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所在平台: Udemy |
课程主页: https://www.udemy.com/course/spatial-data-and-geographic-information-systems-gis-in-r/
课程评论：没有评论
课程名称：完整的3小时R语言空间数据与GIS入门 课程概述： 本课程专为想要进入GIS领域、学习R编程和空间数据的初学者设计。课程内容涵盖了在R环境中处理空间数据的基本知识，适合学术人士、顾问及希望进入GIS领域的个人。您将参与多个项目，例如： - 计算华盛顿州的植被覆盖率，并在感兴趣的点提取数值 - 在缺失数据区域内插补空气质量值 - 根据人口普查的形状文件计算主要社区的平均工资 课程结束时，您将掌握以下技能： - 处理不同类型的数据（向量、栅格等） - 进行GIS分析 - 创建美观的地图 - 使用真实世界的数据 课程额外好处： - 提供所有幻灯片 - 提供所有空间数据集 - 终身访问课程内容 无需任何先前经验，只需带上学习的热情和利用空间数据的积极态度。通过本课程，您将掌握解决空间挑战必需的工具，并自信地踏入更高级的数据科学领域。我的目标是通过用户友好的讲座、动手练习和有趣的项目，引导您发现数据与位置之间迷人的交集。我将逐步指导您，确保您能够自信地使用地理信息系统（GIS）并探索多样的空间数据集。
If you are entering the field of GIS and want to learn how to get started with R programming and spatial data, this is the course for you. We go over all the basis of how to work with spatial data in an R environment. This course is designed for academics, consultants and individuals looking to enter the GIS field. Here are a few examples of the projects you will work on: Calculate vegetation across Washington and extract values at points of interestInterpolate air quality values in areas with missing dataCalculate the average salary across major neighbourhoods using a Census shape file And more!By the end of this course you will have learnt the following: Work with different data types (vector, raster, etc.)Conduct GIS analysisCreate beautiful mapsWork with real-world data.More benefits:All slides are providedAll spatial data sets are providedLifetime access to the course, forever. No prior experience is necessary - just bring your enthusiasm to learn and an eagerness to harness the power of spatial data. By the end of this course, you'll be equipped with the essential tools to tackle spatial challenges and confidently venture into more advanced data science pathways.My goal is to help you discover the fascinating intersection of data and location through user-friendly lectures, hands-on exercises, and engaging projects. I will guide you step-by-step, ensuring you feel confident working with Geographic Information Systems (GIS) and exploring diverse spatial datasets.
