[inaudible background conversations]
Hi, everyone. Hi. We’re going to go ahead and get started.
Thank you all for coming here. Just a few quick announcements.
We will be having another seminar tomorrow.
Our speaker is going to be Sam Johnson.
He’s going to be talking about the northern San Andreas Fault.
And this is a co-hosted seminar with GeoMAC, and it’s going to be at 11:00,
not at 10:30, for anyone who wants to join us again tomorrow.
All right. I would like to introduce our speaker for today.
Gitanjali Bhattacharjee has a bachelor’s of science in architectural engineering
and a bachelor’s degree in liberal arts from the University of …
- Texas. - Texas. Yes. Thank you.
And she’s got her master’s degree in structural engineering, and she has just
finished her first year at Stanford at the Urban Resilience Group with
Jack Baker, who spoke with us – who spoke here a few weeks ago.
And, without, further ado, Gitanjali, please. Thank you.
- Good morning, everyone. Thank you for having me.
Thank you for the invitation to come talk about some work that I’ve done.
So my main work actually focuses more on infrastructure management.
This is just some exciting stuff that I’ve been lucky enough
to get involved with at my time at Stanford.
And specifically, I’ll be talking about developing a better understanding
of post-earthquake building damage information needs and use.
So, to give you some context for how this work came about,
I got involved with some research in crowdsourcing building damage
assessments after earthquakes. And, as part of that larger research
effort, we talked a lot about previous efforts to crowdsource
building damage assessments, and so we talked a lot about
the response to the 2010 earthquake in Haiti.
And what was notable – well, there were many notable things about
that earthquake, but GEO-CAN was initialized specifically after that
earthquake to crowdsource building damage assessments.
And the crowd in that case was a group of expert volunteers. They were
structural engineers, people with lots of relevant experience to the task at hand.
And they were looking at satellite images and trying to assess the
damage to individual buildings. And the whole goal of this massive
GEO-CAN effort was to inform a very specific document called
the Post-Disaster Needs Assessment, or PDNA for short.
And the Post-Disaster Needs Assessment is a document that the government of
the affected country will put together after a disaster in order to, first of all,
take stock of the losses from a disaster to plan for recovery and reconstruction,
and very crucially, it’s the basis upon which they make requests for
financial assistance to other governments around the world.
So GEO-CAN was really trying to inform
the loss estimates that were going to be in the PDNA.
And if you look at what actually gets tabulated in the PDNA,
it looks something like this. So this table is actually from the
2010 Haiti earthquake PDNA. And you can see it’s pretty simple.
You have a number of damaged houses, the corresponding dollar value,
a number of destroyed houses, the corresponding dollar value.
And, at the end, you come up with a number – about $2.3 billion of damage.
When we were talking to people involved in putting this document together,
they really emphasized that they just needed broad regional estimates
of building damage. They didn’t need individual building damage assessments,
which is what GEO-CAN had been working so hard to provide.
And so we saw that there was a pretty clear mismatch between the enormous
very generous effort that these volunteers in GEO-CAN were
putting forth and the main end user’s information needs.
And so, as a group, we started thinking that there’s been a lot of work –
very valuable, exciting, and worthwhile work done to advance building damage
assessment techniques, but we have a comparatively limited understanding
of how responders actually use and need building damage information.
And why does that matter? Well, first of all, it limits the ability
of organizations to respond effectively to disaster.
Not having a common understanding of information needs can also limit
the ability of organizations to effectively share information.
And, in a broader sense, if you think about the bigger
disaster community, it hampers the ability of
the research community to address practitioners’ needs.
One of the people that we talked to gave us a really concrete example
of why having an understanding of information needs is important.
She said, when damage assessment was ongoing on the ground after
this 2014 event, there were apparently 14 different surveys. I mean, they were
the same surveys, asking the same question, doing the same damage
assessment on the same house, but 14 times by different organizations.
That’s an incredible amount of effort to get at one thing.
So what makes understanding information needs difficult in the first place?
I think perhaps the first two bullet points are very self-explanatory,
but maybe they’re worth repeating. Post-disaster contexts are very chaotic.
And no two disaster responses are the same.
So it’s very hard to come up with hard-and-fast rules about
who needs what, when, and how. But previous work in trying to
understand information needs has also found that, even if you were able to get
expert practitioners in a room together talking about their work, they find it
very difficult to actually enumerate the information that they use and need.
So more recently, some researchers have called to shift the focus from
information needs to how information informs specific activities or tasks.
