WEBVTT Kind: captions Language: en-US 00:00:01.789 --> 00:00:07.695 [silence] 00:00:07.695 --> 00:00:09.320 Hi, everyone. 00:00:09.320 --> 00:00:11.559 Welcome to the Earthquake Science Center seminar 00:00:11.559 --> 00:00:14.722 for February 17th, 2021. 00:00:14.722 --> 00:00:17.590 Obviously, you’ve all noticed we’re starting late today in order 00:00:17.590 --> 00:00:20.960 for ESC members to attend the USGS event this morning 00:00:20.960 --> 00:00:24.760 recognizing the career of our own Rufus Catchings in celebration 00:00:24.760 --> 00:00:29.323 of African Americans in the Earth sciences at the USGS. 00:00:29.323 --> 00:00:31.410 As a reminder, before we get started, 00:00:31.410 --> 00:00:33.699 please turn off your cameras and mute your microphones. 00:00:33.699 --> 00:00:36.379 All the functions are available through the menu bar that pops up 00:00:36.379 --> 00:00:41.167 when you hover over the bottom of your – the top of your Teams window. 00:00:41.167 --> 00:00:43.100 Live captioning is available. 00:00:43.100 --> 00:00:48.464 You click the three-dot More button and choose “turn on live captions.” 00:00:48.464 --> 00:00:51.789 Before we begin today, there are just a couple of announcements. 00:00:51.789 --> 00:00:55.210 The next all-hands meeting – we just had one, so the next is about – 00:00:55.210 --> 00:00:59.149 over a month from now on March 19th, but you’ll all have gotten an invitation 00:00:59.149 --> 00:01:03.530 to that for those who are involved. Next week’s seminar will be 00:01:03.530 --> 00:01:07.369 Eric Campbell from LA Seismic talking about the results of 00:01:07.369 --> 00:01:11.979 seismic surveys in Los Angeles and the fault system there. 00:01:12.956 --> 00:01:15.870 If you have questions for today’s speaker, you can either type them 00:01:15.870 --> 00:01:19.210 into the chat or raise your hand. Tamara and I will be monitoring 00:01:19.210 --> 00:01:22.640 the chat in case there’s anything urgent we need to interrupt the speaker with. 00:01:22.640 --> 00:01:27.270 And otherwise, we’ll be emceeing the Q-and-A session at the end. 00:01:27.270 --> 00:01:29.840 And we invite you to feel free to unmute yourself. 00:01:29.840 --> 00:01:32.674 Everyone loves seeing faces and hearing voices these days. 00:01:32.674 --> 00:01:35.070 It’s a little more personal. 00:01:35.070 --> 00:01:37.350 When the time comes. 00:01:37.350 --> 00:01:41.799 So, with that out of the way, it’s my pleasure to introduce the Earthquake 00:01:41.799 --> 00:01:45.560 Science Center’s own Sue Hough to deliver this timely presentation 00:01:45.560 --> 00:01:51.775 on the hidden impacts of societal power structures on our science. 00:01:51.775 --> 00:01:56.860 Introducing Sue is a particular pleasure for me as a USGS seminar coordinator 00:01:56.860 --> 00:02:00.189 because my own career in earthquake science was pretty much launched as 00:02:00.189 --> 00:02:04.229 a high school junior when I went to volunteer at the USGS Pasadena office, 00:02:04.229 --> 00:02:07.799 and Sue mentored me through an archival newspaper project 00:02:07.799 --> 00:02:14.750 re-evaluating the magnitude of a large earthquake in 1892 in Baja 00:02:14.750 --> 00:02:17.550 that itself ended up being a predecessor of an earthquake 00:02:17.550 --> 00:02:21.910 that I went on to study during my Ph.D. later in 2010. 00:02:21.910 --> 00:02:28.000 So Sue has had an illustrious career right exactly at this important 00:02:28.000 --> 00:02:31.230 boundary where earthquake science meets society. 00:02:31.230 --> 00:02:35.580 And, in her research, she shows that that’s a two-way street. 00:02:35.580 --> 00:02:39.380 After graduating from UC-Berkeley and then Scripps, Sue did a postdoc 00:02:39.380 --> 00:02:42.690 at Lamont–Doherty, during which the Loma Prieta earthquake 00:02:42.690 --> 00:02:46.950 interrupted and then stole her attention. Shortly after that, she joined 00:02:46.950 --> 00:02:52.620 the USGS in Pasadena in 1992. And since then, Sue has really handily 00:02:52.620 --> 00:02:55.910 danced between strong motion seismology of modern earthquakes 00:02:55.910 --> 00:02:59.290 and deep insightful investigations of historical earthquakes 00:02:59.290 --> 00:03:04.620 spanning centuries. Somehow, she’s found time among it all 00:03:04.620 --> 00:03:10.770 to write a must-have library of pop science books about earthquakes. 00:03:11.712 --> 00:03:15.210 And, among Sue’s impactful work, she’s also been heavily involved 00:03:15.210 --> 00:03:21.340 in establishing seismic monitoring networks and educating up-and-coming 00:03:21.340 --> 00:03:25.350 career seismologists in countries where these sorts of services are 00:03:25.350 --> 00:03:31.470 badly needed, including Nepal and Haiti, Myanmar, and in India – 00:03:31.470 --> 00:03:35.370 some of the most important places on the planet to establish secure 00:03:35.370 --> 00:03:37.571 home-grown functioning seismic programs. 00:03:37.571 --> 00:03:40.640 Okay, so without further ado, I’ll hand it over to Sue 00:03:40.640 --> 00:03:44.380 to talk to us about which earthquake accounts matter. 00:03:45.071 --> 00:03:51.150 - Well, okay. So I will start sharing. 00:03:51.150 --> 00:03:53.453 See if this works. 00:03:54.461 --> 00:03:55.880 Okay. 00:03:55.880 --> 00:04:00.480 So thank you, Austin, for the very kind introduction. 00:04:00.480 --> 00:04:04.320 This really does feel like coming full circle [laughs] having 00:04:04.320 --> 00:04:07.930 Austin introduce me. Maybe some of you have had 00:04:07.930 --> 00:04:13.590 the experience at the USGS of having volunteers reach out with an interest 00:04:13.590 --> 00:04:19.400 in being involved with projects. And, of the students that I’ve heard 00:04:19.400 --> 00:04:23.730 from, Austin distinguished himself from the beginning. 00:04:23.730 --> 00:04:26.980 And sometimes you get expressions of interest from students who don’t 00:04:26.980 --> 00:04:34.100 really show up. Sometimes you get parents of students reaching out. 00:04:34.100 --> 00:04:39.020 But Austin was engaged and excited about the science, 00:04:39.020 --> 00:04:42.630 and it was a pleasure to work for him, so it was a very happy day when 00:04:42.630 --> 00:04:47.990 I found out that the USGS has actually brought him on board. 00:04:47.990 --> 00:04:54.859 So I wanted to thank Austin, Tamara, Susan for organizing this. 00:04:54.893 --> 00:04:58.130 This is – so I can’t see myself. I just see my screen. 00:04:58.130 --> 00:05:00.070 That’s a little – it’s all a little weird, 00:05:00.070 --> 00:05:06.560 talking into the ether, but hopefully it’ll be okay. 00:05:06.560 --> 00:05:12.720 So the seminar is going to be something a little new and different. 00:05:12.720 --> 00:05:16.840 So let’s start with this. Since current events have brought 00:05:16.840 --> 00:05:21.930 the issue of systemic racism and inequity to the fore last year, 00:05:21.930 --> 00:05:24.850 there’s been a fair amount of soul searching about how to 00:05:24.850 --> 00:05:33.010 improve diversity and inclusion within our science. As this 1897 photo shows, 00:05:33.010 --> 00:05:38.380 geology has historically been very male and very white. 00:05:38.380 --> 00:05:42.900 I found this photo on the web. It’s a USGS group. 00:05:42.900 --> 00:05:45.077 Some of you may recognize the location. 00:05:45.111 --> 00:05:49.790 And I just pulled it out to make the point that, over time, we’ve gotten – 00:05:49.790 --> 00:05:54.760 geology, geosciences has gotten a little less male, but it hasn’t gotten much 00:05:54.760 --> 00:06:00.585 less white. So there’s some overdue conversations that are going on. 00:06:00.618 --> 00:06:06.310 But, as we have these conversations about how to improve diversity 00:06:06.310 --> 00:06:12.540 in geosciences, I want to talk about the possibility that diversity issues 00:06:12.540 --> 00:06:16.748 come into play, not just with our community, but with our science. 00:06:16.788 --> 00:06:20.780 And I’m going to be focusing here on so-called citizen science, 00:06:20.780 --> 00:06:24.960 which I realize is a somewhat controversial term, but if you can 00:06:24.960 --> 00:06:29.358 see this – well, yeah, you can see this because you see my screen. 00:06:29.358 --> 00:06:34.950 I’m using “citizen” in the inclusive sense here – not legal citizens, 00:06:34.950 --> 00:06:39.450 but inhabitants of particular towns or cities. 00:06:39.450 --> 00:06:48.639 So, as the current events – the BLM movement captured the news cycle 00:06:48.639 --> 00:06:54.800 last year, I started talking to a long-time collaborator, Stacey Martin, 00:06:54.800 --> 00:06:58.240 whose picture is shown here. And it turns out – it was one of 00:06:58.240 --> 00:07:04.020 those times when we were both thinking along similar lines. 00:07:04.020 --> 00:07:08.330 If you haven’t had the pleasure of meeting Stacey, his name 00:07:08.330 --> 00:07:13.449 notwithstanding, he is Indian. He’s from Puna, born and raised. 00:07:13.449 --> 00:07:19.460 He spent five years recently at EOS in Singapore, and he’s now at ANU. 00:07:19.460 --> 00:07:23.949 But we’ve worked together over the years on a number of projects 00:07:23.949 --> 00:07:31.190 looking at historical earthquakes, including the 1925 Santa Barbara 00:07:31.190 --> 00:07:37.510 earthquake and its felt reports. And recent earthquakes, 00:07:37.510 --> 00:07:40.090 including the 2015 Gorkha, Nepal, earthquake, 00:07:40.090 --> 00:07:44.010 which you’re going to hear a little more about later. 