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Consuming Location Data Like a Data Scientist

Location comes in different forms, but what if it comes in a simple structured data format and we overlooked it all this time? Most machine learning algorithms, such as random forests, are geared toward creating insights from structured data in tabular form. In this chapter, we will discuss how to leverage spatial data that is masquerading as tabular data and apply machine learning techniques to it as any data scientist would. For this chapter, we will be using New York taxi trip data to predict trip duration for any given New York taxi trip. We are choosing this dataset because of the following reasons:

Predicting trip duration has the right mix of geospatial analytics and machine learning
Finding the time it takes to travel from point A to point B is a routing problem, which will be dealt with in Chapter 6, Let's Build a Routing...

Key benefits

Manipulate location-based data and create intelligent geospatial data models

Build effective location recommendation systems used by popular companies such as Uber

A hands-on guide to help you consume spatial data and parallelize GIS operations effectively

Description

Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease.

Who is this book for?

Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.

What you will learn

Learn how companies now use location data

Set up your Python environment and install Python geospatial packages

Visualize spatial data as graphs

Extract geometry from spatial data

Perform spatial regression from scratch

Build web applications which dynamically references geospatial data

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Learning Geospatial Analysis with Python

$59.99

Mastering Geospatial Development with QGIS 3.x

$43.99

Geospatial Data Science Quick Start Guide

$32.99

Total $ 136.97

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Iyyanki Jun 16, 2022

Contents are good

Amazon Verified review

Kurt Jan 02, 2021

This book is a straightforward guide about geospatial analysis with python. A github repository accompanies the book as well. The only drawback is the black-white print out.

Josh Brown Oct 02, 2020

Two chapters in I'm finding that portions of this book rely on FastAI which is a cutting edge python ML library with a rapidly changing API, and documentation that isn't versioned. The book uses a lot of the extremely useful helper functions included in Fastai, but the modules in this library where these functions are stored have clearly been changed a few times by now. The author suggests using Google Colab to make getting started easier for the reader, but even the github code repo is not kept current with the version of fastai that is pip installed through Colab, or the most current version of fastai which would allow you to use their documentation to sort this out. I've gotten some of the notebooks to work locally in jupyter, but the amount of RAM on my laptop struggles in the WSL Linux environment which it's easiest to install fastai in. Also some of the data source links are broken meaning you have to look elsewhere for the data. This is publicly available data so it's not a huge problem, but this also stands in the way of learning the core concepts.I plan to struggle through this because I recognize there is a bit of a dependency struggle in this domain, and I think that is worth working through for my own learning, but it's worth pointing out that this book forces you to learn a lot of technical detail that is arbitrary to the concepts it is selling. Considering that this is marketed as a quick start guide, I think they've failed magnificently in that regard.If you want to hack away at exploring an out of date, undocumented API while learning geospatial fundamentals then proceed, because the use cases in this book seem valuable, but if you don't want to struggle with fixing the broken code in this book, look for a book that uses less cutting edge or better documented libraries.

Vinicius Oct 29, 2019

Livro básico para uma área carente de fontes. Muito bom!

Johann H. Aug 11, 2019

I really wanted to like the book, but I didn't and can't really recommend it for Data Scientists.The book reads like a medium article. That means, very little to no theory explained, a lot of: how to implement this and that, what tools to use etc. For my personal taste this book has to little content. I felt like I learned close to nothing, even though I am not an experienced GIS user. Hence I wonder what the target group is for this book: Data Scientists know how to use pandas and people who don't know Data Science wont learn it from this book since there is to little theory.Furthermore there are a lot of spelling errors, repetitions etc. that makes me wonder if this book had an editor.One thing made me particularly angry: In pretty much every chapter starts with a formulation like "In this chapter we will learn to do Task X", then they call a python function from a loaded package task_x(your_data_goes_here) and close the chapter with a sentence: "In this chapter we learned Task X". No you did not. You simply loaded a damn library, that has nothing to do with learning.There might be a subset of people for whom this book is useful, for me personally it was not.

Geospatial Data Science Quick Start Guide: Effective techniques for performing smarter geospatial analysis using location intelligence

What do you get with Print?

Geospatial Data Science Quick Start Guide

Consuming Location Data Like a Data Scientist

Exploratory data analysis

Spatial data processing

Taxi zones in New York

Visualization of taxi zones

Error metric

Building the model

Validation data and error metrics

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Geospatial Data Science Quick Start Guide: Effective techniques for performing smarter geospatial analysis using location intelligence

What do you get with Print?

Key benefits

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Who is this book for?

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What do you get with Print?

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