So, in one paper, Tapia and Moore wrote, while improved information
quality and sharing are noble goals, the real aim is to improve relief services.
In another paper, Lallemant et al. wrote, damage assessments are only relevant
in their relation to the response and recovery activities that they enable.
And so we, as a group, were thinking, how can we reframe our question
so that it’s more tractable to answer?
And one way we thought we could do that is to ask, how does building
damage information enable disaster responders to carry out their work?
So the objectives of the work that I’m going to talk about today are as follows.
First of all, we simply wanted to identify post-disaster tasks
that rely on building damage information.
We’re certainly not trying to identify all of them, but at least make a start.
Then our second objective was to describe each task and to do so
according to the people involved in carrying it out according to the
particular building damage information that they require.
And also according to the characteristics of the building
damage information that make it actionable for those users.
So the first half of this talk is really going to focus on the first two bullet
points, which are information needs. And then, in the second half of the talk,
I’m going to focus on information uses and articulate how organizations
actually use the building damage information that they say they need.
So, to give you an idea of our research design, it was pretty straightforward.
The first part involved doing a survey of expert practitioners.
And I’ll give you a better idea of who those are in a second.
To do that survey, we first did 11 in-depth interviews
with those expert practitioners.
We also disseminated a short online survey that got 12 responses.
And then, in the second step, we wanted to develop a framework
that would help us understand and contextualize responders’
building damage information needs.
So, to give you a sense of the people that we were talking to, they worked
in quite a few different capacities. Those included disaster risk
management reduction and financing, urban search and rescue,
emergency mapping, disaster loss estimation,
and also information management and GIS support.
They worked at organizations including the United Nations Office for the
Coordination of Humanitarian Affairs, the American Red Cross,
the World Bank, Pacific Community, ITHACA, the Humanitarian
OpenStreetMap Team, and then we had a couple representatives
from urban search and rescue organizations from the Netherlands
and also from the International Search and Rescue Advisory Group,
which is part of the U.N.
They also had varied backgrounds – seismology,
earthquake structural engineering, economics, geomatics,
marine science, and hazard and risk assessment.
In terms of their practical experience, seven of our 11 interviewees had
been involved in the response to the 2015 earthquake in Nepal.
And five of the 11 had been involved in the 2010 Haiti earthquake response.
And then, smaller numbers had been involved in earthquakes and other
disasters going back to the 2004 Al Hoceima earthquake in
Morocco and most recently in the Amatrice earthquakes in Italy, 2016.
So the interviews that we conducted were semi-structured.
First we wanted to get a sense of who the people are that we were talking to.
So, for example, we might ask, what were your major responsibilities?
Can you give me examples of the types of work that you
were doing, and who were you working with?
But we also really wanted to drill down into the nitty-gritty of the particular
building damage information they were using as the basis for decisions.
We also wanted to know, if they didn’t get to use building damage data,
would they have liked to, and how would that information
have been useful for their work?
So these semi-structured interviews, we used these questions as a guiding basis.
But we really let the conversation go where they directed it.
So our interviews indicated six particular tasks that rely heavily
on building damage information. They are urban search and rescue
operations, which focus on identifying live victims of
building collapse and extracting them from those buildings.
Then the U.N. Office for the Coordination of Humanitarian Affairs
will put together a Multi-cluster Initial Rapid Assessment, called the MIRA.
And that happens soon after a disaster.
The World Bank will also put together a Rapid Impact Assessment,
which is primarily for their internal use.
There’s, of course, the Post-Disaster Needs Assessment,
which I described earlier, that the government of the affected country
will put together in partnership with the
World Bank and other development partners.
Then, in the longer term, the government of the affected country may
offer some housing recovery support to people affected by the disaster.
And then, even longer term, different groups may be interested in developing
and calibrating fragility or loss functions using data from a particular event.
Now, we found these tasks, and listing them is fine, but we really wanted to
contextualize them and give them some relationships to each other.
So we came up with this very simple framework that I will describe here.
It’s basically a plot, and on the X axis, we’re looking at the time elapsed
since the disaster. On the Y axis, we have the operational
spatial resolution of the underlying building damage information.
By operational spatial resolution, I just mean the minimum
required spatial resolution for that information to be useful
to the people carrying out the task.
So we’re looking at time going from response
all the way through recovery and reconstruction.
And then, in terms of spatial resolutions, sort of the least precise would be
having aggregated information of that building damage at the national level
all the way down to really detailed building-level damage information.
