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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
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ISBN-139781788992244
Edition1st Edition
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Shashank Shekhar
Shashank Shekhar
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Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar

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Path

Routes are often found in location datasets to represent road or rail networks. We can plot those types of datasets using the Path visualization option. After uploading the rail routes dataset, either by downloading from the GitHub directory or creating it using the Jupyter Notebook, open it for visualization purposes:

CSV to Database configuration

If you take a look at the Jupyter Notebook, you will find that we create a feature named Polyline. It uses geometry information given in the file. Polylines are an encoded representation of latitudes and longitudes, as follows:

Encoded representation of latitudes and longitudes

The dataset represents rail routes over which crude oil is transported. It might be helpful to locate it on map tiles made of satellite imagery, because we can then see terrain information pertaining to the rail routes.

The bearing value in the Viewport...

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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244

Author (1)

author image
Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar