Using data to bridge sciencepublic divide
[Music]
good afternoon
my name is pradeep khatiwara and i’m the
executive director at youth innovation
lab
i’ll be sharing the story that i’m
working on data
well let me begin with this flickering
light
how many of you have seen your street
lights not working in your community
can i just see some hands
okay how many of you have noticed
these are electrics or solar powered
interesting so we did a survey we did a
survey by deploying
some hundred digital volunteers in
kathmandu valley
we wanted to know what is the present
status of these
street lights in california valley when
we did that
we collected 6 000
geotag streetlights of kathmandu valley
and there was some interesting
stories we could brought up with with
those data
we found that 34 percent of state lights
in catwando valley out of those
6000 collected were not working
these were worrying facts
we started having consultation with
various stakeholders
and we tried to get deep into
those not functional state lights and we
got to know that
some of the facts like these street
lights are not being fixed for
years and there is no direct mechanism
through which
people can communicate with its
authorities
and get these light fix and
the third thing is even more worrying
because these
straight lines absence has caused
the crime rates increase
some road accidents and so many other
issues
and we try to understand the deeper
meaning of this
not just social crimes but also viewing
this or from the national security point
of view
look at our administrative buildings
especially the single daba premises or
some of the vital organizations
even these institutions lack
these solar lights or
these straight lines we think
these data are gold mines these data are
good gold mines
because these data can be used for
various purposes let me give you the
story of
one institution which has used this sort
of data for some meaningful impacts
this is the story of a city in usa
it’s a boston city we just utilize this
this sort of approach so there is a
app called boss 311 through which they
collect
grievances from the community so people
can
report the organ issues like the street
lights not working
or the potholes or anything else they
launched in 2009 but when they
did some assessment in 2010 eight
percent of those
reports were solved from their system
but in 2014 they were able to solve
some 26 percent of the cases and the
number of cases were increasing and i
think
there are some exciting and positive
news if we follow up on that
as i mentioned earlier these data are
gold mines even these data sets
can be utilized to trace all those
places and
get it fixed but do you think our
authorities our government
our institutions are those well informed
how to use these data sets well we are
on the mission
to make them informed and we are on the
mission
to run this advocacy based program
through the digital means
and when we share these facts with the
kathmandu metropolitan officials there
were some administrative issues
who is responsible to handle some
straight lights but when we
asked them about these things so there
was some responses
they are about to install some eight
thousand straight lights in kurfunder
valley
but the demand from the word level is
sixteen thousand plus
and that will cost around fifty million
rupees to get it
uh installed but is that a solution
alone
if there is no mechanism how to bridge
those gaps from public to government
or for a regular maintenance then will
that going to be sustainable for this
country
or for your tax playing money we need to
think about it
at youth innovation lab we talk about
these data we talk about
how we can solve these issues using
right technology and bridging the gap
between public
and science let me show you some of the
examples that we are doing this is the
project we have done in 2017
called hamro moila protein it’s a
repository of
elected female representatives of nepal
when we develop this platform
this was the first ever initiative that
has recognized the elected female
representative of the country
this repository includes the data sets
of
not just these central level governments
but also these
representative of local level
authorities like the municipal
level representatives this has data sets
of 14
000 elected female representative of all
over the country
and 301 those who represent
the federal parliament provincial
level and federal administration
and these leaders would not been rightly
recognized
through the digital means but we are
trying to solve the problem
even bigger than this can you see these
dots
the raid belongs to the communist body
the
green relatively lesser than the rate
this belongs to the congress and other
one the allo on the
province one and two relatively higher
so that’s who’s the
rasta zanta party then
so these data sets and the dots over
there that represent person
the female leaders of this country and
to understand even more
on these data we can do a couple of
things on this
and what we are trying next on these
data sets is that we want to track their
political commitments as
these leaders are not being able to
fulfill the promise that
they have you know like mentioned in the
elections
and that’s our next target but
the people like you i we can check
their facts we have out of these 301
um provincial level uh female leaders
78 are graduate females but sometimes we
make these political
um judgments so quickly about the people
or the
party itself so this will these data
sets will be
helpful for us to understand data better
and understand even the people better
now let’s move to the disaster sites
our political situation of this country
is not less than any
dreadful disaster but i would like to
take you to the journey of
how we are working with government of
nepal to help
breathe this existing gaps through use
of data
i’m sure we all are aware about 2050
nepal earthquake
we have been through all those dreadful
situation and still our recovering
process of
that process is still going on let me
give you some of the facts
about the disaster events happen in 2020
we were going through a global pandemic
at the same time
also peering with other other disasters
you might think the kovit situation of
this country
was higher the you you might have
guessed the number of deaths
due to kobe 2 should be higher than any
other things
but when we look closer into that the
road accident
cases were even higher than the kovid 19
cases
to be precise the total kovid 19 dates
of 2020 was recorded 1856 where
the road accident cases was 2085
and 5000 plus being seriously injured
and the very much worrying thing is that
you might have heard
about the landslide issues as well the
landslide itself
took life of 303 people and
making 64 people still missing
and the lightning thunderbolt that has
taken lives of 82 people
and fire 53 and the snake bites
21 in total making 4496
deaths and still this does not count the
missing reports of 2020
so don’t you think we should be more
careful about these data sets
disaster related data information is one
of the most crucial
components of policy making planning and
implementing
however these data sets are scattered
these are these are not harmonized
and they were not even harmonized when
we built the system
but that’s not going to be solve our
problem that we are currently facing
so we worked with government of nepal
non-governmental organizations
academic institutions that are working
on these things to develop
an integrated platform that i’m showing
right now
so these data sets includes
data sets of 20 sorry 10 years
so these data sets in 10 years has
caused
around 24 800 events
and has taken lives of 14 086
people making lots of orders of
economic loss so
these data sets when you look closely
into all these data sets
we can see that province one being
mostly affected
but these are the incidents
but if you see on various parameters for
example the
people death or estimated loss or
infrastructure destroyed or
livelihood districts that might be the
different cases
so we need to be precise where the most
risk lies
these are the data sets of various
events happening in past 10 years
these data sets plotted in rate marks
fire
and there are multiple disasters we call
it multi-hazard disasters and nepal is
the multi-hazard
disastrous we are not just going through
one disaster at a time
when we were dealing with kovid 19 we
are also dealing with landslide we were
also dealing with flood
and there were multiple other situations
we have to deal every day
so understanding these data sets
understanding whether most risks lies
is an important thing for policy makers
even for us
look at these data sets of earthquake
events happening around the country
in pass from 2000 to
2021 right so these datasheets shows
most of the penetration is on balmati
zone
but look at the other activities going
on in different places and the red dots
somewhere in gorham shows the 2015
earthquake but is it alone there are
lots of data that’s
above the magnitude of four being
recorded on this system
that would help understand where the
risk is i’m focusing more on the risks
aspect because it
it is more um interesting to understand
and to understand further
i think let me take you to the other
other states as well
so this is the flood inundation we all
know that our
southern area is more prone to flood
and these are the data sets of flood
inundation of 2017 and 19 fortunately we
didn’t have to bear
any major plots in 2020
so these were the places for example the
one on the right side
is the river but these all are
settlements where
there was flood inundations and it’s not
happening once it’s happening
every year if you see the data on this
system itself
so that will show you how you can use
this data
for better planning well before wrapping
up
i would like to urge you that 21st
century problems are so big
they are so big that a small institution
or a private institution or
a government itself cannot solve this
issue alone
it requires all of us our collective
effort
collective understanding and
understanding of these meaningful
datasets that can create impact
this is just the beginning i hope we can
all work together to fix these problems
thank you very much