Book Image

Applied Geospatial Data Science with Python

By : David S. Jordan
3 (1)
Book Image

Applied Geospatial Data Science with Python

3 (1)
By: David S. Jordan

Overview of this book

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
Table of Contents (17 chapters)
Part 1:The Essentials of Geospatial Data Science
Free Chapter
Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science
Part 2: Exploratory Spatial Data Analysis
Part 3: Geospatial Modeling Case Studies

Efficient operations with spatial indexing

Over the course of this book, we’ve worked with spatial datasets of varying sizes. However, given the nature of the case studies and the need for simplicity, we haven’t worked with very large spatial datasets. As spatial datasets grow in size and cover larger geographic areas, you will often need to find ways to access and perform operations on the data more efficiently. One way to add efficiency to your spatial data science workflows is through the use of spatial indexing. A spatial index is a way of structuring your data in a way that makes accessing and performing operations on the spatial object more efficient as compared to sequentially scanning every record in the dataset. Spatial indexing, at times, can dramatically increase the speed of spatial operations, including spatial joins and intersections.

There are many types of spatial indexes available in both commercial and open source software, and there are far too...