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