Learning Geospatial Analysis with Python, 2nd Edition (Chinese Translation)

Name: Learning Geospatial Analysis with Python, 2nd Edition (Chinese Translation)
ISBN: 9787115448217

Joel LawheadPosts & Telecom Press2017

4.0

Word count: 235K words
Chapters: 10
ISBN: 9787115448217

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Summary

This is the Chinese translation of the second edition of Learning Geospatial Analysis with Python. Using Python 3 as its tool, it systematically explains the acquisition, processing, analysis, and visualization of geospatial data. The book has 10 chapters. Chapter 1 starts from the relationship between geospatial analysis and daily life, traces the history of GIS, remote sensing, elevation data, and computer cartography, introduces the basic concepts of vector and raster data, and finally builds a minimal GIS from scratch in Python. Chapters 2 and 3 respectively survey mainstream geospatial data formats (Shapefile, CAD, GeoJSON, TIFF, point clouds, web services) and the technology ecosystem (GDAL/OGR, PROJ.4, GEOS, spatial databases, and desktop tools). Chapter 4 focuses on the toolchain used throughout the book, including PyShp, dbfpy3, Shapely, Fiona, GDAL, NumPy, PIL, GeoPandas, PyMySQL, PyFPDF, and Spectral Python. Chapter 5 uses Python to implement distance measurement, bearing calculation, coordinate transformation and reprojection, Shapefile editing and query optimization, dot density and choropleth maps, GPS data, and geocoding. Chapter 6 moves into remote sensing, covering band transformations, histograms, image clipping, classification, feature extraction, and change detection. Chapter 7 deals with elevation data, including ASCII grids, hillshading, contour lines, and LIDAR. Chapter 8 covers advanced modeling, including NDVI, flood inundation models, color hillshading, A* optimal pathfinding and street network planning, and photo geolocation. Chapter 9 discusses real-time data (real-time traffic, weather tracking, regional reports). Chapter 10 uses a GPX reporting program as a comprehensive case study, tying the preceding techniques into a complete application.

Editor's review

很多人做地理分析还停留在“点鼠标、出张图”的阶段，一旦要把同一套流程重复一百次，或者交给同事复用，图形界面就使不上劲了。这本书的价值在于给出了一条“用代码替代点击”的完整路径：它先讲清地理空间分析的概念与历史——从John Snow的伦敦霍乱地图、Minard的拿破仑东征图讲到今天的GIS与遥感，再用Python 3把矢量、栅格、高程、遥感、实时数据一条线串起来。书中示例贯通性强：第5章手工实现距离计算、重投影、Shapefile编辑，第6至8章讲波段变换、分类、变化监测、NDVI、洪水淹没模型与A*路径规划，第10章用GPX-Reporter.py把地图、高程、气象数据拼成一个可交付的小系统。作者Joel Lawhead常年做地理信息集成，也是开源库PyShp的主要开发者，对GDAL/OGR、Shapely、Fiona、GeoPandas等工具链的取舍讲得务实。适合有Python基础、想在GIS与遥感方向动手的人；不过书中代码基于相近年份的库版本，动手时需按当前环境作适配。

Who it's for

A good fit for

有Python基础、希望用代码完成GIS分析的开发者
测绘、遥感、规划、环境等专业的本科生与研究生
需要把重复制图与空间计算流程自动化的GIS从业者
数据分析师，希望把经纬度、地图数据纳入分析流程

Not a good fit for

完全没有编程基础、只想学ArcGIS等桌面软件操作的读者
需要系统学习空间统计理论与数学推导的研究者
只想找现成代码、不愿动手配置GDAL等环境的读者

Reading tips

先按第1章把SimpleGIS示例跑通，建立坐标系与矢量、栅格的概念框架再往下读
动手前先配好GDAL、Fiona、Shapely等环境，示例依赖的具体库版本需按当前Python版本调整
第5至8章的示例建议逐个复现，并替换为自己的Shapefile或影像数据，才能转化为生产力
把第10章的GPX-Reporter.py当作综合模板，改造成自己业务中的报告或制图脚本

About the publisher

Posts & Telecom Press China · Founded in 1953

Posts & Telecom Press is known for its strengths in science and technology, computing, and information and communications titles, and has extended its publishing into photography, art, software learning, and inspirational growth, giving it a fairly diverse range. Its catalog includes computing and communications works such as Refactoring and Patterns (Revised Edition) and Huawei Switch Learning Guide (Second Edition), alongside bilingual collector's editions such as The Little Prince (Chinese-English-French illustrated collector's edition), The Great Gatsby, and Three Days to See. Beijing Ten Years: 1980-1990 and Ghost Blade: WLOP Personal Illustration Collection reflect its presence in photography and art. Its authors include Antoine de Saint-Exupéry, Helen Keller, Joshua Kerievsky, Wang Da, Zhang Zhaozeng, and WLOP.

Chapter 1 Python and Geospatial Analysis
Chapter 2 Geospatial Data
Chapter 3 An Overview of Geospatial Technology
Chapter 4 Geospatial Analysis Tools for Python
Chapter 5 Python and Geographic Information Systems
Chapter 6 Python and Remote Sensing
Chapter 7 Python and Elevation Data
Chapter 8 Advanced Geospatial Modeling with Python
Chapter 9 Real-Time Data
Chapter 10 Putting It All Together

FAQ

这本书讲的是Python 2还是Python 3？

第2版以Python 3为主线，书中示例与工具（如GeoPandas、Fiona）均在Python 3环境下演示。但原书出版于2015年前后，部分库的API与版本已变化，实际运行时需要参考各库当前文档做适配。

零基础能直接读吗？

不太建议。本书默认读者具备Python语法基础并了解环境配置，示例涉及大量文件读写、库安装与命令行操作；对GIS概念（坐标系、投影、矢量与栅格）也只在第1章简要铺垫，最好有一点电子地图或GIS使用经验。

和国内另外的Python GIS图书有何不同？

本书是偏“原理+自建工具”的路线：不依赖ArcGIS等商业软件，而是用OGR、PyShp、Shapely、GDAL等开源库手工实现距离计算、重投影、分类、路径规划等，适合想理解底层过程并能自己搭流程的读者。

需要准备哪些软件和数据？

主要是Python 3环境与若干第三方库（GDAL、Shapely、Fiona、NumPy、PIL、GeoPandas等），示例数据多为公开的Shapefile、栅格影像、ASCII网格、LIDAR与GPS/GPX文件，可随书源码获取。

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