Geospatial Data Science Quick Start Guide
Effective techniques for performing smarter geospatial analysis using location intelligence
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- 2019.09.23. 전자책 출간
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<Geospatial Data Science Quick Start Guide> ▶Book 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.
▶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
- 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
▶Who This Book Is 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 this book covers
- Chapter 1, Introducing to Location Intelligence, introduces the reader to location data and location data intelligence. It provides real-world examples of location data intelligence. This serves as a basic introduction, and has one section of code working on real-world examples of location data intelligence.
- Chapter 2, Consuming Location Data Like a Data Scientist, covers machine learning models for predicting the trip time of taxi journeys based on location factors and other attributes. In this chapter, we will discuss how to leverage spatial data masquerading as tabular data, and apply machine learning techniques to it as any data scientist would.
- Chapter 3, Performing Spatial Operations Like a Pro, lays the groundwork for dealing with geospatial data. In this chapter, we cover the basics of GeoDataFrame, coordinate systems and projections, as well as spatial operations such as buffer analysis and spatial joins. We look into foundational, as well as advanced, location data intelligence techniques using the Foursquare dataset.
- Chapter 4, Making Sense of Humongous Location Datasets, explores ways to aggregate location data into meaningful chunks using machine learning clustering techniques, and deriving more value from it. This chapter further introduces the reader to topics such as spatial autocorrelation, and both global and local spatial autocorrelation are explored and discussed.
- Chapter 5, Nudging Check-Ins with Geofences, introduces to the reader to geofencing, which is a popular tool that sees use in contexts from businesses to conservation work efforts. Geofencing refers to abstract fences that are created around a location, such that an alert or notification system can notify the relevant party should an event happens at or within the fence. The event can be something as simple as a customer entering the vicinity of a business location, or customers moving within range of a cell-phone tower – the applications are unlimited. This chapter moves onto methods for quickly building and deploying a geofencing system using Python.
- Chapter 6, Let's Build a Routing Engine, teaches the reader about navigation and routing, which are indispensable features in today's apps. Be it navigational apps, food delivery apps, or a courier delivery app; routing is a key component to the effective delivery of their services. And most of these apps rely on a few key third-party APIs, such as Google Maps APIs, to provide such services. Do we really need to pay these vendors to build a simple routing app? This topic explains how to build our own routing and navigation solutions using open source data and Python libraries that implement graph algorithms.
- Chapter 7, Getting Location Recommender Systems, is the final chapter of the book and discusses the process of building recommender systems that do not only apply to products on Amazon or movies on Netflix, but also locations. This chapter leverages popular recommender system techniques on offer, including collaborative filtering methods and location-based recommenders. We will use these recommender systems to build a restaurant venue recommendation system.
Geospatial Data Science Quick Start Guide is all about providing a segue for data scientists into mapping technologies, and for GIS scientific researchers into data science and machine learning techniques. Geospatial data science is the core technology used at companies including Uber, Google Maps, Apple Maps, Foursquare, and by real estate search apps such as realtor.com. An integral reason for the enormous success of these companies can be traced to their early successes in effectively identifying and leveraging the power of location data in the context of data science. Location data is a pervasive component of data repositories of any company in Silicon Valley, and the actual benefits of this data are yet to tapped due to a lack of expertise in this area. Geospatial Data Science Quick Start Guide will try to bridge this gap by equipping the reader with important skills for handling location data and effectively synergizing location data with machine learning techniques.
▶About the Author
- Abdishakur Hassan
Abdishakur Hassan is a geographic information systems (GIS) expert and consultant with over 5 years of experience working with UN Habitat. He holds an MSc in geoinformation science and earth observations. During his tenure as a GIS expert, Abdishakur has developed fully fledged GIS applications in the urban planning and land administration domains. He is interested in all things related to geospatial data science.
- Jayakrishnan Vijayaraghavan
▶TABLE of CONTENTS
1. Introducing Location Intelligence
2. Consuming Location Data Like a Data Scientist
3. Performing Spatial Operations Like a Pro
4. Making Sense of Humongous Location Datasets
5. Nudging Check-Ins with Geofences
6. Let's Build a Routing Engine
7. Getting Location Recommender Systems
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