Book Image

Geospatial Data Science Quick Start Guide

By : Abdishakur Hassan, Jayakrishnan Vijayaraghavan
Book Image

Geospatial Data Science Quick Start Guide

By: Abdishakur Hassan, Jayakrishnan Vijayaraghavan

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.
Table of Contents (9 chapters)

Introducing Location Intelligence

"Everything that happens, happens somewhere."
- The first law of geography by Waldo Tobler

Location data is data with a geographic dimension. Location data is everywhere as all actions that occur in or near the Earth's surface happen to use geographic aspects. It is generally referred to as any data with coordinates (latitude, longitude, and sometimes altitude) but also encompasses different aggregated geographic units, including addresses, zip codes, landmarks, districts, cities, regions, and much more.

Location intelligence, on the other hand, is the process of turning geographic (spatial) data into insights and business outcomes. Any data with a geographical position, either implicitly or explicitly, requires location-aware preprocessing methods, visualization, as well as analytical methods to derive insights from it. Thus, location intelligence applications can reveal hidden patterns of spatial relationships that cannot be derived through other normal means. It leads to better decision making on spatial problems, where things happen, why they happen in some places, and the spatial trends in time-series analysis. Understanding the location dimension of today's challenges in, industrial, retail, agricultural, climate, and environment, can lead to a better understanding of why economic, social, and environmental activities tend to locate where they are.

In this chapter, we give an overview of location data and location data intelligence. Here, we briefly introduce different location data types and location data intelligence applications and examples. We cover how to identify location data from publicly available open datasets. We briefly discuss and highlight the difference between location data and other non-geographic data. At the end of this chapter, we explore how location data fits into data science and what opportunities and challenges bring location data into the interdisciplinarity of data science.

We will specifically focus on the following topics:

  • Location data
  • Location data intelligence
  • Location data and data science
  • A primer on Google Colab and Jupyter Notebooks