And so we can start to situate the tasks that we found in this framework.
So urban search and rescue operations happen – they can start happening
within six hours of an event anywhere in the world.
And those operations continue, at maximum, to about 10 days
after the onset of the event. And they really rely on very detailed
building-level damage information. They need to know construction
details that might be specific to the particular country that they’re in.
And that may affect the likelihood of people surviving if buildings collapse.
At the same time, the World Bank will start putting together its
Rapid Impact Assessment, which is sort of a coarse first-order
look at the losses that come out of a particular event.
And that’s primarily for internal use but is also
used to check government statistics.
Then, as I mentioned, the MIRA, or the Multi-cluster Initial Rapid Assessment,
is put together at the same time as these other assessments.
And it really focuses on humanitarian needs.
So what are the food, water, and shelter needs of people
who have been affected by an event?
And they really rely on city-level building damage information.
In contrast to the Rapid Impact Assessment,
which only really needs regional information.
As we move through the recovery timeline, the PDNA starts about
two months after an earthquake or other disaster.
And that’s put together by the national government in partnership with
many development partners. And, again, they rely on regional
building-level – sorry – regional building damage information.
And then, as we go on, housing recovery support that might be offered
by the national government and other development partners
will require really detailed information about the damage
to specific buildings as well as property ownership.
And, to calibrate fragility and loss functions, you might even
need component-level damage information
that’s more detailed than building-level damage information.
So now that we’ve plotted all of these tasks that rely on building damage
information, what does that help us see that we couldn’t see before?
Well, first of all, you’ve noticed that the tails on these different numbered
bubbles have different lengths, and that indicates the task duration.
So all of these tasks take some amount of time, and different information
will be useful to different people at different times.
So this helps us see, for example, if you come into – if you happen to
have some regional damage information,
it might be useful to different people at different times.
We can also see that there are many opportunities for information
sharing that might not have been taken advantage of.
So different groups may need the same building-level damage information –
so information gathered earlier by urban search and rescue teams
might be useful to housing recovery support and calibrating fragility
parameters in later stages of the recovery timeline.
And we can also see that there are really two spatial precisions at which
building damage information is needed, primarily.
There’s highly detailed building-level damage information that’s required for
different operations, and then there’s regional estimates of building damage.
Anything in between is less useful to different groups.
We can also get a sense of who’s involved in all of these different tasks
that rely on building damage information.
And what becomes clear very quickly is that the national government happens
to be involved in nearly all of these activities that have to do with buildings.
And so it’s important, as other researchers have pointed out, to support
them getting back up to capacity if they’re affected by the disaster as well.
So what we can see from this first part on responders’ information needs is that
building damage information needs among responders vary by operational
spatial precision and also by the time at which information is required.
But by doing this work to link building damage information to the responders
and the tasks that it supports, we can highlight strategic opportunities
for research for information sharing and other partnerships.
We also a Blume Report that includes more information on this if you’re
interested. And, of course, I’m happy to take questions about that as well.
We also wanted to look beyond frameworks to information use.
So what happened after we came up with this framework and analyzed
what we could from it, is that we saw it really captures the availability
and the suitability of information for users’ needs.
But when we went back and looked at all the conversations we had with
practitioners, they had so many stories that seemed to suggest there are more
factors that influence whether or not they use building damage information.
And so we wanted to take account of those stories and anecdotes that they
had provided, and so we conducted a qualitative analysis of the
interviews and the survey data to better understand responders’
information use as distinct from their information needs.
And in doing that qualitative analysis, we identified some other factors that
tend to influence responders’ use of building damage information.
And these are impediments to information sharing,
implications of information adoption, varying understandings of disasters,
and their attitudes toward emerging technologies.
So I want to address each of these and give you a sense of what they mean just
by sharing some of the anecdotes that we gathered from expert practitioners.
So when it comes to impediments to information sharing, we found that there
are really three issues that were present in our responders’ experiences.
And those were issues of semantic interoperability,
systemic disincentives to sharing information,
and a lack of feedback from information users to information producers.
So semantic interoperability we’ll define as the ability to share information
without creating ambiguities in what it means.
And something that happens after an earthquake, for example,
is that different responders will tag buildings according to different
schemes to indicate their status. So urban search and rescue teams
will tag buildings to indicate whether they’re safe for urban search and
rescuers to enter to rescue people. And building damage assessment teams
will tag buildings to indicate whether they’re safe to reoccupy.
