00:19:13,470 It's using. 324 00:19:13,860 --> 00:19:20,280 The, the 2010 round of national censuses as its basis, it's using geospatial 325 00:19:20,280 --> 00:19:25,920 data sets that were around in the, in 20 17, 20 18, which as we know are 326 00:19:25,920 --> 00:19:30,660 now massively advanced in terms of our ability to map even individual 327 00:19:30,660 --> 00:19:35,670 buildings across continents, whereas we didn't have that ability before. 328 00:19:36,060 --> 00:19:40,290 So the new set of data covers 2015 to 2030. 329 00:19:40,290 --> 00:19:42,300 It's funded by the Gates Foundation. 330 00:19:42,600 --> 00:19:42,690 It. 331 00:19:43,515 --> 00:19:47,805 Brings in both the 2010 round of censuses and the 2020 round of 332 00:19:47,805 --> 00:19:52,735 censuses, , new types of demographic models to fill, fill those gaps. 333 00:19:53,690 --> 00:19:58,640 , And estimate those populations, new sets of covariates to be 334 00:19:58,640 --> 00:20:02,750 able to model and predict those populations in that spatially. 335 00:20:03,050 --> 00:20:07,340 We're bringing in those building maps that come from satellites 336 00:20:07,340 --> 00:20:11,090 that, that are produced by, by groups like Google and Microsoft. 337 00:20:11,750 --> 00:20:14,450 Uh, and then yes, a, a, a new. 338 00:20:14,690 --> 00:20:18,050 Master grid, new sets of coastlines, new water bodies. 339 00:20:18,350 --> 00:20:23,060 So everything has, has been, I guess, given a, an upgrade in terms 340 00:20:23,060 --> 00:20:25,195 of, , those, those output data sets. 341 00:20:25,495 --> 00:20:27,685 Anything else you'd like to highlight about World Pop in general? 342 00:20:27,925 --> 00:20:30,685 On the one side, there's this new data that's covered that are 343 00:20:30,685 --> 00:20:32,365 globally and uh, and I kind of. 344 00:20:32,775 --> 00:20:38,205 Co-developed with other data providers and research institutes, but the other half 345 00:20:38,205 --> 00:20:43,605 of our work is very much co-developing individual country data sets with 346 00:20:43,815 --> 00:20:49,665 national statistics offices, ministries of health in lower middle income countries. 347 00:20:50,025 --> 00:20:53,085 And perhaps, yeah, Heather has good example of working 348 00:20:53,085 --> 00:20:54,615 with Zambia, for instance, on, 349 00:20:55,125 --> 00:20:58,425 I guess there's quite a lot of different examples we could, we could talk about. 350 00:20:59,560 --> 00:21:02,470 Some of this stems back to this early work that we, we talked about 351 00:21:02,470 --> 00:21:06,100 earlier in, in Afghanistan when we were working with the statistics 352 00:21:06,100 --> 00:21:09,220 agency there back in 20 18, 20 17. 353 00:21:09,790 --> 00:21:12,130 And since then we've worked with a lot of different statistics 354 00:21:12,130 --> 00:21:13,360 agencies around the world. 355 00:21:13,960 --> 00:21:17,020 And this might be in context where they haven't had a census for a long time, 356 00:21:17,020 --> 00:21:20,080 or they're preparing for a census, or they've conducted a census, but then 357 00:21:20,080 --> 00:21:23,830 have issues with gaps in coverage in particular geographic regions. 358 00:21:24,130 --> 00:21:27,675 So in each of those cases, it's a. It's a case of understanding what the data 359 00:21:27,675 --> 00:21:31,485 gaps are that exist, and then trying to develop methods that aim to address 360 00:21:31,485 --> 00:21:36,615 those gaps, fill those gaps in using the available data as best we can. 361 00:21:36,825 --> 00:21:40,875 And there's very much the focus on these estimates always being co-developed. 362 00:21:41,145 --> 00:21:43,615 It's not, we're going in there and using a, a specific, , a 363 00:21:43,645 --> 00:21:44,815 standard set of methods. 364 00:21:45,055 --> 00:21:45,475 It's. 365 00:21:46,010 --> 00:21:50,270 Working very closely with staff in national statistics agencies to 366 00:21:50,270 --> 00:21:53,930 understand the problem and then come up with a solution together with a big 367 00:21:53,930 --> 00:21:55,880 focus on capacity strengthening as well. 368 00:21:55,880 --> 00:21:56,960 So we don't want to. 369 00:21:57,725 --> 00:22:00,245 Just do this piece of work once and then leave. 370 00:22:00,245 --> 00:22:04,505 It's a case of trying to improve the statistical understanding, the geospatial 371 00:22:04,505 --> 00:22:08,525 understanding around these methods and so that hopefully further down the line 372 00:22:08,645 --> 00:22:12,785 if these estimates need to be updated, then actually there's more capacity 373 00:22:12,785 --> 00:22:15,935 within statistics agencies to do at least some of this work themselves. 374 00:22:16,385 --> 00:22:19,620 And I think one of the, one of the contexts in which this has been really. 375 00:22:20,645 --> 00:22:24,905 Sort of quite well established, I think is in working with particularly U-N-F-P-A 376 00:22:25,535 --> 00:22:30,335 to bring together regional workshops where multiple countries have come together. 377 00:22:30,335 --> 00:22:33,965 There's been training and then U-N-F-P-A have gone on to support 378 00:22:34,145 --> 00:22:37,205 those, um, national statistics offices with their country offices 379 00:22:37,205 --> 00:22:41,165 to try and enable these methods to be more widely applied and supported. 380 00:22:41,285 --> 00:22:43,145 Yeah, and it's quite, uh, quite important this. 381 00:22:43,450 --> 00:22:47,680 Co-development for actual use and impact of the data. 382 00:22:47,860 --> 00:22:50,770 'cause these are kind of new types of methods. 383 00:22:50,770 --> 00:22:55,720 This for, although all of us here are quite familiar with the world of 384 00:22:55,720 --> 00:22:59,800 geospatial and the abilities and, and what's possible with satellite imagery 385 00:22:59,800 --> 00:23:04,360 and what's possible with statistical and spatial methods nowadays in busy, 386 00:23:04,570 --> 00:23:08,680 low resourced statistics offices, it's, it's very much something 387 00:23:08,710 --> 00:23:11,255 new and, and population data is. 388 00:23:12,125 --> 00:23:14,375 Uh, underlies so much of decision making. 389 00:23:14,375 --> 00:23:18,575 It's, it's your GDP, it's your allocation of resources. 390 00:23:18,875 --> 00:23:22,265 It's the voting, it's a representation in parliament so 391 00:23:22,265 --> 00:23:26,975 they can be incredibly sensitive and important data to get right and. 392 00:23:27,695 --> 00:23:32,555 To, to switch from, uh, the methods that have been used for years and years 393 00:23:32,555 --> 00:23:38,195 of just sometimes just a straight line projection from the last census 10 or 394 00:23:38,195 --> 00:23:44,795 20 years ago to something that is very alien to the, to, uh, the government. 395 00:23:45,185 --> 00:23:51,125 It takes a lot of work to ensure that those, those statistics offices understand 396 00:23:51,215 --> 00:23:55,775 and, and are convinced in the methods enough that they can convince their 397 00:23:55,775 --> 00:24:00,155 president, they can convince the public that this, these types of methods are 398 00:24:00,155 --> 00:24:02,495 producing more reliable and accurate. 