>> Azure Maps is a great service part of the Azure IoT family,
but what you might not know is the power of
Azure Maps is not just in the rendering of the maps themselves,
and ShiSh is here to tell us everything
about what's really behind Azure Maps as a service.
[MUSIC]
Hi, everyone. You're watching the IoT Show.
I'm Olivier, your host.
Today, we have ShiSh from the Azure IoT Team.
ShiSh is here to talk to us about Azure Maps. ShiSh?
>> Hi. I'm part of
the IoT business Acceleration Team
and I really focused on Azure Maps.
>> So Azure Maps is one of the many services that
the Azure IoT Team actually builds and develops,
and very often people are seeing maps for the visual part of it,
and so they think when we tell them
Azure Maps is a new service for
building IoT applications and so on,
they see the map and what can be done there.
>> Exactly.
>> But there's more to it, right?
>> Yeah, and that's the big challenge that I always face.
When I talk about mapping,
the perception is always about that this
is how I can render maps and places,
but get really a lot beyond that.
It is about the location of things and what you can do spatially.
Like for example, I want to be able to geo-fence something,
or I want to be able to find out what is nearby a certain area,
and be able to perform a lot of actions on it.
So there is a lot of geospatial actions
that is really the core part of Azure Maps,
and I'm going to be talking about those in details as well.
>> Awesome. Yeah. I like just to say that
the IoT data that come
from devices makes sense in context, all right?
>> Right.
>> There's a time contexts,
and there's also the location contexts.
>> Exactly. When we're talking about big data and you have
IoT devices really sending out telemetric data.
All that telemetric data doesn't have the location contexts.
Depending on where that data is coming from,
it has specific significance
and that's what geospatial data really does.
It provides it's location context just to say, absolutely.
>> So we had Ricky several times on
the show talking about these feeds, some of the features.
This is a specific scenario you've been working on,
that you've build a sample for it
and it's like little GitHub repo and so on.
Let's talk about that scenario,
that's really highlights, just what we talked about.
>> Super. So I spent about 15 years in
the Retail Team at Microsoft
before actually working with geospatial,
and during that entire time,
I actually worked with geospatial and retail.
One of the big challenges really working with retailers,
they're looking at how can they improve
the profitability of their physical stores.
As you know, retail is are really facing a big challenge.
Their stores are closing.
>> Yeah.
>> So they're trying to figure out what are
the things that influences
or disrupts their business in a spatial manner around.
>> Okay.
>> Geospatial platforms really enable you to solve that.
So in this slide here,
what I have is actually a visualization.
This is a term that is typically
used in the retail catchment areas.
Where Catchment Area in retail really is,
the area around a store where they
generate a lot of their pitfall in their business.
Typically, it is a 15 minutes zone around the store and
what is in that area really
impacts the performance of the store in a big way.
For example, if there is enough,
ample parking around the store it will drive a lot of traffic.
If there's public transit within a 2-3 minute area,
it definitely means better traffic.
If there's a competitor right across the street,
then it actually disrupts the business.
So one of the things that many retailers
want to be able to do is to be
able to analyze the catchment area for retail.
>> Okay.
>> Geospatial platforms, Azure Maps in particular,
what it can do is,
it can create a catchment area.
It's called an isochrone where typically with
an API called Route Range,
where I can actually say,
from a certain point which is the retail store itself,
can I create an area around it.
If I walk for 10 minutes in any direction,
it creates these points.
The farthest point that I can walk to.
>> Considering the buildings you have
to go around and everything like that.
>> Exactly. Then it connects all those points
together and that's the isochrone.
>> Okay.
>> That direction could also be a drive time that I
create 15-minute drive around the store or walk, whatever it is.
>> Okay.
>> Once I create the isochrone,
the other aspect of what Azure Maps can do as well,
is really run searches within that isochrone.
To say, what is in that area,
is there a competitor in there,
is there a parking in there,
is there a bus stop in there.
