[inaudible background conversations]
Okay. Well …
This would be the time that we should be starting.
People are slowly drifting in. We really need to figure out the
viscosity of scientists to really work out this at a better pace.
So, as the scientists viscously return to the room, I will turn things
over to our excellent moderator, Jen Andrews from Caltech,
who will start off the wonderfulness that is our final session of the day.
- Thank you, Sara. And thank you, everyone, for joining us for the final
session of the day, which is ShakeAlert earthquake early warning rollout.
And, as several previous speakers alluded, it’s been a busy and exciting
year for ShakeAlert going public. And I’m particularly excited about
the presentations today because, as a humble worker bee on the
algorithm side, what I’m really interested in is the end product,
the result, how do we issue meaningful and useful alerts,
and how do they get turned into actions. So three great presentations.
We will be saving the discussion for the end following the previous
sessions of the workshop. And so I’ll turn it over to Jessie
Saunders, our first speaker, who is talking on examining regional EEW
alerting strategies through the lens of the Ridgecrest earthquake.
[silence]
- [inaudible]
Good afternoon, everybody. My name is Jessie Saunders.
I am one of the Mendenhall postdocs here at the ESC
in Moffett Field studying earthquake early warning.
And, before I begin, I would just like to thank all of my co-authors
listed here who have contributed to the work that I’m presenting today,
and then, as well as all of the ShakeAlert team.
So the ShakeAlert earthquake early warning system is the
early warning system for the West Coast of the United States.
It is operated through the USGS with partner state and academic
institutions listed here. And the goal for ShakeAlert is
to generate early warning alerts for MMI 4 shaking and higher shaking
for earthquakes that occur in California, Oregon, and Washington – so the
orange dotted lines on the map. And operationally, the algorithms that
are in ShakeAlert use seismic data to rapidly estimate the earthquake source –
so magnitude and location, and then finite fault effects.
And then there are several additional algorithms that are currently
in development. So there are three algorithms that use high-rate
geodetic data to refine the finite source characterization.
And then there’s also the PLUM ground motion-based algorithms.
That uses direct observations of strong ground motions to generate alerts rather
than using rapid source characterization. And, as was mentioned several times
earlier today, the public rollout for ShakeAlert has already begun.
So, at the end of December in 2018, public alerts began for residents of
L.A. County through the ShakeAlertLA cellphone app.
And then, in October of 2019, public alerting began for the entire
state of California where ShakeAlerts will be issued through the wireless
emergency alert system as well as other cellphone apps, including MyShake.
Now, for this talk, I’m going to be using the magnitude 6.4 and
magnitude 7.1 Ridgecrest earthquakes to examine different strategies for
improving the magnitude – or, the MMI 4-plus early warning alert regions.
And so, as a reminder, the way that MMI 4 is defined for USGS Did You
Feel It? as well as ShakeMap is that MMI 4 is felt by many to all in the
regions, and most observers describe this intensity of shaking as strong shaking.
So, in the first part of my talk, I will be focusing on how we can
improve the accuracy of MMI 4 alert regions, mainly for the point
source alert distance computation. And then briefly, at the end, I will
also talk about how we’ve used the Ridgecrest earthquakes to evaluate
the alert accuracy and timeliness of the PLUM ground motion-based
early warning algorithms with different regional alerting strategies.
So the way that ShakeAlert generates alert regions can be
broken up into four main steps. So, in the first step, an earthquake
is detected, and the magnitude and the location are estimated.
Then this magnitude is then input into a ground motion prediction equation to
estimate the peak ground acceleration and peak ground velocity with distance.
And then, at a given distance, the PGA and PGV are then input
into a ground motion-to-intensity conversion equation, or GMICE,
to estimate the MMI at that distance. And then the distance that corresponds
to MMI 4.0, which is the alerting target, that becomes the radius
of the alert region. And so that is shown here for the Ridgecrest earthquake.
And this is using the actual magnitude estimate in this case.
So, for this process, ShakeAlert currently uses the median expected
ground motions. But we know that there is variability, both with PGA
and PGV, at a given distance. And then, with PGA and PGV
with MMI. And so, because of that, even if we get accurate source
estimation, this will still produce an alert region that has missed alerts.
And so, for the magnitude 7 earthquake, this is the
Did You Feel It? map for that. And you can see that there are some
regions that have MMI 4 and higher shaking that are outside the alert zone.
So our question is – moving forward, is how do we improve the accuracy
of this MMI 4-plus alert region? And for this, we are going to
move forward assuming accurate source characterization –
so with accurate magnitude estimation.
So the way that we want to improve the accuracy is by
expanding the alert region. And we want to make sure that we
can do that with having the MMI 4 alert targets stay the same.
And so our approach for this is to incorporate ground motion
model uncertainties. So we have the uncertainty
in PGA and PGV at a given distance from the GMPE.
And then we also have uncertainty in MMI for a given PGA and PGV
value from co-located data. And so we propagate these errors
in the ground motion-to-intensity conversion equation to form
a single standard deviation. And then we use that standard deviation to then
expand the alert distance calculation. And so that looks something like this.
So here, this – there we go. So this green dot shows what
the MMI 4 alert distance is with one sigma uncertainty.
And then we have the same thing with two sigma uncertainty.
And so that corresponds to the alert regions we see on this map.
And so now, let’s quantify what the overall quality
of the alert is in terms of population.
So that looks like that here.
So all of these colors show the different alert quality.
And so this middle map shows the total population that is inside the alert region
as a function of alert distance. So this is the radius of our alert
region as it goes out. And so here we quantify correct and missed alerts by
the population that experiences MMI 4 and larger shaking according to
Did You Feel It? And if these people are inside the alert region,
that’s a correct alert in green. And, if they are outside the alert region,
that means it is a missed alert because they did not get an alert in this case.
And, as you can see, as we increase the alert distance, more and more of these
people are now inside the alert region, which is great.
However, as we increase the size of the alert region, we are also
including a lot of people who do not feel MMI 4 and larger shaking.
And these are false alerts.
And that’s shown with all the population up here.
And so I’ve separated these different false alerts into whether or not people
still feel MMI 3 shaking, which means that, if they are indoors, they are still
likely to feel some weak shaking. And that’s shown in the yellow.
And then the population shown in orange are people who are
unlikely to feel any shaking. And then, in gray, which is just
a little bit up here, these are people inside ZIP codes that were not –
that were not represented in Did You Feel It?
And that could either be because they did not feel any shaking,
and therefore were less inclined to submit a Did You Feel It? report,
or because they were in a location of very sparse population,
and so there were fewer people in that region to submit a report.
Okay, so now the three black lines on this plot shows what the alert
quality is for our three alert regions. And then also I have shown in pink
what the 95% correct alert rate is in terms of population.
And then, in blue, shows the same thing except in terms of area.
