[silence]
Hi, everyone.
Welcome to the Earthquake Science Center seminar
for February 17th, 2021.
Obviously, you’ve all noticed we’re starting late today in order
for ESC members to attend the USGS event this morning
recognizing the career of our own Rufus Catchings in celebration
of African Americans in the Earth sciences at the USGS.
As a reminder, before we get started,
please turn off your cameras and mute your microphones.
All the functions are available through the menu bar that pops up
when you hover over the bottom of your – the top of your Teams window.
Live captioning is available.
You click the three-dot More button and choose “turn on live captions.”
Before we begin today, there are just a couple of announcements.
The next all-hands meeting – we just had one, so the next is about –
over a month from now on March 19th, but you’ll all have gotten an invitation
to that for those who are involved. Next week’s seminar will be
Eric Campbell from LA Seismic talking about the results of
seismic surveys in Los Angeles and the fault system there.
If you have questions for today’s speaker, you can either type them
into the chat or raise your hand. Tamara and I will be monitoring
the chat in case there’s anything urgent we need to interrupt the speaker with.
And otherwise, we’ll be emceeing the Q-and-A session at the end.
And we invite you to feel free to unmute yourself.
Everyone loves seeing faces and hearing voices these days.
It’s a little more personal.
When the time comes.
So, with that out of the way, it’s my pleasure to introduce the Earthquake
Science Center’s own Sue Hough to deliver this timely presentation
on the hidden impacts of societal power structures on our science.
Introducing Sue is a particular pleasure for me as a USGS seminar coordinator
because my own career in earthquake science was pretty much launched as
a high school junior when I went to volunteer at the USGS Pasadena office,
and Sue mentored me through an archival newspaper project
re-evaluating the magnitude of a large earthquake in 1892 in Baja
that itself ended up being a predecessor of an earthquake
that I went on to study during my Ph.D. later in 2010.
So Sue has had an illustrious career right exactly at this important
boundary where earthquake science meets society.
And, in her research, she shows that that’s a two-way street.
After graduating from UC-Berkeley and then Scripps, Sue did a postdoc
at Lamont–Doherty, during which the Loma Prieta earthquake
interrupted and then stole her attention. Shortly after that, she joined
the USGS in Pasadena in 1992. And since then, Sue has really handily
danced between strong motion seismology of modern earthquakes
and deep insightful investigations of historical earthquakes
spanning centuries. Somehow, she’s found time among it all
to write a must-have library of pop science books about earthquakes.
And, among Sue’s impactful work, she’s also been heavily involved
in establishing seismic monitoring networks and educating up-and-coming
career seismologists in countries where these sorts of services are
badly needed, including Nepal and Haiti, Myanmar, and in India –
some of the most important places on the planet to establish secure
home-grown functioning seismic programs.
Okay, so without further ado, I’ll hand it over to Sue
to talk to us about which earthquake accounts matter.
- Well, okay. So I will start sharing.
See if this works.
Okay.
So thank you, Austin, for the very kind introduction.
This really does feel like coming full circle [laughs] having
Austin introduce me. Maybe some of you have had
the experience at the USGS of having volunteers reach out with an interest
in being involved with projects. And, of the students that I’ve heard
from, Austin distinguished himself from the beginning.
And sometimes you get expressions of interest from students who don’t
really show up. Sometimes you get parents of students reaching out.
But Austin was engaged and excited about the science,
and it was a pleasure to work for him, so it was a very happy day when
I found out that the USGS has actually brought him on board.
So I wanted to thank Austin, Tamara, Susan for organizing this.
This is – so I can’t see myself. I just see my screen.
That’s a little – it’s all a little weird,
talking into the ether, but hopefully it’ll be okay.
So the seminar is going to be something a little new and different.
So let’s start with this. Since current events have brought
the issue of systemic racism and inequity to the fore last year,
there’s been a fair amount of soul searching about how to
improve diversity and inclusion within our science. As this 1897 photo shows,
geology has historically been very male and very white.
I found this photo on the web. It’s a USGS group.
Some of you may recognize the location.
And I just pulled it out to make the point that, over time, we’ve gotten –
geology, geosciences has gotten a little less male, but it hasn’t gotten much
less white. So there’s some overdue conversations that are going on.
But, as we have these conversations about how to improve diversity
in geosciences, I want to talk about the possibility that diversity issues
come into play, not just with our community, but with our science.
And I’m going to be focusing here on so-called citizen science,
which I realize is a somewhat controversial term, but if you can
see this – well, yeah, you can see this because you see my screen.
I’m using “citizen” in the inclusive sense here – not legal citizens,
but inhabitants of particular towns or cities.
So, as the current events – the BLM movement captured the news cycle
last year, I started talking to a long-time collaborator, Stacey Martin,
whose picture is shown here. And it turns out – it was one of
those times when we were both thinking along similar lines.
If you haven’t had the pleasure of meeting Stacey, his name
notwithstanding, he is Indian. He’s from Puna, born and raised.
He spent five years recently at EOS in Singapore, and he’s now at ANU.
But we’ve worked together over the years on a number of projects
looking at historical earthquakes, including the 1925 Santa Barbara
earthquake and its felt reports. And recent earthquakes,
including the 2015 Gorkha, Nepal, earthquake,
which you’re going to hear a little more about later.