Something that one of our interviewees mentioned in Haiti was that there was
a misinterpretation, and that was that the color coding for safety
for the purpose of rescue was interpreted as the color coding for viability.
Those things are, in fact, very different and should not be confused.
Another very interesting anecdote we heard from the urban search
and rescue team leaders that we talked to is about systemic disincentives
to sharing information. So, after an earthquake or other disaster happens,
multiple urban search and rescue teams may deploy to the affected region.
And each of them is designed to be a self-sufficient unit.
And so they have to have a set of detailed coordination guidelines.
And they do. A key part of those coordination guidelines
is tagging buildings. So here at the bottom of the slide,
I’ve included a couple of examples of what those tags might look like.
So, on the left, you can see they’re in fluorescent ink.
They indicate some dates when work is happening.
At the top, they might indicate some hazards.
On the left, we have asbestos. On the right, we’ve got chemicals,
gases, and rats. So that’s important for them – for their teams to know.
And if you look at the tag on the right, at the bottom, you’ll see a count.
It says, 12, question mark, two dead in elevator.
And so teams are supposed to note the potential for rescuing
live victims from buildings.
And urban search and rescue teams also have multiple other information-sharing
mechanisms. They have a virtual coordination platform.
They have emails. They share paper forms.
They do phone calls. And they have coordination meetings.
So there’s no shortage of mechanisms for them to share information.
But when we were talking with a USAR team leader, he listed all of those
coordination and information-sharing mechanisms, and then he sort of took
a step back and said, well, there’s another one, and that is,
you don’t share information. And, of course, we were curious.
Why would you not share information if there are lives potentially at risk?
And he sort of went on to say, because information is valuable.
The moment you go to another country and you try to save people,
it’s very important that you save somebody because it’s an
operation that will cost a lot of money. And we all have sponsors.
So he wanted to explain that there are teams who will occasionally
not tag buildings or share information about locations of potential victims
or locations where there have been confirmed fatalities because they
don’t want other teams to get a head start in finding live people.
And that’s because there are external pressures to do with
how these teams are funded that disincentivize information sharing.
Even though they have so many coordination mechanisms.
A less dramatic example, perhaps, is just having a lack of feedback
from the people using your information.
So we talked to people who do emergency mapping in the wake
of disasters. And one of them noted, well, there’s a mechanism of a feedback
form in the Copernicus Emergency Mapping Service.
But it’s quite difficult to have details in how they actually use that analysis.
There’s no direct relationship between the people who are doing the
emergency mapping and the people using the emergency maps.
And this lack of feedback was something he cited as
frustrating and that could inhibit improvements to the
production of building damage information.
Another theme that we noticed is that information adoption has certain
implications. Using information is not neutral, in other words.
So if you choose to use a piece of information, in some situations,
you’re actually sending a signal of trust to the person who’s providing
you that information – a signal that you trust their version of events
and you trust what they have to say.
And, in some cases, you might want, as a responder, to send that signal.
You might want to establish or maintain or even build a good
working relationship with the people who are giving you information.
And we sort of noticed this because of two very different behaviors
that came up in our interviews. One was by members of a World Bank
team who are working with the government of the affected country
long-term. So they work together on the PDNA and then on efforts to recover
and reconstruct in the affected country. And then we also noticed a very
different behavior from urban search and rescue teams.
Now, they have very time-limited deployments to the affected countries,
which means they have different relationships with the governments
of those countries. And so, when we were talking
to the World Bank employee about how they used building damage information,
they said, well, unfortunately, in the context of PDNAs or
rapid assessments that we do, we have to use the government
report that they send. They have their own way of
collecting data on the ground, although it’s not very good.
This stands in really dramatic contrast to how the urban search and rescue team
leader talked about his team’s deployment after the 2015 Nepal
earthquake. He said, initially the government had sent them to a
particular area, or said that they should go there.
And he said, in the end, we sent a reconnaissance party to that area,
just to discover that the government really was telling us the truth, but we
had so much information coming from other people who said you have to go to
these other places, we said, well, let’s just confirm and see if it’s really true.
So these examples show, first of all, that the relationships between the
urban search and rescue teams and World Bank teams and the
government are very different. And they’re interested in sending
different signals to the government. So the World Bank has that
long-lasting relationship. It’s important to build that relationship.
And so signaling trust by using information, even if you consider it
substandard, may be in your interest for other reasons.
