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

Exploring the Location Set Covering Problem (LSCP)

LSCPs fall within a class of problems known as set covering problems. The set covering problem class is an NP-hard problem within the combinatorial optimization space. These problems typically aim at minimizing the number of sets within a space that cover all the demand within that space. Take, for example, the emergency services problem we mentioned at the start of this chapter, where a given number of emergency service facilities must service the demand of all the residents in a given community. The emergency planning departments within this community may want to know if the demand from the community can be met with the existing infrastructure. If there are too few facilities to triage the community’s needs, then additional facilities may need to be constructed. Conversely, there may be too many facilities in the community than needed. This may present opportunities to consolidate the facilities, resulting in operational savings...