00:07:44.010 --> 00:07:47.130 So this is Stacey. The other introduction is that I assume people 00:07:47.130 --> 00:07:52.880 are familiar by now with the traditional intensity color scale. 00:07:52.880 --> 00:08:00.690 So this is the intensities, or the scale, that goes back to the 19th century to 00:08:00.690 --> 00:08:06.911 assign numerical values for the severity of shaking at any one location. 00:08:06.911 --> 00:08:13.389 And we’re using, in all of the maps, the color palette – the color scale 00:08:13.389 --> 00:08:18.161 that was – is used for ShakeMaps and Did You Feel It?. 00:08:18.161 --> 00:08:24.005 Okay, so the thought that – so, okay. 00:08:25.080 --> 00:08:29.800 The felt reports of earthquake shaking are sometimes called 00:08:29.800 --> 00:08:35.120 macroseismic data, distinguished from instrumental data. 00:08:35.120 --> 00:08:40.419 From historical earthquakes, we use archival accounts, which are often the 00:08:40.419 --> 00:08:44.829 only source of information we’ll have to study historical earthquakes. 00:08:44.829 --> 00:08:51.160 For modern earthquakes, increasingly, people use reports that are contributed 00:08:51.160 --> 00:08:58.740 via online websites, including Did You Feel It?, and now there’s some others. 00:08:58.740 --> 00:09:03.829 So what Stacey and I both started to think about, watching the news 00:09:03.829 --> 00:09:08.930 play out, was the question of representation in citizen science. 00:09:08.930 --> 00:09:12.610 For historical earthquakes, whose accounts were recorded? 00:09:12.610 --> 00:09:14.480 Which accounts might be missing? 00:09:14.480 --> 00:09:18.621 How can that shape our view of historical earthquakes? 00:09:18.621 --> 00:09:21.670 And, for modern earthquakes, you know, we’re relying on people 00:09:21.670 --> 00:09:26.279 to come to us – to the web. Which accounts are contributed? 00:09:26.279 --> 00:09:29.569 Which accounts might be missing? And how does that shape our view 00:09:29.569 --> 00:09:32.149 of earthquake effects? In modern times, we don’t really 00:09:32.149 --> 00:09:36.019 use these effects to locate earthquakes or determine their magnitude, 00:09:36.019 --> 00:09:42.550 but we can still use them to look at the effects of earthquake shaking. 00:09:42.550 --> 00:09:51.399 So questions like this arise naturally in India, as this old map of the Indian 00:09:51.399 --> 00:09:57.180 Empire, quote, unquote, illustrates. So this was back during the 00:09:57.180 --> 00:10:00.260 British colonial period. And the different colors – 00:10:00.260 --> 00:10:04.144 it’s probably hard to see, but the red areas are – 00:10:04.144 --> 00:10:10.004 were under British control during the colonial period. 00:10:10.004 --> 00:10:13.929 But it – and we rely on British sources very heavily to study 00:10:13.929 --> 00:10:17.660 historical earthquakes in India. But that wasn’t the entire 00:10:17.660 --> 00:10:21.480 Indian subcontinent. The yellow areas were – 00:10:21.480 --> 00:10:28.600 have always been independent, including Nepal and some other areas – 00:10:28.600 --> 00:10:34.399 Balochistan. And there are some areas that remained under Indian control. 00:10:34.399 --> 00:10:39.089 So, you know, the question arises of whether we have 00:10:39.089 --> 00:10:42.100 accounts from all of these regions. 00:10:42.100 --> 00:10:47.409 And then you get up into Tibet for important historical earthquakes. 00:10:47.409 --> 00:10:53.885 In the U.S., the configuration of the map has obviously changed over time. 00:10:53.885 --> 00:11:01.160 And much of the country historically has been Indian territory – 00:11:01.160 --> 00:11:05.809 Native American territory. So there’s issues of representation 00:11:05.809 --> 00:11:09.910 that are going to come into play there. 00:11:09.910 --> 00:11:14.199 To illustrate the issues that can arise with historical earthquakes, 00:11:14.199 --> 00:11:17.050 I’m going to talk about a misplaced earthquake 00:11:17.050 --> 00:11:22.550 in Oklahoma that serves as a illustrative example. 00:11:22.550 --> 00:11:27.830 So, in recent years, we kind of know Oklahoma as the 00:11:27.830 --> 00:11:33.588 induced earthquake capital of the world – or the U.S. 00:11:33.588 --> 00:11:40.360 This 2014 hazard map, when it was made, the group NSHMP drew boxes 00:11:40.360 --> 00:11:46.170 around zones with induced earthquakes and didn’t consider them to come up 00:11:46.170 --> 00:11:51.041 with the hazard map for tectonic earthquakes. 00:11:51.041 --> 00:11:57.182 And you can see that Oklahoma does have hazard associated with tectonic 00:11:57.223 --> 00:12:03.616 earthquakes, most especially the Meers Fault in southwest Oklahoma. 00:12:04.230 --> 00:12:08.730 So the Meers Fault I assume a lot of you know about. 00:12:08.730 --> 00:12:16.000 It’s arguably the most dramatic recent fresh scarp east of the Rockies. 00:12:16.000 --> 00:12:20.860 It’s believed to have hosted two, I think, surface-rupturing events 00:12:20.860 --> 00:12:24.564 in the Holocene, magnitude 7-ish. 00:12:24.564 --> 00:12:26.499 I’m not going to be talking about the Meers Fault. 00:12:26.499 --> 00:12:33.900 It’s been remarkably quiet over recent decades. 00:12:33.900 --> 00:12:37.220 But I’m going to be talking about the earliest known earthquake 00:12:37.220 --> 00:12:42.501 in present-day Oklahoma. It occurred on October 22nd, 1882. 00:12:42.501 --> 00:12:46.959 It pre-dates statehood, which was in 1907. 00:12:46.959 --> 00:12:48.869 It was reported in a number of places, 00:12:48.869 --> 00:12:55.665 including Arkansas, where it was described as a lively shaking. 00:12:55.665 --> 00:13:00.279 Until a few years ago – this work actually goes back a few years 00:13:00.279 --> 00:13:02.959 when I was looking back at the historical catalog 00:13:02.959 --> 00:13:07.459 for Oklahoma at the suggestion of Rob Williams. 00:13:07.459 --> 00:13:13.227 This is what the known effects of that earthquake look like. 00:13:13.227 --> 00:13:15.350 And you can see the earthquake was big enough 00:13:15.350 --> 00:13:20.339 to be felt into several neighboring states. 00:13:20.339 --> 00:13:23.629 The location of the earthquake has bounced around like 00:13:23.629 --> 00:13:29.420 a ping-pong ball over time. The earliest suggestion by Branner 00:13:29.420 --> 00:13:33.516 and Hansell was that it was in western Arkansas here. 00:13:33.516 --> 00:13:40.569 In 1952, Heinrich suggested that it was over near the El Reno earthquake 00:13:40.569 --> 00:13:45.360 that happened that year. Later, it was mis-located down 00:13:45.360 --> 00:13:53.209 in Texas, and that turned out to be based on a mis-reading of some reports. 00:13:53.209 --> 00:13:57.920 More recently, the generally accepted location is Fort Gibson. 00:13:57.920 --> 00:14:01.940 It was an early U.S. fort in this location where relatively 00:14:01.940 --> 00:14:05.258 strong effects were described. 00:14:05.258 --> 00:14:09.350 The USGS location, at least as of a couple months ago, 00:14:09.350 --> 00:14:13.114 was down close to Texas. 00:14:14.133 --> 00:14:19.769 For this earthquake, it turns out that historical context matters. 00:14:19.769 --> 00:14:23.579 Before statehood, Oklahoma was divided up. 00:14:23.579 --> 00:14:29.170 It was known as Indian Territory. This was the end of the Trail of Tears, 00:14:29.170 --> 00:14:32.860 which was the forced march that brought a lot – a number of Native 00:14:32.860 --> 00:14:38.839 tribes from their ancestral homelands that were further east into Oklahoma, 00:14:38.839 --> 00:14:44.459 as you see here. And I’ll call your attention to the Choctaw Nation. 00:14:44.459 --> 00:14:47.439 That was southeastern-most Oklahoma. 00:14:47.439 --> 00:14:53.351 And this was established by an act in 1889. 00:14:53.389 --> 00:14:55.439 Or, no – well … 00:14:57.336 --> 00:15:01.459 Yeah. okay. The dates – as of – as of 1882, this was – 00:15:01.459 --> 00:15:07.488 this was – this was already the Choctaw Nation. 00:15:08.601 --> 00:15:14.459 This is the lively shaking account. In the process of looking at the 00:15:14.459 --> 00:15:19.773 historical catalog, I came across a report that had not been considered 00:15:19.773 --> 00:15:24.968 before by seismologists. And it was – it’s a little hard to sort out. 00:15:24.968 --> 00:15:31.310 It was written from Dallas to the editor of a newspaper in Arkansas from 00:15:31.310 --> 00:15:34.319 an individual who’s describing a number of different places. 00:15:34.319 --> 00:15:39.259 He was returning from one county, and then he describes effects of 00:15:39.259 --> 00:15:45.440 an earthquake at a place called Venetia Grove, where the shock upset a pile 00:15:45.440 --> 00:15:51.538 of lumber, shook up the dishes, etc. And then it goes on to say that 00:15:51.569 --> 00:15:57.639 70 miles west of Venetia Grove, in the Choctaw Nation, 00:15:57.639 --> 00:16:02.694 chimneys fell and a general turning over of things took place. 00:16:02.694 --> 00:16:08.100 So pulling out the key information, in Venetia Grove, you have effects, 00:16:08.100 --> 00:16:12.600 and then, in the Choctaw Nation, it was clearly quite a bit stronger. 00:16:12.600 --> 00:16:17.720 Even conservatively, that suggests intensity of 7. 