So this is Stacey. The other introduction is that I assume people
are familiar by now with the traditional intensity color scale.
So this is the intensities, or the scale, that goes back to the 19th century to
assign numerical values for the severity of shaking at any one location.
And we’re using, in all of the maps, the color palette – the color scale
that was – is used for ShakeMaps and Did You Feel It?.
Okay, so the thought that – so, okay.
The felt reports of earthquake shaking are sometimes called
macroseismic data, distinguished from instrumental data.
From historical earthquakes, we use archival accounts, which are often the
only source of information we’ll have to study historical earthquakes.
For modern earthquakes, increasingly, people use reports that are contributed
via online websites, including Did You Feel It?, and now there’s some others.
So what Stacey and I both started to think about, watching the news
play out, was the question of representation in citizen science.
For historical earthquakes, whose accounts were recorded?
Which accounts might be missing?
How can that shape our view of historical earthquakes?
And, for modern earthquakes, you know, we’re relying on people
to come to us – to the web. Which accounts are contributed?
Which accounts might be missing? And how does that shape our view
of earthquake effects? In modern times, we don’t really
use these effects to locate earthquakes or determine their magnitude,
but we can still use them to look at the effects of earthquake shaking.
So questions like this arise naturally in India, as this old map of the Indian
Empire, quote, unquote, illustrates. So this was back during the
British colonial period. And the different colors –
it’s probably hard to see, but the red areas are –
were under British control during the colonial period.
But it – and we rely on British sources very heavily to study
historical earthquakes in India. But that wasn’t the entire
Indian subcontinent. The yellow areas were –
have always been independent, including Nepal and some other areas –
Balochistan. And there are some areas that remained under Indian control.
So, you know, the question arises of whether we have
accounts from all of these regions.
And then you get up into Tibet for important historical earthquakes.
In the U.S., the configuration of the map has obviously changed over time.
And much of the country historically has been Indian territory –
Native American territory. So there’s issues of representation
that are going to come into play there.
To illustrate the issues that can arise with historical earthquakes,
I’m going to talk about a misplaced earthquake
in Oklahoma that serves as a illustrative example.
So, in recent years, we kind of know Oklahoma as the
induced earthquake capital of the world – or the U.S.
This 2014 hazard map, when it was made, the group NSHMP drew boxes
around zones with induced earthquakes and didn’t consider them to come up
with the hazard map for tectonic earthquakes.
And you can see that Oklahoma does have hazard associated with tectonic
earthquakes, most especially the Meers Fault in southwest Oklahoma.
So the Meers Fault I assume a lot of you know about.
It’s arguably the most dramatic recent fresh scarp east of the Rockies.
It’s believed to have hosted two, I think, surface-rupturing events
in the Holocene, magnitude 7-ish.
I’m not going to be talking about the Meers Fault.
It’s been remarkably quiet over recent decades.
But I’m going to be talking about the earliest known earthquake
in present-day Oklahoma. It occurred on October 22nd, 1882.
It pre-dates statehood, which was in 1907.
It was reported in a number of places,
including Arkansas, where it was described as a lively shaking.
Until a few years ago – this work actually goes back a few years
when I was looking back at the historical catalog
for Oklahoma at the suggestion of Rob Williams.
This is what the known effects of that earthquake look like.
And you can see the earthquake was big enough
to be felt into several neighboring states.
The location of the earthquake has bounced around like
a ping-pong ball over time. The earliest suggestion by Branner
and Hansell was that it was in western Arkansas here.
In 1952, Heinrich suggested that it was over near the El Reno earthquake
that happened that year. Later, it was mis-located down
in Texas, and that turned out to be based on a mis-reading of some reports.
More recently, the generally accepted location is Fort Gibson.
It was an early U.S. fort in this location where relatively
strong effects were described.
The USGS location, at least as of a couple months ago,
was down close to Texas.
For this earthquake, it turns out that historical context matters.
Before statehood, Oklahoma was divided up.
It was known as Indian Territory. This was the end of the Trail of Tears,
which was the forced march that brought a lot – a number of Native
tribes from their ancestral homelands that were further east into Oklahoma,
as you see here. And I’ll call your attention to the Choctaw Nation.
That was southeastern-most Oklahoma.
And this was established by an act in 1889.
Or, no – well …
Yeah. okay. The dates – as of – as of 1882, this was –
this was – this was already the Choctaw Nation.
This is the lively shaking account. In the process of looking at the
historical catalog, I came across a report that had not been considered
before by seismologists. And it was – it’s a little hard to sort out.
It was written from Dallas to the editor of a newspaper in Arkansas from
an individual who’s describing a number of different places.
He was returning from one county, and then he describes effects of
an earthquake at a place called Venetia Grove, where the shock upset a pile
of lumber, shook up the dishes, etc. And then it goes on to say that
70 miles west of Venetia Grove, in the Choctaw Nation,
chimneys fell and a general turning over of things took place.
So pulling out the key information, in Venetia Grove, you have effects,
and then, in the Choctaw Nation, it was clearly quite a bit stronger.