So we can see that there is a pretty big difference if we look at
population versus area. And so that could affect what
our overall target should be – so much uncertainty should we
actually include moving forward. And this also looks pretty different
for the magnitude 6.4 Ridgecrest earthquake, which is shown here.
So the magnitude 6.4 Ridgecrest earthquake – obviously, the MMI 4
shaking didn’t extend as far. So there’s fewer people who need to be alerted.
But, because some of those people who need to be alerted extend into
the densely populated L.A. area, we can see that there is a pretty
big spike in false alerts when we hit that densely populated region.
So now, once again, our question becomes, how much uncertainty should
we include in our alerting strategy? And so these two tables show the totals
in population for the correct, missed, and false alerts for both of these
earthquakes for our three example alerting strategies here.
And so they have actually pretty similar characteristics.
So if we were just to use the median expected alert distance for MMI 4
shaking, we only get 7 and 6% correct alerts.
And then, if we include one sigma of uncertainty, we end up getting
that increased to over 80%. And then, if we get two sigma
uncertainty, that gets us to over 99%, which is great, except getting to that
99% causes us to have to send false alerts to tens of millions of people.
And, for the magnitude 7 Ridgecrest earthquake, that ends up being about
twice as many people over-alerted who actually require alerts –
or, than people who require alerts. And then, for the magnitude 6.4
Ridgecrest earthquake, that ends up being about 15 times as many people
who receive unnecessary alerts compare to the people who actually need alerts.
And so, when we move forward and look at more earthquakes to try to
pick out how much uncertainty should we include and what our
alerting strategy should be, it’s really important to take into account this
tradeoff between trying to minimize the number of false alerts and then also
trying to minimize the amount of over- alerting that we might expect to happen.
So then, for the second half of my talk, I will now kind of go away from
source-based early warning and then talk a bit about
ground motion-based early warning.
So the ground motion-based early warning algorithm that currently is
in use in Japan and is under consideration for incorporation to
the West Coast is called PLUM. It’s down – it stands for
the Propagation of Local Undamped Motion.
And so, instead of determining the earthquake source characteristics
in order to issue an alert, what it does is that it monitors
a station, and when a station observes MMI 4 or larger shaking, it issues
an alert to regions within 30 kilometers of that station.
However, in order for this to work, we need to have a set of pre-defined
alerting regions. And we do not have that yet for the West Coast.
And, fortuitously, PLUM was actually running on an internal development
server with real-time data streams during the Ridgecrest earthquakes.
And so that has allowed us to perform a postmortem analysis of PLUM using
real-time data availability and latencies. And so, using that, we were able to
test two – or, three candidate regional alerting strategies.
So we have county boundaries here in this middle map, which represent
kind of the largest alerting regions.
You can see, particularly in southern California, these are very large.
And then, second are the National Weather Service public
forecast zones, which are shown here. And these are essentially county
boundaries that are broken up into regions of similar weather.
And one of the ways that that’s broken up is in terms of topography.
So we actually happen to end up seeing a really nice correlation between the
National Weather Service forecast zones as well as topographically
derived Vs30 estimates, which is what the map is colored by.
And then, for our third alerting strategy,
we do a very simple 50-kilometer-by-50-kilometer grid.
So let’s jump right into the results.
So here, these maps show the results for PLUM with the magnitude 6.4
Ridgecrest earthquake on top and the magnitude 7 Ridgecrest earthquake
on the bottom for our three different candidate regional alerting strategies.
And so each of these regions are colored by the alert quality.
And so, sorry, these colors are a little bit different than earlier,
but they’re basically the same thing. So, in blue, those are – those indicate
a correct alert. So that would be a region that is alerted by PLUM,
and then it also experiences MMI 4-plus shaking somewhere –
excuse me – somewhere within that boundary.
And, in the red, we have the missed alerts.
So these are regions that are not alerted, but then there is some MMI 4 or larger
shaking inside of that. And then yellow is for false alerts.
And then, in gray, these are regions that are not alerted.
And so, for all three alert strategies, they all do a really well –
a really good job. There are some regions of missed alerts, notably
up here, where there’s a tiny bit of MMI 4 and larger shaking
in some of these regions. And then there’s also some missed
alerts down here by the Salton Sea. And this was because the way that
PLUM computes MMI is very slightly different than the way that ShakeMap
does it, which is what we’re using for this comparison.
And so that also explains some of the false alerts over in here where some of the
stations that participate in the alert – so that measure MMI 4 and larger
shaking are shown. But that wasn’t included –
or that wasn’t the case for ShakeMap for some reason.
And so, if we look at this in terms of the total population that was alerted,
all three strategies actually achieved 99% correct alerts, which is great.
And then, if we look at the smaller alerting regions, those actually reduce
the total area that is falsely alerted. So that could indicate that the smaller
50-kilometer grid might actually be the strategy to select.
However, how does alert timeliness factor in?
So, because we were able to use the real-time data streams for this, we’re
actually able to estimate the warning times for these different regions.
So what we do for this is that we assume that strong shaking
arrives with the S wave. So then, for any location,
the warning time for that location is the S wave arrival minus the time
that that region is alerted. And so, once again, we are able to
include the data telemetry latencies in our alerting time.
However, we do not include any alert delivery – alert delivery latencies.
So these would be the time it takes from when a ShakeAlert is issued to when it
goes through, say, the WEA system and then arrives on your phone.
So that’s not included in this.
Okay. And so the amount of warning time for these
different cases are shown here.
We got the longest warning times for the largest alerting regions.
And so you can see that really clearly with the counties over here.
However, most of the warning times – or, the longest warning times are
in regions that do not experience MMI 4 shaking.
And so the boundary for MMI 4 is the black lines on these maps.
So one other thing that’s pretty noticeable is that
there are some regions in gray. And regions in gray means that there
is no useful warnings for these regions. And so there will always be a late alert
zone near the epicentral region. And this has to do with the amount of
time that it takes to issue the first alert, for the most part.
And then we also see that, when we have smaller alerting regions, there ends
up being additional late alert zones that are beyond the epicentral region.
And so, because of this, if we were to look at these two earthquakes,
even though the 50-kilometer alerting grid does a better job with reducing
the overall area of false alerts, because there are more regions
that have late-alert zones, even without the additional late –
or, additional delivery latencies, we might say that this style of
regional alerting strategy might not be the optimal one to use.
We might want to use a slightly bigger one.
Okay, so in conclusion, in the first part, we developed a method to incorporate
ground motion uncertainties into the alert distance computation.
And so that actually is able to improve the accuracy of the
MMI 4-plus alert regions for source-based alerting regions.
And then, in Part 2, we examined potential regional alerting strategies
for the ground motion-based early warning algorithms.