399 00:24:03,755 --> 00:24:08,585 Population numbers that people can trust and that they have the ability 400 00:24:08,585 --> 00:24:12,875 and understanding to be able to replicate those methods and, and 401 00:24:12,875 --> 00:24:17,855 explain them to, to the public so that these types of data can be adopted. 402 00:24:18,275 --> 00:24:22,805 And yeah, as a, as a result of some of these co-development, we've, we've seen 403 00:24:22,955 --> 00:24:25,535 these types of geospatial methods be. 404 00:24:25,835 --> 00:24:30,845 Used and, uh, the outputs adopted by governments in places like Papua New 405 00:24:30,845 --> 00:24:37,775 Guinea in South Sudan, and we've seen them be used for vaccination campaigns, uh, in, 406 00:24:37,895 --> 00:24:42,065 in Nigeria, in Zambia, uh, in Afghanistan. 407 00:24:42,335 --> 00:24:43,535 And so, yeah, if. 408 00:24:44,020 --> 00:24:48,370 It takes, it takes some pain and it takes more time than if we were just 409 00:24:48,370 --> 00:24:51,790 sitting here at the university and producing those estimates ourselves. 410 00:24:52,300 --> 00:24:57,430 But it really brings, I think, a lot more impact and, and yeah, really great 411 00:24:57,430 --> 00:25:00,640 to see those numbers actually changing. 412 00:25:01,050 --> 00:25:04,740 Changing how resources are allocated and reaching populations who've 413 00:25:04,740 --> 00:25:06,810 sometimes never been counted before. 414 00:25:07,140 --> 00:25:11,280 Can you, uh, mention very quickly as we're heading out, uh, some of 415 00:25:11,280 --> 00:25:14,910 the information about the upcoming coming launch of the product? 416 00:25:15,180 --> 00:25:16,620 Uh, yes. 417 00:25:16,620 --> 00:25:21,030 So we are launching, , these new global data, uh, through a webinar 418 00:25:21,030 --> 00:25:23,275 on the 4th of September will be. 419 00:25:24,255 --> 00:25:28,155 , Giving details on that data where you can access it, how it was put together. 420 00:25:28,205 --> 00:25:32,495 , We will also be having contributions from the Director General 421 00:25:32,495 --> 00:25:34,565 of Statistics, Sierra Leone. 422 00:25:34,895 --> 00:25:38,945 We will have contributions from regional advisor from U-N-F-P-A, um, 423 00:25:38,975 --> 00:25:40,985 from our Gates Foundation funder. 424 00:25:41,315 --> 00:25:43,055 Um, we will have a, a technical. 425 00:25:43,135 --> 00:25:47,665 Question and answer session, and it's free to sign up to. 426 00:25:47,755 --> 00:25:51,445 There's, uh, an event right link that you can access that through our website 427 00:25:51,805 --> 00:25:54,685 and we'll be sure to include a link to it in our show notes as well. 428 00:25:54,865 --> 00:25:57,595 Well, it is important work and it is good. 429 00:25:57,805 --> 00:26:03,505 Uh, in this current time when data is sometimes disappearing to 430 00:26:03,505 --> 00:26:07,585 see new data sets, uh, making its way onto the, the world stage. 431 00:26:07,705 --> 00:26:10,615 Um, both to support global initiatives as well as local 432 00:26:10,615 --> 00:26:12,140 initiatives in, in various countries. 433 00:26:12,800 --> 00:26:14,660 So thank you for, uh, doing this work. 434 00:26:14,960 --> 00:26:16,490 And of course, thank you for joining us today. 435 00:26:16,730 --> 00:26:17,000 Great. 436 00:26:17,240 --> 00:26:17,750 Thank you. 437 00:26:17,960 --> 00:26:18,440 Thank you. 438 00:26:18,440 --> 00:26:19,220 Thanks for the time. 439 00:26:19,235 --> 00:26:19,455 Now, 440 00:26:33,770 --> 00:26:37,820 kicking off with, uh, one that means absolutely very little to 441 00:26:37,820 --> 00:26:42,110 most people, but uh, at the recent. 442 00:26:43,505 --> 00:26:44,525 What conference was that? 443 00:26:44,585 --> 00:26:47,825 Psychograph Conference, which still exists I had forgotten about. 444 00:26:48,395 --> 00:26:52,895 Kronos announced, which is an open, uh, source group, highlighted that 445 00:26:52,895 --> 00:26:57,245 they were working with the OGC and Niantic, spatial Anes and ri, I 446 00:26:57,245 --> 00:27:06,245 guess, to put out Gian Splats in WebGL transmission format or GLTF for short. 447 00:27:06,995 --> 00:27:08,195 Um, and this isn't. 448 00:27:09,365 --> 00:27:10,775 A huge thing for most people. 449 00:27:11,225 --> 00:27:15,455 But in the space where Gian Splats are replacing a lot of the 450 00:27:15,455 --> 00:27:19,055 3D models that we were creating with point clouds, it's relevant. 451 00:27:19,115 --> 00:27:23,555 So if you are someone who is doing that, basically creating 3D data 452 00:27:23,885 --> 00:27:26,795 and wanna look how gian splats are working, and you're probably 453 00:27:26,795 --> 00:27:30,845 already using applications that are generating gian splats, check that out. 454 00:27:30,845 --> 00:27:36,545 And if you're someone who just wants to get things, uh, in 3D onto the web. 455 00:27:37,010 --> 00:27:40,520 This is another group that should be looking at that, but we're not gonna 456 00:27:40,520 --> 00:27:42,110 go into details about what it's though 457 00:27:42,260 --> 00:27:44,420 the too long didn't read is if you're doing 3D stuff, 458 00:27:44,420 --> 00:27:45,170 there's a new way to do it. 459 00:27:45,350 --> 00:27:47,390 Uh, kind of new in the open format. 460 00:27:47,690 --> 00:27:47,840 Yeah. 461 00:27:48,230 --> 00:27:51,830 Something that you should comment on is that the draft part 1 0 8 462 00:27:52,070 --> 00:27:57,015 rules for the FAA, uh, this is the beyond visual line of sight. 463 00:27:58,115 --> 00:27:58,745 Rules. 464 00:27:58,805 --> 00:28:06,035 Uh, these are ones that, uh, were already mandated to be created in the near future 465 00:28:06,335 --> 00:28:08,915 by the last administration in Congress. 466 00:28:09,695 --> 00:28:13,385 And then a couple of months ago, the current administration said You 467 00:28:13,385 --> 00:28:14,825 have to have this done right now. 468 00:28:14,825 --> 00:28:17,165 'cause of course, it's a moneymaking thing, so they're 469 00:28:17,165 --> 00:28:18,455 gonna be pushing it really hard. 470 00:28:18,875 --> 00:28:24,185 Um, and, uh, there are some things in there that are, you know, less 471 00:28:24,185 --> 00:28:27,035 rule than I would like to see for beyond visual line of sight. 472 00:28:27,605 --> 00:28:30,695 So go through, take a look at it, see what you think of it. 473 00:28:31,085 --> 00:28:31,385 Yes. 474 00:28:31,385 --> 00:28:36,515 If you are already flying, UAVs, drones, whatnot, or are thinking about it. 475 00:28:36,905 --> 00:28:41,255 And a term you may hear a lot, which you may not necessarily put together as 476 00:28:41,255 --> 00:28:43,295 beyond visual line of sight is bev loss. 477 00:28:43,595 --> 00:28:45,815 That's the word for it. 478 00:28:46,175 --> 00:28:50,195 Which, because government has to have acronyms that sound like words. 479 00:28:50,705 --> 00:28:53,195 Well, I mean, to be fair beyond visual line of sight is a long phrase. 480 00:28:54,095 --> 00:28:54,965 It is one. 481 00:28:55,025 --> 00:28:57,215 But um, again. 