Even conservatively, that suggests intensity of 7.
So historical seismology can be fun.
An initial question is, where is Venetia Grove.
And if you go to Google Earth, it’s no help at all.
It doesn’t recognize the location. It no longer exists as a place name.
So you start poking around. You look at old newspapers and maps.
And lo and behold, eventually, you figure out where Venetia Grove is.
And then, from there, you measure 70 miles to the west
to get the Choctaw Nation location.
So then you put that data point on the map.
So just one – well, two – Venetia Grove and Choctaw Nation.
And that one data point changes the view quite a bit.
If using the method of Bakun and Wentworth to find an – excuse me –
optimal location, with the expanded data set, yields this circled star here.
And a magnitude of 4.8 to 5-ish.
So let’s zoom into – yeah, so here – obviously, there’s some pretty big
uncertainties on exactly where the location is, but somewhere
close to the Choctaw Nation. And we suggest that
that’s the appropriate name for this event.
So, looking at that part of Oklahoma, some topography jumps out
immediately. That turns out to be the Ouachita Fold and Thrust Belt,
which winds its way through Texas, corner of Oklahoma,
into Arkansas and neighboring states.
I’m not the best person to talk about this part of the world in geology,
but the Ouachita Fold and Thrust Belt is a late Paleocene orogeny.
And the leading edge of the thrust zone is now defined by the Choctaw Thrust
Fault in Oklahoma and then the Ross Creek Fault in Arkansas.
Zooming into that map, you can see, these are – the circles are small
instrumentally recorded earthquakes. You can see the induced events.
Most of these are not likely to be induced.
And then there’s two white stars. These are both magnitude 4.3 –
estimated 4.3 events, instrumentally recorded, which are also falling
generally along these trends. So the – oh, let me – let me
point out that some of these areas are fairly remote,
but the city of Little Rock is right here.
This is – the 1882 event is the largest known tectonic
earthquake in Oklahoma. It falls along this interesting
structural trend, which also has produced other magnitude 4-ish
earthquakes in recent times. And it raises the question of whether
or not this is a central-eastern U.S. source zone that we haven’t –
we haven’t really paid much attention to. Whether or not
it’s active in the current stress regime, I don’t know.
But it’s an interesting question. It’s an interesting zone.
And the point for this talk is that one serendipitous account
changed our whole view of the earthquake and calls attention
to a region that we haven’t really thought about before.
For historical earthquakes, the old quotes from
Donald Rumsfeld comes to mind. For the most part, we don’t know
what we don’t know. For the 1882 event,
this one serendipitous account surfaced.
But in general, there are effects that we don’t have accounts of.
So that’s going to be difficult to get at [audio cuts out].
But for now, let me move forward and talk about representation issues
and so-called citizen science. So, starting in 1999, thanks to
Dave Wald and colleagues, we have the Did You Feel It? system,
which I assume people know about. It lets people come to the web
and fill out the standard questionnaire from which
intensities are determined and used to produce a map.
It’s been successful, I think, beyond wildest expectations.
People like Did You Feel It?. So, at this point, if you have
a magnitude 4.5 earthquake in the greater L.A. area, as we did
last September, 40,000 people come and fill out this questionnaire,
and we get these amazing maps that show the distribution of shaking.
What’s more, a number of studies by a number of people have shown
that there’s a remarkably good correspondence between
Did You Feel It? intensities and instrumental parameters
like PGA, PGV. This is from a figure – a paper by Gordon et al.
Gail Atkinson has also looked at this. I’ve looked at it.
So it turns out these contributed accounts are
giving us a quite reliable view of what the shaking distribution is.
And so, as these data points find their way into important USGS products,
in some cases, they’re used to flesh out ShakeMaps, especially
if instruments are sparse. You have a lot more
Did You Feel It? data than you have instrumental data.
And then the PAGER system, which uses ShakeMaps to forecast
likely impact – fatalities and losses – relies on ShakeMaps, which can
rely on these contributed values as important constraints.
So – oh, and this is – I find this amusing.
The Did You Feel It? system has collected quite a bit of data
retroactively for earthquakes that pre-date the 1999 introduction
of the system.
This is the ShakeMap for the 1971 Sylmar earthquake,
the 50th anniversary of which was just last week.
There’s over 1,200 Did You Feel It? reports for this event, which,
as I’ve said, is not too shabby for an earthquake that’s
old enough to join AARP.
Okay, let’s get back to the question at hand.
You know, which accounts are being contributed,
and which accounts might be missing?
So there was a paper a couple years ago by Mak and Schorlemer.
They had considered a similar question. They looked at a whole lot of data,
put it into a hopper, and they concluded that, to first order,
participation in Did You Feel It? depends on the severity of shaking
and population density, so – neither of which is too surprising.
So [audio cuts out] this study, we looked in more detail
at individual events. So I’m going to start with
a tale of three earthquakes – Loma Prieta, Northridge, and Ridgecrest.
And so, for this analysis, I’m looking at Did You Feel It? data averaged
within ZIP codes. And I could talk a lot about this.
That’s why the Ridgecrest map looks funny.
Because there weren’t a lot of ZIP codes going up into the near-field area.