00:16:17.720 --> 00:16:20.559 So historical seismology can be fun. 00:16:20.559 --> 00:16:24.780 An initial question is, where is Venetia Grove. 00:16:24.822 --> 00:16:27.869 And if you go to Google Earth, it’s no help at all. 00:16:27.869 --> 00:16:33.230 It doesn’t recognize the location. It no longer exists as a place name. 00:16:33.230 --> 00:16:36.609 So you start poking around. You look at old newspapers and maps. 00:16:36.609 --> 00:16:41.420 And lo and behold, eventually, you figure out where Venetia Grove is. 00:16:41.420 --> 00:16:45.230 And then, from there, you measure 70 miles to the west 00:16:45.230 --> 00:16:48.834 to get the Choctaw Nation location. 00:16:48.834 --> 00:16:52.269 So then you put that data point on the map. 00:16:52.269 --> 00:16:57.639 So just one – well, two – Venetia Grove and Choctaw Nation. 00:16:57.639 --> 00:17:01.009 And that one data point changes the view quite a bit. 00:17:01.009 --> 00:17:07.120 If using the method of Bakun and Wentworth to find an – excuse me – 00:17:07.120 --> 00:17:15.549 optimal location, with the expanded data set, yields this circled star here. 00:17:15.549 --> 00:17:20.600 And a magnitude of 4.8 to 5-ish. 00:17:20.600 --> 00:17:27.569 So let’s zoom into – yeah, so here – obviously, there’s some pretty big 00:17:27.569 --> 00:17:31.250 uncertainties on exactly where the location is, but somewhere 00:17:31.250 --> 00:17:38.090 close to the Choctaw Nation. And we suggest that 00:17:38.090 --> 00:17:40.880 that’s the appropriate name for this event. 00:17:40.880 --> 00:17:46.716 So, looking at that part of Oklahoma, some topography jumps out 00:17:46.716 --> 00:17:51.750 immediately. That turns out to be the Ouachita Fold and Thrust Belt, 00:17:51.750 --> 00:17:55.583 which winds its way through Texas, corner of Oklahoma, 00:17:55.608 --> 00:18:00.180 into Arkansas and neighboring states. 00:18:00.180 --> 00:18:06.930 I’m not the best person to talk about this part of the world in geology, 00:18:06.930 --> 00:18:14.039 but the Ouachita Fold and Thrust Belt is a late Paleocene orogeny. 00:18:14.039 --> 00:18:20.460 And the leading edge of the thrust zone is now defined by the Choctaw Thrust 00:18:20.460 --> 00:18:29.333 Fault in Oklahoma and then the Ross Creek Fault in Arkansas. 00:18:29.333 --> 00:18:33.409 Zooming into that map, you can see, these are – the circles are small 00:18:33.409 --> 00:18:37.559 instrumentally recorded earthquakes. You can see the induced events. 00:18:37.559 --> 00:18:40.598 Most of these are not likely to be induced. 00:18:40.598 --> 00:18:45.679 And then there’s two white stars. These are both magnitude 4.3 – 00:18:45.679 --> 00:18:50.929 estimated 4.3 events, instrumentally recorded, which are also falling 00:18:50.929 --> 00:18:58.860 generally along these trends. So the – oh, let me – let me 00:18:58.860 --> 00:19:02.860 point out that some of these areas are fairly remote, 00:19:02.860 --> 00:19:06.910 but the city of Little Rock is right here. 00:19:09.817 --> 00:19:13.360 This is – the 1882 event is the largest known tectonic 00:19:13.360 --> 00:19:18.240 earthquake in Oklahoma. It falls along this interesting 00:19:18.240 --> 00:19:24.299 structural trend, which also has produced other magnitude 4-ish 00:19:24.299 --> 00:19:29.179 earthquakes in recent times. And it raises the question of whether 00:19:29.179 --> 00:19:35.566 or not this is a central-eastern U.S. source zone that we haven’t – 00:19:35.566 --> 00:19:38.707 we haven’t really paid much attention to. Whether or not 00:19:38.707 --> 00:19:41.909 it’s active in the current stress regime, I don’t know. 00:19:41.909 --> 00:19:45.840 But it’s an interesting question. It’s an interesting zone. 00:19:45.840 --> 00:19:49.870 And the point for this talk is that one serendipitous account 00:19:49.870 --> 00:19:54.200 changed our whole view of the earthquake and calls attention 00:19:54.200 --> 00:19:59.191 to a region that we haven’t really thought about before. 00:19:59.191 --> 00:20:03.309 For historical earthquakes, the old quotes from 00:20:03.309 --> 00:20:07.410 Donald Rumsfeld comes to mind. For the most part, we don’t know 00:20:07.410 --> 00:20:11.570 what we don’t know. For the 1882 event, 00:20:11.570 --> 00:20:16.448 this one serendipitous account surfaced. 00:20:16.448 --> 00:20:23.919 But in general, there are effects that we don’t have accounts of. 00:20:23.919 --> 00:20:29.370 So that’s going to be difficult to get at [audio cuts out]. 00:20:29.405 --> 00:20:33.769 But for now, let me move forward and talk about representation issues 00:20:33.769 --> 00:20:41.933 and so-called citizen science. So, starting in 1999, thanks to 00:20:41.933 --> 00:20:45.020 Dave Wald and colleagues, we have the Did You Feel It? system, 00:20:45.020 --> 00:20:49.380 which I assume people know about. It lets people come to the web 00:20:49.380 --> 00:20:52.429 and fill out the standard questionnaire from which 00:20:52.429 --> 00:20:57.000 intensities are determined and used to produce a map. 00:20:57.000 --> 00:21:01.784 It’s been successful, I think, beyond wildest expectations. 00:21:01.784 --> 00:21:06.300 People like Did You Feel It?. So, at this point, if you have 00:21:06.300 --> 00:21:10.960 a magnitude 4.5 earthquake in the greater L.A. area, as we did 00:21:10.960 --> 00:21:16.340 last September, 40,000 people come and fill out this questionnaire, 00:21:16.340 --> 00:21:22.112 and we get these amazing maps that show the distribution of shaking. 00:21:22.112 --> 00:21:26.130 What’s more, a number of studies by a number of people have shown 00:21:26.130 --> 00:21:30.580 that there’s a remarkably good correspondence between 00:21:30.580 --> 00:21:33.960 Did You Feel It? intensities and instrumental parameters 00:21:33.960 --> 00:21:40.460 like PGA, PGV. This is from a figure – a paper by Gordon et al. 00:21:40.460 --> 00:21:45.100 Gail Atkinson has also looked at this. I’ve looked at it. 00:21:45.100 --> 00:21:49.509 So it turns out these contributed accounts are 00:21:49.509 --> 00:21:57.080 giving us a quite reliable view of what the shaking distribution is. 00:21:57.080 --> 00:22:04.690 And so, as these data points find their way into important USGS products, 00:22:04.690 --> 00:22:09.690 in some cases, they’re used to flesh out ShakeMaps, especially 00:22:09.690 --> 00:22:13.580 if instruments are sparse. You have a lot more 00:22:13.580 --> 00:22:17.039 Did You Feel It? data than you have instrumental data. 00:22:17.039 --> 00:22:24.169 And then the PAGER system, which uses ShakeMaps to forecast 00:22:24.169 --> 00:22:31.039 likely impact – fatalities and losses – relies on ShakeMaps, which can 00:22:31.039 --> 00:22:37.322 rely on these contributed values as important constraints. 00:22:37.322 --> 00:22:41.759 So – oh, and this is – I find this amusing. 00:22:41.759 --> 00:22:46.243 The Did You Feel It? system has collected quite a bit of data 00:22:46.243 --> 00:22:50.450 retroactively for earthquakes that pre-date the 1999 introduction 00:22:50.450 --> 00:22:52.779 of the system. 00:22:52.779 --> 00:22:58.509 This is the ShakeMap for the 1971 Sylmar earthquake, 00:22:58.509 --> 00:23:03.269 the 50th anniversary of which was just last week. 00:23:03.269 --> 00:23:11.350 There’s over 1,200 Did You Feel It? reports for this event, which, 00:23:11.350 --> 00:23:14.000 as I’ve said, is not too shabby for an earthquake that’s 00:23:14.000 --> 00:23:17.665 old enough to join AARP. 00:23:19.532 --> 00:23:23.670 Okay, let’s get back to the question at hand. 00:23:23.670 --> 00:23:27.040 You know, which accounts are being contributed, 00:23:27.040 --> 00:23:30.850 and which accounts might be missing? 00:23:30.850 --> 00:23:33.950 So there was a paper a couple years ago by Mak and Schorlemer. 00:23:33.950 --> 00:23:37.809 They had considered a similar question. They looked at a whole lot of data, 00:23:37.809 --> 00:23:43.954 put it into a hopper, and they concluded that, to first order, 00:23:43.954 --> 00:23:48.980 participation in Did You Feel It? depends on the severity of shaking 00:23:48.980 --> 00:23:54.227 and population density, so – neither of which is too surprising. 00:23:54.227 --> 00:23:58.409 So [audio cuts out] this study, we looked in more detail 00:23:58.409 --> 00:24:01.330 at individual events. So I’m going to start with 00:24:01.330 --> 00:24:07.179 a tale of three earthquakes – Loma Prieta, Northridge, and Ridgecrest. 00:24:07.179 --> 00:24:13.789 And so, for this analysis, I’m looking at Did You Feel It? data averaged 00:24:13.789 --> 00:24:17.080 within ZIP codes. And I could talk a lot about this. 00:24:17.080 --> 00:24:20.260 That’s why the Ridgecrest map looks funny. 00:24:20.260 --> 00:24:26.180 Because there weren’t a lot of ZIP codes going up into the near-field area. 00:24:26.180 --> 00:24:32.547 But, yeah, so there’s fairly – there’s quite a bit of Did You Feel It? 00:24:32.547 --> 00:24:35.039 data for all three events, even though two of them 00:24:35.039 --> 00:24:39.179 do pre-date the introduction of the system. 00:24:39.179 --> 00:24:46.830 The reason for looking at data averaged within ZIP codes is that 00:24:46.830 --> 00:24:52.440 you can compare that then to average household income data, 00:24:52.440 --> 00:24:56.590 which I’m going to use as the – sort of the proxy for socioeconomic 00:24:56.590 --> 00:25:00.289 factors in California. And that information – 00:25:00.289 --> 00:25:07.549 average within ZIP codes is available from U.S. census data from 2010-2011. 