And we were able to use real-time data streams from the Ridgecrest
earthquakes in order to do that. For both alerting styles in Part 1 and
Part 2, we were able to achieve 99% correct alerts for MMI 4-plus shaking.
However, this does require falsely alerting tens of millions of people.
And, for the magnitude 6.4 earthquake, once again, that’s about 15 to 20 times
more false alerts than necessary alerts. And, for the magnitude 7.1,
that’s about 2 to 2-1/2 more false alerts than necessary alerts.
And, of those people who are falsely alerted, about 20 to 40%
of them may not even feel shaking.
So, in this case, there is this tradeoff between minimizing the amount of
missed alerts and then also minimizing the amount of over-alerting.
And it’s – and we would need to examine more earthquakes in order to
better pick out what type of alerting strategy would be the most optimal.
But we can’t just look at the overall alerts.
With Part 2, with looking at the alert timeliness, we have found that it’s
really important to also factor in what our expected alert timeliness is in order
to find the correct alerting strategy. And then finally, we really need to
understand what the public’s expectations are for early warning,
particularly their false alert tolerance.
If we expect – if people are really false alert-tolerant, then we would be able to
tailor our alerting strategy in order to better minimize the missed alerts
without having to worry about as much over-alerting.
But, once again, we need to make sure that that’s something that
everybody is okay with. And so we would need lots of
social science in order to do that, and hopefully Sara will
talk about that in the next talk.
Thank you.
[applause]
- Great. Thank you, Jessie. Our next speaker is the social
scientist Sara McBride on failure to alert – exploring
perceptions of ShakeAlert during the 2019 Ridgecrest earthquake sequence.
[silence]
- Good afternoon, everyone. I’m Sara McBride.
I’m the social science coordinator for ShakeAlert.
Oh. I need to get the presentation going first. That would be helpful.
[silence]
[inaudible whispers]
[silence]
All right. Sorry for that false start. What a great way to start this
failure to alert talk. A failure to start, really.
So I am the social science coordinator for ShakeAlert,
so I’m sort of an expert on human perception around ShakeAlert.
And I actually know a lot about failure, so I think that
this is a good topic for me [chuckles] to talk about today.
So I just want to thank my legion of co-authors on this particular one.
And it is quite a legion there who helped get together
all of this information and help with the analysis.
Particularly Jason Baumgartner from Pushshift who scraped Twitter for me,
and we got 600,000 tweets regarding Ridgecrest.
We had a huge data set that I’m going to talk about today.
I just quickly want to talk – give a little plug for social science and ShakeAlert
and talk about the Joint Committee for Communication, Education,
and Outreach and our national committee so that you know that we’re
embedding social science within the ShakeAlert endeavor at the USGS.
And we have a number of other activities going on, including
working with all the geological surveys on state levels.
All the emergency management groups – our wonderful friends,
who are our emergency managers.
And then our technical implementation and engagement group, which Dr. Jen
Strauss is in the room here, who is one of the heads of the committee.
And Jen is actually a vice chair of this initiative.
And we have colleagues working on this in British Columbia
as well as Oregon, Washington, and California.
And, just quickly on the ShakeAlert CEO Plan – that’s communication,
education, and outreach – we really have five focus areas, which are public
safety, preparedness, and resilience. Technical implementation and user
engagement, of which Jen Strauss is one of the leaders on that.
Consistent messaging and communication.
Integration with other federal and state earthquake hazard products.
And education resource development and dissemination.
And these are our five strains. And really just talking about the social
science initiative, we have seven projects that are currently ongoing
right now from 2019 and 2020, of which this project is a part of it.
And we really want to develop an understanding of where our
people are currently in terms of risk perception and protective action,
knowledge, and basic earthquake preparedness in Washington,
Oregon, and California. And we also want to establish a long-term
monitoring and evaluation plan for ShakeAlert to see how ShakeAlert
changes our communities and builds in more community resilience.
And so we do have these seven active projects working with
nine universities and research agencies from
five different states and two different countries.
So I just want to talk about this project now and about
our alerting platforms that were available
to the public on July 4th and July 6th of 2019 for Ridgecrest.
There was only one platform that was really publicly available at the time,
which was ShakeAlertLA, and that was developed in conjunction with
the city of Los Angeles. And the alerting thresholds
there were set at a magnitude 5 with an MMI shaking of 4.
And, due to the public response and perception that people wanted an alert
for lesser magnitudes and lesser MMI shakings, the thresholds were lowered.
Also just a note to Caltech. They have developed UserDisplay,
which is not – it’s not available to the public but to select ShakeAlert users.
But it was filmed live during a media conference, and I want to talk
a little bit about that and what that did in the media environment.
Now just quickly about the Ridgecrest earthquakes and ShakeAlertLA.
Alerts were not sent out via the ShakeAlert app for either the magnitude
6.4 or the magnitude 7.1 due to neither really meeting the
estimated shaking threshold. And that’s – the “estimated”
is a real important term here, and we’ll get to that in a minute.
And while the thresholds might have been met for the magnitude 7.1,
the ShakeAlert system estimated the earthquake at a 6.3 rather than an M 7.1.
And the app did not provide information on the earthquakes on their
map or in their recent earthquake list after the earthquake occurred.
So, when people looked for more information about shaking
on their ShakeAlertLA app, they just couldn’t find anything.
And so I just quickly want to go through – so that’s really
what happened, and that was for both kind of earthquakes.
So, for the data collection analysis, I analyzed 69 media stories between
July and November of 2019 about the Ridgecrest earthquakes.
I went through Google News. And I usually wait about six months.
I collect – I go through and collect new stories every month because of
the way that Google archives differently at different times using pretty
consistent search terms. The searches – my searches to
gather media started 36 hours post the first event on the July 4th and
then continued monthly and ongoing until November 2019.
And all the stories were coded in Nvivo, which is a content
analysis software program, using interpretive thematic analysis.
Again, there were 600,000 tweets with the data set that I was provided
by Pushshift by Jason Baumgartner.
The collection began – I think it was about six hours post the M 6.4 quake.
So it was in time to collect real rich, real-time data from the magnitude 7.1.
The problem is, with social media, is that people often delete their
tweets after a period of time or they delete their accounts.
And so you don’t always get really good, rich data that is representative
of the conversation that was going on if you, say, search Twitter and look,
you know, months or years afterwards.
And so what we did with the social media analysis is we analyzed
2,100 tweets, and we coded 1,600 because, in the hashtags and in the –
using the ShakeAlert terms #shakealertla, #shakealert –
sometimes you just get, like, junk tweets that have absolutely
nothing to do with the event. Someone just wants to tag along
on the conversation and then add their own whatever to the –
[chuckles] to the discourse on that hashtag. Again, all the tweets were
coded in NVivo, and I used interpretive thematic analysis there.
So the types of media I analyzed were print – so newspapers, television,
radio, electronic media – which is only available online, magazines, blogs.