But, yeah, so there’s fairly – there’s quite a bit of Did You Feel It?
data for all three events, even though two of them
do pre-date the introduction of the system.
The reason for looking at data averaged within ZIP codes is that
you can compare that then to average household income data,
which I’m going to use as the – sort of the proxy for socioeconomic
factors in California. And that information –
average within ZIP codes is available from U.S. census data from 2010-2011.
So let’s start with Loma Prieta.
I will show you a bunch of panels, starting with this one.
This is plotting the response rate, normalized by the population
in a ZIP code, versus average household income in that ZIP code.
And you can see the data look pretty ratty. There’s a lot of scatter.
There is some increase with income.
But then, if you look – interestingly, if you look at the intensity level
versus household income for this event, there’s a quite
strong increase, which is sort of surprising.
That richer households that people reported in felt stronger shaking.
Sort of curious.
So, if you look, then, at the response rate versus the intensity level, you see
the standard correlation that people who felt stronger shaking, as has been
shown before, were more likely to report into Did You Feel It?.
So, basically, what’s going on here, to first order, appears to be that the
people who responded – the richer households felt stronger shaking.
They were more likely to respond. And so you get this correlation.
At least to first order, that that’s a consistent explanation.
So what’s going on with this bottom panel – when you stand back –
this is looking at household income mapped across the state of California.
And there’s been some discussion about how Loma Prieta impacted
disadvantaged areas more strongly than more affluent areas, for the most part,
and how the disadvantaged areas were slow to recover.
But, overall, this earthquake generated its strongest effects
in the most affluent part of California.
So you can think of this as a, quote, rich California earthquake.
It predominantly impacted a very affluent part of the state.
The Did You Feel It? response does not appear to be
grossly un-representative, at least not to first order.
Yeah.
So, moving on to Northridge. Similar plots.
This time I’ll start with response rate versus intensity.
So you see the typical correlation.
And the other two panels – this event was more egalitarian
in that the shaking level did not depend strongly on household income.
It was fairly flat, at least of the reported data.
If you look at response rate versus income, you do see a –
you do see a correlation – a trend.
In all of these plots, the data tend to get ratty at the higher household incomes
because there are relatively few data points at those – at those income levels.
So Northridge, compared to Loma Prieta, was more egalitarian
in the effects that it generated in the, more or less, affluent areas.
The shaking in affluent ZIP codes was better characterized than the –
there’s more by Did You Feel It? than the less affluent areas.
And this trend here is with – the response rate versus income trend
is fairly strong of the events that I’ve looked at.
And one can think of the reasons for that.
There’s – there could be issues with mobility and people leaving
the area and not being around to come back to Did You Feel It?.
Awareness of the system. There’s also potential language issues.
A Spanish-language questionnaire has been available at times,
then for a while, it went away when the site was redesigned,
and then it came back. So language barriers could be a part of this.
Lastly, for – well, the last big earthquake to look at is Ridgecrest,
which happened just last year – well, 2019 now.
So, again, response rate depends on intensity level.
But the rest of the panels get interesting.
The response rate actually tends to decrease with household income.
And – which is apparently because, for this earthquake, the intensity level
tends to decrease with increasing household income.
So let’s see. Which slide do I have next? Yeah.
So, again, what’s controlling response rate appears to be the intensity level.
But it’s an interesting comparison for this earthquake versus Loma Prieta.
If Loma Prieta can be considered a, quote,
rich California earthquake, Ridgecrest is different.
It primarily impacted one of the least affluent parts of the state.
What does this matter? Interestingly, Duncan Agnew had
a recent paper where he talked about so-called celebrity earthquakes.
And I encourage you to read that if you haven’t seen it. It’s interesting.
So this plot from his paper shows the number of scientific papers about
different earthquakes as a function of magnitude.
And showing that generally, bigger earthquakes get more attention,
but there are what he calls celebrity earthquakes that got sort of more
than their share of attention. And you can find Napa’s on this plot.
Ridgecrest is down here – not much of a celebrity – Parkfield, Christchurch.
So I don’t – I didn’t have his data set,
but this inspired me to go look at some related data.
So Loma Prieta, which – looking at scientific publications – sorry –
there’s 42,000 Google Scholar publications on Loma Prieta,
versus only 22,000 for Landers. I didn’t look at Ridgecrest because
it’s kind of recent, whereas, Landers and Loma Prieta
were around the same time. Landers was quite a bit bigger,
but it’s gotten less scientific attention. But Napa is definitely a celebrity.
It was only a magnitude 6, but a whole lot of papers have
been written on that compared to Ridgecrest and Joshua Tree.
So there is Ridgecrest here, but – which is a paper –
earthquake that people are still writing papers about, but still.
One can look at newspaper articles about the different earthquakes.
And so, for this, looking at Loma Prieta, Landers, and Northridge,
they happened around – within five years of each other.
I looked at newspaper articles within 10 years on a –
on an online searchable database of newspapers.
And Loma Prieta – 2,900 articles in 10 years,
far outstripping Northridge and way outstripping Landers.
So …
Again, an earthquake that impacts an affluent area is getting
a lot more attention scientifically and in the media than Northridge,
which impacted generally an overall less affluent part of California.
