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所在平台: Udemy |
课程主页: https://www.udemy.com/course/urban-analytics-with-python/
课程评论：没有评论
Coursera “Urban Analytics with Python” 课程总结 本课程“Urban Analytics with Python: Geospatial Data Science and OpenStreetMap”是一门以 Python 编码为核心的实践课程，旨在深入讲解城市数据分析。 **课程内容概述：** * **地理空间数据基础：** 介绍地理空间数据的概念，区分矢量数据和栅格数据，并重点介绍如何将 OpenStreetMap (OSM) 作为强大的数据源。 * **Python 环境与库：** 指导学员搭建 Python 环境，并学习使用 GeoPandas 和 Shapely 等核心地理空间库。学员将立即开始编码，处理几何数据类型和地理空间数据结构。 * **OSM 数据获取与处理：** 学习使用 OSMNx 和 OverPy 等 Python 包，从 OSM 中获取不同类型的城市数据集，包括点、多边形和图数据（如建筑轮廓、道路网络）。所有操作均涉及 Python 编码。 * **高级城市分析与实践：** 教授高级城市分析技术，通过实际项目分析道路网络、建筑属性，并进行可视化探索。课程最终将通过一个综合性的小项目，结合各项关键绩效指标 (KPI)，利用 Python 创建城市宜居性指数。 **学习成果：** 完成课程后，学员将对地理空间数据科学有扎实的掌握，并能够运用 Python 和 OSM 数据进行高级城市分析项目。 **课程资源：** * 作者录制的章节介绍和总结视频。 * 配有 PDF 补充讲义的演示文稿。 * 包含屏幕共享的编码视频，并提供录制代码文件及 Jupyter Notebook 格式的优化代码。
IntroductionWelcome to "Urban Analytics with Python: Geospatial Data Science and OpenStreetMap"! In this course, you'll dive deep into the world of urban data analysis with a hands-on Python coding approach. This isn't just a theoretical overview - it's a practical course where you'll actively write code to manipulate, analyze, and visualize geospatial data from OpenStreetMap (OSM).The course starts with an introduction to geospatial data, including the distinctions between vector and raster data types, while offering a foundation in using OSM as a robust data source. As you move forward, we'll guide you through setting up your Python environment and introduce essential geospatial libraries like GeoPandas and Shapely. You'll begin coding right away, working with geometric data types and handling geospatial data structures.Once you're comfortable with Python and geospatial basics, we'll focus on acquiring different urban datasets from OSM using powerful Python packages like OSMNx and OverPy. You'll learn how to collect and work with point, polygon, and graph data, from building footprints to road networks. Every step will involve Python coding, ensuring you gain the technical skills to handle real-world geospatial data tasks.Finally, we'll wrap up with advanced urban analytics techniques. You'll engage in practical projects, analyzing road networks, building profiles, and creating visualizations to explore urban areas. The course concludes with a comprehensive mini-project, where you'll apply all the techniques you've learned to create a livability index for a city, combining various urban KPIs using Python.By the end, you'll have a solid grasp of geospatial data science and be able to use Python and OSM data to conduct advanced urban analytics projects. Let's get coding and unlock the power of urban data together!And what you get here:- Chapter intro and summary videos directly with the author- Presentations with PDF supplementary slides- Coding videos with screen sharing, which comes with the recorded live code files as well as cleaned-up version fo the odes in Jupyter Notebook formats