00:25:07.549 --> 00:25:09.921 So let’s start with Loma Prieta. 00:25:09.921 --> 00:25:14.100 I will show you a bunch of panels, starting with this one. 00:25:14.100 --> 00:25:20.580 This is plotting the response rate, normalized by the population 00:25:20.580 --> 00:25:25.570 in a ZIP code, versus average household income in that ZIP code. 00:25:25.570 --> 00:25:30.090 And you can see the data look pretty ratty. There’s a lot of scatter. 00:25:30.090 --> 00:25:37.085 There is some increase with income. 00:25:37.085 --> 00:25:40.789 But then, if you look – interestingly, if you look at the intensity level 00:25:40.789 --> 00:25:44.000 versus household income for this event, there’s a quite 00:25:44.000 --> 00:25:47.519 strong increase, which is sort of surprising. 00:25:47.519 --> 00:25:53.130 That richer households that people reported in felt stronger shaking. 00:25:53.130 --> 00:25:55.530 Sort of curious. 00:25:55.530 --> 00:26:01.340 So, if you look, then, at the response rate versus the intensity level, you see 00:26:01.340 --> 00:26:07.159 the standard correlation that people who felt stronger shaking, as has been 00:26:07.159 --> 00:26:11.470 shown before, were more likely to report into Did You Feel It?. 00:26:11.470 --> 00:26:16.970 So, basically, what’s going on here, to first order, appears to be that the 00:26:16.970 --> 00:26:22.330 people who responded – the richer households felt stronger shaking. 00:26:22.330 --> 00:26:26.686 They were more likely to respond. And so you get this correlation. 00:26:26.686 --> 00:26:32.233 At least to first order, that that’s a consistent explanation. 00:26:32.275 --> 00:26:36.370 So what’s going on with this bottom panel – when you stand back – 00:26:36.370 --> 00:26:42.865 this is looking at household income mapped across the state of California. 00:26:42.865 --> 00:26:48.129 And there’s been some discussion about how Loma Prieta impacted 00:26:48.129 --> 00:26:54.679 disadvantaged areas more strongly than more affluent areas, for the most part, 00:26:54.679 --> 00:26:58.220 and how the disadvantaged areas were slow to recover. 00:26:58.220 --> 00:27:04.200 But, overall, this earthquake generated its strongest effects 00:27:04.200 --> 00:27:08.979 in the most affluent part of California. 00:27:11.248 --> 00:27:16.039 So you can think of this as a, quote, rich California earthquake. 00:27:16.039 --> 00:27:20.809 It predominantly impacted a very affluent part of the state. 00:27:20.809 --> 00:27:23.289 The Did You Feel It? response does not appear to be 00:27:23.314 --> 00:27:27.788 grossly un-representative, at least not to first order. 00:27:28.451 --> 00:27:29.451 Yeah. 00:27:30.357 --> 00:27:33.412 So, moving on to Northridge. Similar plots. 00:27:33.412 --> 00:27:36.360 This time I’ll start with response rate versus intensity. 00:27:36.360 --> 00:27:41.130 So you see the typical correlation. 00:27:41.130 --> 00:27:45.740 And the other two panels – this event was more egalitarian 00:27:45.740 --> 00:27:50.929 in that the shaking level did not depend strongly on household income. 00:27:50.929 --> 00:27:55.570 It was fairly flat, at least of the reported data. 00:27:55.570 --> 00:28:01.059 If you look at response rate versus income, you do see a – 00:28:01.059 --> 00:28:05.360 you do see a correlation – a trend. 00:28:05.360 --> 00:28:10.380 In all of these plots, the data tend to get ratty at the higher household incomes 00:28:10.380 --> 00:28:18.411 because there are relatively few data points at those – at those income levels. 00:28:18.411 --> 00:28:22.580 So Northridge, compared to Loma Prieta, was more egalitarian 00:28:22.580 --> 00:28:30.710 in the effects that it generated in the, more or less, affluent areas. 00:28:30.710 --> 00:28:36.520 The shaking in affluent ZIP codes was better characterized than the – 00:28:36.520 --> 00:28:41.620 there’s more by Did You Feel It? than the less affluent areas. 00:28:41.620 --> 00:28:45.460 And this trend here is with – the response rate versus income trend 00:28:45.460 --> 00:28:49.929 is fairly strong of the events that I’ve looked at. 00:28:49.929 --> 00:28:53.230 And one can think of the reasons for that. 00:28:53.230 --> 00:28:57.690 There’s – there could be issues with mobility and people leaving 00:28:57.690 --> 00:29:01.320 the area and not being around to come back to Did You Feel It?. 00:29:01.320 --> 00:29:06.580 Awareness of the system. There’s also potential language issues. 00:29:06.580 --> 00:29:11.809 A Spanish-language questionnaire has been available at times, 00:29:11.809 --> 00:29:15.429 then for a while, it went away when the site was redesigned, 00:29:15.429 --> 00:29:20.629 and then it came back. So language barriers could be a part of this. 00:29:20.629 --> 00:29:26.440 Lastly, for – well, the last big earthquake to look at is Ridgecrest, 00:29:26.440 --> 00:29:31.690 which happened just last year – well, 2019 now. 00:29:31.690 --> 00:29:38.199 So, again, response rate depends on intensity level. 00:29:38.199 --> 00:29:41.279 But the rest of the panels get interesting. 00:29:41.279 --> 00:29:46.558 The response rate actually tends to decrease with household income. 00:29:46.558 --> 00:29:53.519 And – which is apparently because, for this earthquake, the intensity level 00:29:53.519 --> 00:29:59.040 tends to decrease with increasing household income. 00:29:59.040 --> 00:30:03.355 So let’s see. Which slide do I have next? Yeah. 00:30:03.355 --> 00:30:12.009 So, again, what’s controlling response rate appears to be the intensity level. 00:30:12.009 --> 00:30:16.440 But it’s an interesting comparison for this earthquake versus Loma Prieta. 00:30:16.440 --> 00:30:18.620 If Loma Prieta can be considered a, quote, 00:30:18.620 --> 00:30:22.700 rich California earthquake, Ridgecrest is different. 00:30:22.700 --> 00:30:31.726 It primarily impacted one of the least affluent parts of the state. 00:30:32.288 --> 00:30:35.779 What does this matter? Interestingly, Duncan Agnew had 00:30:35.779 --> 00:30:40.250 a recent paper where he talked about so-called celebrity earthquakes. 00:30:40.250 --> 00:30:44.240 And I encourage you to read that if you haven’t seen it. It’s interesting. 00:30:44.240 --> 00:30:49.620 So this plot from his paper shows the number of scientific papers about 00:30:49.620 --> 00:30:52.800 different earthquakes as a function of magnitude. 00:30:52.825 --> 00:30:58.720 And showing that generally, bigger earthquakes get more attention, 00:30:58.720 --> 00:31:02.320 but there are what he calls celebrity earthquakes that got sort of more 00:31:02.320 --> 00:31:07.470 than their share of attention. And you can find Napa’s on this plot. 00:31:07.470 --> 00:31:13.049 Ridgecrest is down here – not much of a celebrity – Parkfield, Christchurch. 00:31:13.049 --> 00:31:15.490 So I don’t – I didn’t have his data set, 00:31:15.490 --> 00:31:21.330 but this inspired me to go look at some related data. 00:31:21.330 --> 00:31:29.320 So Loma Prieta, which – looking at scientific publications – sorry – 00:31:29.320 --> 00:31:35.049 there’s 42,000 Google Scholar publications on Loma Prieta, 00:31:35.049 --> 00:31:39.380 versus only 22,000 for Landers. I didn’t look at Ridgecrest because 00:31:39.380 --> 00:31:43.159 it’s kind of recent, whereas, Landers and Loma Prieta 00:31:43.159 --> 00:31:46.950 were around the same time. Landers was quite a bit bigger, 00:31:46.950 --> 00:31:52.039 but it’s gotten less scientific attention. But Napa is definitely a celebrity. 00:31:52.039 --> 00:31:54.879 It was only a magnitude 6, but a whole lot of papers have 00:31:54.879 --> 00:31:59.249 been written on that compared to Ridgecrest and Joshua Tree. 00:31:59.249 --> 00:32:03.369 So there is Ridgecrest here, but – which is a paper – 00:32:03.369 --> 00:32:08.630 earthquake that people are still writing papers about, but still. 00:32:09.439 --> 00:32:13.940 One can look at newspaper articles about the different earthquakes. 00:32:13.940 --> 00:32:18.690 And so, for this, looking at Loma Prieta, Landers, and Northridge, 00:32:18.690 --> 00:32:23.769 they happened around – within five years of each other. 00:32:23.769 --> 00:32:28.179 I looked at newspaper articles within 10 years on a – 00:32:28.179 --> 00:32:31.590 on an online searchable database of newspapers. 00:32:31.590 --> 00:32:36.190 And Loma Prieta – 2,900 articles in 10 years, 00:32:36.190 --> 00:32:42.978 far outstripping Northridge and way outstripping Landers. 00:32:42.978 --> 00:32:44.299 So … 00:32:47.548 --> 00:32:52.320 Again, an earthquake that impacts an affluent area is getting 00:32:52.320 --> 00:32:58.749 a lot more attention scientifically and in the media than Northridge, 00:32:58.749 --> 00:33:04.400 which impacted generally an overall less affluent part of California. 00:33:04.400 --> 00:33:06.799 And then Landers, forget it. 00:33:07.759 --> 00:33:14.340 If you look just a Ridgecrest with – so a recent earthquake, 00:33:14.340 --> 00:33:20.929 you can see how wealthier areas get more attention just by comparing 00:33:20.929 --> 00:33:24.899 the number of Los Angeles Times articles that mention Trona, 00:33:24.899 --> 00:33:29.909 which had more significant damage than Ridgecrest – 00:33:29.909 --> 00:33:33.299 was a significantly less affluent area. 00:33:33.299 --> 00:33:37.250 So – and, again, this idea of, quote, rich California earthquakes. 