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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
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Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Data Analysis 2. Data Preprocessing FREE CHAPTER 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java A. Java Tools Index

The MongoDB extension for geospatial databases


MongoDB supports the GeoJSON object types Point, LineString, Polygon, MiltiPoint, MultiLineString, MultiPolygon, and GeometryCollection. These are used in two-dimensional geometry and geographic surface-of-the-earth data.

Mongo provides a nice tutorial on geospatial databases with an application on restaurant locations in New York City here: https://docs.mongodb.com/manual/tutorial/geospatial-tutorial/.

A GeoJSON object has the form:

<field>: { type: <GeoJSON-type>, coordinates: [longitude, latitude]}

Here, <GeoJSON-type> is one of the seven types listed previously, and longitude and latitude are decimal numbers, with range -180 < longitude < 180 and -90 < latitude < 90. For example, the following is the GeoJSON object for Westminster Abbey in London:

"location": {"type": "Point", "coordinates": [-0.1275, 51.4994]}

Notice that GeoJSON lists longitude before latitude, as with (x, y) coordinates. This is the reverse of...

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