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)

Performing Spatial Operations Like a Pro

Spatial operations play a crucial role in location data analysis. In fact, they are what distinguishes location data analysis from other data analysis. Spatial analysis derives insights from input data using geometry functions and operators. There are a number of different spatial operations, including buffer analysis, spatial joins, overlay analysis, and topological operations (such as intersect and contain). We will use the Foursquare dataset to apply spatial operations in a real-world project.

The NYC Foursquare Check-In dataset first appeared in Fine-Grained Preference-Aware Location Search Leveraging Crowdsourced Digital Footprints by Dingqi Yang, Daqing Zhang, Zhiyong Yu, and Zhiwen Yu, from LBSNs. This was released before the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), September 8...