00:33:37.250 --> 00:33:39.529 They’re getting more scientific studies. 00:33:39.529 --> 00:33:42.529 They’re getting more media attention. 00:33:42.529 --> 00:33:45.600 More public awareness. And this is – the next one is 00:33:45.600 --> 00:33:52.470 speculative, but are they having a bigger long-term impact on legislation 00:33:52.470 --> 00:33:56.570 and societal response, I think, is a – I haven’t shown that. 00:33:56.570 --> 00:34:07.156 I haven’t looked at it, but it’s kind of an obvious suggestion from the other stuff. 00:34:07.156 --> 00:34:11.882 Okay, getting back to the questions of representation, to look at it in 00:34:11.882 --> 00:34:16.490 a little more detail, I’ve looked at moderate earthquakes between 00:34:16.490 --> 00:34:23.500 magnitude 4.2 up to Napa, is the biggest, between 2002 and 2021. 00:34:23.500 --> 00:34:27.040 So these are – they’re moderate earthquakes. 00:34:27.040 --> 00:34:30.980 They’re all – they all postdate the Did You Feel It? system. 00:34:30.980 --> 00:34:34.580 They each produced at least 15,000 reports, 00:34:34.580 --> 00:34:39.440 and they were all widely felt in either Los Angeles or San Francisco. 00:34:39.440 --> 00:34:41.080 And so I’m going to show a bunch of panels. 00:34:41.080 --> 00:34:46.170 This one is northern California on the left, southern California on the right. 00:34:46.170 --> 00:34:49.940 Did You Feel It? response rate on a logarithmic scale 00:34:49.940 --> 00:34:53.920 versus household income. And I’ve broken it out 00:34:53.920 --> 00:34:58.530 before 2018 on the bottom and after 2018 on the top. 00:34:58.530 --> 00:35:02.670 So you can see there is a tendency for response rate to increase 00:35:02.670 --> 00:35:07.860 with household income for these earlier events, and maybe things 00:35:07.860 --> 00:35:12.710 flatten out more recently, which would suggest that 00:35:12.710 --> 00:35:15.700 Did You Feel It? is getting more inclusive over time. 00:35:15.700 --> 00:35:21.530 I didn’t average the data in the top left panel because there’s only two events. 00:35:21.530 --> 00:35:25.390 But it’s interesting to look at the data a little more. 00:35:25.390 --> 00:35:28.660 So remember that response rate depends, to first order, 00:35:28.660 --> 00:35:32.660 on intensity level. If you look at the intensity level, 00:35:32.660 --> 00:35:36.260 similar panels, but intensity level versus income rather than response 00:35:36.260 --> 00:35:42.046 rate, northern California, things, on average, are pretty flat. 00:35:42.046 --> 00:35:48.790 Southern California, for these events, the trends are pretty flat. 00:35:48.790 --> 00:35:53.980 Interestingly, for recent events in southern California, by chance, 00:35:53.980 --> 00:36:01.350 they all generated stronger intensities at the – in less affluent areas. 00:36:01.350 --> 00:36:04.940 So what’s going on in southern California, if you put that together, 00:36:04.940 --> 00:36:12.190 is that, over time, this apparent – the trend in response rate – 00:36:12.190 --> 00:36:16.710 the correlation between response rate and household income appears to have 00:36:16.710 --> 00:36:22.140 flattened out more recently but, for these earthquakes, 00:36:22.140 --> 00:36:28.180 the intensities were less strong. So, even though – for – among affluent 00:36:28.180 --> 00:36:32.430 households, so even though people – affluent households were feeling less 00:36:32.430 --> 00:36:39.740 severe shaking, they’re still reporting in equal numbers, about. 00:36:39.740 --> 00:36:44.410 And then – so here’s an example from northern California. 00:36:44.410 --> 00:36:51.560 So this is response rate versus intensity. For this event, there’s no strong trend 00:36:51.560 --> 00:36:57.780 in intensity versus income. There is some trend in response rate 00:36:57.780 --> 00:37:05.070 versus income. So what these basically say is that the system is fairly inclusive. 00:37:05.070 --> 00:37:09.131 There’s evidence that it’s getting more inclusive with time. 00:37:09.131 --> 00:37:15.020 But there’s still some room for improvement in – if our goal is to 00:37:15.020 --> 00:37:23.568 characterize shaking levels equally well in – across all socioeconomic groups. 00:37:23.568 --> 00:37:26.540 So now let me just talk quickly about something interesting 00:37:26.540 --> 00:37:30.420 that came out of all of this. And you may have noticed that 00:37:30.420 --> 00:37:32.890 a number of these – I don’t know if you can see my cursor 00:37:32.890 --> 00:37:36.930 or if I’m just doing this for my own edification or not. 00:37:36.930 --> 00:37:42.170 But, if you look at a lot of these plots of household income versus normalized 00:37:42.170 --> 00:37:48.340 response rate, they have a funny hook where, at the lowest income levels, 00:37:48.340 --> 00:37:53.021 you get fairly high response rates. And, taken at its face, that suggests 00:37:53.021 --> 00:38:00.370 that the least-affluent households are more likely to report into 00:38:00.370 --> 00:38:04.849 Did You Feel It?, which, if true, would be interesting. 00:38:04.849 --> 00:38:08.850 So, for this plot, what I’ve done is pulled out three separate events that 00:38:08.850 --> 00:38:12.950 were widely felt in southern California and plotted them with different colors. 00:38:12.950 --> 00:38:18.830 Blue is the Chino Hills earthquake, which was quite widely felt. 00:38:18.830 --> 00:38:21.973 And then green and red are two other moderate events. 00:38:21.973 --> 00:38:28.300 And so I plotted the average response rate versus household income. 00:38:28.300 --> 00:38:33.810 And then I pulled out one particular ZIP code that I noticed had 00:38:33.810 --> 00:38:40.430 a unusual high response rate for some earthquakes in particular. 00:38:40.430 --> 00:38:47.161 So what I’m going to do – well, I’ll show you the ZIP code – 90089. 00:38:47.161 --> 00:38:51.800 And I’m going to leave that on the screen until somebody out there 00:38:51.800 --> 00:38:57.680 tells me what ZIP code that is. Because I know – I know some of you know it. 00:38:59.996 --> 00:39:03.012 - [whispering] 00:39:07.238 --> 00:39:10.518 [shuffling sounds] 00:39:10.543 --> 00:39:13.387 - Okay. Austin, you know this. 00:39:15.121 --> 00:39:18.684 - [chuckles] Yes, indeed. Harkening back to college days. 00:39:18.684 --> 00:39:23.363 I believe that’s USC or around the campus. 00:39:23.363 --> 00:39:26.480 - That is USC. [laughs] 00:39:26.505 --> 00:39:32.390 So, that ZIP code, as far as I can tell, is just the campus footprint. 00:39:32.390 --> 00:39:38.066 I don’t think it extends into the – into the adjoining neighborhoods. 00:39:38.066 --> 00:39:42.580 And, for some events in particular, it’s an over-achiever with 00:39:42.580 --> 00:39:47.680 Did You Feel It? reports. Well, then I – but it’s not every event. 00:39:47.680 --> 00:39:52.371 So then I defined the USC factor. So, for all the earthquakes in the data 00:39:52.371 --> 00:39:57.980 set, I took the normalized response rate for this ZIP code and divided it 00:39:57.980 --> 00:40:03.870 by the average response rate for similar household incomes. 00:40:03.870 --> 00:40:08.777 And here’s the plot. Here’s the USC factor. 00:40:08.777 --> 00:40:14.730 It’s plotting it versus hours – time of day in local time. 00:40:14.730 --> 00:40:20.720 So basically, between 9:00 and 5:00, if earthquakes happen, you get 00:40:20.720 --> 00:40:25.680 a whole bunch of reports from the USC community. 00:40:25.680 --> 00:40:28.560 If earthquakes happens in the wee hours of the morning, 00:40:28.560 --> 00:40:35.370 you don’t get so many reports. And evenings tend to be a mixed bag. 00:40:35.370 --> 00:40:41.390 So USC basically – it’s not controlling these data points at low incomes 00:40:41.390 --> 00:40:45.920 entirely, but it illustrates the issue. There are some other ZIP codes, 00:40:45.920 --> 00:40:51.530 like downtown L.A., where you’re getting high response rate for 00:40:51.530 --> 00:40:54.220 daytime earthquakes. It’s clearly people who are feeling 00:40:54.220 --> 00:41:01.190 earthquakes and reporting them in places where they don’t live. 00:41:01.190 --> 00:41:03.540 And so – let’s see, before I move on, 00:41:03.540 --> 00:41:12.026 there’s one earthquake for which there are zero reports from 90089. 00:41:12.026 --> 00:41:16.890 And I won’t throw that out there as a quiz, but I imagine, if some of you 00:41:16.890 --> 00:41:21.080 think about it, you can probably figure out which earthquake that was. 00:41:25.987 --> 00:41:30.360 It was the – an earthquake near South El Monte – the one 00:41:30.360 --> 00:41:32.490 last September that I showed you the map for. 00:41:32.490 --> 00:41:38.010 This was just before midnight, so definitely after hours. 00:41:38.010 --> 00:41:41.560 It was during the lockdown. So we have a pandemic. 