482 00:28:58,085 --> 00:29:00,245 If you heard Bev loss, you go, what the heck is that? 483 00:29:00,545 --> 00:29:01,325 That's what that is. 484 00:29:01,835 --> 00:29:03,965 Nobody else any has anything to say about Bev loss? 485 00:29:04,505 --> 00:29:05,195 I, I do. 486 00:29:05,195 --> 00:29:08,285 I have a lot of things to say about Bev loss, and I agree with you. 487 00:29:08,285 --> 00:29:16,625 It's not as rule based as I would like, and the secretary said, we 488 00:29:16,625 --> 00:29:21,005 need to do these things for these industries that will grow using drones. 489 00:29:21,065 --> 00:29:27,005 And I understand the point of view, but I think beyond visual implies like. 490 00:29:27,590 --> 00:29:32,030 At least in my head, not too far beyond, but the way they're talking about it 491 00:29:32,150 --> 00:29:34,850 is like way be beyond visual, you know? 492 00:29:34,850 --> 00:29:39,530 Like don't worry about it, it out there doing a thing, it'll be fine. 493 00:29:39,770 --> 00:29:43,010 Which I'm a little, I know we'll eventually get there. 494 00:29:43,100 --> 00:29:45,110 I'm just not sure we're quite ready for that yet. 495 00:29:45,695 --> 00:29:50,525 Uh, especially whenever we also could talk about how it seems that maybe 496 00:29:50,855 --> 00:29:53,315 DJI is already just saying whatever. 497 00:29:53,315 --> 00:29:58,895 We're not gonna deal with the stuff that's going on and letting drones run out in the 498 00:29:58,895 --> 00:30:02,015 US and not releasing other products just because they don't wanna deal with it. 499 00:30:02,645 --> 00:30:06,320 So, yeah, one of the largest manufacturers is, is kind of calling it. 500 00:30:06,875 --> 00:30:09,125 Somewhat a pause for the us. 501 00:30:09,575 --> 00:30:10,955 So that's a question mark. 502 00:30:11,045 --> 00:30:14,825 Um, you have, I mean, we can go all the way back to, what was it, dark Angel 503 00:30:14,825 --> 00:30:21,065 back in the late nineties where you had, uh, military slash police drones 504 00:30:21,305 --> 00:30:26,480 being common cause uh, there, and those were always doing questionable things. 505 00:30:26,500 --> 00:30:29,705 And that's from the 1990s before we even had a lot of these. 506 00:30:30,245 --> 00:30:31,055 That's say cool, but 507 00:30:31,055 --> 00:30:33,890 underappreciated TV show for those of you who aren't familiar. 508 00:30:34,130 --> 00:30:34,170 Yeah, 509 00:30:34,210 --> 00:30:37,505 I was like that, that is a pop culture minutia. 510 00:30:37,535 --> 00:30:37,955 Callback. 511 00:30:37,955 --> 00:30:38,015 Yeah. 512 00:30:38,015 --> 00:30:38,975 But that's awesome though. 513 00:30:39,545 --> 00:30:44,285 But it was also one of the earlier like, um, aspirations of the, 514 00:30:44,285 --> 00:30:45,755 the drone industry originally. 515 00:30:45,905 --> 00:30:47,495 Um, at least in the industry I was in, 516 00:30:47,705 --> 00:30:51,275 and to go even further the callback, um, I never, I can never remember 517 00:30:51,515 --> 00:30:54,635 there was a movie and a TV show and one was called Blue Thunder and I 518 00:30:54,635 --> 00:30:57,455 can't remember which one is, is, and they couldn't call it Blue Thunder. 519 00:30:57,905 --> 00:30:59,315 For the other thing, whatever. 520 00:30:59,375 --> 00:31:01,295 But the helicopter itself was, oh, the helicopter thing. 521 00:31:01,295 --> 00:31:02,465 It was just called Blue Thunder. 522 00:31:02,465 --> 00:31:05,465 But for legal reasons, they couldn't call. 523 00:31:05,465 --> 00:31:07,295 I think the TV show, they weren't allowed to call Blue Thunder. 524 00:31:07,295 --> 00:31:10,775 Anyway, it's a whole thing, but it had the ability, why am I making 525 00:31:11,075 --> 00:31:15,485 this up with Airwolf, one of those two had the ability to, to put out 526 00:31:15,485 --> 00:31:19,355 drones that did this and bring it back into like the motherships, which is, 527 00:31:19,835 --> 00:31:21,425 if that is older, this, it's not much older. 528 00:31:22,310 --> 00:31:23,750 No, it is about 10 years older. 529 00:31:23,750 --> 00:31:25,280 But you're right, it's not much older. 530 00:31:25,370 --> 00:31:28,430 I, I just have to say though, in their list of industries, this is 531 00:31:28,430 --> 00:31:32,180 going to impact, or they have, you know, very big ones that, that use 532 00:31:32,180 --> 00:31:35,390 this, and I know they have, you know, standards within the industry 533 00:31:35,390 --> 00:31:39,860 for them, like agriculture, aerial surveying, public safety, recreation. 534 00:31:39,860 --> 00:31:43,760 But then you get into package delivery and other areas where I, I don't think 535 00:31:43,760 --> 00:31:47,750 they internally within the industry have as much, you know, outlined. 536 00:31:48,305 --> 00:31:49,325 Best practices. 537 00:31:49,505 --> 00:31:53,435 But what I wanted to tag onto there also was wedding photography, because that's 538 00:31:53,495 --> 00:31:58,475 what I see a whole lot of, you know, beyond lot, you know, Bev laws would 539 00:31:58,475 --> 00:32:03,665 be going on without knowing what the rest of the industry is saying to do. 540 00:32:04,145 --> 00:32:09,725 Um, I will say that, you know, whenever we talk about deliveries, Amazon and 541 00:32:09,725 --> 00:32:13,355 lots of other companies, uh, everything to Wal, even Walgreens, I think. 542 00:32:13,700 --> 00:32:18,170 Uh, has gotten in on it, have been testing these, so they are building at 543 00:32:18,170 --> 00:32:23,270 least internal best practices, if not industry best practices, uh, because 544 00:32:23,270 --> 00:32:29,450 they want and have wanted this to happen since what, 2017 or whatever it was. 545 00:32:30,080 --> 00:32:34,910 Uh, whenever we first saw, uh, the first part, 1 0 7 come through. 546 00:32:35,690 --> 00:32:35,960 Yeah. 547 00:32:35,960 --> 00:32:38,540 So I, I think there's some of that. 548 00:32:38,570 --> 00:32:40,730 Um, but yes, it's, I think. 549 00:32:41,465 --> 00:32:44,795 It goes back to what Frank said in his mind, beyond visual line of sight is 550 00:32:44,795 --> 00:32:46,925 just, it's, it's just over the hill. 551 00:32:46,925 --> 00:32:48,095 I can't see it for a second. 552 00:32:48,095 --> 00:32:49,325 It's gonna come back anyway. 553 00:32:49,835 --> 00:32:52,655 Versus this is really, I would use the word autonomous. 554 00:32:52,700 --> 00:32:52,990 Yeah. 555 00:32:52,990 --> 00:32:53,230 Autonom. 556 00:32:53,230 --> 00:32:54,870 I mean it really is a better term. 557 00:32:54,870 --> 00:32:55,270 Autonomous. 558 00:32:56,075 --> 00:32:57,155 Yeah, that's what they're talking about. 559 00:32:57,155 --> 00:32:59,855 There's just like, send this thing out and then it'll come back later or not, 560 00:33:00,065 --> 00:33:01,145 you know, in a few days, whatever. 561 00:33:01,145 --> 00:33:03,635 It doesn't matter 'cause it's autonomous and I'm like, that's, 562 00:33:03,725 --> 00:33:05,165 that's a different thing. 563 00:33:05,165 --> 00:33:06,935 And again, it's not necessarily a bad thing. 564 00:33:06,935 --> 00:33:08,735 We have in agriculture, for example. 565 00:33:09,770 --> 00:33:13,400 A lot of autonomous systems out there, but they're, you know, kind 566 00:33:13,400 --> 00:33:14,750 of rooted in the ground usually. 567 00:33:14,750 --> 00:33:17,090 And, but there's a lot of that out there. 