Big Data Visualization platform

Big Data Visualization platform


Hi. This is a demo for our Big Data Visualization
platform, particularly focused on the Real Estate online portal sector. So, here, I’m showing some of the major real
estate online portal websites — Zillow.com, Trulia, RedFin and Realtor.com. So, in the United States, people who want
to buy or rent a home, they first go to these real estate online portals and these are very
well-known brand names. Zillow, for example, has about 160 million
visits on its web and mobile pages. And similarly, Trulia, … These are the major
portals. And Zillow itself is traded in the market
for, in the stock market, its market capitalization is worth some 10 billion dollars. So, these are very major, established corporate
enterprises in the real estate online portal sector. The structure of these portals is like this
… There are two major components. One is the listings themselves, which I’m
showing here. The listings, where if you have a house to
sell, then you can put up a few pages — photographs and details of the house. And through various channels, Zillow gets
the feeds of all the listings. The other major component of the portal is
the mapping technology. The map displays maps of neighborhoods, and
shows various details on the map. For example, here, I’m showing the East Village
neighborhood in New York city. So, if you see, the map — Zillow, Trulia,
RedFin, all of them, I’m showing the East Village neighborhood. You can see on the map, they usually show
you some details about crime in that area, schools, commute — you know, drive, transit,
walking — shop & eat — restaurants, groceries, cafes, some entertainment, etc … And the technology is the same across all
these corporate enterprises, not just one. It’s pretty much the same, where they show
a couple of data fields. But the notable thing is this technology was
developed sometime, 10 or 15 years ago. At that time, the approach was that if you
needed data on schools or crime, you parceled it out to third-party vendors, who provided
the data. Then you simply got it and populated it on
the map. So, in particular, there was no intermediate
analytics layer. And you can see, all of the portals, they
have this, kind of, crime or schools, … Moreover, the third-party data vendors, Crime
is provided by SpotCrime.com and CrimeReports.com. Schools by Great Schools, and so on. Similarly, same in Zillow also. See, schools data is provided by Great Schools. So this was how it was, when the technology
was developed. But one thing notable is here, the neighborhood
of East Village, for example, this is the finest level of details they will go into. Most fine-grained detail. But neighborhood has about 30 or 40 blocks. A block is like the square … This already
contains about 30 or 40 blocks. And so, this is a large area, especially if
200 million visits are happening on the web pages, and people are looking for more and
more detail, before they will buy a house which can cost anywhere from 100,000 to 6
or 700,000 dollars. So these two main things to observe. One is that, the number of fields they are
showing, even if you look at this crime, schools, commute, and we add up all of these fields,
it’s only about 20 or 30 data fields. And the level of granularity is at the neighborhood
level, which already has some 30 or 40 blocks. Ok, so now, we will show … This technology
is, across the board, it is the same. We will show our implementation for East Village,
for example. Here, this is our app that we are developing. We allow data at a variety of geographic levels. So, if you, say, for example, you choose a
block. Here you get a Topic-Tree, sort of, cogently
organized for a huge variety, huge collection of data, using Big Data Visualization techniques. And so, we have data available on various
topics, including demographics, household relations, family formation, mortgages &homeownership,
lifestyles, culture, consumer behavior, jobs, income &economic activity. Over 10,000 data fields which we have organized in this way. All the data fields which we organized in
this way, cogently, our data sources are all accredited public data sources — U.S. Census,
American Community Survey, American Housing Survey, Bureau of Labor Statistics, Common
Core Education Data, FBI Uniform Crime Reporting. So, for example, now let’s say you choose
the topic. Then you can go and select, by clicking on
any block, immediately you get back the data for that topic, concerning that block. So this is the East Village neighborhood. Now you can get block-by-block details. For example, say, you changed the topic to
‘Male By Age’. Then immediately, it refreshes and provides
you data by age-graded male population in that area. Now, if you choose another area. For example, this block here. Immediately, it refreshes, centers and updates
the data. You can do this for census-tract, zip code. And you can do this for any area in the United
States. There are 11 million blocks in the United
States. Say, for example, San Francisco. …Here, choose the topic and choose a block. Any block, and whatever the shape, it picks
up. Eleven million blocks. You can see it comes back very fast. So this is really, a new generation of technology
we have developed, you know, … Here, again, you can access the data on various levels
of geography. Now, again, if you go back and see what is
existing on the online real estate portals, like Zillow, Trulia, Realtor … All this
is older generation of technology. We’ve identified this opportunity, where we
are able to serve data cogently organized, and serve it in a way that is much accessible. And our data … This is our Web App version. We are also making it available on mobile
with React Native framework. First on iOS Apple devices. And the same content is also available for
sharing with other apps as well as Javascript APIs for corporate clients. Thank you!

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