If you’re a urban search and rescue team leader, and you’re going to be out of
the country within a week, you really don’t care, perhaps,
about your relationship with the government of the affected country.
You don’t need to signal that you trust them.
You can operate in a very different manner.
Another theme that emerged from our analysis is that different responders
have really varying understandings of the same disaster.
And those varying understandings stem from things like language differences,
from the different organizational mandates that they carry out,
and from the differences in their roles or in their backgrounds.
So something that came up when we were talking to a loss estimation
specialist was that the languages responders speak may actually shape
their understanding of how a disaster happened or unfolded.
So he noted, after the Nepal earthquake – the one in 2015 –
he said, all the information was actually coming out.
It was just all in Nepalese. So I’m afraid millions of different
papers said it took the government four days or five days to get around to
bringing out loss estimates, but that’s not correct.
And so here he’s showing that many people’s understanding of what was
happening after the Nepal earthquake was actually not accurate
because they didn’t take into account that
the information might be in a different language.
He also made a note about language barriers that can influence the building
damage data to which responders actually have access.
So he said, in most countries, 90% of the best data is in their local language.
So, in China, you don’t search in English.
If you want a Chinese vulnerability function, if you want Chinese
damage data, you search in Chinese. He made the point that not many people
think to do that when they’re putting together loss estimates.
In addition, how responders actually understand building damage information
and act upon it can differ pretty significantly according to the particular
organization that they work for and the mission of that organization.
So the person that we were talking to here worked on doing building
damage assessments after the 2010 earthquake in Haiti.
Those were structural damage estimates.
She was working on doing those assessments to inform the PDNA
and not for humanitarian purposes. And she was reflecting on her
experience, saying, you also get an idea of what’s left, and that’s really
important because that’s your elasticity. That’s where you can absorb people
into rental shared housing, temporary housing, into what’s left.
And we never accounted for that in Haiti.
We were just, like, oh my god, all of the stuff’s been damaged.
We didn’t say what’s left, and where is it, and how can we
open that up so people can use that housing?
That, of course, would have been really valuable from a humanitarian
needs perspective, but she didn’t think of it at the time because
her organizational mandate was completely different.
And then, people’s different roles and backgrounds can also really
influence how they receive information that’s packaged differently.
One person we were talking to – an information manager – sort of summed
it up, saying, maps, figures, different people take data in different ways.
A disaster reduction specialist reflected a little bit more, saying, showing a
disaster manager a map, it’s not going to enthuse them if they just want numbers.
And sometimes these numbers are something as simple as a table that
shows this is what’s been impacted and the needs are then this.
So those different roles and backgrounds can really impact
how people react to information that’s packaged in different ways.
And then the final theme that emerged really strongly from the qualitative
analysis that we did is that the responders all have different attitudes
toward emerging technologies. Previous work looking at responders’
attitudes toward emerging technologies has stated that they tend to be reluctant
to adopt those technologies if they’re not problem-free at the outset or if, to do so,
they would be replacing tried-and-true solutions – methods that they’ve used
in the past that have worked before. But we found out something different.
We found that responders actually have a really wide range of attitudes
toward emerging technologies. And those attitudes range from,
like, real pessimism toward extant technologies to real optimism about
technologies with which they have no operational experience or that
might not even exist yet. We also found that they tend to
use emerging technologies in tandem with tried-and-true methods.
They don’t replace those tried-and-true methods.
And we also have this sort of conditional optimism about
emerging technologies that I’ll describe in a minute.
So one way to illustrate this – we asked a lot of people about their thoughts on
using drone imagery of buildings. And we got many different responses.
So the last estimation specialist we talked to was really enthused
about drone imagery. He said, you can get that 3D image.
You can get a lot better idea of the damage sites in the location.
When we talked to someone who works in humanitarian needs
assessments, he was not at all thrilled. He said, there are too many other things
flying around in the air, then trying to do an aerial imagery assessment,
that could be done faster by getting in a Jeep and driving 30 miles an hour.
We also talked to the urban search and rescue team leaders about how they
might use drone imagery to inform their operations, and they didn’t
really see it as useful at all. They said it can’t tell me specific
information about which building is probably a possible rescue situation.
Some interviews also showed really great optimism towards untested
technologies. So when we were talking to the urban search and rescue team
leader, we asked whether he thought about using social media scraping.
And he got really excited about the potential of that.
So he said, so if you can take – if you can actually make a platform
where you can analyze these tweets and pick a place.
We see so many hits of people sending us tweets or sending information into
the world that there really are a lot of victims, you can use that as a source.
What was interesting about his response was that he didn’t really address
what this platform would be. He seemed to view it as a black box
solution, and he didn’t talk about how it would address the problems
that he had found with other – interacting with people even on
the ground, and their information not being reliable.
Then we talked to someone else who was working in damage assessment.
We didn’t ask about virtual reality, but she was very excited to talk about it.
She said, I threw around ideas with people when I came back about some
kind of virtual reality – some kind of tool which would allow anyone with a
smartphone to take a useful, informative photograph to start to get images in the
locations that you need to know to help inform the guidance that you’re going to
give people who are going to rebuild.
So she seemed very enthused about the potential of this emerging technology.
Really optimistic about creating a tool that harnessed virtual reality
for rebuilding, but didn’t have any operational experience with that.
And then we saw some more tempered reactions to emerging technologies.
We found evidence that people are already using emerging technologies
as a kind of check on their tried-and-true solutions.
So one loss estimation specialist said of drone satellite imagery that,
we actually just check against what we see on the ground to
see if their estimates are reasonable. And then other interviewees –
multiple ones emphasized the importance of new information
products regardless of the emerging technologies on which they are based.
They should fit into existing workflows. So we threw around a few ideas about
crowdsourced building damage assessments, and the reaction
was pretty much, well, if you could add that into
our tools chain, that would be really, really useful.
So in terms of responders’ information use, what we found is that their use of
building damage information depends on many factors that can’t be captured
by simple availability and suitability of the information for their work.
Those factors, again, are impediments to information sharing, implications
of information adoption, their varying understandings of disasters,
and also their attitudes toward emerging technologies.
To wrap up, I’d just like to acknowledge the many people that have contributed
to this work – Robert Soden, David Lallemant, Karen Barns, and
Sabine Loos contributed to the work. Greg Deierlein, Jack Baker, and
Anne Kiremidjian have been advisers on this project.
And then we’ve had great collaborations with the University of
Colorado-Boulder, the Humanitarian OpenStreetMap Team, the University
of Heidelberg’s GIScience Research Group, and also the
World Bank Global Facility for Disaster Risk Reduction.
And this work was sponsored by the National Science Foundation.
And I’m happy to take any questions that
you might have about the presentation.
[Applause]
- That was – that was [inaudible].
That was a great talk. Thank you so much.
Does anyone have any questions for Gitanjali?
I know Anne will have many questions. Hold on.
- You can go to someone else [inaudible].
- No, no. Does anyone else – before Anne?
Because she’s going to ask a lot of questions.
- No, no, no. Just ask one. - Just ask one. Really?
- Just for now. - Okay.
- Can you go back to your slide with the graph – the figure of time
and building resolution? Yeah. That was interesting.
So I wondered if you missed out the block- and city-level uses of
the information because of the people you spoke to.
So you’ve got national, mostly. Whereas, a lot of recovery decisions
are made at the local level. And did you tap in – did you have
any people that were – that were representing that role in your sample?
- We had not many people representing work that’s done during the recovery
process, so that’s entirely possible that we didn’t represent those tasks well.
- Yeah. Anyway, that would be my hypothesis on that because I think,
in general, the concentration of damage – the pattern of damage
in an area on buildings is quite important for how you recover.
- Okay. - Yeah.
- Okay. Thank you.
- I have a couple of questions. For those of us that aren’t
qualitative researchers, can you please walk through your methodology a little
bit more and explain the method? - Sure.
Let me grab a slide on this.
Sure. So we did a qualitative analysis. What that meant in practice is that
we got all of the interviews that we’ve done transcribed.
We sat down and read them multiple times.
And we tried to see what themes were emerging from the interviews
about how people were specifically using and interacting with building
damage information. So that was a process of inductive coding.
And that process, which had many, many iterations,
led us to identify those four codes in particular.
- Great. - Does that answer your question?
- Any other questions for Gitanjali?
[Silence]
All right. Well, we might end the seminar a little bit early today.
Before we say goodbye, I just want to have everyone thank Susan Garcia for
helping us set up on – every week. So thanks, Susan, for all your hard work.
[Applause]
And we’re going to go and take Gitanjali out to lunch now
in the café on campus. Anyone is welcome to join us.
And thank you all for coming, and hopefully we’ll see you
here again tomorrow. Thanks.
[Applause]
[inaudible conversations]