And then Landers, forget it.
If you look just a Ridgecrest with – so a recent earthquake,
you can see how wealthier areas get more attention just by comparing
the number of Los Angeles Times articles that mention Trona,
which had more significant damage than Ridgecrest –
was a significantly less affluent area.
So – and, again, this idea of, quote, rich California earthquakes.
They’re getting more scientific studies.
They’re getting more media attention.
More public awareness. And this is – the next one is
speculative, but are they having a bigger long-term impact on legislation
and societal response, I think, is a – I haven’t shown that.
I haven’t looked at it, but it’s kind of an obvious suggestion from the other stuff.
Okay, getting back to the questions of representation, to look at it in
a little more detail, I’ve looked at moderate earthquakes between
magnitude 4.2 up to Napa, is the biggest, between 2002 and 2021.
So these are – they’re moderate earthquakes.
They’re all – they all postdate the Did You Feel It? system.
They each produced at least 15,000 reports,
and they were all widely felt in either Los Angeles or San Francisco.
And so I’m going to show a bunch of panels.
This one is northern California on the left, southern California on the right.
Did You Feel It? response rate on a logarithmic scale
versus household income. And I’ve broken it out
before 2018 on the bottom and after 2018 on the top.
So you can see there is a tendency for response rate to increase
with household income for these earlier events, and maybe things
flatten out more recently, which would suggest that
Did You Feel It? is getting more inclusive over time.
I didn’t average the data in the top left panel because there’s only two events.
But it’s interesting to look at the data a little more.
So remember that response rate depends, to first order,
on intensity level. If you look at the intensity level,
similar panels, but intensity level versus income rather than response
rate, northern California, things, on average, are pretty flat.
Southern California, for these events, the trends are pretty flat.
Interestingly, for recent events in southern California, by chance,
they all generated stronger intensities at the – in less affluent areas.
So what’s going on in southern California, if you put that together,
is that, over time, this apparent – the trend in response rate –
the correlation between response rate and household income appears to have
flattened out more recently but, for these earthquakes,
the intensities were less strong. So, even though – for – among affluent
households, so even though people – affluent households were feeling less
severe shaking, they’re still reporting in equal numbers, about.
And then – so here’s an example from northern California.
So this is response rate versus intensity. For this event, there’s no strong trend
in intensity versus income. There is some trend in response rate
versus income. So what these basically say is that the system is fairly inclusive.
There’s evidence that it’s getting more inclusive with time.
But there’s still some room for improvement in – if our goal is to
characterize shaking levels equally well in – across all socioeconomic groups.
So now let me just talk quickly about something interesting
that came out of all of this. And you may have noticed that
a number of these – I don’t know if you can see my cursor
or if I’m just doing this for my own edification or not.
But, if you look at a lot of these plots of household income versus normalized
response rate, they have a funny hook where, at the lowest income levels,
you get fairly high response rates. And, taken at its face, that suggests
that the least-affluent households are more likely to report into
Did You Feel It?, which, if true, would be interesting.
So, for this plot, what I’ve done is pulled out three separate events that
were widely felt in southern California and plotted them with different colors.
Blue is the Chino Hills earthquake, which was quite widely felt.
And then green and red are two other moderate events.
And so I plotted the average response rate versus household income.
And then I pulled out one particular ZIP code that I noticed had
a unusual high response rate for some earthquakes in particular.
So what I’m going to do – well, I’ll show you the ZIP code – 90089.
And I’m going to leave that on the screen until somebody out there
tells me what ZIP code that is. Because I know – I know some of you know it.
- [whispering]
[shuffling sounds]
- Okay. Austin, you know this.
- [chuckles] Yes, indeed. Harkening back to college days.
I believe that’s USC or around the campus.
- That is USC. [laughs]
So, that ZIP code, as far as I can tell, is just the campus footprint.
I don’t think it extends into the – into the adjoining neighborhoods.
And, for some events in particular, it’s an over-achiever with
Did You Feel It? reports. Well, then I – but it’s not every event.
So then I defined the USC factor. So, for all the earthquakes in the data
set, I took the normalized response rate for this ZIP code and divided it
by the average response rate for similar household incomes.
And here’s the plot. Here’s the USC factor.
It’s plotting it versus hours – time of day in local time.
So basically, between 9:00 and 5:00, if earthquakes happen, you get
a whole bunch of reports from the USC community.
If earthquakes happens in the wee hours of the morning,
you don’t get so many reports. And evenings tend to be a mixed bag.
So USC basically – it’s not controlling these data points at low incomes
entirely, but it illustrates the issue. There are some other ZIP codes,
like downtown L.A., where you’re getting high response rate for
daytime earthquakes. It’s clearly people who are feeling
earthquakes and reporting them in places where they don’t live.
And so – let’s see, before I move on,
there’s one earthquake for which there are zero reports from 90089.
And I won’t throw that out there as a quiz, but I imagine, if some of you
think about it, you can probably figure out which earthquake that was.
It was the – an earthquake near South El Monte – the one
last September that I showed you the map for.
This was just before midnight, so definitely after hours.
It was during the lockdown. So we have a pandemic.
We have a lockdown. There’s no one at USC.