And I looked at local, regional, national, and international news,
particularly the AP and Reuters wire services.
And just quickly, on the media analysis, so what I found really
interesting was that there was a pretty significant spike – you know,
obviously, after July the 4th. But what I found was interesting was
I couldn’t find as many stories on the 6th, even though that was
a larger earthquake and ostensibly felt by more people and more intense.
So I’m not entirely sure why that is. I probably need to rerun some of
my searches and just double-check my analysis.
But the stories I analyzed were the – I analyzed 12 stories in that –
from that first area in that first couple of days and then nine from the – you
know, day three. And then, as time went on, obviously, the story sort of died.
And what’s interesting is you can see a long tail, right, on these stories.
You can start seeing August and October is when these really
rich science stories are coming out in the media.
And they can see the scientists, you know, were publishing in
Science and in Nature, and our journal articles are coming out.
And those were – those are what those stories are in the
longer tail of the analysis. So just quickly – so 25 out of the
69 articles I analyzed mentioned ShakeAlert specifically.
It was the third most common theme in all the media stories – the first,
of course, was aftershocks, which was 52 stories – other than
the original earthquake itself. And the top three themes underneath
that about ShakeAlert were ShakeAlertLA, which had 19 articles
mentioning it; threshold, which had 19 articles mentioned it;
and failure, which had 10 articles mentioning it.
And I thought that was really interesting that failure was kind of
a discourse that was mentioned in social media, and I’ll talk about
that in a second. But that the media really did focus on talking about thresholds.
So when they – I noted that, when they talked about failure,
they also talked about the thresholds.
The media did a pretty good job at explaining what had occurred
with ShakeAlertLA and why people didn’t receive the alert.
So I want to talk about failure. And I just want to say, before I start
this, what I’m going to talk about now is not how the USGS itself or the
scientists in this room necessarily perceive the performance of ShakeAlert.
This is the perception and the discourse and the commentary
of people on Twitter. So I’m going to go through the dark
belly of [chuckles] Twitter right now. And this is not me saying this is
what I think. This is me just reflecting on discourse that was
going on using these terms, okay?
So I’m just going to – that’s my caveat here.
So failure was one of the biggest themes mentioned – 446 tweets out of
1,668 mentioned that people were disappointed in the app.
But, while they mentioned failure, they also just mentioned general
disappointment in going to the app and not receiving any more information.
And then the poor performance was also mentioned about the app with
battery drains and locality issues – so location issues.
And so that was another issue as well with 38 tweets just exploring
sort of issues with the app there. And the media stories were
highly re-tweeted. But after sort of 72 hours after
the largest earthquake, on Twitter, I noted that really
it was just media stories being shared. People weren’t really commenting
on it anymore. They were just – these media stories were just
being shared along to each other. So what I found interesting was that
a lot of the discourse wasn’t just people being frustrated that
they didn’t receive an alert. It was that they didn’t receive
an alert despite perceived intense shaking by people that was felt widely
throughout southern California. But, on top of that, when they searched
for authoritative information from the app, and within the app,
they couldn’t find any. And then, when they went to
the USGS website, they couldn’t find the USGS – the USGS website
experienced some technical issues as well and was unavailable for
a period of time after both earthquakes. Now, I just want to say, I have
personally talked with people who work on the USGS on the website,
and we had a long conversation about me mentioning it, and they really
wanted to say, yep, this happened. We want to be transparent about it.
We did have connectivity issues.
But we fixed it, and we fixed it right away.
So they’re pretty confident that we’re not going to have those issues again.
But they were comfortable with me talking about that
being mentioned on Twitter.
So there was also some – so just – sorry, backing up.
I just – failure wasn’t just the lack of an alert.
Failure in people’s minds was the lack of authoritative
information that they could find from their app or from the USGS.
So I just want to get that in people’s minds a little bit.
And I found that personally really interesting.
And then people had some confusion points, right?
They were confused between MMI and magnitude.
And I don’t think that’s necessarily surprising to anyone in the room.
But people were most confused about why the magnitude – you know,
what is MMI, what is magnitude, why are they different thresholds.
And then we had also conducted wireless emergency alert tests
in San Diego 10 days prior. And some people in San Diego
thought, well, we have ShakeAlert and wireless emergency alerts
because you just did a test on it. And so they were confused that
they didn’t actually even have ShakeAlert yet because
they thought they had it because we had had those tests.
And then ShakeAlert and ShakeAlertLA and UserDisplay
were sort of used interchangeably. People didn’t – people confused –
the UserDisplay they were seeing on the – on the – on the media
interviews that went off during the press conferences, they thought
that’s what they were going to receive in ShakeAlertLA.
And then they thought ShakeAlert was the same as ShakeAlertLA, which is,
you know, kind of understandable because of the name sharing.
[chuckles] And then misinformation on Twitter.
Of course, we always have the delightful earthquake prediction
people come out in force claiming that, you know, their cat told them this
was going to happen and that – you know, 21 tweets on that.
So just some questions and comments about ShakeAlert.
So 57 tweets were dedicated to asking about having it in their county,
which I found really interesting. Even though the discourse was
around failure to alert, people were, like, hey, your app didn’t work,
but we still want it. [laughs]
And that was in San Bernardino, Kern County, and San Diego County.
Eleven tweets out of the 1,600 claimed to uninstall the app, and five explored
issues of loss of trust in the ability for the app to deliver messages.
And four tweets talked about testing the system.
And I found those tweets really interesting.
They came from, like, software engineers who were, like, oh, well,
yeah. You’re just testing the product and seeing if it works.
And, hey, you guys learned some things.
I thought that was, you know, kind of a very charitable approach from people.
And then 85 tweets suggested for improvements of the app.
Mostly around people being allowed to choose their own alerting thresholds.
So one thing – if you know anything about me personally,
you know that I do love humor. And I coded humor 60 times.
And humor is something that is well-known of a way of coping
around stressful situations. So I just want to read these out
because they’re kind of small.
But my favorite one is all, like – is the second one, which is, true,
the ShakeAlert app failed to notify L.A. residents of two major earthquakes
this weekend, but animals are supposed to sense quakes, too,
and my dog didn’t say a damn word.
So I’m feeling all kinds of betrayed right now. [laughter]
Which is by far my favorite one.
And then one of my other – the other good ones was,
did ShakeAlertLA work? Asking for 2 million friends.
And, I hope ShakeAlert works better than Shake Weight.
And there were some that I can’t share in polite company.
But this is one of the delightful parts of the job.
So just quickly on who had the most popular tweets about ShakeAlert.
Well, Representative Adam Schiff had the most re-tweeted ShakeAlert
tweet with, then, L.A. city and then some personal accounts.
L.A. Times – Ron Lin, congratulations. You made the top 10.