568 00:33:17,090 --> 00:33:21,590 So this is something that is inevitable, but I think it's really 569 00:33:21,590 --> 00:33:26,480 fundamentally different than, I mean, you know, you can make the argument 570 00:33:26,480 --> 00:33:29,765 that I've sit here in West Virginia and if I launch a drone in, uh. 571 00:33:30,530 --> 00:33:31,340 Mongolia. 572 00:33:31,340 --> 00:33:33,020 That's beyond my visual line of sight. 573 00:33:33,020 --> 00:33:33,290 Right. 574 00:33:33,620 --> 00:33:35,300 That's, well, since it's in Mongolia, it 575 00:33:35,300 --> 00:33:37,130 doesn't fall under FAA anyway, so whatever. 576 00:33:37,220 --> 00:33:40,970 Well, I know, but, but the point being is that, you know, it feels like there should 577 00:33:40,970 --> 00:33:42,470 be some limits there when I say that. 578 00:33:42,620 --> 00:33:47,810 Yeah, I, I mean, to be fair, I almost never actually fly my drone. 579 00:33:47,810 --> 00:33:52,340 I use it autonomously, but I use it autonomously within my line of sight. 580 00:33:53,075 --> 00:33:56,495 So I set up the expectation of where I want it to go. 581 00:33:56,495 --> 00:33:59,075 I say, you know, grab images in this area. 582 00:33:59,075 --> 00:34:01,565 I want to use it for creating a 3D model, or just creating 583 00:34:01,565 --> 00:34:04,025 an ortho photo and launch. 584 00:34:04,025 --> 00:34:07,715 And I just sit there, watch it, watch the screen, basically 585 00:34:07,715 --> 00:34:10,475 making sure that if something does happen, if it loses a propeller or 586 00:34:10,475 --> 00:34:12,275 something, I'm there to hopefully. 587 00:34:12,705 --> 00:34:16,455 Maybe be able to guide it a little bit away from anything that it might hit. 588 00:34:16,515 --> 00:34:19,665 But really if it, if something like that does happen, it's, 589 00:34:20,175 --> 00:34:22,095 you said anyway, like I said, we do a whole podcast on this, but 590 00:34:22,185 --> 00:34:24,435 yeah, then maybe we should, I don't know. 591 00:34:24,435 --> 00:34:29,265 We never really have done a big, these, our threats on drones, but, uh, something 592 00:34:29,355 --> 00:34:32,025 a little bit less to talk about though. 593 00:34:32,150 --> 00:34:35,210 If there are reason to talk about it, uh, we're seeing more natural 594 00:34:35,210 --> 00:34:37,430 language searches in the MAP apps. 595 00:34:37,460 --> 00:34:43,490 So instead of having to just say, find X or directions to X, uh, 596 00:34:43,490 --> 00:34:47,150 you can say, you know, find the best sandwiches that are nearby. 597 00:34:47,210 --> 00:34:52,430 Or, you know, how do I get from here to there instead of having to, just the 598 00:34:52,430 --> 00:34:55,190 fact that we are seeing more use of LLMs. 599 00:34:56,075 --> 00:35:01,115 In how we talk to the software and how they give us information back. 600 00:35:01,445 --> 00:35:04,685 Um, and I highlight this because, uh, one, it's already in Google 601 00:35:04,685 --> 00:35:08,735 Maps to some extent, but uh, it's definitely a feature in iOS 26. 602 00:35:09,125 --> 00:35:12,335 So I've been testing that and it, it's there. 603 00:35:12,665 --> 00:35:13,085 It works. 604 00:35:13,325 --> 00:35:14,555 Is it any good? 605 00:35:14,860 --> 00:35:17,945 And, and the reason I ask this is because I'm perennially frustrated. 606 00:35:17,945 --> 00:35:22,055 So my car has CarPlay, which means that you, or Apple, I can't 607 00:35:22,055 --> 00:35:23,645 remember the terms, uh, yeah. 608 00:35:23,705 --> 00:35:27,545 CarPlay, uh, which means that I, and I have like a Hud. 609 00:35:28,490 --> 00:35:31,400 But I can only get the symbols in the hood if I use apple maps. 610 00:35:31,400 --> 00:35:34,370 So basically I use Apple maps everywhere I go because that's just 611 00:35:34,370 --> 00:35:37,220 the least pain in the neck to do. 612 00:35:37,580 --> 00:35:42,980 But when I use the the voice thing and I say, plot me of course to blah, blah, 613 00:35:42,980 --> 00:35:51,770 blah, I find the system to be way worse than I expect it to be, considering 614 00:35:51,770 --> 00:35:53,150 it's supposed to be location based. 615 00:35:53,480 --> 00:35:56,330 So for example, um, there's a place that I like to go to. 616 00:35:57,140 --> 00:35:59,420 It's a guitar store and it's called Empire Music. 617 00:35:59,510 --> 00:36:03,200 Now, that's probably not a unique name in North America. 618 00:36:04,130 --> 00:36:08,360 I know it's not a unique name in North America, but when I say plot 619 00:36:08,360 --> 00:36:12,440 me a course to Empire Music, why is it showing me places in Montana? 620 00:36:13,745 --> 00:36:16,415 No, I'm not driving to Montana. 621 00:36:17,345 --> 00:36:21,485 I'm in, I'm in a location that, why is it not smart enough to go, well, let's 622 00:36:21,485 --> 00:36:26,195 start with the closest one and then we'll work our way outward, you know? 623 00:36:26,195 --> 00:36:27,515 Did you mean this one? 624 00:36:27,515 --> 00:36:27,755 Yes. 625 00:36:27,755 --> 00:36:28,415 That's the one I met. 626 00:36:28,625 --> 00:36:30,365 Anyway, that's my little pet peeve. 627 00:36:30,905 --> 00:36:34,775 What, what I'm wondering is, is it any good at actually discerning 628 00:36:34,775 --> 00:36:38,345 things and making those connections, particularly with regard to 629 00:36:38,345 --> 00:36:40,355 location, get me the best sandwich. 630 00:36:40,835 --> 00:36:42,635 Near me for God's sakes. 631 00:36:44,015 --> 00:36:50,225 I think the answer to that is it's kind of okay, but I think that that is a 632 00:36:50,225 --> 00:36:58,475 larger contextual conversation about AI and Geo, is that it understands 633 00:36:58,475 --> 00:37:03,875 language and it can understand nearby as long as there's some agent in there 634 00:37:03,875 --> 00:37:08,255 that then goes out, checks to see what your nearby is and brings it back. 635 00:37:08,615 --> 00:37:11,975 To the LLM and gives you that answer, uh, or he gives it that 636 00:37:11,975 --> 00:37:13,595 answer so it knows where to search. 637 00:37:14,135 --> 00:37:14,495 But 638 00:37:16,535 --> 00:37:19,025 right now it, it's, it's a loosely coupled model. 639 00:37:19,025 --> 00:37:23,885 And, um, so yeah, I think there's, I think there are issues and there 640 00:37:23,885 --> 00:37:24,905 are gonna continue to be issues. 641 00:37:24,905 --> 00:37:29,735 Right now, of course, iOS 26 is still in beta, but even once it's out of 642 00:37:29,735 --> 00:37:33,755 beta, this isn't gonna really change the way the LLMs themselves work. 643 00:37:34,295 --> 00:37:37,265 And I think that's one of the big questions for us with geo. 644 00:37:37,775 --> 00:37:46,535 And AI is finding a way to branch that area between location and language. 645 00:37:46,955 --> 00:37:52,205 Because right now for most of us, AI is deep learning, machine learning, 646 00:37:52,655 --> 00:37:55,325 uh, tools as opposed to the LLMs. 647 00:37:55,655 --> 00:38:00,455 Uh, even with Esri's recent rollout, that focus was on, you know, how 648 00:38:00,455 --> 00:38:03,455 do you use LLMs to help you find. 649 00:38:04,205 --> 00:38:07,085 How to do things or where things are in the dataset. 