相關主題
商品描述
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.
商品描述(中文翻譯)
《使用 Python 進行地理計算》是一本全面的資源，專注於使用世界上最受歡迎的程式語言處理地理數據。這本書概述了 Python 在空間數據分析方面的能力，以及涵蓋標準 GIS 操作的多個實作範例。這本書的一個獨特賣點是其對向量和光柵地理數據模型的連貫且整合的覆蓋，以及一致的學習曲線。對於初學者來說，這本書是使用 Python 處理地理數據的絕佳起點，非常適合剛開始接觸 Python 的學生和從業者。
主要特點：
- 展示向量和光柵數據集操作的整合。
- 提供書中每行代碼的解釋，以減少意外情況。
- 包含示例數據集和有意義的操作，以說明地理研究的應用性。
另一個獨特的特點是這本書是更大社群的一部分。《使用 Python 進行地理計算》是《使用 R 進行地理計算》（Lovelace, Nowosad, 和 Muenchow 2019）的姊妹項目，後者是一本關於使用 R 程式語言進行地理數據分析、可視化和建模的書籍，擁有眾多貢獻者和活躍的社群。
這本書教你如何使用 Python 導入、處理、檢查、轉換、計算和導出空間向量和光柵數據集，Python 是數據科學和許多其他領域中最廣泛使用的語言。閱讀這本書並運行其中的可重現代碼片段，將使你熟練掌握生態系統中的關鍵套件，包括 shapely、geopandas 和 rasterio。這本書還展示了如何利用數十個額外的套件來完成各種任務，從互動地圖製作到地形建模。《使用 Python 進行地理計算》為更高級的主題提供了堅實的基礎，包括空間統計、涉及空間數據的機器學習和空間網絡分析，並為進入活躍且支持的社群開啟了大門，該社群正在開發 Python 及其他領域的地理工具。
作者簡介
Michael Dorman, 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.
Anita Graser, Ph.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.
Jakub Nowosad, Ph.D. is an Associate Professor at Adam Mickiewicz University in Poznań and a visiting scientist at the University of Münster. 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.
Robin Lovelace, 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.
作者簡介(中文翻譯)
邁克爾·多曼（Michael Dorman），博士，是內蓋夫本-古里昂大學環境、地理資訊與城市規劃科學系的程式設計師及講師。他與研究人員和學生合作，開發空間分析的計算工作流程，主要通過使用 Python、R 和 JavaScript 進行程式設計，並教授相關課程。
安妮塔·格拉瑟（Anita Graser），博士，是奧地利科技研究院（AIT）的高級科學家，QGIS PSC 成員及 MovingPandas 的首席開發者。安妮塔出版了幾本有關 QGIS 的書籍，包括《學習 QGIS》（Learning QGIS）和《QGIS 地圖設計》（QGIS Map Design），並教授 QGIS 的 Python 課程，還撰寫了一個受歡迎的空間數據科學部落格。
雅庫布·諾沃薩德（Jakub Nowosad），博士，是波茲南亞當·密茲基維奇大學的副教授，並在明斯特大學擔任訪問科學家。他專注於環境研究中的空間模式分析，將研究與教育和開放科學原則相結合。諾沃薩德博士致力於開發科學軟體，並通過教學和開源貢獻促進可獲得的知識。
羅賓·洛夫雷斯（Robin Lovelace），博士，是利茲大學的交通數據科學教授。他是高影響力應用程式的開發者，旨在促進更以數據為驅動的交通規劃和政策。他在地理數據的數據科學研究和教學方面擁有十年的經驗，並開發了多種工具以支持更以數據為驅動的政策，包括獲獎的騎行傾向工具（Propensity to Cycle Tool），該工具改變了英國戰略性主動出行網絡規劃的實踐。