High-five to you. Keep doing your great work.
USGS ShakeAlert – yeah, L.A. Times was three and then Ron, so I just count
you guys as one, so you guys got four of the top 10 tweets there.
So, well done there on social media.
And then personal accounts. Four out of the top 10 personal
accounts were linked to media stories. So their re-tweets were the most
re-tweeted, but they were just re-tweeting media stories.
Only one of the top re-tweeted tweets came from a top 10 personal account.
And four out of the top 12 tweets came from personal accounts
rather than organizational, media, and political accounts.
Which means that, while organizational, media, and political accounts are
really important, we can’t dismiss influencers and
social media influencers in the discourse here.
So what did we learn? So ShakeAlert is now live and in testing
mode in California with MyShake and QuakeAlert also available.
So we have different apps now than ShakeAlertLA.
ShakeAlerts are also available via wireless emergency alert.
And the ShakeAlert website has been updated and has held up
in a recent earthquake in Berkeley.
And ShakeAlertLA – and we have lowered the thresholds for public apps.
So I think that we learned the lessons really quickly.
And we were able to assess what the important lessons were and
make those changes internally. And I think that that’s a really
laudable thing for an organization to do so quickly.
So just conclusions. The communication of failure is
not always straightforward. And people see failure in many different
dimensions, and it’s not just about necessarily not receiving an alert,
but it’s really important about what information they receive afterwards.
And that they can find authoritative information to confirm their experience.
Media stories did talk about failure but also talked about thresholds.
So the media was very curious and wanted to report correctly
what had occurred and why.
And, you know, I think it’s important that failure is perceived more as
a lack of information in our communication ecosystem.
So diversity in our communication channels could improve this
outcome for future earthquakes. So, if one channel fails, people can
go to another channel to find the information that they’re looking for.
And, while – I found this really interesting, which I didn’t mention here,
but I thought was intriguing that lowering the thresholds was popular
on Twitter, but there was definitely dissenting opinions.
I found about 40 tweets that said, no, we shouldn’t
lower the thresholds. And they wanted to keep the thresholds.
And then media continues to have a really critical role in the
communication of ShakeAlert. You know, as they shift their platforms
from broadcast to social media, media is a main social media influencer.
And so getting messages out quickly to media, I think,
is a really critical thing to do.
So that’s the end of my presentation, so thank you, everyone, for listening.
[applause]
- Thanks, Sara, for exploring our failures there.
And finally, our last talk of the day, Jill Barnes on ShakeAlert
and disaster readiness in LAUSD.
- I’m not sure I’ll be able to switch this. - Oh, [inaudible].
- Because I’m so not a Mac person.
[silence]
- [inaudible] Yeah, that one.
[silence]
There we are.
So I’m not sure if I should be insulted by the fact that
I was preceded by failure. [laughter]
So hopefully I won’t give you that. [chuckles]
Just to give you a little bit of context on the LAUSD.
The Los Angeles Unified School District is the
second-largest school district in the country.
It’s the largest with an elected board. We have about 650,000 K-12 students.
We have 100,000 adult school students.
We have almost 100,000 preschool students.
We have 10 infant programs.
We have a couple of overnight programs.
And we’ve got about 80,000 employees.
So, all told, we have direct contact with about a million people within
Los Angeles – the Los Angeles area. And we cover multiple cities
in Los Angeles. I don’t know that there’s a more complicated
setup than L.A. We are one of 83 school districts in Los Angeles County.
So the L.A. Unified is not L.A. County schools, but we
cover more than Los Angeles city. We do have L.A. city, and we have
parts or all of 23 different other cities and unincorporated areas of the county.
We don’t do anything simply.
So we cover 710 square miles, and the number of schools – you’ll get
a different number depending on who you ask and what your criteria are.
It’s between 900 and 1,400, depending on how you’re asking. But many.
In general, when we’re thinking about disasters in school, we’re really
putting things into four buckets. And, within those buckets,
each bucket has two. So you’re looking at either –
you’re getting outside – you’re either getting outside to get away from
an emergency that’s inside, rather like we did today earlier.
Or we’re coming inside because the danger is outside.
We have 20 different types of emergencies in our plan.
I’m not going to go over all of this in detail.
There are several different laws that cover some of the disaster planning
and preparedness that we do have in schools, including earthquake drills.
And, for us, we plan for – we have three days of supplies at every
single one of our schools. Because we know that,
for K-12 schools, we care for those kids until each one of them
is reunited with a custodial parent.
So we know we’ll be in it for a while there with them.
So, in terms of ShakeAlert, we started with ShakeAlert with
a desktop beta test – beta testing of the desktop program at
Eagle Rock High School, which is – they’ve been a really great partner.
They have a very vibrant science program at the school and a
fantastically motivated principal. They’ve done a lot there.
So we started by putting the desktop version of the program into all of
the science classrooms and on the principal’s laptop as well.
And they used it in science lessons, and they would actually kind of
manually trigger the system so they could use it in drills as well.
And so we used that for a couple of years there.
We started that in 2015.
And then we actually were able to get them the device.
So most of our schools – we’re planning on doing it
through the PA systems because that’s a system we already have.
And actually, that’s generally where – if there’s an announcement made
about an earthquake, which is often generally when there’s –
the earthquake is actually occurring, it’s going to be the principal
on the PA system telling everyone to drop, cover, and hold on.
And that’s generally how we run our drills as well for earthquakes.
So the PA system is already being used in that way.
It’s also used – that’s how we announce lockdowns and shelter-in-place events.
So it’s already a system that we’re using to announce emergencies.
So it makes sense for us to do that. I know that there may be – as we move
further into technology, apparently some of the new fire alarm systems
have some vocal capabilities as well, and we’re just barely beginning
to explore whether or not we can do anything with
early warning with that as well.
So right now, what we’ve done is, we’ve taken the device – we’re working
with Early Warning Labs, and we have the device actually put into the PA
system at Eagle Rock High School. And it’s actually giving out the alerts.
And it can also be used for tests, which is wonderful because
that way the school uses it for all of its drills.
We have two more schools that we – that we will be putting the devices in.
Hopefully sooner rather than later.
And the other piece of this is we have about 50 schools that
have upgraded PA systems. And, because of those upgraded
PA systems, we would actually be able to do all of the ShakeAlert
software kind of over the air without even going to those school sites,
which makes it a lot simpler. Unfortunately, that’s only going to
cover about 50 of our schools. But we’ve lumped those 50 together
and put in an application with Cal OES for hazard mitigation grant funding.
But we haven’t yet received that funding.
So we’re kind of waiting on that one.
So one thing that I learned through this is that it helps to get –
I like to say yes to things. And I like to forge ahead.
There’s a lot on this that I can’t do myself.
I can’t go out there and do the technical components of it.