And, poof, there’s zero Did You Feel It? reports from that ZIP code,
even though the shaking was quite strong enough to wake people up.
All right. Let me close on a somewhat more serious note.
It’s sort of fun to dig into Did You Feel It? reports and,
you know, see where things are coming from.
But let me, in closing, talk about intensities in India.
So the system does collect information – reports from around
the world, including India. There were quite a few reports
contributed for the 2015 Gorkha earthquake.
And so this is – you’ll recognize India. I’ll explain the colors in a second.
Here’s the location of the Gorkha earthquake.
But Nepal is – I’m not going to be talking about Nepal or Tibet,
so that’s why some of the areas are missing.
And then there’s one Indian state where the census data was missing.
So, in India – well, in the U.S., there’s a big gulf between rich and poor.
There’s a bigger gulf in India.
And that gulf translates into a lack of access to basic education,
in rural areas especially. So rural India, there’s quite a few
people living there, but the poverty is much worse, and the
education levels are much worse. So this is a graph I saw – I found
comparing the monthly per capita expenditure on education
in rural areas versus urban areas. And you can see how the poorest
to the richest folks in each of the – in both rural and urban.
So there isn’t universal access to public education in India.
And that translates into real differences in literacy rates.
So these plots that I’m showing with the blues and the greens
are literacy rate defined as the ability of people over the age of 8 to read and
write in any language. And the values range up to 100% in the richer areas.
And then they get down to less than 50%,
especially in some of the rural areas.
So that’s one of the bits of information we’re going to use
to look at socioeconomic factors in India.
So this is the Did You Feel It? data plotted for Gorkha
plotted on the literacy map.
This is, by comparison, what we call a traditional intensity data set.
So, after the earthquake happened, Stacey Martin rolled up his sleeves
and made an exhaustive effort to collect newspaper and other accounts –
a lot of them were online – of shaking effects throughout India and Nepal.
You can see there’s a huge number. There’s, like, 1,000 or more different
locations represented in this traditional data set.
So you can see that there’s a lot more data that came in collected
this way versus Did You Feel It?.
But you can compare, then, the two types of data.
Oh, there is an issue with – there’s different types of
Did You Feel It? data. So we looked at the city data, which are values that are
averaged within cities if more than one response come in.
And those are the circles. There’s also increasingly
geocoded data that uses web services and other information
to pinpoint location, and those are the squares.
And so, you know, you can – you know,
maybe the two data sets are – they’re a little bit different.
And I can – there’s some more details I could talk about here,
but the main point is that the conclusions don’t differ.
You could use one data set or the other. We are using the city data.
So, for – we looked at three different earthquakes that were widely
felt in India – 2011 Sikkim – and I’ll show you a map in a second.
I should have showed it first. 2015 Gorkha, 2015 Dolakha –
the large aftershock. And, for this, we’re looking at the
average intensity level plotted versus the literacy rate from the census data.
And the gray line shows the average literacy in India, which is 74%.
So what you see on the left is that there’s an oddball data set here.
But, for all three earthquakes, the Did You Feel It? data are preferentially –
they’re coming in from places that have higher-than-average literacy.
And the interesting comparison is with the traditional data sets,
where especially it’s – it’s especially striking for Gorkha and Dolakha.
The strongest shaking levels are coming from areas with low literacy.
So you can zoom in on the area around Bihar, which is here.
It’s India’s poorest state. Oh, and here’s the map I should have shown you.
Here’s the 2011 Sikkim earthquake, Gorkha, Dolakha.
So all of these events shook this part of India relatively strongly.
But, again, it is the least-affluent part of India.
And, on these panels, we show – the shading indicates –
it’s not literacy now. It’s whether regions are urban or rural.
You can see here that the literacy rate is generally fairly low.
But these areas – there’s a few urban centers within them.
And the Did You Feel It? values are coming from, almost exclusively,
the urban areas. Whereas, these traditional values –
you know, there are people out there. They’re impacted by earthquakes.
Their reports make their way to media sources, as you see here.
It’s a much – it’s a much better spatial coverage of this area,
where the average intensities were up around 5 to 6.
So, if you – so that was just Bihar. If you stare at the map, you can –
you can see, without doing any analysis, that the Did You Feel It?
values, not surprisingly, are coming from the blue areas where people
are relatively affluent. They’re relatively well-educated.
They are more likely to have internet access.
They’re more likely to be fluent in English.
So, yeah, again, you know, if we’re missing big areas
where people aren’t coming to the internet,
we’re going to be missing potentially important information.
So what do you do about it? How do you improve representation?
You know, fundamentally, in a place like India,
you want to improve literacy and the internet access.
So those are beyond our pay grade as seismologists.
There’s other things that you can think about to improve the –
to bring in more participants. Outreach. The group –
Rémy Bossu’s group, EMSC, has pioneered some
interesting approaches. They use – instead of a questionnaire,
they use thumbnails, and they ask people, instead of answering questions,
to just point to the cartoon that matches their experience.
They’ve optimized a phone app rather than a full website.
You can make information available in other languages.
The one I want to mention is outreach and engagement.
Sorry. My voice is cutting out.
Did You Feel It? is hugely popular. People like the system.