I can then score the isochrone based on
how many competitors exist within a certain area around the store.
How many parking spots and bus stops and all of those,
and based on that I could give it a positive or a negative score.
>> Okay.
>> As a result, I could have a rating for that area.
So for a retailer when selecting a location for a new store,
they could look at the score.
Also, if they're trying to close down a store,
they could look at the scores of multiple stores and say,
here's a store that's got multiple competitors around it.
>> Yeah.
>> It doesn't have enough parking.
So if you have to close one of the stores,
that's probably the best one to do and so on.
>> Or build a parking.
>> Exactly.
>> Yeah, totally. Interestingly, so
Azure Maps as a service provides the tools,
the APIs to do all of that.
>> Right.
>> The data, you mentioned the fact that it's like a big thing,
in the scenario you describe is to get
the information about the competitors,
and the parkings, and the transit information.
So Azure Maps partners with
different companies that actually
have certain types of data, all right?
>> Right. Yeah, and today we actually get data from TomTom.
So a lot of the mapping data itself,
like the locations and businesses exist in certain areas,
all that comes from TomTom, including traffic.
If traffic in an area is important,
we can actually get information about how
bad is the traffic and so on.
In addition to that,
we also partner with MOVEit.
So MOVEit gives us a lot of the public transit data.
It gives us ride shares and transit data and all that.
So we know exactly how many bus routes are around there,
and bus stops, and all of those things, as well.
Also in addition to that,
we have the potential of really
ingesting data from any data provider.
So today for example,
we work with the companies that provide human mobility data.
So you can look at the chart here,
there's a lot of blobs moving around.
>> Yeah.
>> This is actually human mobility data over the period of a day.
This is information about people moving around.
Companies typically gather this information from cell phones,
and if you do have that data,
you can actually upload that to Azure Maps.
You can analyze how busy is the area around your store.
Does your store get traffic and if another store gets
traffic or an area around your store gets enough traffic,
what you can do is, the other aspect of
it is you can start using geo-fencing.
So geo-fencing what it enables you to do,
is to look for areas where there is traffic
with the right audience that you want in your store,
and you can then geo-fence that area,
and then do targeted promotions to
those people to actually drive the traffic in.
>> As in on digital signs and things like that?
>> Right. Exactly.
>> On their phones?
>> Yeah. Exactly.
>> Okay.
>> So it is a fascinating way where they can
figure out what's going on geospatially.
They can then drive actions for that to drive traffic.
So a lot of retailers are really looking
at geospatial platforms to
really understand how to improve their business and profitability,
and all of that using these capabilities.
>> I love it. That's pretty impressive.
>> Yeah.
>> So do you want to go to
the demo itself and work the demo from there.
>> Yeah.
>> So Rakesh, I see the value of all of that.
Now what we want to do is to see a demo.
>> Super. So what I did is actually put together a demo
and I'm not a seasoned developer.
So what I did is I actually use
Jupiter Notebooks and the Azure version for Azure Notebooks.
>> Okay.
>> So what I wanted to do is use a few of
the APIs to illustrate what you can do with mapping.
>> Okay.
>> What you see here is really that is not about
the maps but it's about the geospatial capabilities,
and functions right there.
>> Okay.
>> So this is a demo that walks through
some of those capabilities that I talked about for retail.
What I did here is I'm trying to analyze a particular address.
So in this case what I did is,
I was looking for a Starbucks store in Downtown Seattle.
>> Okay.
>> I wanted an analyze for them.
What's the disrupters around them?
What's the things that there are business drivers around them?
So I'll pick the address here and Azure Maps has
a geocoding service because when I'm using
those APIs that actually looks for latitude and longitude,
and what I've done here is I've actually called
the M API that does a geocoding for me.
>> Okay, from your address and bookings.
>> Exactly. So when I run this code what it'll do is,
it'll actually pass that address over,
it'll tell me what the lat long is for that particular address.