650 00:38:07,145 --> 00:38:08,765 So, yeah. 651 00:38:08,795 --> 00:38:11,345 Or not, sorry, not in the dataset, but in the, the interface. 652 00:38:12,425 --> 00:38:12,830 So I don't know. 653 00:38:13,175 --> 00:38:13,235 I, 654 00:38:13,985 --> 00:38:16,715 I just want to make a caveat there for anyone who may be Googling it. 655 00:38:16,715 --> 00:38:19,415 I don't actually remember if the other empire is in Montana. 656 00:38:19,415 --> 00:38:21,605 I just remember it's out west somewhere, but it's like really 657 00:38:21,605 --> 00:38:23,285 it's days away for me to drive 658 00:38:24,155 --> 00:38:27,395 and I can't imagine there's more than, or there, there's only two. 659 00:38:27,575 --> 00:38:28,235 I mean, that's, 660 00:38:28,445 --> 00:38:31,985 yeah, there's, there's multiple ones and, and in fact Empire music. 661 00:38:32,015 --> 00:38:34,955 Um, one of them is a record store, which I love record stores, don't get me wrong, 662 00:38:34,955 --> 00:38:38,135 but it's, no, again, one of the ones that's maybe California or someplace 663 00:38:38,375 --> 00:38:42,005 is a record store, and I'm looking specifically for a guitar store, so. 664 00:38:42,890 --> 00:38:43,340 It. 665 00:38:43,340 --> 00:38:44,870 It's just those type of things. 666 00:38:45,140 --> 00:38:49,160 Another place where it happens is when we're in Pittsburgh, a lot of times we'll 667 00:38:49,160 --> 00:38:52,370 go to Whole Foods because there's no Whole Foods near us and there's some things 668 00:38:52,370 --> 00:38:53,990 we can get to Whole Foods that we like. 669 00:38:54,350 --> 00:38:59,270 So we'll be in a place and I'll say, plot me of course, to Whole Foods, and it 670 00:38:59,270 --> 00:39:04,460 will automatically pick the one that is furthest away, almost always, which is. 671 00:39:05,825 --> 00:39:10,775 Weird, like why I wanna wanna go further and longer because I'm driving to a city. 672 00:39:11,285 --> 00:39:17,765 It's a very odd, the, the model as tied to location to me is very frustrating. 673 00:39:17,765 --> 00:39:19,955 I find it not as robust as I want it to be. 674 00:39:20,165 --> 00:39:23,525 Do you think you find it more frustrating because you do have a sense 675 00:39:23,525 --> 00:39:28,595 of what it's capable of and you know the terms you should be able to use? 676 00:39:28,595 --> 00:39:31,835 Or do you think that people who don't have that background and 677 00:39:31,835 --> 00:39:33,545 use it are even more frustrated? 678 00:39:34,340 --> 00:39:35,270 That's a really good question. 679 00:39:35,270 --> 00:39:38,540 I mean, I have some conceptualization of, even in a place like Pittsburgh where if 680 00:39:38,540 --> 00:39:40,010 you've never navigated Pittsburgh, it's, 681 00:39:40,760 --> 00:39:41,390 it's ridiculous. 682 00:39:42,020 --> 00:39:42,860 It's unique. 683 00:39:42,950 --> 00:39:43,730 I was gonna say unique. 684 00:39:44,000 --> 00:39:46,280 It is a very weird place to navigate. 685 00:39:47,150 --> 00:39:48,710 Even with that weird navigation. 686 00:39:48,740 --> 00:39:53,630 I do have a sense of roughly where and how to get places very roughly. 687 00:39:54,110 --> 00:39:56,240 Um, I would imagine anyone. 688 00:39:57,440 --> 00:40:00,290 Navigating Pittsburgh that's not familiar with it, which just takes the default. 689 00:40:00,290 --> 00:40:00,800 I think you're right. 690 00:40:00,800 --> 00:40:03,800 They would just go, okay, and then not really. 691 00:40:04,190 --> 00:40:09,380 So maybe it is, my frustration is a partially a function of having some 692 00:40:09,380 --> 00:40:17,120 idea of where these non uh, unique places in an area can be or should be. 693 00:40:17,810 --> 00:40:19,550 I've just gone on for mental that tangent. 694 00:40:19,610 --> 00:40:21,470 This is another good podcast episode. 695 00:40:21,530 --> 00:40:22,220 We should do 696 00:40:22,970 --> 00:40:25,430 continue with things that I don't think anybody's gonna say 697 00:40:25,430 --> 00:40:26,480 anything about, but they might. 698 00:40:27,080 --> 00:40:29,750 Uh, OSM has released vector tiles. 699 00:40:30,950 --> 00:40:36,920 So if you're using OSM and you've wanted to be able to use vector tiles 700 00:40:38,210 --> 00:40:43,520 instead of the uh, OSM stream, you now have access to some layers, I think. 701 00:40:44,180 --> 00:40:44,930 I don't think they're all in there. 702 00:40:45,260 --> 00:40:46,850 I was gonna say like, how much so far? 703 00:40:47,030 --> 00:40:47,390 Yeah. 704 00:40:48,680 --> 00:40:51,350 If you're not using Vector telles, you should play with them a bit. 705 00:40:51,410 --> 00:40:55,670 I mean, they're a very nice way to, to lower your footprint. 706 00:40:56,660 --> 00:40:59,780 In terms of how much data comes across and how much stuff you're generating on 707 00:40:59,780 --> 00:41:01,305 the backend and all that stuff, so, yeah. 708 00:41:01,610 --> 00:41:01,700 Yeah. 709 00:41:01,700 --> 00:41:04,130 And this is based on their entirely new backend. 710 00:41:04,880 --> 00:41:06,980 Um, so it's one of the reasons it can be so quick. 711 00:41:07,640 --> 00:41:11,180 And another one, which I rearranged so that everybody 712 00:41:11,180 --> 00:41:13,610 knows, is now Instagram maps. 713 00:41:15,410 --> 00:41:18,020 If you've used Snap Maps before, apparently it looks the same. 714 00:41:18,530 --> 00:41:20,360 It's been a long time since I looked at Snap Maps. 715 00:41:20,360 --> 00:41:21,140 It's been a long time. 716 00:41:21,650 --> 00:41:24,410 Or it's been a week since I looked at Instagram maps and I only looked 717 00:41:24,410 --> 00:41:28,430 at it to make sure I wasn't posting to it, even though I rarely post. 718 00:41:28,730 --> 00:41:32,120 Yeah, I'm safe from that since I literally haven't posted anything on my 719 00:41:32,120 --> 00:41:35,420 Instagram since I joined it whenever ago. 720 00:41:36,680 --> 00:41:42,830 Yeah, I, I use it a lot and uh, I'll say I find it a weird, confusing 721 00:41:42,830 --> 00:41:47,900 app anyway, and I find that I don't. 722 00:41:47,960 --> 00:41:52,160 Uh, I, I don't, I wouldn't have run across this on my own. 723 00:41:52,160 --> 00:41:55,940 'cause I, I purposely go get your garbage outta my way and let me 724 00:41:55,940 --> 00:41:58,130 just do the thing that I want to do and leave me alone on Instagram. 725 00:41:58,490 --> 00:41:59,510 That's how I feel about Instagram. 726 00:41:59,990 --> 00:42:02,720 And that's, that's kind of problematic because apparently for some 727 00:42:02,720 --> 00:42:06,440 people, uh, depending on what your settings were for privacy anyway, 728 00:42:07,700 --> 00:42:09,710 you were automatically posting. 729 00:42:09,950 --> 00:42:14,870 So your, your posts were automatically being mapped, um, to everyone. 730 00:42:15,500 --> 00:42:17,150 And so if you were posting things from home. 731 00:42:18,170 --> 00:42:18,920 They were there. 732 00:42:18,920 --> 00:42:23,240 And of course if you were yeah, posting things, uh, you know, and 733 00:42:23,240 --> 00:42:28,190 you're someone who people are likely to try to find, then they would 734 00:42:28,190 --> 00:42:29,780 know where you were when you posted. 