00:41:41.560 --> 00:41:43.500 We have a lockdown. There’s no one at USC. 00:41:43.500 --> 00:41:49.160 And, poof, there’s zero Did You Feel It? reports from that ZIP code, 00:41:49.160 --> 00:41:52.885 even though the shaking was quite strong enough to wake people up. 00:41:54.127 --> 00:41:56.930 All right. Let me close on a somewhat more serious note. 00:41:56.930 --> 00:42:00.346 It’s sort of fun to dig into Did You Feel It? reports and, 00:42:00.379 --> 00:42:03.400 you know, see where things are coming from. 00:42:03.400 --> 00:42:08.820 But let me, in closing, talk about intensities in India. 00:42:08.820 --> 00:42:14.530 So the system does collect information – reports from around 00:42:14.530 --> 00:42:18.220 the world, including India. There were quite a few reports 00:42:18.220 --> 00:42:21.360 contributed for the 2015 Gorkha earthquake. 00:42:21.360 --> 00:42:25.780 And so this is – you’ll recognize India. I’ll explain the colors in a second. 00:42:25.780 --> 00:42:30.490 Here’s the location of the Gorkha earthquake. 00:42:30.490 --> 00:42:33.870 But Nepal is – I’m not going to be talking about Nepal or Tibet, 00:42:33.870 --> 00:42:36.330 so that’s why some of the areas are missing. 00:42:36.330 --> 00:42:41.486 And then there’s one Indian state where the census data was missing. 00:42:41.486 --> 00:42:47.571 So, in India – well, in the U.S., there’s a big gulf between rich and poor. 00:42:47.571 --> 00:42:51.400 There’s a bigger gulf in India. 00:42:51.400 --> 00:42:58.620 And that gulf translates into a lack of access to basic education, 00:42:58.620 --> 00:43:02.970 in rural areas especially. So rural India, there’s quite a few 00:43:02.970 --> 00:43:06.680 people living there, but the poverty is much worse, and the 00:43:06.680 --> 00:43:11.204 education levels are much worse. So this is a graph I saw – I found 00:43:11.204 --> 00:43:15.660 comparing the monthly per capita expenditure on education 00:43:15.660 --> 00:43:21.270 in rural areas versus urban areas. And you can see how the poorest 00:43:21.270 --> 00:43:30.380 to the richest folks in each of the – in both rural and urban. 00:43:30.380 --> 00:43:37.950 So there isn’t universal access to public education in India. 00:43:37.950 --> 00:43:43.410 And that translates into real differences in literacy rates. 00:43:43.410 --> 00:43:46.280 So these plots that I’m showing with the blues and the greens 00:43:46.280 --> 00:43:52.770 are literacy rate defined as the ability of people over the age of 8 to read and 00:43:52.770 --> 00:44:00.260 write in any language. And the values range up to 100% in the richer areas. 00:44:00.260 --> 00:44:02.789 And then they get down to less than 50%, 00:44:02.789 --> 00:44:06.220 especially in some of the rural areas. 00:44:06.220 --> 00:44:11.671 So that’s one of the bits of information we’re going to use 00:44:11.671 --> 00:44:17.400 to look at socioeconomic factors in India. 00:44:17.400 --> 00:44:22.390 So this is the Did You Feel It? data plotted for Gorkha 00:44:22.390 --> 00:44:25.250 plotted on the literacy map. 00:44:25.250 --> 00:44:32.020 This is, by comparison, what we call a traditional intensity data set. 00:44:32.020 --> 00:44:36.230 So, after the earthquake happened, Stacey Martin rolled up his sleeves 00:44:36.230 --> 00:44:47.030 and made an exhaustive effort to collect newspaper and other accounts – 00:44:47.030 --> 00:44:53.380 a lot of them were online – of shaking effects throughout India and Nepal. 00:44:53.380 --> 00:44:57.960 You can see there’s a huge number. There’s, like, 1,000 or more different 00:44:57.960 --> 00:45:01.470 locations represented in this traditional data set. 00:45:01.470 --> 00:45:06.300 So you can see that there’s a lot more data that came in collected 00:45:06.300 --> 00:45:09.220 this way versus Did You Feel It?. 00:45:09.220 --> 00:45:14.187 But you can compare, then, the two types of data. 00:45:14.187 --> 00:45:16.780 Oh, there is an issue with – there’s different types of 00:45:16.780 --> 00:45:22.130 Did You Feel It? data. So we looked at the city data, which are values that are 00:45:22.130 --> 00:45:27.700 averaged within cities if more than one response come in. 00:45:27.700 --> 00:45:30.560 And those are the circles. There’s also increasingly 00:45:30.560 --> 00:45:35.070 geocoded data that uses web services and other information 00:45:35.070 --> 00:45:39.046 to pinpoint location, and those are the squares. 00:45:39.913 --> 00:45:42.490 And so, you know, you can – you know, 00:45:42.490 --> 00:45:47.210 maybe the two data sets are – they’re a little bit different. 00:45:47.210 --> 00:45:51.160 And I can – there’s some more details I could talk about here, 00:45:51.160 --> 00:45:56.210 but the main point is that the conclusions don’t differ. 00:45:56.210 --> 00:46:01.913 You could use one data set or the other. We are using the city data. 00:46:01.913 --> 00:46:09.040 So, for – we looked at three different earthquakes that were widely 00:46:09.040 --> 00:46:13.500 felt in India – 2011 Sikkim – and I’ll show you a map in a second. 00:46:13.500 --> 00:46:19.460 I should have showed it first. 2015 Gorkha, 2015 Dolakha – 00:46:19.460 --> 00:46:23.950 the large aftershock. And, for this, we’re looking at the 00:46:23.950 --> 00:46:32.270 average intensity level plotted versus the literacy rate from the census data. 00:46:32.270 --> 00:46:37.760 And the gray line shows the average literacy in India, which is 74%. 00:46:37.760 --> 00:46:41.650 So what you see on the left is that there’s an oddball data set here. 00:46:41.650 --> 00:46:45.730 But, for all three earthquakes, the Did You Feel It? data are preferentially – 00:46:45.730 --> 00:46:52.050 they’re coming in from places that have higher-than-average literacy. 00:46:52.050 --> 00:46:55.660 And the interesting comparison is with the traditional data sets, 00:46:55.660 --> 00:47:00.810 where especially it’s – it’s especially striking for Gorkha and Dolakha. 00:47:00.810 --> 00:47:10.088 The strongest shaking levels are coming from areas with low literacy. 00:47:11.224 --> 00:47:16.090 So you can zoom in on the area around Bihar, which is here. 00:47:16.090 --> 00:47:20.280 It’s India’s poorest state. Oh, and here’s the map I should have shown you. 00:47:20.280 --> 00:47:24.920 Here’s the 2011 Sikkim earthquake, Gorkha, Dolakha. 00:47:24.920 --> 00:47:29.669 So all of these events shook this part of India relatively strongly. 00:47:29.669 --> 00:47:35.818 But, again, it is the least-affluent part of India. 00:47:35.818 --> 00:47:42.330 And, on these panels, we show – the shading indicates – 00:47:42.330 --> 00:47:48.480 it’s not literacy now. It’s whether regions are urban or rural. 00:47:48.480 --> 00:47:53.950 You can see here that the literacy rate is generally fairly low. 00:47:53.950 --> 00:47:59.080 But these areas – there’s a few urban centers within them. 00:47:59.080 --> 00:48:03.410 And the Did You Feel It? values are coming from, almost exclusively, 00:48:03.410 --> 00:48:06.580 the urban areas. Whereas, these traditional values – 00:48:06.580 --> 00:48:10.660 you know, there are people out there. They’re impacted by earthquakes. 00:48:10.660 --> 00:48:17.120 Their reports make their way to media sources, as you see here. 00:48:17.120 --> 00:48:24.450 It’s a much – it’s a much better spatial coverage of this area, 00:48:24.450 --> 00:48:30.890 where the average intensities were up around 5 to 6. 00:48:30.890 --> 00:48:35.740 So, if you – so that was just Bihar. If you stare at the map, you can – 00:48:35.740 --> 00:48:40.020 you can see, without doing any analysis, that the Did You Feel It? 00:48:40.020 --> 00:48:45.110 values, not surprisingly, are coming from the blue areas where people 00:48:45.110 --> 00:48:50.496 are relatively affluent. They’re relatively well-educated. 00:48:50.496 --> 00:48:53.290 They are more likely to have internet access. 00:48:53.290 --> 00:49:01.160 They’re more likely to be fluent in English. 00:49:01.195 --> 00:49:08.770 So, yeah, again, you know, if we’re missing big areas 00:49:08.770 --> 00:49:11.060 where people aren’t coming to the internet, 00:49:11.060 --> 00:49:17.290 we’re going to be missing potentially important information. 00:49:17.290 --> 00:49:22.238 So what do you do about it? How do you improve representation? 00:49:22.238 --> 00:49:26.060 You know, fundamentally, in a place like India, 00:49:26.060 --> 00:49:28.750 you want to improve literacy and the internet access. 00:49:28.750 --> 00:49:33.879 So those are beyond our pay grade as seismologists. 00:49:33.879 --> 00:49:40.160 There’s other things that you can think about to improve the – 00:49:40.160 --> 00:49:45.440 to bring in more participants. Outreach. The group – 00:49:45.440 --> 00:49:50.011 Rémy Bossu’s group, EMSC, has pioneered some 00:49:50.011 --> 00:49:53.040 interesting approaches. They use – instead of a questionnaire, 00:49:53.040 --> 00:49:57.760 they use thumbnails, and they ask people, instead of answering questions, 00:49:57.760 --> 00:50:02.652 to just point to the cartoon that matches their experience. 00:50:02.652 --> 00:50:11.690 They’ve optimized a phone app rather than a full website. 00:50:11.690 --> 00:50:15.580 You can make information available in other languages. 00:50:15.580 --> 00:50:20.620 The one I want to mention is outreach and engagement. 00:50:20.620 --> 00:50:23.690 Sorry. My voice is cutting out. 00:50:23.690 --> 00:50:28.280 Did You Feel It? is hugely popular. People like the system. 00:50:28.280 --> 00:50:32.180 When they experience an earthquake, people generally want to talk about 00:50:32.180 --> 00:50:35.480 their experiences. They like to share. 