Geocomputation With Python Pdf
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Geocomputation with Python
Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python. Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community. The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.
Geocomputation with Python
"Geocomputation with Python (geocompy) is motivated by the need for an introductory, yet rigorous and up-to-date, resource for working with geographic data with the most popular programming language in the world. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent place to start for those starting our working on geographic data with Python, making it ideal for students and practitioners beginning their journey with Python. There are many resources on Python on 'GeoPython' but none that fill this need for an introductory resource that provides strong foundations for future work. This book fills an 'ecological niche' in the wider free and open-source software for geospatial ecosystem. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has 60+ contributors and an active community, not least in the associated Discord group. Links with the vibrant 'R-spatial' community, and other communities such as GeoRust and JuliaGeo, lead to many opportunities for mutual benefit across open-source ecosystems"--
Geocomputation
Geocomputation is the use of software and computing power to solve complex spatial problems. It is gaining increasing importance in the era of the ‘big data’ revolution, of ‘smart cities’, of crowdsourced data, and of associated applications for viewing and managing data geographically - like Google Maps. This student focused book: Provides a selection of practical examples of geocomputational techniques and ‘hot topics’ written by world leading practitioners. Integrates supporting materials in each chapter, such as code and data, enabling readers to work through the examples themselves. Chapters provide highly applied and practical discussions of: Visualisation and exploratory spatial data analysis Space time modelling Spatial algorithms Spatial regression and statistics Enabling interactions through the use of neogeography All chapters are uniform in design and each includes an introduction, case studies, conclusions - drawing together the generalities of the introduction and specific findings from the case study application – and guidance for further reading. This accessible text has been specifically designed for those readers who are new to Geocomputation as an area of research, showing how complex real-world problems can be solved through the integration of technology, data, and geocomputational methods. This is the applied primer for Geocomputation in the social sciences.