When they experience an earthquake, people generally want to talk about
their experiences. They like to share.
And so I think there’s an opportunity here to make use of Did You Feel It?
to reach out to groups that maybe don’t know about it as a way to improve our
overall engagement to communities that have maybe been underserved.
But the last – oh, and then, for historical earthquakes, it is tough.
You know, how do you – how do you figure out what you don’t know?
It’s not easy answers. The archival research is like
looking for needles in haystacks, going – looking more for original
sources in native languages. Looking for transcribed
oral histories for peoples that didn’t have a written language.
These sometimes exist.
Collaborating with people outside of seismology – with historians, say.
And then one thing that’s more within our wheelhouse as seismologists,
and this is something Stacey is thinking about, is the analysis of data to consider
what the effect is of potentially missing accounts from areas where you
don’t have information. What happens with these methods
that we use to locate earthquakes from intensities is that, if – especially
if it’s a one-sided intensity distribution, if you’re missing near-field values,
the methods tend to pull the location to the – to where you do have accounts,
and it typically lowers the magnitudes if you assume the location is
closer to the accounts than it really is.
So that’s something that one could consider without adding extra data.
But the main conclusion of all of this is that I think we’re all aware
now that representation matters for our community. It matters for society.
But the point is that representation actually can matter for our science
in ways that we maybe haven’t even thought about before.
So, with that, I will stop speaking into the void and see if anyone’s
still out there and has questions. Thank you.
- Thanks, Sue.
Here’s your round of applause on everyone’s behalf. [clapping]
Thanks for that really interesting talk walking us through your research that
sort of looks at the intersection of a lot of different variables here.
If anyone has questions, again, feel free to write them in the chat
or raise your hand, and we’ll field them.
I’ll kick it off with a question about the ZIP codes and looking at the –
looking at the average income in ZIP codes.
Do you – have you made an attempt to look at the range or distribution
of income within those ZIP codes? I mean, not all ZIP codes are created
equal as far as the unity of their affluence, right?
- Ah. So …
The short answer is no. Ridgecrest was pretty clear
that there were real differences. Ridgecrest is a single ZIP code
in China Lake. It was clear that there were differences
in effects and, I suspect, participation.
One could get at that. You’d need finer socioeconomic data.
Yeah, you could look at – like, now there’s Did You Feel It?
data available in 1-kilometer geocoded cells.
So you could look at response rate in those cells versus
the overall ZIP code, say. You’d need to have the population
in those cells, though, which …
Yeah. It would be interesting. - Yeah [inaudible] …
- It would get progressively more complicated.
- … having finer resolution would clearly illuminate
a lot of things even better, so … - That’s – yeah.
- Steve Hickman asks – he says, fascinating talk, how do you control
for site amplification effects? Steve, do you want to go on?
I see you joined us. - I just want to show my face.
Talking into the void is not much fun. - [laughs]
- Anyway, so I did think it was a great talk, and curious about that.
It doesn’t – you know, I thought about it in your Loma Prieta
versus Northridge example, but also in the Indian example.
You know, Loma Prieta, of course, you have mountain and topographic effects.
Potentially, rich people live up in the mountains.
And Northridge is mostly an urban basin.
So how do you control for that sort of thing in your analysis?
- Yeah, I don’t. I mean, I don’t really worry about
what’s controlling the ground motions.
So, yeah, think it through. If less-affluent people are experiencing
more amplification, they’re experiencing stronger shaking.
You might expect them to report in more because they are just –
everything else being equal, if you experience more shaking,
no matter what the cause is – yeah. So …
- Yeah. That’s what I was thinking. You know, would they – if they’re
shaken more harshly, would they be more likely to dial in Did You Feel It?
than if the same person with the same income lived in the low-lying – or a
hard rock site or something like that. It doesn’t work in some cases
because it goes the opposite. - Yeah. No, there’s all sorts of
fun stuff to – I mean, there’s important stuff but interesting stuff to think about.
There’s a whole range of sociological issues that Stacey
and I didn’t want to touch. Because we just didn’t feel qualified.
But why are people going to Did You Feel It? is a really
interesting question that I think it would be fun to look at.
I just don’t feel like I’m the right person to do it.
You know, for – if you – if you look at some of the trends, it really looks like
participation increases from a household income of about
20,000 to around 100,000, 120,000. It looks like it comes down again
after that. The data get more ratty. But I sort of wondered about that
and if sociological issues aren’t coming into play.
- Yeah, it’s interesting. And also you pointed out
internet access, literacy rates – these things all matter.
- Yeah, and in India, the language proficiency.
Even in the U.S., the – you know, not everyone in California speaks English.
And, you know, if you have a really modern browser, there’s automatic
translation now, but that hasn’t always existed, and it’s not –
it’s not universally available even now. - Yeah. Okay, thanks.
- Yeah. And there’s also this question along these same lines of – that you
brought up with this example of USC, that, within the ZIP codes and areas,
there’s – you know, the average income as a representation of what’s going on
and who is populating those areas when the earthquake happens is
not necessarily a really direct thing. And these cases in California where
you have the sort of opposing effects of affluence, depending where the
earthquake is, that suggests that there’s some sort of deeper variable coming in,
like, you know, are the – in affluent ZIP codes, do you also have lots of
businesses and places that aren’t residential and don’t count towards
the income but are counting towards [inaudible].