00:50:35.480 --> 00:50:40.310 And so I think there’s an opportunity here to make use of Did You Feel It? 00:50:40.310 --> 00:50:46.410 to reach out to groups that maybe don’t know about it as a way to improve our 00:50:46.410 --> 00:50:52.964 overall engagement to communities that have maybe been underserved. 00:50:52.964 --> 00:50:58.180 But the last – oh, and then, for historical earthquakes, it is tough. 00:50:58.180 --> 00:51:01.870 You know, how do you – how do you figure out what you don’t know? 00:51:01.870 --> 00:51:06.940 It’s not easy answers. The archival research is like 00:51:06.940 --> 00:51:11.030 looking for needles in haystacks, going – looking more for original 00:51:11.030 --> 00:51:15.150 sources in native languages. Looking for transcribed 00:51:15.150 --> 00:51:20.930 oral histories for peoples that didn’t have a written language. 00:51:20.930 --> 00:51:23.323 These sometimes exist. 00:51:23.323 --> 00:51:30.390 Collaborating with people outside of seismology – with historians, say. 00:51:30.390 --> 00:51:34.791 And then one thing that’s more within our wheelhouse as seismologists, 00:51:34.791 --> 00:51:40.840 and this is something Stacey is thinking about, is the analysis of data to consider 00:51:40.840 --> 00:51:45.520 what the effect is of potentially missing accounts from areas where you 00:51:45.520 --> 00:51:50.180 don’t have information. What happens with these methods 00:51:50.180 --> 00:51:57.140 that we use to locate earthquakes from intensities is that, if – especially 00:51:57.140 --> 00:52:02.486 if it’s a one-sided intensity distribution, if you’re missing near-field values, 00:52:02.486 --> 00:52:10.790 the methods tend to pull the location to the – to where you do have accounts, 00:52:10.790 --> 00:52:15.850 and it typically lowers the magnitudes if you assume the location is 00:52:15.850 --> 00:52:18.540 closer to the accounts than it really is. 00:52:18.540 --> 00:52:26.120 So that’s something that one could consider without adding extra data. 00:52:26.120 --> 00:52:30.120 But the main conclusion of all of this is that I think we’re all aware 00:52:30.120 --> 00:52:37.040 now that representation matters for our community. It matters for society. 00:52:37.040 --> 00:52:43.940 But the point is that representation actually can matter for our science 00:52:43.940 --> 00:52:49.384 in ways that we maybe haven’t even thought about before. 00:52:49.384 --> 00:52:54.493 So, with that, I will stop speaking into the void and see if anyone’s 00:52:54.493 --> 00:52:59.630 still out there and has questions. Thank you. 00:53:02.571 --> 00:53:03.634 - Thanks, Sue. 00:53:03.634 --> 00:53:07.329 Here’s your round of applause on everyone’s behalf. [clapping] 00:53:07.329 --> 00:53:11.310 Thanks for that really interesting talk walking us through your research that 00:53:11.310 --> 00:53:15.298 sort of looks at the intersection of a lot of different variables here. 00:53:15.298 --> 00:53:19.640 If anyone has questions, again, feel free to write them in the chat 00:53:19.640 --> 00:53:23.321 or raise your hand, and we’ll field them. 00:53:23.321 --> 00:53:30.563 I’ll kick it off with a question about the ZIP codes and looking at the – 00:53:30.563 --> 00:53:34.548 looking at the average income in ZIP codes. 00:53:34.548 --> 00:53:39.670 Do you – have you made an attempt to look at the range or distribution 00:53:39.670 --> 00:53:43.350 of income within those ZIP codes? I mean, not all ZIP codes are created 00:53:43.350 --> 00:53:48.420 equal as far as the unity of their affluence, right? 00:53:48.420 --> 00:53:52.134 - Ah. So … 00:53:52.985 --> 00:53:57.060 The short answer is no. Ridgecrest was pretty clear 00:53:57.060 --> 00:54:01.541 that there were real differences. Ridgecrest is a single ZIP code 00:54:01.541 --> 00:54:04.720 in China Lake. It was clear that there were differences 00:54:04.720 --> 00:54:10.165 in effects and, I suspect, participation. 00:54:10.165 --> 00:54:17.673 One could get at that. You’d need finer socioeconomic data. 00:54:17.673 --> 00:54:20.770 Yeah, you could look at – like, now there’s Did You Feel It? 00:54:20.770 --> 00:54:25.188 data available in 1-kilometer geocoded cells. 00:54:25.188 --> 00:54:32.460 So you could look at response rate in those cells versus 00:54:32.460 --> 00:54:35.810 the overall ZIP code, say. You’d need to have the population 00:54:35.810 --> 00:54:38.751 in those cells, though, which … 00:54:40.337 --> 00:54:43.087 Yeah. It would be interesting. - Yeah [inaudible] … 00:54:43.087 --> 00:54:45.510 - It would get progressively more complicated. 00:54:45.510 --> 00:54:48.420 - … having finer resolution would clearly illuminate 00:54:48.420 --> 00:54:52.833 a lot of things even better, so … - That’s – yeah. 00:54:54.040 --> 00:54:58.140 - Steve Hickman asks – he says, fascinating talk, how do you control 00:54:58.140 --> 00:55:01.649 for site amplification effects? Steve, do you want to go on? 00:55:01.649 --> 00:55:04.250 I see you joined us. - I just want to show my face. 00:55:04.250 --> 00:55:06.410 Talking into the void is not much fun. - [laughs] 00:55:06.410 --> 00:55:09.560 - Anyway, so I did think it was a great talk, and curious about that. 00:55:09.560 --> 00:55:12.040 It doesn’t – you know, I thought about it in your Loma Prieta 00:55:12.040 --> 00:55:17.160 versus Northridge example, but also in the Indian example. 00:55:17.160 --> 00:55:20.350 You know, Loma Prieta, of course, you have mountain and topographic effects. 00:55:20.350 --> 00:55:23.550 Potentially, rich people live up in the mountains. 00:55:23.550 --> 00:55:25.680 And Northridge is mostly an urban basin. 00:55:25.680 --> 00:55:29.860 So how do you control for that sort of thing in your analysis? 00:55:29.860 --> 00:55:33.470 - Yeah, I don’t. I mean, I don’t really worry about 00:55:33.470 --> 00:55:36.670 what’s controlling the ground motions. 00:55:36.670 --> 00:55:44.310 So, yeah, think it through. If less-affluent people are experiencing 00:55:44.310 --> 00:55:48.580 more amplification, they’re experiencing stronger shaking. 00:55:48.580 --> 00:55:55.370 You might expect them to report in more because they are just – 00:55:55.370 --> 00:56:01.024 everything else being equal, if you experience more shaking, 00:56:01.051 --> 00:56:02.890 no matter what the cause is – yeah. So … 00:56:02.890 --> 00:56:05.950 - Yeah. That’s what I was thinking. You know, would they – if they’re 00:56:05.950 --> 00:56:11.500 shaken more harshly, would they be more likely to dial in Did You Feel It? 00:56:11.500 --> 00:56:15.570 than if the same person with the same income lived in the low-lying – or a 00:56:15.570 --> 00:56:18.280 hard rock site or something like that. It doesn’t work in some cases 00:56:18.280 --> 00:56:23.440 because it goes the opposite. - Yeah. No, there’s all sorts of 00:56:23.440 --> 00:56:29.900 fun stuff to – I mean, there’s important stuff but interesting stuff to think about. 00:56:29.900 --> 00:56:36.480 There’s a whole range of sociological issues that Stacey 00:56:36.480 --> 00:56:40.000 and I didn’t want to touch. Because we just didn’t feel qualified. 00:56:40.000 --> 00:56:45.140 But why are people going to Did You Feel It? is a really 00:56:45.140 --> 00:56:50.100 interesting question that I think it would be fun to look at. 00:56:50.100 --> 00:56:54.868 I just don’t feel like I’m the right person to do it. 00:56:54.868 --> 00:56:59.030 You know, for – if you – if you look at some of the trends, it really looks like 00:56:59.030 --> 00:57:02.510 participation increases from a household income of about 00:57:02.510 --> 00:57:08.700 20,000 to around 100,000, 120,000. It looks like it comes down again 00:57:08.700 --> 00:57:14.800 after that. The data get more ratty. But I sort of wondered about that 00:57:14.800 --> 00:57:18.970 and if sociological issues aren’t coming into play. 00:57:18.970 --> 00:57:20.880 - Yeah, it’s interesting. And also you pointed out 00:57:20.880 --> 00:57:25.048 internet access, literacy rates – these things all matter. 00:57:25.048 --> 00:57:30.580 - Yeah, and in India, the language proficiency. 00:57:30.580 --> 00:57:35.570 Even in the U.S., the – you know, not everyone in California speaks English. 00:57:35.570 --> 00:57:42.095 And, you know, if you have a really modern browser, there’s automatic 00:57:42.095 --> 00:57:47.649 translation now, but that hasn’t always existed, and it’s not – 00:57:47.683 --> 00:57:52.600 it’s not universally available even now. - Yeah. Okay, thanks. 00:57:52.600 --> 00:57:56.759 - Yeah. And there’s also this question along these same lines of – that you 00:57:56.759 --> 00:58:03.288 brought up with this example of USC, that, within the ZIP codes and areas, 00:58:03.321 --> 00:58:10.770 there’s – you know, the average income as a representation of what’s going on 00:58:10.770 --> 00:58:14.380 and who is populating those areas when the earthquake happens is 00:58:14.380 --> 00:58:17.962 not necessarily a really direct thing. And these cases in California where 00:58:17.997 --> 00:58:24.150 you have the sort of opposing effects of affluence, depending where the 00:58:24.150 --> 00:58:29.712 earthquake is, that suggests that there’s some sort of deeper variable coming in, 00:58:29.712 --> 00:58:34.450 like, you know, are the – in affluent ZIP codes, do you also have lots of 00:58:34.450 --> 00:58:38.640 businesses and places that aren’t residential and don’t count towards 00:58:38.640 --> 00:58:41.712 the income but are counting towards [inaudible]. 