In terms of – this has a lot of different pieces in the district that it touches.
It’s not just our IT personnel. And it’s not just our maintenance
personnel. It’s a combination of those two departments, who generally
don’t do a lot of things collaboratively. Plus the emergency warning piece.
So it helps, when you’re pursuing this, to make sure that you’ve got all of
that support lined up ahead of time before you
start doing anything, which is great.
Which we do have, but it still is managing to proceed rather slowly.
So the thing that I really like about ShakeAlert is there
really is no downside. It’s really great for the life safety angle.
It gives us extra time. And that extra time is really important
for us with a couple of different groups. It’s very important for the kids who
use wheelchairs or have other devices or difficulties getting –
some of them need extra time. They can get under something to drop,
cover, and hold on, and it just takes them a little extra time.
Some of them cannot. The ones in earthquakes, then you
need to have – or, the ones in wheelchairs that needed to
have somebody roll them into a protective corner of the classroom.
And then that person needs to be able to drop, cover, and hold on.
So having that extra time comes in really handy.
There are also some kids with processing disabilities, where –
these are the kids who kind of freeze when you tell them something.
And they need a minute to kind of get over that before they
actually start moving. And ShakeAlert actually provides them that time so that
they actually get a chance to process – do their freeze, process, and then
they’re still able to drop, cover, and hold on before the shaking starts.
It really does have a really fantastic protective capability for those kids.
It’s also going to be really nice with our – with our younger ones,
with our preschoolers, with our infant programs because it gives us
a chance to do something for those kids and then have the adults that
are taking care of them also be able to have an adequate time to drop,
cover, and hold on for themselves. I think it’s a really great way
to look at resilience. I’m all about the resilience from the
social aspect of it, that community disaster resilience aspect of it.
I think that, in putting ShakeAlert in the schools – as we know,
kids tends to take the lessons that they’ve learned home with them.
If the warning tones are standardized, if the messaging is standardized,
if they hear that out in the community, they will then communicate
to their parents. If they know there’s an app, they may ask
their mom or dad to download it. If there’s translation that needs
to take place, the kids can kind of get on board with that.
By the time these kids grow up, then everybody will be used to it,
and it’ll be wonderful. So I think that it will do a lot to help us,
as a region, as the West Coast, to move forward by getting it into
more of our schools, hopefully. And it – the nice thing for me –
a lot of people have asked, well, you know, what are you
going to have to redo? You know, you’re going to have to do
all this training. You’re going to have to do all these things.
You’re going to have to really go out there and teach people different things.
Well, I don’t. Because it doesn’t change what we do.
What did we do when we heard the alert? We drop, cover, and hold on.
We’re doing the exact same action. We’re just doing it earlier.
Which is really, really nice because it makes – the learning is –
we’re that much further down the road with learning.
All I have to do, then, is update the procedures to make sure that it accounts
for having alerts or tones given through ShakeAlert, as well as, if you don’t –
if you don’t get those alerts, then if you, of course, then feel the shaking, then
you’ll still drop, cover, and hold on.
And then it’s really nice that we can use it for the drills as well.
Because they do drill with the drop, cover, and hold on every month.
So that will be a lot of practice with the –
with it that we’ll be experiencing.
So I spend a lot of my time making sure that our kids with disabilities
have the support that they need. And I’ve already mentioned a little bit
about that in terms of ShakeAlert. But we really do get a nice microcosm
of society going through our public schools.
And you’ll get kids with mobility issues, visual issues, the deaf and
hard of hearing, cognitive, autism spectrum, the medically fragile,
kids with emotional disabilities, medication-dependent,
and multiple disabilities. Because nobody ever says you get just one.
And, for most of these populations,
ShakeAlert does give them a little something extra in it.
One other thing. We do have a school for the deaf.
And it is a – it’s all taught in ASL, which means that the majority of the
staff that works at that school is also deaf because who’s fluent in ASL?
People who are deaf. So the majority of the staff is deaf.
All the students are deaf. They start their programs there
at 3 years old, and it goes through the age of 20.
So it’s a comprehensive K-12 program, and they have a very specialized –
they have a one-of-a-kind – I don’t even think it’s produced
anywhere else in the country from what I’ve heard – they have a very
unique – communication devices that are – that are throughout
the school that were developed specifically for them.
And I think it will be interesting to see how we can incorporate
ShakeAlert for that population as well. Because then we’ll be able to start
seeing the benefits of ShakeAlert in some of those specialized
communities as well. And that is short and sweet.
And I think I’m done with that.
[applause]
[inaudible]
[silence]
- Okay, great. All right. Do we have any questions for Jessie, Sara, and Jill?
- So a question for Jessie. What – did you ever look at what
the difference in – the improvement would be in all of the aspects if you
simply took the MMI 3 level for alerting instead of MMI 4 plus
sigma or plus two sigma? Because if you just say MMI 3-plus,
then you don’t have all of that stuff that’s MMI 3
and not quite MMI 4, but you still alerted it.
- Okay, so I just want to try to understand your question correctly.
So you’re saying that, instead of trying to expand the alert region
by incorporating uncertainty, we just lower the MMI threshold …
- Yeah. - … and expand it that way?
- Yeah. - Okay. So, yes, it does help.
For the – so incorporating one sigma of uncertainty ends up expanding the
alert region by close to 100 kilometers. And, with lowering the threshold
by one MMI unit, that also does approximately the same thing.
But our goal with what we were trying to do is we were trying
to improve the alert accuracy while keeping the MMI target the same.
So you can do both methods.
[silence]
- This question is for Sara.
I’m wondering – well, I know why I sometimes see people posting
on social media is to complain. And I’m wondering if you came across
a lot of tweets, or any tweets, of people saying that they were
happy with ShakeAlert? Or if it was all 100% negative.
[laughter]
Not necessarily 100% negative, but just …
- It is Twitter, right? - Exactly.
- Like, you know – you know what I’m working with.
- Right. - Right?
Yeah. Like, what I found really interesting was the discourse for
people who didn’t want to see the threshold lowered.
That liked only being alerted when they thought it was really serious.
And that was – you know, I found 30, 40 tweets that were,
like, hey, I don’t want to be – I don’t want to be aware of every little shake.
And I’m fine with that, so don’t lower the threshold right away.
And I found that really interesting. That wasn’t really a complaint.
And it wasn’t really, I really like the app the way that it is.
It was more, I don’t want to see this threshold change.
So I wouldn’t say that that wasn’t necessarily – it wasn’t necessarily
positive or negative. It was just people being happy
with what – you know, they liked the status quo
of only being alerted at certain levels of shaking.
- So, sort of on that note – this is kind of a question for both Jessie and Sara.
Other than people opining about that sort of thing, what kind of research
is going into whether or not there are benefits of the threshold
being higher or lower? You know, does it –
does it hurt people to hear about just minor shaking?
