《Python地理空间分析指南 (第2版) 》由Joel Lawhead著作, 人民邮电出版社于2017年4月出版。本书全面阐述了Python在地理空间分析中的应用, 内容系统完整。书中首先介绍了地理空间分析的基础概念、历史发展及其与地理信息系统 (GIS) 、遥感等相关领域的联系。接着, 深入探讨了地理空间数据的各个方面, 包括数据格式、结构和空间索引等关键内容。随后, 书中系统梳理了地理空间技术的概览, 涵盖了数据访问、计算几何等技术环节。本书的核心内容集中在Python在地理空间分析中的工具应用, 具体涉及其与地理信息系统及遥感技术的结合、地形数据处理、高级建模以及实时数据处理等多个领域。书中详细介绍了Python相关模块的安装、距离和方位计算、图像波段变换、归一化植被指数应用等操作与应用技巧。此外, 本书还设有综合应用章节, 通过实际案例展示Python在地理空间分析中的具体应用。作为一本系统性、实用性的指南, 本书旨在帮助希望利用Python进行空间地理分析、建模和GIS分析的开发者、研究人员, 乃至对测绘数字化和分析感兴趣的读者, 掌握Python在该领域中的核心技能与应用方法, 推动相关领域的实践与研究进展。

“Learning Geospatial Analysis with Python (Second Edition) ”, authored by Joel Lawhead, was published by Posts & Telecom Press in April 2017. This book comprehensively elaborates on the application of Python in geospatial analysis, with systematic and comprehensive content. It begins by introducing the fundamental concepts of geospatial analysis, its historical development, and its connections with related fields such as Geographic Information Systems (GIS) and remote sensing. Subsequently, it delves into various aspects of geospatial data, including key content such as data formats, structures, and spatial indexing. The book then systematically reviews geospatial technologies, covering technical aspects such as data access and computational geometry. The core content of this book focuses on the application of Python tools in geospatial analysis, specifically involving its integration with GIS and remote sensing technologies, terrain data processing, advanced modeling, and real-time data processing, among multiple areas. It provides detailed introductions to the installation of Python-related modules, operations and application techniques such as distance and azimuth calculations, image band transformations, and the application of Normalized Difference Vegetation Index (NDVI) . Additionally, the book includes a chapter on comprehensive applications, showcasing the practical application of Python in geospatial analysis through real-world cases. As a systematic and practical guide, this book aims to help developers, researchers, and even readers interested in surveying and mapping digitization and analysis who wish to utilize Python for spatial geographic analysis, modeling, and GIS analysis to master the core skills and application methods of Python in this field, thereby promoting practical and research advancements in related areas