- Right. Yeah, and I think you see that in the – some of the downtown areas –
San Bernardino, Los Angeles – you have places where people
are working, whether or not – they’re relatively poor areas,
but they’re not – they’re not living there.
- It would – it would certainly be very interesting
to dive deeper into that, right, because those demographics –
those will still have an effect on who is reporting it and how
and what environment they’re in. - Yeah.
- Okay, there are a couple of questions in the chat again.
So Peter Haeussler says, are there – the subject is fresh on his mind
working on updating NEHRP external grants program priorities.
A colleague was asking, is there a way to help better represent
under-represented communities with the call for proposals,
possibly in ways to get hazard information to the communities,
or how can we better communicate this information to them?
So I guess this is a question about sort of, how do you solicit –
in this particular case, how would you go about
remedying this and soliciting the right information?
- Yeah, that’s a great question. And, as I understand it, the awareness
of Did You Feel It? has just grown organically.
I don’t think there was ever any big PR push, but people found it.
They find the website. And, again, people like it.
So I haven’t given it a whole lot of thought, but it really does strike me
as an opportunity to – you know, there are efforts to reach out to
under-represented communities to use it as a – as a tool to engage
with people and, you know, something – if an earthquake happens,
just to do what we can to raise awareness.
I don’t have any really specific suggestions for that.
- Yeah, I guess, clearly, you see the effects of more people using
Did You Feel It? over time in the number of responses.
So somehow, it’s growing. I think [inaudible] …
- Yeah. - .. advertising that.
- Yeah, and I think, you know, overall, the message is good,
that more people are finding it. We are getting these tens of thousands
of responses. It does appear to be getting more inclusive with time.
Well, in California, at least, and presumably most of the U.S.
That’s good.
- So there’s one more question here from Lauren Harrison.
Lauren, I don’t know if you want to ask it yourself, but the gist of it
is essentially, how constant are the affluence designations
of ZIP codes over time? - Yeah.
- She lists a variety of ways in which they might change.
- Yeah, no. That’s an issue. And the census data goes back to 2010.
Our assumption is that affluence changes, but the relative affluence
is probably fairly constant. So rich areas are still rich.
There are – there are, you know, if areas are starting to really gentrify,
things may change over time. But, yeah, and I mean, this is getting
into the whole realm of interesting issues that one could look at.
- Yeah. I see a note that some of the other examples that Lauren
brought up point to effects that are dependent on prior earthquakes.
So, you know … - Oh.
- … either damage or other things that sort of scare or chase people
or displace people from a neighborhood and then whether or not it gets built
back correctly or not. Those things. - Yeah I wondered – I wondered for
Northridge, where there is a fairly strong correlation between response
rate and income, which can’t be – is not explained by the correlation
in intensity versus income. And I wondered, you know,
did people move away and not come back.
It’s hard to know. What you would really like to have,
and we’re not going to have, is demographic data for
Did You Feel It? responses. That’s far beyond the approval for
the Did You Feel It? questionnaire. It would be to ask people just some
basic information. But we don’t do that, and I don’t think we ever will do that.
- We’ll do one more question in the formal Q-and-A, and then we’ll stop
the recording, and people can hang out and ask more questions.
But this is from Glenn Biasi, and it sort of mirrors another
question I was going to ask. In India, could Did You Feel It? reports
reflect a floor, or a base, education level, such as high school,
below which earthquakes do not make the curriculum, and even
the concept of a report is a non-thing. I was going to add to that hypothesizing
about what concentrates Did You Feel It? reports in the –
in the highly literate areas. Is it just a statistics thing, right?
You know, the reports are coming from big cities, and there aren’t that many.
And so, if you’re getting a certain reporting rate,
you’re just not capturing – you know, you’re super unlikely
to capture the rural population at all anyway.
- Yeah. I think – I mean, that’s true. If you have more, there are less likely
to be – if you have fewer, they’re less likely to be distributed.
But I think, if you – if you know India, and Stacey knows it very well,
the results aren’t surprising. The differences – the socioeconomic
differences between rural and urban areas is so stark, and the literacy –
it’s just such stark contrast. So the fact – I mean, it’s kind of
a no-brainer if you ask where would Did You Feel It? responses come from.
The answer is, oh, of course they’re going to come from
the cities where people have – you know, they can speak English.
They have access to the internet.
Sort of the surprising result in all of that, to me, was that the traditional data
sets, when they’re really undertaken in the exhaustive way that Stacey’s
been able to do, you do get quite good coverage.
So there are people out there. They’re impacted by earthquakes.
They’re living in vulnerable houses in a lot of cases.
So that information is out there. And it does get – it can be collected.
It’s just not flowing to us over the internet.
- Yeah. It’s nice to look at ways that you can – we can get
the appropriate spatial coverage for that sort of deal.
All right. So, with that, let’s give Sue a round of applause for the
great work and the talk and answering these questions.
Thanks very much.
[applause]
So, with that, we’ll end the recording and see you all next week.
But anyone who wishes …