You know, at a lower – you know, intensity 3 instead of 4?
- Is this one for me? I mean, we are looking at the
different thresholds and why people like them the way that they do.
And a lot of that, we’re still kind of – we have a project of doing surveying
in Ridgecrest and L.A. about this very question and really unpacking
more what people think about the thresholds.
And what motivates personal desires or personal preferences
in terms of thresholds. I would only be speculating
at this point, which I love to do, and we can do later.
But maybe not in front of these people. [laughs]
- So I have maybe a follow-on question there. Thanks, Austin, for the lead-in.
To Jill, so you mentioned that ShakeAlert is only positive, and it will
give your students, and particularly maybe students who have a disability
or need a few extra moments to drop, cover, and hold on.
But, based on what Jessie presented and what Sara sort of just said,
you can expect some false alerts. So that you’ll get a ShakeAlert,
and the students maybe drop, cover, and hold on, and will not feel shaking.
So do you – can you speculate, I guess, on – or, from your experience,
particularly for students with disabilities for whom drop, covering,
and hold on might not be trivial, how do you – will that be a negative
experience, or what would you expect? Or maybe it’s just more practice.
They already do drills. - Yeah. I don’t anticipate it being
too much of a – of a big deal for the schools.
Because they do have to – they are required to do an earthquake drill
every month anyway. And, if they – if they do that drill
through an alert that doesn’t turn out to result in shaking, then, hey,
they can check that box that they’ve done that drill.
A little – a little extra practice isn’t a bad thing.
And it generally doesn’t take more than a couple of minutes, especially if we’re
just doing drop, cover, and hold on, and we’re not evacuating buildings.
It really only takes a couple of minutes in the classroom.
So it doesn’t have a large impact on instructional time, which is
what teachers and administrators are usually concerned about.
- [inaudible]
[laughter]
- [inaudible] the other side of the room.
- So, for Sara, you mentioned that there were some earthquake
prediction tweets. And it only recently came to my
attention, but there was actually, I gather, quite a well-known example
of Kern County Fire tweeting – and not only that, but Jennifer Lazo
pointed out that they also did a Nixle alert.
So that was an official agency tweet, and it was based on some wacky
something, I don’t know where, from California City Police.
You look like you know what this is about.
I just wanted to cue you up on that. - Yeah. Yeah. I actually do know
a little bit about what this – what you’re asking.
Yeah. Essentially, someone called – from what we can gather, because we
haven’t been able to really assess from the people who actually sent the tweet.
There’s been numerous attempts to talk to them back channel and find out
what’s going on – not from myself in particular, but from other – from other
emergency managers that do social media and researchers as well.
It looks like someone called in from a – pretending to be from the military base
saying that they had a prototype of some description that was –
equipment of some description that was telling them that
an earthquake was going to occur in 15 minutes.
And I can kind of emphasize with the Kern County officials because they –
you know, a new situation had arisen. There was a lot of stress.
They couldn’t – I mean, I don’t know all the details of the decision-making
process, but someone sounded authoritative saying that they had
special information, and they made the best decision they could at the time.
And I’m not going to stand up here and criticize any other agency
in terms of their decision-making process.
But I think what’s interesting is that we also had a very similar case
occur in Anchorage as well. So – and fortunately, that got stopped
before it went to sending out a message. But what was interesting and –
you know, I’m not a conspiracy theorist, either, but what was interesting was,
is that the messages were quite similar and claimed to come from
official sources or military institutions.
So there is – there is a – there is more to unpack here.
And I know a couple of researchers who are looking at this piece
of misinformation. And hopefully they will
have their articles and research published soon.
[silence]
- So this is kind of jointly for all three of you all, I guess,
back to the haters-gonna-hate question.
So, short of having to launch these massive social science surveys,
where you go, post-earthquake, and have a specific question about
thresholding or asking a school district, what is your specific, you know,
response to a false alert, because social media and a lot of the traditional ways
that we can information from the public is very biased towards haters gonna
hate, you know, and you don’t get a whole lot of people taking the time
while they’ve, you know, experienced something, and they’re, like, yay, you
know, we’re happy with the status quo. How can we, as ShakeAlert or,
you know, users of the system actually probe what’s happening?
How do we, you know, not get bogged down in the Google Play, you know,
responses or tweetstorms or a random article in the small village gazette?
You know, how do we actually probe what people want?
Because individual surveys, while important and gives you
empirical evidence, is not something we can do on a monthly basis.
- Crickets. [laughter]
- I mean, I don’t really have an answer to that question, but perhaps one way
that we could pursue that that’s not sending out separate surveys, why not
just add a couple of extra questions to, say, the Did You Feel It? survey?
And have that be some ShakeAlert- specific questions that don’t necessarily
go into say, the estimations for MMI, but then perhaps can give us some of
the answers that we might want in terms of, did you feel strong shaking and,
like, did you get a ShakeAlert, or would you want to.
And maybe that could be one way that we get those answers that
don’t necessarily require mobilizing a bunch of people to survey
everybody every time that there’s an earthquake.
- So, yeah, we’re actually already doing that.
So we’re in the process of developing questions for Did You Feel It? with
Dave Wald. He’s kindly said, hey, we would like to add a few questions.
So we’re working with him on what the most appropriate questions are.
Because we agree. Like, collecting data on a regular basis and
making it a part of a process the public is already – some people in the public are
already familiar with, I think, is a really good thing to do.
I mean, I’m an advocate for doing social science research.
The other thing that we’re looking at doing is analyzing human behavior
via CCTV footage. So we watch what people actually do.
Because I think understanding human behavior, versus what
they tell us they did, which is – which is fantastic to learn.
People are, like, yeah, of course, I dropped, covered, and held on,
and I grabbed my whole family. And actually, they, you know,
ran somewhere else.
So analyzing people’s actually behavior is another way that
we’re collecting sort of more latent data that doesn’t require asking people
necessarily their perceptions. Because I love that question,
Jen, about haters gonna hate. Of course, in Twitter, you’re going to
get people who are going to be wanting to be funny, wanting to
make a statement. I think that that asking for
2 million friends tweet was, like, re-tweeted 500 times,
and that person was really happy and then posted their sound cloud.
[laughter]
You know, so there is that whole thing of, if I say something quite funny
and complain-y on Twitter, I will get recognition.
And so you have to bear that in mind when you are doing social media
analysis is that people are making a comment because they want to be seen,
not necessarily because this is actually what they feel or what they perceive.
And surveys tell you a certain piece of information, but they tell you
what people want you to think they think they think.
So I’ll just let you guys think about that. [laughter]
And then, you know, we also have focus groups.
And I could go through all the methods of – the many methods of social science
if we want to, but I’m sure we have other things to talk about today.