735 00:42:29,900 --> 00:42:30,680 So it's, it's. 736 00:42:30,905 --> 00:42:34,325 I mean, there's some of that there that's always been there, that you've been, 737 00:42:34,325 --> 00:42:39,455 have been able to get to the location information, just not as easily, and not 738 00:42:39,455 --> 00:42:42,185 just, Hey, here's a map of where it is. 739 00:42:43,085 --> 00:42:46,865 It does look like it's mostly for friends as opposed to followers, 740 00:42:47,495 --> 00:42:54,935 which is, yeah, some level of niceness, but also you know it since 741 00:42:54,935 --> 00:42:56,735 it's tied to your Facebook friends. 742 00:42:57,935 --> 00:43:00,335 You know, you may have friend at a whole lot of people that you don't really 743 00:43:00,965 --> 00:43:04,025 want to know where you're at at any given moment or anything like that. 744 00:43:04,085 --> 00:43:04,865 So then it may have 745 00:43:04,865 --> 00:43:05,315 changed. 746 00:43:05,315 --> 00:43:08,705 They may have changed the default after the backlash whenever it first rolled out. 747 00:43:08,705 --> 00:43:13,535 Because at beginning it was kind of a, here's the map, everybody can 748 00:43:13,535 --> 00:43:17,945 see it unless you have it marked to be only friends or to be private. 749 00:43:18,695 --> 00:43:19,535 Um, so yeah. 750 00:43:20,555 --> 00:43:23,825 And you still, I think you do still have the option to, at least 751 00:43:23,825 --> 00:43:28,895 that's a couple of days ago, to market as everybody if you want to. 752 00:43:29,555 --> 00:43:29,855 Yeah. 753 00:43:29,915 --> 00:43:31,925 Which, you know, if you wanna live your life out loud, go ahead. 754 00:43:31,925 --> 00:43:33,275 But yeah, no thank you. 755 00:43:33,485 --> 00:43:36,125 Well, it's just really interesting 'cause I know I saw a lot of. 756 00:43:36,530 --> 00:43:40,160 You know, people that were on Instagram, you know, saying this is going on, and 757 00:43:40,160 --> 00:43:44,750 it feels like the rollout was that they, they didn't realize this was happening. 758 00:43:44,750 --> 00:43:47,480 And it's really interesting that a, a rollout like this could happen where 759 00:43:47,480 --> 00:43:53,000 it's, uh, you know, an opt out instead of an opt in, um, for the Instagram map. 760 00:43:53,240 --> 00:43:53,420 Yeah. 761 00:43:53,480 --> 00:43:56,420 Um, and hopefully they've, you know, adjust. 762 00:43:57,005 --> 00:44:01,445 Future rollouts like that, um, with any location based, um, changes. 763 00:44:02,345 --> 00:44:05,465 But we have seen many examples over the years where that is not the case. 764 00:44:06,005 --> 00:44:07,745 It, it's, yeah. 765 00:44:07,745 --> 00:44:08,495 No, it's interesting. 766 00:44:08,495 --> 00:44:13,595 Like, it, it feels like a, a throwback to, uh, all those things like Google 767 00:44:13,595 --> 00:44:17,705 attitude and all those kinds of things where, and also a reminder again to 768 00:44:18,275 --> 00:44:20,975 so many people that don't realize that the metadata in your phone. 769 00:44:22,550 --> 00:44:25,640 Or the metadata that's being processed from your phone has location in it. 770 00:44:26,450 --> 00:44:30,200 And so, 'cause some people I know don't really think about Instagram 771 00:44:30,200 --> 00:44:32,900 as being something that's necessarily recording place, unless they 772 00:44:32,900 --> 00:44:36,860 specifically caption it or say, Hey, this is me at the beach, or whatever. 773 00:44:37,010 --> 00:44:40,070 It just, it doesn't, it does not register that, that. 774 00:44:40,480 --> 00:44:42,610 You know, every, every photo has a place. 775 00:44:43,000 --> 00:44:45,670 There's, I mean there's whole questions about things from meta. 776 00:44:45,670 --> 00:44:49,270 This is one of the few that have been MAP related this summer, but there 777 00:44:49,270 --> 00:44:50,830 have been WhatsApp questionable things. 778 00:44:50,830 --> 00:44:54,910 There have been just Facebook and meta in general, questionable things. 779 00:44:54,940 --> 00:45:00,220 Uh, Sue and I both have the meta glasses where we have essentially 780 00:45:00,640 --> 00:45:04,000 disabled the AI portions of it just. 781 00:45:04,385 --> 00:45:08,465 So I'm now into the world of, of localized LLMs more than anything 782 00:45:08,465 --> 00:45:10,175 else, I guess, in my mind. 783 00:45:10,205 --> 00:45:15,095 But that said, I also then will randomly go to copilot and ask it random questions. 784 00:45:15,785 --> 00:45:18,425 No, it will offer me random answers before I ask it. 785 00:45:18,425 --> 00:45:19,445 I'm like, get away from me. 786 00:45:19,685 --> 00:45:21,005 It's clippy on steroids. 787 00:45:21,095 --> 00:45:21,185 It's 788 00:45:22,775 --> 00:45:23,135 it, yeah. 789 00:45:24,095 --> 00:45:27,395 Copilot is a bit aggressive and, uh, what, what I find annoying, 790 00:45:27,395 --> 00:45:28,655 I don't know about anyone else's. 791 00:45:28,805 --> 00:45:33,245 Office 365 is I used to go to Office 365 and I would had the 792 00:45:33,305 --> 00:45:36,125 Chevron, uh, what are the dots? 793 00:45:36,665 --> 00:45:37,475 I always forget what they called. 794 00:45:38,675 --> 00:45:40,265 Yeah, the waffle, the nine dots, that's, I 795 00:45:40,265 --> 00:45:41,435 will always just call it that. 796 00:45:41,645 --> 00:45:41,735 Yeah. 797 00:45:41,735 --> 00:45:42,125 But yeah, 798 00:45:42,335 --> 00:45:44,675 you could go to that and you could go to like, I need to get a PowerPoint, 799 00:45:44,675 --> 00:45:45,755 or I need to get a accelerator. 800 00:45:45,755 --> 00:45:46,265 You know, whatever. 801 00:45:46,295 --> 00:45:47,615 Go to what you needed to go to. 802 00:45:47,615 --> 00:45:51,875 Oftentimes I would use that as my easy way to get to OneDrive for work, but now like 803 00:45:51,875 --> 00:45:56,015 co-pilot's in the way, and I'm just like, I just want to go to my OneDrive, man. 804 00:45:56,015 --> 00:45:57,455 Just get out of the way. 805 00:45:57,725 --> 00:45:58,505 Here's my response 806 00:45:58,505 --> 00:45:58,805 to that. 807 00:45:59,645 --> 00:46:05,375 I had removed the link bar from all browsers like years, 808 00:46:05,375 --> 00:46:06,400 like decade or more ago. 809 00:46:07,205 --> 00:46:12,905 A month ago when this happened, I re-added the link bar just so I could 810 00:46:12,965 --> 00:46:17,195 have a link that's direct to the apps page so I don't have to go through 811 00:46:17,195 --> 00:46:21,005 copilot to then click two more times just to get to the list of apps. 812 00:46:21,485 --> 00:46:22,565 I don't even know how to do it. 813 00:46:22,565 --> 00:46:24,365 I'm not sure where you go. 814 00:46:24,425 --> 00:46:27,095 Uh, over on the left hand side at the bottom, yeah. 815 00:46:27,155 --> 00:46:29,255 It says Apps click that. 816 00:46:29,255 --> 00:46:32,255 And that is essentially the same as clicking the waffle menu before 817 00:46:32,945 --> 00:46:34,395 we have a, we have, because of the. 818 00:46:35,285 --> 00:46:39,065 At, at work, at our higher education institution, we have these, this card 819 00:46:39,065 --> 00:46:40,835 page that has a link to all those. 