{"product_id":"geocomputation-with-python-paperback","title":"Geocomputation with Python - Paperback","description":"\u003cp\u003eby \u003cb\u003eMichael Dorman\u003c\/b\u003e (Author), \u003cb\u003eAnita Graser\u003c\/b\u003e (Author), \u003cb\u003eJakub Nowosad\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eGeocomputation with Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003eKey features: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eShowcases the integration of vector and raster datasets operations.\u003c\/li\u003e \u003cli\u003eProvides explanation of each line of code in the book to minimize surprises.\u003c\/li\u003e \u003cli\u003eIncludes example datasets and meaningful operations to illustrate the applied nature of geographic research.\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eAnother unique feature is that this book is part of a wider community.\u003ci\u003e Geocomputation with Python\u003c\/i\u003e is a sister project of\u003ci\u003e Geocomputation with R\u003c\/i\u003e (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.\u003c\/p\u003e\u003cp\u003eThe 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. \u003ci\u003eGeocomputation with Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMichael Dorman\u003c\/b\u003e, Ph.D. is a programmer and lecturer at The Department of Environmental, Geoinformatics and Urban Planning Sciences, Ben-Gurion University of the Negev. He is working with researchers and students to develop computational workflows for spatial analysis, mostly through programming in Python, R, and JavaScript, as well as teaching those subjects.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAnita Graser, \u003c\/b\u003ePh.D. is a Senior Scientist at the Austrian Institute of Technology (AIT), QGIS PSC member and lead developer of MovingPandas. Anita has published several books about QGIS, including \"Learning QGIS\" and \"QGIS Map Design\", teaches Python for QGIS, and writes a popular spatial data science blog.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJakub Nowosad\u003c\/b\u003e, Ph.D. is an Associate Professor at Adam Mickiewicz University in Poznań and a visiting scientist at the University of M?nster. Specializing in spatial pattern analysis in environmental studies, he combines research with a dedication to education and open science principles. Dr. Nowosad is committed to developing scientific software and fostering accessible knowledge through teaching and open-source contributions.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRobin Lovelace\u003c\/b\u003e, Ph.D. is a Professor of Transport Data Science at the University of Leeds., He is the developer of high impact applications for more data-driven transport planning and policy. He has a decade's experience researching and teaching data science with geographic data and has developed numerous tools to support more data-driven policies, including the award-winning Propensity to Cycle Tool which has transformed the practice of strategic active travel network planning in the UK.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 344\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.65 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 14, 2025\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42733963214911,"sku":"9781032460659","price":126.34,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/64f5ad5797fdb4a0745b70b1c1364cc0.webp?v=1765138127","url":"https:\/\/dhl-adrianne.myshopify.com\/products\/geocomputation-with-python-paperback","provider":"BBB","version":"1.0","type":"link"}