Python for Geospatial Data Analysis

Chapter 1. Introduction to Geospatial Analytics

Are you a geographer, geologist, or computer scientist? Impressive, if you answered yes! I’m none of those: I am a spatial data analyst, interested in exploring data and integrating location information into data analysis.

Geospatial data is collected everywhere. Appreciating the where in data analyses introduces a new dimension: comprehending the impact of a wider variety of features on a particular observation or outcome. For instance, I spend a lot of professional time examining large open source datasets in public health and health care. Once you become familiar with geocoding and spatial files, not only can you curate insights across multiple domains, but you can also recognize and target areas where profound social and economic gaps exist.

Early in my evolution as a data analyst, I began to realize I had bigger and more complex questions to consider, and I needed more resources. With an eye toward working with United States census data, I enrolled in a course in applied analytics. I had worked in the R programming language, but this course was taught in Python. I made it through, but I discovered a lot in the following months that I wish I had learned along with basic Python. This book is meant to share what I wish I’d been taught.

What I hope to share here is not the complete coding paradigm of Python, nor is it a Python 101 course. Instead, it is meant to supplement your Python learning by showing you how to write actionable ...