Once it does that,
what I can do now is for that address
itself I want to build an isochrone
and I pass some parameters in here.
For example, I want to do
a 600 second zone around it, which is 10 minutes,
and I've also set this to be a pedestrian which is
someone walking for 10 minutes from
that Starbucks what can get fined around it.
So to do that I actually call this
the service which does a route range,
and it'll actually give me the area around it.
It'll mark that area out.
I'll run that and when I run this,
it'll actually give me back a whole bunch of points.
How far can you go with your walk 10 minutes in
every possible road that leads up from that Starbucks .
Once I do that,
the other aspect of what I want to do is I'm
extracting all of the polygons from it.
Once I extract the data,
the other element to what I want to do is
really look for what's around that Starbucks.
So I could look for example,
the fast food businesses' become a disrupter for
Starbucks and McDonald's for example started selling coffee.
Exactly, so I want to know,
is there any fast food joints around that's Starbucks.
So the key Word that I'm looking for here is "fastfood".
I'm doing a geometric search.
So this is an API call that I'm
using which will only search within that isochrone,
for everything within that.
So here I'll make that call and what it does at this point is,
it'll tell me how many fast food places exist.
It's about 11, and I'll list it out and you'll
notice I have a list right
there of there's 11 places right around that.
If I were to change that,
for example, I go back there and say.
"These are disrupters but what about parking?
What does that look like?"
So I could edit the code very interactively.
One of the reasons I'd like using notebooks, is that,
I could just interactively edited [inaudible]
code right from there without having to rerun it.
I notice there's 50 parking spots
and I can see all the parking spots right there.
So once I do this,
I can now upload all of these data points,
I want to visualize it in a map.
So that's the other aspect of what Azure Maps really does.
So what it's doing at this point is it's
passing the data up and generating the map.
So what I see finally up
here is really the tile that was returned.
You can also see the isochrone marked in red.
>> Yeah.
>> The other aspect of it you can
see in this case what I did was the parking.
Parking is a positive for the Starbucks.
With more parking I have,
the more traffic I'm going to get in there.
>> Yeah.
>> So this was again an example of how
those APIs would help those business decisions,
where a retailer could really
understand what's happening around that store,
know what's a business disruptor,
and be able to score the area as well.
So you could enhance this code even
more by scoring each one of those things and saying,
"What's the overall score based
on all of these different factors you have now?".
>> That's what's really impresses me is that
you just call like what for APIs.
>> Exactly.
>> Actually you end up with something that is
very useful and that you are production,
you just have to call simple APIs from Azure Maps.
>> Exactly.
>> I love it.
>> I'm fascinated by what's possible.
>> Yeah, we are all fascinated,
I'm sure here right now. Let's bring up.
We had this finding, it was like links,
let's actually invite people to learn more advantage of Maps.
So this is where you guys should go azuremaps.com.
>> Exactly.
>> It's the main sites.
>> Yeah. So that's where everything about
Maps and including the links to the APIs,
and all that is available.
I actually managed the Twitter handle as well.
So there's a lot of information that we post through there.
>> Okay.
>> We've got a Reddit.
Subreddit as well, so you can get information out there,
and most importantly if you want that coat.
>> For the demo?
>> For the demo, if we've got it on the IoT Tech community.
>> Okay. So aka.ms/iottechcommunity
where we have the code for that demo
but also blog posts and discussion boards
for Azure Maps and other Azure IoT tactics.
>> Exactly.
>> Awesome. ShiSh loved it and it's a great demo of
the value of Azure Maps which is not
just the graphics in rendering.
>> Exactly.
>> I think it was very insightful,
I'm sure we're going to see you again soon
for other Azure Maps demos.
>> Super thank you.
>> Looking forward to it.
>> Thank you very much.
>> Thanks for watching the IoT Show, I hope to see you soon.
>> Thank you.
[MUSIC]