00:58:41.712 --> 00:58:46.080 - Right. Yeah, and I think you see that in the – some of the downtown areas – 00:58:46.080 --> 00:58:49.340 San Bernardino, Los Angeles – you have places where people 00:58:49.340 --> 00:58:52.770 are working, whether or not – they’re relatively poor areas, 00:58:52.770 --> 00:58:59.227 but they’re not – they’re not living there. 00:58:59.227 --> 00:59:01.360 - It would – it would certainly be very interesting 00:59:01.360 --> 00:59:04.210 to dive deeper into that, right, because those demographics – 00:59:04.210 --> 00:59:07.561 those will still have an effect on who is reporting it and how 00:59:07.561 --> 00:59:10.240 and what environment they’re in. - Yeah. 00:59:10.240 --> 00:59:12.750 - Okay, there are a couple of questions in the chat again. 00:59:12.750 --> 00:59:16.830 So Peter Haeussler says, are there – the subject is fresh on his mind 00:59:16.830 --> 00:59:21.759 working on updating NEHRP external grants program priorities. 00:59:21.759 --> 00:59:24.670 A colleague was asking, is there a way to help better represent 00:59:24.670 --> 00:59:27.485 under-represented communities with the call for proposals, 00:59:27.485 --> 00:59:30.477 possibly in ways to get hazard information to the communities, 00:59:30.477 --> 00:59:33.290 or how can we better communicate this information to them? 00:59:33.290 --> 00:59:37.170 So I guess this is a question about sort of, how do you solicit – 00:59:37.170 --> 00:59:40.673 in this particular case, how would you go about 00:59:40.708 --> 00:59:44.626 remedying this and soliciting the right information? 00:59:44.626 --> 00:59:48.080 - Yeah, that’s a great question. And, as I understand it, the awareness 00:59:48.080 --> 00:59:50.750 of Did You Feel It? has just grown organically. 00:59:50.750 --> 00:59:54.340 I don’t think there was ever any big PR push, but people found it. 00:59:54.340 --> 00:59:57.720 They find the website. And, again, people like it. 00:59:57.720 --> 01:00:03.110 So I haven’t given it a whole lot of thought, but it really does strike me 01:00:03.110 --> 01:00:08.860 as an opportunity to – you know, there are efforts to reach out to 01:00:08.860 --> 01:00:14.840 under-represented communities to use it as a – as a tool to engage 01:00:14.840 --> 01:00:20.240 with people and, you know, something – if an earthquake happens, 01:00:20.240 --> 01:00:22.990 just to do what we can to raise awareness. 01:00:22.990 --> 01:00:28.230 I don’t have any really specific suggestions for that. 01:00:30.399 --> 01:00:33.070 - Yeah, I guess, clearly, you see the effects of more people using 01:00:33.070 --> 01:00:36.400 Did You Feel It? over time in the number of responses. 01:00:36.400 --> 01:00:39.830 So somehow, it’s growing. I think [inaudible] … 01:00:39.830 --> 01:00:41.673 - Yeah. - .. advertising that. 01:00:41.673 --> 01:00:44.600 - Yeah, and I think, you know, overall, the message is good, 01:00:44.600 --> 01:00:47.620 that more people are finding it. We are getting these tens of thousands 01:00:47.620 --> 01:00:52.813 of responses. It does appear to be getting more inclusive with time. 01:00:52.813 --> 01:00:57.212 Well, in California, at least, and presumably most of the U.S. 01:00:57.242 --> 01:00:58.845 That’s good. 01:00:58.845 --> 01:01:01.946 - So there’s one more question here from Lauren Harrison. 01:01:01.946 --> 01:01:04.700 Lauren, I don’t know if you want to ask it yourself, but the gist of it 01:01:04.700 --> 01:01:07.960 is essentially, how constant are the affluence designations 01:01:07.960 --> 01:01:10.470 of ZIP codes over time? - Yeah. 01:01:10.470 --> 01:01:14.852 - She lists a variety of ways in which they might change. 01:01:14.852 --> 01:01:19.310 - Yeah, no. That’s an issue. And the census data goes back to 2010. 01:01:19.310 --> 01:01:25.220 Our assumption is that affluence changes, but the relative affluence 01:01:25.220 --> 01:01:30.884 is probably fairly constant. So rich areas are still rich. 01:01:30.919 --> 01:01:35.130 There are – there are, you know, if areas are starting to really gentrify, 01:01:35.130 --> 01:01:40.000 things may change over time. But, yeah, and I mean, this is getting 01:01:40.000 --> 01:01:46.940 into the whole realm of interesting issues that one could look at. 01:01:48.595 --> 01:01:51.360 - Yeah. I see a note that some of the other examples that Lauren 01:01:51.360 --> 01:01:55.970 brought up point to effects that are dependent on prior earthquakes. 01:01:55.970 --> 01:01:57.350 So, you know … - Oh. 01:01:57.350 --> 01:02:02.280 - … either damage or other things that sort of scare or chase people 01:02:02.280 --> 01:02:05.980 or displace people from a neighborhood and then whether or not it gets built 01:02:05.980 --> 01:02:11.400 back correctly or not. Those things. - Yeah I wondered – I wondered for 01:02:11.400 --> 01:02:14.990 Northridge, where there is a fairly strong correlation between response 01:02:14.990 --> 01:02:20.860 rate and income, which can’t be – is not explained by the correlation 01:02:20.860 --> 01:02:24.540 in intensity versus income. And I wondered, you know, 01:02:24.540 --> 01:02:28.712 did people move away and not come back. 01:02:28.712 --> 01:02:31.120 It’s hard to know. What you would really like to have, 01:02:31.120 --> 01:02:35.310 and we’re not going to have, is demographic data for 01:02:35.310 --> 01:02:39.430 Did You Feel It? responses. That’s far beyond the approval for 01:02:39.430 --> 01:02:43.120 the Did You Feel It? questionnaire. It would be to ask people just some 01:02:43.120 --> 01:02:51.485 basic information. But we don’t do that, and I don’t think we ever will do that. 01:02:51.485 --> 01:02:56.120 - We’ll do one more question in the formal Q-and-A, and then we’ll stop 01:02:56.120 --> 01:02:58.890 the recording, and people can hang out and ask more questions. 01:02:58.890 --> 01:03:02.050 But this is from Glenn Biasi, and it sort of mirrors another 01:03:02.050 --> 01:03:06.430 question I was going to ask. In India, could Did You Feel It? reports 01:03:06.430 --> 01:03:12.220 reflect a floor, or a base, education level, such as high school, 01:03:12.220 --> 01:03:14.770 below which earthquakes do not make the curriculum, and even 01:03:14.770 --> 01:03:20.720 the concept of a report is a non-thing. I was going to add to that hypothesizing 01:03:20.720 --> 01:03:24.830 about what concentrates Did You Feel It? reports in the – 01:03:24.830 --> 01:03:30.190 in the highly literate areas. Is it just a statistics thing, right? 01:03:30.190 --> 01:03:33.180 You know, the reports are coming from big cities, and there aren’t that many. 01:03:33.180 --> 01:03:35.210 And so, if you’re getting a certain reporting rate, 01:03:35.210 --> 01:03:38.840 you’re just not capturing – you know, you’re super unlikely 01:03:38.840 --> 01:03:42.391 to capture the rural population at all anyway. 01:03:42.391 --> 01:03:47.230 - Yeah. I think – I mean, that’s true. If you have more, there are less likely 01:03:47.230 --> 01:03:49.760 to be – if you have fewer, they’re less likely to be distributed. 01:03:49.760 --> 01:03:56.510 But I think, if you – if you know India, and Stacey knows it very well, 01:03:56.510 --> 01:04:02.040 the results aren’t surprising. The differences – the socioeconomic 01:04:02.040 --> 01:04:10.040 differences between rural and urban areas is so stark, and the literacy – 01:04:10.040 --> 01:04:13.890 it’s just such stark contrast. So the fact – I mean, it’s kind of 01:04:13.890 --> 01:04:18.540 a no-brainer if you ask where would Did You Feel It? responses come from. 01:04:18.540 --> 01:04:20.700 The answer is, oh, of course they’re going to come from 01:04:20.700 --> 01:04:23.720 the cities where people have – you know, they can speak English. 01:04:23.720 --> 01:04:26.970 They have access to the internet. 01:04:26.970 --> 01:04:32.340 Sort of the surprising result in all of that, to me, was that the traditional data 01:04:32.340 --> 01:04:36.720 sets, when they’re really undertaken in the exhaustive way that Stacey’s 01:04:36.720 --> 01:04:40.090 been able to do, you do get quite good coverage. 01:04:40.090 --> 01:04:42.430 So there are people out there. They’re impacted by earthquakes. 01:04:42.430 --> 01:04:47.930 They’re living in vulnerable houses in a lot of cases. 01:04:47.930 --> 01:04:54.087 So that information is out there. And it does get – it can be collected. 01:04:54.087 --> 01:04:58.618 It’s just not flowing to us over the internet. 01:04:58.618 --> 01:05:01.070 - Yeah. It’s nice to look at ways that you can – we can get 01:05:01.070 --> 01:05:04.493 the appropriate spatial coverage for that sort of deal. 01:05:04.493 --> 01:05:08.290 All right. So, with that, let’s give Sue a round of applause for the 01:05:08.290 --> 01:05:11.180 great work and the talk and answering these questions. 01:05:11.180 --> 01:05:13.083 Thanks very much. 01:05:13.083 --> 01:05:16.251 [applause] 01:05:16.251 --> 01:05:22.010 So, with that, we’ll end the recording and see you all next week. 01:05:22.010 --> 01:05:23.190 But anyone who wishes …