Jordan Applied Geospatial Data Science with Python

Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
1. Auflage 2023
ISBN: 978-1-80324-034-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 308 Seiten

No detailed description available for "Applied Geospatial Data Science with Python".

Autoren/Hrsg.

Jordan, David S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

Table of Contents - Introducing Geographic Information Systems and Geospatial Data Science
- What Is Geospatial Data and Where Can I Find It?
- Working with Geographic and Projected Coordinate Systems
- Exploring Geospatial Data Science Packages
- Exploratory Data Visualization
- Hypothesis Testing and Spatial Randomness
- Spatial Feature Engineering
- Spatial Clustering and Regionalization
- Developing Spatial Regression Models
- Developing Solutions for Spatial Optimization Problems
- Advanced Topics in Spatial Data Science

Über Autor(innen)

Jordan David S.:
David S. Jordan has made a career out of applying spatial thinking to tough problem spaces in the domains of real estate planning, disaster response, social equity, and climate change. He currently leads distribution and geospatial data science at JPMorgan Chase & Co. In addition to leading and building out geospatial data science teams, David is a patented inventor of new geospatial analytics processes, a winner of a Special Achievement in GIS (SAG) Award from Esri, and a conference speaker on topics including banking deserts and how great businesses leverage GIS.