We
are so excited to show you a whole new way of working with ArcGIS. We
will be using Python
for GIS
to perform some spatial analysis content management and GIS
administration. Just using a simple and expressive language that
every one of you can understand and use, not just the developers
only. While the Python API can be used wherever Python runs, it
shines in the Jupyter
Notebook.
The
Jupyter Notebook is an open-source browser-based application. It lets
you create and share these documents that can contain live code
visualizations, explanatory text, and now maps using ArcGIS.
Getting Started with ArcGIS in a Notebook
So
let's see ArcGIS in a notebook you can make notes. You can use that
for narrative, telling your story, you can type in Python code and
math expressions to see the results interactively. You just brought
in the ArcGIS API in this notebook you can begin using it, and you've
logged onto your GIS. GIS could be ArcGIS Online or ArcGIS
Enterprise, and in three lines of code, you've brought in a live map
of the place you have selected.
# First NOTEBOOK
How
cool is that?
Let's
bring in some content! Suppose you were searching for your learn
ArcGIS
online organization for content related to your selected area and
displaying the results that you get back. Looking at the results, you
can see that the first layer is a layer of places in your selected
area. The second result is a layer of trolley stations and creating a
new map zooming in this time and adding the layers on that map.
Now
your map has layers! You want to do some analysis, or you want to
visit some of these places, and for that, you can call to create.
Drive-Time Areas tool with the travel mode of walking, to identify
locations within five minutes of walking distance. Those trolley
stations are then overlaying the layers of places to see in your
selected area with those walkable areas. That gives me a shortlist of
sites that are within walking distance from the trolley stations.
You
could take the trolley to see those places. You'll get back your
results as a layer, and you want to be able to query them as a table
and visualize them on a map. One of the beautiful things done is that
it is integrated this API with pandas. For those of you who don't
know, pandas are pythons library for data access. It's like bringing
excel in your notebook. You can also visualize the results, adding it
to a map. That was a quick tour of getting started.
- You can do so much more, and these notebooks are great for sharing.
- You know collaborating with others, sharing your analysis with others.
# Second NOTEBOOK
Let's
take a look at a second notebook that has an analysis on finding the
best places to go running in the area you have selected. In the
mornings, you like to go out for a run, and it feels great if it's a
place with low elevation, flattering, you can run hills, and it's out
in nature, that's the most important thing.
Let's
look at the Python code to solve this problem. Notice how we waited
for overlay analysis, which is not complex code; it's a simple math
expression that's about as easy to understand. Map algebra for the
Web GIS, and now with distributed raster analysis, it's more potent
than ever. You can do interactive raster processing right within
Jupyter notebook, and see the results inline in real-time. You can
add the results to a map, and this is using dynamic image processing.
If you want to, you can also save the results out as a new imagery
layer using an image server.
# Third NOTEBOOK
Next,
let's look at a third notebook and a different kind of use. This
notebook helped you to make your boss happy another day, so when he
came in on a Friday evening and wanted you to configure a new ArcGIS
Enterprise. Customize the home page to match your organization, add
all the users from your division to the appropriate groups for
collaboration, and also have the enterprise-ready to go with content.
The workflow will look like; you were there to do it manually. Maybe
you might have to stay back late on Friday and hopefully not come in
on Saturday, but since it's a workflow that you may need to repeat
often, you can also make a script for it.
It's
not complicated, and all you need to do is click run all, and the
notebook will do everything for you. We've seen how we use these
notebooks on the Python API to do some powerful analytics and GIS
administration using a straightforward, expressive language.
Machine-Learning Libraries and Python
Next,
let's see the fantastic things that you can do by combining ArcGIS
with the rich set of data science and machine learning libraries and
Python. We want to share how we can enhance the experience of Jupyter
Notebook by adding Open Python components, like an image library, and
invoking IBM Watson's deep learning.
Georgia
Power performs inspections of their transmission lines from
helicopters, and they look for things like insulators that are
broken, or contaminated, or flash. Now an inspection flight generates
thousands of Geotag images and these are just a small sample here,
but when we get the data set like that, the first thing we want to do
is to draw it on the map.
Here,
we will be using an image library to extract the spatial location of
an image and using the API. We can now turn it into a feature on a
layer on a map using the address API for Python. You will love how
easily it can integrate all that in the Jupyter notebook, and the
experience is so secure. But what we are more excited about is the
following. What you see here is just one mile out of one flight, and
in that one mile, there are 400 images.
Now
Georgia Power has over 17,000 miles of transmission lines that means
today they are manually classifying over 100 thousand images. It is a
daunting task. There has to be a better way to do this, and that is
Deep Learning. We've trained IBM Watson to recognize broken
insulators and images. In such a way that now, by using the Watson
API, we can easily pass pixels of a brand new geotagged image into
Deep Learning visual recognition system. And it will come back with
the classification of damage or not.
We
can take that information now and turn it into a feature layer, but
what's exciting here is we're going to see it all in action, and what
we're going to be doing here is dynamically invoking the API. As
Watson recognizes broken insulators, we can highlight them in red in
here.
So
let's see the image, Awesome! Great job here, and if we look at it
here again, even in blurred images, which is very cool. So what we've
done here a combination of Python modules, like the image library, to
extract spatial coordinates from images. We automated the future
identification using Watson Deep Learning, and we combine all that
with the ArcGIS to gain us a deeper understanding and to enable us to
do further analysis like emerging hotspots or create the third
workflow in maximum.
Author’s Bio
Kapil Sharma serves as a Seo Executive
in the leading Institute
named Edunbox.com
which provides ArcGis
Training,
there I handle all works related to SEO, SMO, SMM, Content Writing
and Email Marketing, etc.
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