But, yes, so we are looking at doing that, and we’ve got other
social science projects on the way to explore more
about people’s perceptions on ShakeAlert.
- All very fascinating. Are the thresholds based on
what people feel outside?
And do you know when you get false readings if people are inside or outside?
It makes quite a difference if you’re in a multi-story building.
- So, right now, ShakeAlert does not tailor the alerts based on the type of
building that someone might be in. So right now, it’s just a pretty simple –
we just use the magnitude and epicenter estimate of the earthquake and
then also some finite fault information. and then it’s just one map – like, one
very simple ShakeMap, essentially. And then we use that to define the
alert region. And, for MMI 4 shaking, which is our minimum threshold target
that we’re trying to alert for, that’s felt by pretty much everybody, both
inside buildings and outside as well. But it isn’t a level of damaging shaking.
- I appreciate that. - Yeah.
- Just interested, if somebody reports that they feel shaking, and they’re in a
four-story building, the people on the ground will not feel it [inaudible].
- Yeah. So in – I know in the Did You Feel It? surveys, they ask about whether
or not you were inside a building or outside, but I don’t think they ask about
the story of building that you were on. So that information is not something
that we use to tease out the MMI. - Might be something to keep in mind.
- Yeah. - It is radically different.
- Yeah. For sure.
[silence]
- Question is for Jessie. I haven’t really used ShakeAlert,
but is there any follow-up at all? An alert goes out, and then does
anything else go out immediately? Say within a minute or so
to let people know? And also, given that we have
instruments everywhere, is there any feedback from the system
whereby the strong ground motion instruments can then be queried
to see where things are really shaking?
- So, to answer the last part of your question first, for the PLUM earthquake
early warning algorithm that’s in development – so this is not
in operation for ShakeAlert, it’s essentially real-time monitoring of
the ground shaking that’s happening. So PLUM would potentially capture
some of the non-uniform behavior that we would expect to see.
Whereas, for the ShakeAlert algorithms that are in operation, their main
objective is to rapidly characterize the earthquake.
So they’re not – so those algorithms aren’t necessarily adding additional
complexity, such as, for example, basin amplification in L.A.,
were that to come to play. And then, when an alert is issued –
Sara might know more about this – I don’t believe there’s any follow-up
messaging that’s happening yet.
So, Sara, do you want to answer that? [laughs]
- Yeah, we’ve actually been developing post-alert messaging for about
18 months. Because we know that it’s critical that people receive information
very quickly after they receive an alert. And so we have templated WEA
messages and templated media releases for successful alerts and false alerts
and missed alerts at the moment. And then we can make modifications
as we learn more about the earthquake behavior, particularly
in the media template. Because, yeah, as we saw in the failure
to alert, it wasn’t just that people didn’t receive an alert.
It was that they didn’t receive any information afterwards.
So post-alert messaging is a really critical part of the project, and we’re
in the process of finalizing that development.
- So it only goes to the media? It didn’t – why not just send out the
same message again with [inaudible] … - No, we do – I said that we had WEA –
wireless emergency alert messages that do explain a bit of that.
The complexity with that is that we’re trying to work in
a 90-character message. Now, we have – we do know that
the FCC has expanded some of the character allowances, but it is quite
complex to explain why people might not have received an alert or why
they did receive an alert in a 90 – in a 90-character limit.
And that’s been part of it, but we do have some in draft, so when
people do receive a WEA, and it’s quite a big area, we’re
prepared to say something via wireless emergency alerts, if required.
- [laughs] Hi. I just had a question about whether or not there’s been
a comparison with the National Weather Service’s, for example,
tornado alerts and similar weather alerts.
Obviously, that’s probably a lot higher probability, but people are probably
going to tweet about those, too, and, you know, have similar responses
about whether or not – well, probably want to know about a tornado just
about – like a big earthquake. - Yeah, so to develop the post-alert
messaging, we took a lot of – we took a real look at rapid-onset
hazard warning systems. And tornadoes were definitely one of them.
And so we looked at the messaging on there.
And there is – there are differences. There are fundamental differences
in behaviors of the systems and timeframes.
But we did use some of the messaging, particularly around the shortness and
the directiveness around the messaging to develop these messages.
We looked at lahar warning systems. We looked at tornadoes.
We looked at flash flooding messages. So we looked at all of these hazards.
We also looked at other earthquake early warning messages, you know,
in Japan and Mexico – well, Mexico City has the sirens, among other things.
So, yeah, we did take a pretty broad look at the
different hazards and their messaging.
- Do we have one more quick question from anyone?
- Am I doing this right? This is a little bit of a follow-up
on Rufus’ question because I think I didn’t quite understand the answer.
If you have – you talked about the follow-up messaging being –
it sounded to me, with respect to, did the alert work well for you,
and that’s what you follow up. Like, oh, you got a false alert.
Oh, we forgot to give you an alert.
But, in one of your slides, you said something about –
you showed that they wanted follow-up information.
And is there a – does this happen, or is it in the works to have a follow-up
tweet to the actual alert with information about the event –
where was it, actually, and the kinds of things that people –
that, well, for example, I immediately go to my computer and look up
all kinds of things. But is that kind of follow-up stuff?
- Yeah. So we actually have our own Twitter account – USGS_ShakeAlert.
I recommend everyone follow it. Because it makes Bob de Groot really
happy when he sees numbers go up. And he’s, like, hey, Sara, we got
95,000 – we got 9,500 followers today.
And that makes me happy when he’s happy.
And that’s actually the account to follow that we’ll be updating
when people receive an alert. And we do post follow-up information
about what occurred and where to go to get more information.
And you can also look at the USGS websites as well.
It’s unlikely we’ll post that in a WEA because a WEA is a very specific
type of public safety message, and there are very specific constraints regarding
wireless emergency alerts. And you can’t just send out wireless
emergency alerts for public information and follow-up information.
You need to – it needs to be very directive and very specific
to people’s situations. So there is limitations in terms of the
purpose of wireless emergency alerts. But, in terms of social media and
Twitter, yeah, we’ve been definitely developing that
and in the media templates as well.
- I just wanted to say something too. Since I didn’t help develop it,
I can say this. The MyShake cellphone app does provide
follow-up information. So if you get an alert on MyShake,
there’s – [inaudible] to have it right now, you can follow the link to learn
more about the earthquake, go to the USGS website and get the definitive
solution and ShakeMaps and all those sort of things for that event.
So there is – depends on the app, but at least that one does have follow-up.
- Thanks. This is great discussion, but I’m mindful that we’ve reached 4:30.
Sarah, did you want to say – I’m happy for the discussion to go on, but did you
have any closing messages [laughs] if people do want to sneak out?
- Good night, everyone. Drive safe. See you in the morning?
[laughter]
- Great. [laughs]
[applause]
- Thanks to our speakers again. Thank you.
[silence]