820 00:46:40,835 --> 00:46:46,085 And lately I just wanted that 'cause it's quicker, but I've never, I'm just 821 00:46:46,085 --> 00:46:47,165 like, where the hell the thing go? 822 00:46:47,525 --> 00:46:48,455 I'm not gonna draw this. 823 00:46:48,455 --> 00:46:50,195 Go to the card page and I click on it. 824 00:46:51,215 --> 00:46:51,455 Yes. 825 00:46:51,455 --> 00:46:54,095 I don't, I don't understand why they had to say, okay, well you 826 00:46:54,095 --> 00:46:57,540 know this portal office.com used to take you to what you wanted. 827 00:46:57,560 --> 00:47:03,215 Go to office.com, but now it's gonna take you to M 360 five.microsoft dot. 828 00:47:03,635 --> 00:47:04,415 Slash whatever. 829 00:47:04,685 --> 00:47:05,195 I don't know. 830 00:47:05,555 --> 00:47:08,045 It's just, and and, and it's really annoying 'cause I 831 00:47:08,045 --> 00:47:09,395 got, I got snarky about it. 832 00:47:09,395 --> 00:47:12,275 I was like, I typed into co-pilot, how do I open office? 833 00:47:12,920 --> 00:47:16,010 And it was like, oh, well you just go to the start menu and you click on it. 834 00:47:16,010 --> 00:47:19,040 I'm like, I know how to do that stupid machine. 835 00:47:19,370 --> 00:47:19,970 Anyway, sorry. 836 00:47:20,300 --> 00:47:23,870 Yeah, it's just the dumbest, it's the dumbest thing. 837 00:47:23,875 --> 00:47:23,955 The, 838 00:47:23,960 --> 00:47:26,660 the machine is training us and our syntax. 839 00:47:26,720 --> 00:47:29,300 So rather than the other way around, 840 00:47:29,660 --> 00:47:33,320 uh, which I, I think kind of goes to the last one a little bit, I guess. 841 00:47:33,320 --> 00:47:34,160 No, not really. 842 00:47:35,000 --> 00:47:35,270 Nah. 843 00:47:35,840 --> 00:47:39,110 But, uh, Google has released Alpha Earth Foundations, which 844 00:47:39,170 --> 00:47:42,200 is, uh, in my mind a dataset. 845 00:47:43,295 --> 00:47:44,375 More than anything else. 846 00:47:45,035 --> 00:47:48,695 Um, the link in the show notes will say that it's an ai, what do they call it? 847 00:47:48,695 --> 00:47:53,855 The, the first line, an advanced AI model, but it's the results of a deep 848 00:47:53,855 --> 00:47:59,795 learning model to create essentially 10 meter pixel representations of the earth 849 00:48:00,905 --> 00:48:08,315 that they call the 10 by 10 embeddings as opposed to a 10 by 10 meter thing. 850 00:48:09,095 --> 00:48:09,395 Yeah. 851 00:48:09,395 --> 00:48:09,995 So it's. 852 00:48:10,805 --> 00:48:12,485 Something we're just gonna point you to. 853 00:48:12,515 --> 00:48:15,065 There's information about it. 854 00:48:15,515 --> 00:48:17,675 Uh, it has set, quote, unquote, 64. 855 00:48:17,915 --> 00:48:19,085 Well, it depends on where you look at it. 856 00:48:19,505 --> 00:48:26,705 Uh, on the same page it says components, dimensions, and bands. 857 00:48:27,365 --> 00:48:30,185 It's referred to all three ways, and one place. 858 00:48:30,185 --> 00:48:32,105 It's in two of those in the same sentence. 859 00:48:32,855 --> 00:48:37,475 No idea what any of those things mean, whether they're different colors. 860 00:48:38,915 --> 00:48:39,305 Don't worry. 861 00:48:39,305 --> 00:48:43,760 So it's been, it's like land use, uh, land cover data set that's 862 00:48:43,760 --> 00:48:45,980 already been pre-processed for you. 863 00:48:46,160 --> 00:48:50,120 And I assume that's what it is, but I'm not sure what those 64 are. 864 00:48:50,240 --> 00:48:52,010 'cause I haven't been able to find that page yet. 865 00:48:52,880 --> 00:48:55,220 Uh, yeah, I'll let you guys talk about it. 866 00:48:55,640 --> 00:48:56,195 I have questions 867 00:48:56,195 --> 00:48:56,360 about it. 868 00:48:57,050 --> 00:48:57,800 Yeah, don't worry about it. 869 00:48:57,800 --> 00:48:58,310 It's Google. 870 00:48:58,310 --> 00:48:59,810 It'll be shut down in two years, 871 00:49:01,040 --> 00:49:04,910 so if you, so it is, the idea is that this is, uh. 872 00:49:05,930 --> 00:49:09,230 A data set that could be used to look at essentially land cover and other 873 00:49:09,230 --> 00:49:10,730 types of elements related to that. 874 00:49:11,120 --> 00:49:13,880 And it is available, uh, what they've done so far. 875 00:49:13,880 --> 00:49:16,940 So I think that covers the year 2017 to 2024. 876 00:49:17,480 --> 00:49:19,280 And I don't know if it's one data set. 877 00:49:19,850 --> 00:49:23,060 Uh, I guess the idea in the beddings that you can get to the, 878 00:49:23,090 --> 00:49:26,360 the data sources, but it's called the satellite embedding data set. 879 00:49:26,360 --> 00:49:29,720 And you have to use it in Google Earth Engine, which is, which is free if you're 880 00:49:29,720 --> 00:49:31,670 using it for research and education. 881 00:49:32,180 --> 00:49:33,050 And a couple other things. 882 00:49:33,050 --> 00:49:38,120 So it, it doesn't seem that it's easily able to be used in other platforms 883 00:49:38,120 --> 00:49:42,950 right now, but, uh, anyway, I think you should check it out because I 884 00:49:42,950 --> 00:49:45,680 mean, one of the things it's trying to address is sparsity of data. 885 00:49:46,115 --> 00:49:51,725 Uh, at certain resolutions, uh, which when you're using remotely sensed, 886 00:49:51,905 --> 00:49:57,065 uh, data sets for understanding, uh, agricultural issues or ecosystems or 887 00:49:57,065 --> 00:50:01,175 things like that, the resolution's really important and some of our most complete 888 00:50:01,175 --> 00:50:05,375 data sets don't have necessarily the best resolution for what we can do now. 889 00:50:06,215 --> 00:50:09,455 So anyway, uh, if you're interested, uh, check it out. 890 00:50:09,875 --> 00:50:13,955 Um, there's a, a writeup in an article about kind of the, the technology, 891 00:50:13,955 --> 00:50:15,395 math and everything behind it. 892 00:50:15,395 --> 00:50:18,995 But, um, if you want to see the dataset, you can, uh, download 893 00:50:18,995 --> 00:50:21,155 Google Earth Engine and check it out. 894 00:50:21,155 --> 00:50:24,635 It's called the satellite embedding dataset and just to, to kind of, 895 00:50:24,695 --> 00:50:25,925 it's just what they think of it. 896 00:50:25,925 --> 00:50:28,595 They're, they're thinking of it as a virtual satellite, right? 897 00:50:28,595 --> 00:50:30,185 So to cover things. 898 00:50:30,785 --> 00:50:35,165 That the sensors and data sets we have, uh, just can't quite capture. 899 00:50:35,495 --> 00:50:36,395 That's the idea anyway. 900 00:50:36,395 --> 00:50:38,075 So do either of you use, 901 00:50:38,105 --> 00:50:40,085 do any of you use Google Earth Engine? 902 00:50:41,105 --> 00:50:41,765 I, I don't. 903 00:50:41,765 --> 00:50:42,370 That's why I'm asking. 904 00:50:42,370 --> 00:50:42,965 Not used it 905 00:50:43,205 --> 00:50:44,225 since before. 906 00:50:44,255 --> 00:50:45,695 In the Before times. 907 00:50:46,505 --> 00:50:46,925 Yeah. 908 00:50:47,015 --> 00:50:48,305 And thats you. 909 00:50:48,305 --> 00:50:49,085 It's been a really long time. 910 00:50:49,175 --> 00:50:52,930 There was some reason why maybe somebody asked me or something. 911 00:50:53,045 --> 00:50:57,005 I downloaded it and played with it for like a half an hour. 912 00:50:57,605 --> 00:50:58,445 So I do not use it. 913 00:50:58,445 --> 00:50:58,655 No, 914 00:50:59,225 --> 00:50:59,675 Jesse. 