SHANE GLASS: Hey, there, and welcome to Google Cloud Next
On Air.
Today we'll be talking about how public data could help data
scientists, researchers, public health agencies,
and others combat the novel 2019 coronavirus, also known
as COVID-19, and help flatten the curve
of cases around the globe.
My name is Shane Glass.
I'm a developer advocate here at Google Cloud,
covering business intelligence, and I'm also
the tech lead for the cloud public data sets program.
I'm thankful to be joined today by my co-presenter Javier.
Javier, you want to take a minute to introduce yourself?
JAVIER DE LA TORRE: Sure.
Hi, everybody.
My name is Javier de la Torre, and then
the co-founder of CARTO.
SHANE GLASS: Awesome, thanks, Javier.
So here's a quick outline of what we intend to cover today.
So let's start with a quick overview of Google Cloud's
public data sets program.
We currently offer more than 180 public machine learning
ready data sets available in our products,
both in structured data sets in our serverless data warehouse
BigQuery and unstructured data sets,
like satellite imagery in Google Cloud Storage.
And we work hard to ensure that each of these data sets
are as up to date as possible according to the requirements
of each individual data set.
Sometimes this means real time updates,
like our cryptocurrency blockchains,
and sometimes this means annual updates, like US Census Bureau
boundaries.
We're also working with our partners, team members,
and customers to identify new data sets of interest that
can expand our catalog as well.
So ultimately, what does this mean for you?
There's no need to worry about ETL
for some of the most high value public data sets in the world.
We preload these core data sets, and it
makes life easier for the data analyst and data scientist
by reducing time to get started and helping
you unlock new insights in your existing data.
Now as the COVID-19 pandemic truly
began to spread across the globe,
we recognized the need for expanded access
to analyze data that documents the spread
of the and its impacts.
In March of 2020, we announced our COVID-19
public data sets program, which now contains
more than 20 data sets.
To make it as easy as possible for researchers and data
scientists to combat COVID-19, these data sets
are preloaded into BigQuery and continually
updated to reduce the time to analyze the data.
In addition to the free access we provide
for all public data sets, queries
against COVID-19 public data sets are provided at no cost
through September 15 to maximize the ability for researchers
to fight COVID-19.
These include data sets detailing COVID-19 confirmed
cases and deaths around the world,
like those available from "The New York
Times," the Italian Department of Civil Protection,
and the European Center for Disease and Prevention
and Control, data sets that contain information about how
governments are acting to combat COVID-19, such as the Oxford
COVID-19 Government Response Tracker, that describes policy
actions that have been put into place to mitigate
the impacts of COVID-19, and data
sets that can contextualize outbreaks,
such as the US Census Bureau's American Community Survey.
We think this can truly make a difference
in flattening the curve.
Let's pause here for a second for those unfamiliar
with BigQuery.
BigQuery is Google Cloud's highly scalable enterprise data
warehouse solution.
It's fully managed and serverless,
offers real time insights from streaming data,
has built ML out of the box, and has
a high speed in memory BI Engine for faster and reporting
and analysis.
BigQuery is designed to make data analysts and data
scientists more productive, and it's
a secure offering that offers data encryption by default
both in transit and at rest to ensure
that your data are protected.
So let's get back to the public data to combat COVID-19.
Now we believe that the COVID-19 public data sets
we are hosting, combined with the high powered analysis
platforms like BigQuery and CARTO,
can help support the mitigation of the impact of COVID-19
in three primary ways.
The first of these is informing individual decision making.
And in my opinion, this might be the most important.
This is critical to slowing the spread of the virus
by helping individuals understand
the importance of social distancing,
the importance of wearing a mask, and other safeguards.
It can also help researchers and public health officials better
understand which policy actions are having a positive impact.
Now, the second of these use cases
is analyzing the second order of public health
impacts of the pandemic.
Secondary public health impacts of the virus
include potential food security among employees and industries
that are most likely to be displaced by the COVID-19
impact on the economy, according to the public health
officials we've worked with.
Now these data sets are typically
used by public health officials to identify people and regions
that are dealing with impacts of the pandemic other
than the virus itself.
Data sets such as Google's Community Mobility Reports,
the Census Bureau's American Community Survey,
and global health and population trends from the World Bank
are crucial to help public health officials better
understand and ensure that the full public health impact
of the pandemic is mitigated.
And finally, we're helping inform
individual economic decision making, such as helping
governments make an informed data driven decision
about reopening economies.
Combining these data sets, such as data
about COVID-19 cases from USA Facts,
the Oxford university Government Policy Response Tracker,
aggregated hospital capacity from the American Hospital
Association, and Geotab's commercial mobility impacts,
government officials and businesses
can make an informed decision about the risks and benefits
of reopening their economies.
This can help states, such as the five Western states that
make up the Western states pack, take a data driven approach
to measuring their published criteria for reopening
economies, including the ability of hospitals
to handle a new wave of patients and protect those most
vulnerable to COVID-19.
