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所在平台: Udemy |
课程主页: https://www.udemy.com/course/spatial-data-science-with-python/
课程评论：没有评论
**课程名称:** 使用Python进行空间分析和地理空间数据科学 **课程概述:** 本课程是地理空间数据科学的入门，地理空间数据科学是数据科学的一个分支，专注于处理和分析具有空间属性的数据。课程将带您深入了解地理空间数据科学的核心概念和技术，而不仅仅是地图制作。您将学习如何使用Python中的`GeoPandas`库，这是地理空间数据科学领域的核心工具。 **课程内容:** * **基础入门:** 介绍地理空间数据科学的概念，以及`GeoPandas`库的强大功能。 * **空间数据处理:** 学习如何有效地读取、操作和处理各种空间数据格式。 * **核心空间分析:** 深入学习常用的空间分析技术，包括： * **缓冲区分析 (Buffer analysis):** 创建围绕地理要素的区域。 * **空间连接 (Spatial joins):** 根据空间关系合并数据集。 * **最近邻分析 (Nearest Neighbourhood analysis):** 找出最接近的地理要素。 * **空间数据可视化:** 掌握使用`GeoPandas`以及`Folium`, `IpyLeaflet`, `Plotly Express`等交互式库创建精美地理空间可视化图表的方法，涵盖多种主流地图类型。 * **进阶主题:** * **地理编码 (Geocoding) 和反向地理编码 (Reverse geocoding):** 将地址转换为坐标，反之亦然。 * **OpenStreetMap数据访问:** 学习如何在Python中访问和利用OpenStreetMap数据。 * **大型数据集处理技巧:** 掌握处理大规模地理空间数据集的高级技巧。 **学习目标:** 完成本课程后，您将能够使用Python执行大多数地理空间数据科学操作，并建立坚实的地理空间Python基础知识。 **教学方式:** 课程包含概念讲解、代码示例演示，并通过实践性的作业和项目帮助您巩固学习。
Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. It is beyond creating maps and merely focusing on where things happen but instead incorporates spatial analysis and insights derived from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get introduced with Geopandas, the workhorse of Geospatial data science Python libraries.The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science. We will be learning how to read spatial data effectively, manipulate and process spatial data, and carry out spatial operations. A large portion of the course deals with spatial operations like Buffer analysis, Spatial joins and Nearest Neighbourhood analysis. Each video contains a brief overview of the topic and a walkthrough with code examples. We conclude each section Geospatial data science assignment and project, that will help you learn more effectively.We will also cover spatial data visualization using both Geopandasa and other interactive libraries like Folium, IpyLeaflet and Plotly Express. We cover how to make stunning Geo visualization for the most widely used map types.The final section covers some advance features including Geocoding, reverse geocoding, accessing OpenStreetMap data in Python and some advanced tips and tricks to process large Geospatial datasets.At the end of this course, you will be able to perform most of Geospatial data science operations in Python and also build a strong foundational knowledge in Geospatial Python.