915 00:51:01,130 --> 00:51:03,530 Not, like Sue said a while. 916 00:51:03,530 --> 00:51:03,770 Yeah, 917 00:51:04,855 --> 00:51:09,020 I, I played with it and I was going to use it, but I remember running 918 00:51:09,020 --> 00:51:12,950 into the issue of there was a cost association associated with it. 919 00:51:13,700 --> 00:51:15,170 Um, so maybe I just didn't figure out. 920 00:51:15,170 --> 00:51:16,760 Yeah, it's a full stuff now. 921 00:51:16,760 --> 00:51:17,090 Maybe 922 00:51:17,090 --> 00:51:19,340 they just like with, with Google Earth maybe. 923 00:51:19,370 --> 00:51:21,350 'cause there used to be premium stuff there too. 924 00:51:21,350 --> 00:51:25,370 But yeah, I haven't, I haven't tried to use it in a long time. 925 00:51:26,015 --> 00:51:29,705 I was just curious 'cause I feel like it's, uh, I mean it was the bee's 926 00:51:29,705 --> 00:51:35,705 knees at one time, but it, it, I'm not sure it's made a ca Well, okay. 927 00:51:35,705 --> 00:51:38,795 In fairness, probably because I have access to all the ESRI stuff, that's 928 00:51:39,245 --> 00:51:43,235 the dominant reason I have other op other things that I can use to answer 929 00:51:43,235 --> 00:51:44,195 the questions I want to answer. 930 00:51:44,615 --> 00:51:47,885 So I was just kind of curious is it is just not a tool set that I turned to. 931 00:51:48,395 --> 00:51:52,685 None of us are doing research on a large area. 932 00:51:53,540 --> 00:51:54,935 Yeah, so I think that's true. 933 00:51:55,010 --> 00:51:58,520 That's, that's where Google Earth Engine, um, shines a little bit more. 934 00:51:58,550 --> 00:52:04,220 A lot of us do a, at the table, do a lot more of very, uh, large scale, 935 00:52:04,280 --> 00:52:07,220 small area, uh, kind of research. 936 00:52:07,700 --> 00:52:13,400 And so, you know, being able to map the whole world is, is less useful for us. 937 00:52:13,400 --> 00:52:15,590 We just want to be able to focus on certain areas. 938 00:52:15,590 --> 00:52:17,570 We want lidar, we want, you know. 939 00:52:18,530 --> 00:52:22,700 Drone data for imagery, we want, you know, someone who's gone out 940 00:52:22,700 --> 00:52:26,330 with a GPS unit and collected data for us more than anything else. 941 00:52:26,330 --> 00:52:30,740 So it, it, I think it is a scale issue between what we do and 942 00:52:30,740 --> 00:52:32,540 what Google Earth Engine offers. 943 00:52:32,985 --> 00:52:37,400 I, I think that's what I see usually when I, when I, um, see different forms where 944 00:52:37,400 --> 00:52:41,750 people are discussing it, is they tend to be working with, um, those coastal 945 00:52:41,750 --> 00:52:44,150 and, and large scale world environments. 946 00:52:44,330 --> 00:52:46,130 But we want people to know about it, even though. 947 00:52:46,565 --> 00:52:50,315 I who started talking about it then kind of had questions about it, I still 948 00:52:50,315 --> 00:52:51,815 want you to go out and look at it. 949 00:52:51,935 --> 00:52:56,795 Just be aware that it, the, the metadata is not as. 950 00:52:57,755 --> 00:53:00,755 Transparent as I would like it to be, but maybe in Google Earth 951 00:53:00,755 --> 00:53:01,715 engine it's a little bit better. 952 00:53:02,405 --> 00:53:03,280 And that's it for the news. 953 00:53:16,205 --> 00:53:18,485 All right, so, and onto the web corner. 954 00:53:19,175 --> 00:53:25,175 Alan Carroll has a book about his journey telling stories with maps, 955 00:53:25,385 --> 00:53:28,715 um, lessons from a lifetime of creating place-based narratives. 956 00:53:29,105 --> 00:53:36,565 Um, it brings together the stories of a. How he thought up this idea with 957 00:53:36,655 --> 00:53:40,705 his background at National Geographic as the chief cartographer there. 958 00:53:41,035 --> 00:53:45,415 Um, the fundamentals of storytelling for humanity, um, talking about best 959 00:53:45,415 --> 00:53:50,215 practices and how maps help us to tackle who, what, when, where, why, and how. 960 00:53:50,365 --> 00:53:55,975 The connections between maps and memory, and also gives you a way to learn 961 00:53:55,975 --> 00:53:59,575 about approaches to storytelling and place-based topics through story maps. 962 00:54:00,055 --> 00:54:04,015 If you've read any of a Aaron Carroll's very well written. 963 00:54:04,070 --> 00:54:06,320 Articles, they, they tend to be. 964 00:54:07,145 --> 00:54:09,335 Just like story maps, they flow really well. 965 00:54:09,545 --> 00:54:11,915 They tend to be succinct, but they give you what you need to 966 00:54:11,915 --> 00:54:13,505 know to, to tell that story. 967 00:54:13,865 --> 00:54:18,935 Um, so I'm looking forward to getting this book and using in it in classes. 968 00:54:19,145 --> 00:54:23,015 He also focuses on practical features and functions for story 969 00:54:23,015 --> 00:54:27,095 maps, um, so that he can inspire you to apply them to your own stories. 970 00:54:27,830 --> 00:54:31,610 Um, so I had the opportunity to go to a workshop he taught virtually 971 00:54:31,610 --> 00:54:35,840 online several years ago, and it was for primarily museum. 972 00:54:37,640 --> 00:54:41,690 Geographers people working with exhibits inside and outside and how 973 00:54:41,690 --> 00:54:45,560 to make that connection to the stories with the exhibits and the places. 974 00:54:45,590 --> 00:54:50,480 Um, a lot of the Smithsonian and the, the cherry blossoms, which, you know, 975 00:54:50,480 --> 00:54:54,950 is one of the earlier and still most impactful story maps out there about the 976 00:54:54,950 --> 00:54:57,170 story of the, the cherry blossoms in DC 977 00:54:57,470 --> 00:54:57,680 Yeah. 978 00:54:57,680 --> 00:55:00,920 If you're interested in either Island Carroll's career or. 979 00:55:01,565 --> 00:55:03,425 Story maps, go check it out 980 00:55:19,025 --> 00:55:19,925 onto the events corner. 981 00:55:19,925 --> 00:55:23,765 As always, we encourage you to go check out events such as 982 00:55:24,185 --> 00:55:27,815 State of the Map, October 3rd through fifth in Manila, and 983 00:55:27,815 --> 00:55:29,195 there is a call for posters. 984 00:55:29,990 --> 00:55:34,730 Geo Week 2026 will be February 16th through the 18th. 985 00:55:34,730 --> 00:55:38,030 So now we're getting into 2026 in uh, Denver, Colorado. 986 00:55:38,720 --> 00:55:41,750 Uh, of course if you'd like us to add your event to the podcast, send us 987 00:55:41,750 --> 00:55:43,520 an email to podcast@veryspatial.com. 988 00:55:44,300 --> 00:55:47,600 If you'd like to reach us individually, I can be reached at you@veryspatial.com. 989 00:55:48,710 --> 00:55:51,410 I could be reached at barb@veryspatial.com and you can 990 00:55:51,410 --> 00:55:53,030 reach me atFrank@veryspatial.com. 991 00:55:53,270 --> 00:55:55,820 I'm available at kind of spatial and of course you can find to. 992 00:55:56,480 --> 00:56:01,040 All of our contact information over at very spatial.com/contact. 993 00:56:02,705 --> 00:56:03,205 As always. 994 00:56:04,100 --> 00:56:05,390 We're the folks from very spatial. 995 00:56:05,450 --> 00:56:06,110 Thanks for listening, 996 00:56:06,260 --> 00:56:07,310 and we'll see you in a couple weeks.