So let's take a look at some examples of how Google Cloud
customers are already seeing success
using COVID-19 public data sets in the three categories
we've laid out.
The first of these is the state of Oklahoma.
State officials in Oklahoma collaborated with teams
from across Google Cloud to develop and launch
a Looker dashboard that helps keep their citizens informed
of the current status.
This helps reinforce the importance of public health
efforts by providing citizens transparency
into the decision making process and the impacts
of these decisions.
The second of these is analyzing the second order public health
impacts of the pandemic.
Descartes Labs has built and widely published
an analysis that shows the reduction of pollution
and other emissions within areas that
have high number of COVID-19 cases
and that are currently under stay at home
or safer at home orders.
And the final example is from the Emergent Alliance,
a data analytics coalition that's
spearheaded by Rolls Royce.
The Emergent Alliance is a response
from a growing number of global brands
who believe in using the power of big data for the greater
good.
By combining private and public data sets,
the Emergent Alliance aims to accelerate and smooth
economic recovery from the COVID-19 crisis.
Their stated mission is to help governments, businesses,
and communities help each other in the recovery
and provide better insights through better data that
provide better outcomes for everybody.
Now a lot of this amazing work is only possible
because of the powerful analytics platforms
that are available.
Now one of these is CARTO, a leading spatial analytics
company headquartered in New York City.
I'm joined today all the way from Madrid, Spain
by their founder and chief strategy officer, Javier de la
Torre, to talk a little more about how
they are helping their customers leverage
public data to combat COVID-19.
Javier.
JAVIER DE LA TORRE: Thanks, Shane.
I'm pretty excited to be here today collaborating with Google
on COVID-19 mitigation.
We have seen an explosion on usage of maps and location
intelligence to fight COVID-19.
It's like the entire population had cut almost a 101 course
of maps and mobility recently.
And that's because, I'd like to say,
there's never been a more important time to geography.
Understanding the geography of the pandemic
and the mobility of the population
is one of the few tools that we have right now
available to fight COVID.
At Carto, we have been pretty busy over the last months,
at the beginning of the pandemic,
working with different governments and organizations
on the response to COVID.
And BigQuery has been a fundamental part
to handle very large spatial datasets on these use cases.
But before [INAUDIBLE] we do that,
I just want to give you a little quick intro about Carto.
So Carto is a location intelligence platform.
We help organizations analyze and visualize location data.
Let me tell you a little bit about what it means.
One way that I like to explain what location is-- location
intelligence is, is by explaining
the difference between understanding where
and understanding why.
You want to look at where there's
more COVID-19 cases, you will likely use a BI tool,
such as Looker.
But if you want to understand why
there are more cases on one place versus another, if you
want to understand what are the characteristics of those places
that makes them feel more vulnerable to COVID,
then you need to create what we call a spatial model.
And for doing that, you need Carto.
So with Carto, think about creating
spatial models that help you understand
why something happened so.
Now we provide these capabilities
in a product with different interfaces.
We work with data scientists, analysts, and developers.
And more important, I think very relevant in this case, Carto.
We like to say, comes also with batteries included.
Most of our users use Carto with data observatory, which
is our repository of spatial data, things like demographics,
mobility, financial data, that is useful for doing
a lot of this analysis.
A lot of these datasets will be collaborating
with the BigQuery public datasets,
to make them available to the Query community.
Now more widely, Carto and Google Cloud Platform
provides a full solution of what we call a next generation
spatial data infrastructure.
With components like the AI Platform and BigQuery,
Google Cloud Platform together with Carto,
provides a great new way of thinking
about the spatial analysis.
And we have been leveraging it, like we
said, a lot during COVID-19.
So let me show you a few use cases.
So the first one, as we were describing,
combining location data with COVID-19 cases,
government can actually create what we call risk models.
It can do risk analysis to understand
which areas are more vulnerable depending
on things like demographics, like age,
to have a bigger effect COVID-19.
Right, so these type of analysis are
possible by combining a lot of the datasets, some Shane,
you were talking before.
Now, another use case that I like a lot,
and you put it before very well.
There's been an incredible pressure on governments
to explain the reasons why they are closing up or open up,
those lockdowns having affected so many people.
And in order to do that, they had
to be very responsible in the way they publicize the data,
and how they explain the stories behind it.
So helping to tell those stories and make trends
get transparency, so that makes it way more effective,
and makes it citizens understands the importance
of those measurements.
Of course, another typical call or use case
that we've been seeing, is associating human mobility,
with changes on the economy.
We know when lockdowns happens, the economy shrinks.
So trying to understand the correlation between these two
factors, is a pretty important factor.
It can help you to collect, understand, and mitigate
the economic impact of COVID-19.
The next one is even like a more practical things.
