Geospatial Data Science Quick Start Guide

By : Abdishakur Hassan, Jayakrishnan Vijayaraghavan

4 (6)

Buy this Book

Geospatial Data Science Quick Start Guide

4 (6)

By: Abdishakur Hassan, Jayakrishnan Vijayaraghavan

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introducing Location Intelligence

Location data

Location data intelligence

Location data science versus data science

A primer on Google Colaboratory and Jupyter Notebooks

Summary

Consuming Location Data Like a Data Scientist

Exploratory data analysis

Spatial data processing

Error metric

Building the model

Summary

Performing Spatial Operations Like a Pro

GeoDataFrames and geometries

Spatial operations

Location data visualization

Summary

Making Sense of Humongous Location Datasets

K-means clustering

Density-Based Spatial Clustering Applications with Noise

Spatial autocorrelation

Summary

Nudging Check-Ins with Geofences

Geofencing

Geometry and topology (lines and polygons)

Geofencing with Plotly

Summary

Let's Build a Routing Engine

Fundamentals of graph data structure

Shortest path analysis on a simple graph

Building a graph based on a road network

Shortest path analyses on the road network graph

Summary

Getting Location Recommender Systems

Exploratory data analysis

Recommender systems

Summary

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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...