<p><strong>Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python</strong></p><p><strong>The book includes colored images of important concepts</strong></p><p><br></p><p><strong>Key Features: </strong></p><ul><li>Learn how to integrate spatial data and spatial thinking into traditional data science workflows</li><li>Develop a spatial perspective and learn to avoid common pitfalls along the way</li><li>Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded</li></ul><p><br></p><p><strong>Book Description: </strong></p><p>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.</p><p>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