Like this situation has made us, well,
have to collect deal about like, how many people come
with feet on a beach, while maintaining secure distance,
right?
So all those type of analysis also
require a lot of light data crunching and data analytics.
We see that particular in cases where
we work with governments on understanding the importance
of the lockdowns.
But also we've seen quite a lot on the private sector.
So understanding the impact for a particular retailer
of those lockdowns in the different countries where
they're operating, so those are also a very classical cases
where you're mixing data that you have on the public datasets
together with your own enterprise data,
in this case to see how COVID-19 is affecting your business.
And lastly, I think also something that we've seen
and we've been working a lot is understanding
and prepared working on data around mobility inside offices.
As we know, more businesses are opening up.
And we hear more and more the need
for understanding like mobility indoors, right?
So if you track movements of employees while at the office,
you can obtain reports like who's
been in contact with whom, within three meters,
or in the same room.
Which parts of the office produce more contacts?
How can you adapt timetables to reduce those contacts?
Well, as you can see, there's many use cases
where you can apply spatial analytics together
with a spatial they don't like, the one
that is available on the BigQuery public datasets,
to fight on COVID-19.
So, what do you think, Shane?
SHANE GLASS: I think it's a great idea.
Why don't we look at some demos?
I'll go first.
I'll show a couple of queries in BigQuery
and we can come back to you and show
how we can visualize those.
JAVIER DE LA TORRE: Sounds great.
SHANE GLASS: OK.
Now let's take a look at some examples
of how COVID-19 data can be help used to flatten the curve.
We'll start here by looking at COVID-19 cases
by county across the US, and joining demographic data
from the Census Bureau's American Community Survey.
The demo that we're looking at here
gives us cases and deaths by US county, and also
a litany of different demographic factors,
including total population, population density, income,
age, gender, and race.
This data can be used to help contextualize an outbreak
and better understand some potential factors that
are in play.
We can also use it to create a dashboard in Looker, the newest
member of Google Cloud's family for business intelligence.
This allows us to visualize data, drill down
by county or state, and provide contextual visualizations
around it.
Now let's take a look at our next demo, which
is COVID-19 cases normalized by county population,
in this case per 100,000.
Now we can quickly run this query as well.
And we'll get a result for the number
of cases within the last day by US county.
But now, Javier, what if we wanted to visualize a query
like this.
Could you show us what that would look like?
JAVIER DE LA TORRE: BigQuery is known
for handling large amounts of data,
so visualizing large amounts of data
sounds something that's pretty much needed.
So for example, this data set from New York City and the taxi
trips that is available on BigQuery public data, that's
around 131 million GPS points.
We have found a way to visualize this.
Here's a demo of that.
This is your 131 million points visualized on the map.
And as you can see as I zoom in, you
can see how the maps gets loaded progressively
until if I zoom enough each of these points
represents one of the records in that table.
And this is possible because we process the data
into what we call tile sets.
We've been working over the last few months
to generate this task starting from a query.
And we have a solution for it.
So you can see now we can progress not hundreds of them,
we can process billions of records
in very, very effectively, using a query.
So using this tile sets generation,
dialing from a query without moving the data out of it,
you can get datasets like this one, 300 million buildings
in the US, all the buildings in the United States,
and territories, or from within the query.
So if you're interesting to use this type of technology
for tile set generation, we have an open beta program.
And you can get access by sending us
an email to BigQuery@Carto.
We think this will be pretty relevant to people
who are working with very large spatial datasets.
SHANE GLASS: That's awesome, Javier.
Thanks for the demo.
I personally can't wait to use Carto
to dig in a little deeper, to figure out
why things are happening where they're happening.
OK well, that's all for us here today at Google Cloud Next
OnAir.
If you'd like to learn more about how
you can use public data to combat COVID-19,
check out the following resources.
You can check out more specific examples of how data science
and public data can support public health efforts to fight
COVID-19 by watching our Cloud OnAir webinar from May,
titled "Data science for public health--
Working with public COVID-19 datasets."
You can also check out the full catalog
of public datasets available on Google Cloud
by visiting the Google Cloud Marketplace using
the short link on the right.
Additionally there are a number of other Cloud Next OnAir
sessions talking about how Google Cloud is helping support
the response to COVID-19.
There's so many to choose from.
From the industrial insights session
on "Planning for retail's new priorities,"
hosted by our VP of retail, Carrie Tharp,
to the breakout session "Data driven responses to COVID-19
using Looker and BigQuery," hosted
by my friend and colleague, Leigha Jarett,
who runs the product demonstration for Looker.
In particular, I recommend checking out
Aashima Gupta's talk on reimagining health
care in COVID-19 era, and Mike Daniels's session
on reimagining the public sector in the COVID-19 era.
Both of these talks do a really great job
of expanding on the concepts we covered today.
That's all for us.
Thanks for watching Google Cloud Next OnAir.