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所在平台: Udemy |
课程主页: https://www.udemy.com/course/arcprogeoanalytics/
课程评论：没有评论
**课程名称：** 利用桌面计算机进行GIS并行处理 **课程概述：** 本课程面向GIS从业者，特别是当您遇到的数据量已经影响到工作效率时。课程将深入探讨如何利用桌面计算机的并行处理能力来处理大规模地理空间数据。我们将重点介绍使用Esri和开源工具（如GeoAnalytics Desktop和Postgres/PostGIS）来实现这一目标的最佳实践。 **核心要点：** * **桌面并行处理潜力：** 现代桌面PC通常拥有8个或更多处理核心（CPU），这些是提升GIS分析 T 速度的宝贵资源。 * **关键技术：** * **Esri GeoAnalytics Desktop：** 利用Apache Spark技术，将ArcGIS Pro中的地理处理工具进行并行化，操作方式与现有工具类似，易于集成。 * **Postgres/PostGIS：** 通过其worker进程机制，为GIS分析提供了一个强大的并行处理框架，虽然需要一些配置，但能带来卓越的数据处理性能。 * **优化并行处理效果：** 课程将不仅介绍GeoAnalytics Desktop和Postgres的具体功能，还将分享在硬件、软件和数据管理方面的最佳实践，以确保并行工具的有效性。 * **学习内容涵盖：** * 处理大规模空间数据的硬件考量。 * 存储大规模空间数据的数据库类型。 * 处理大规模空间数据的不同坐标系统。 * 提高数据库搜索速度的索引策略。 * 优化GIS数据格式以提升空间分析效率。 * **学习方式：** 课程结合理论讲解和实践操作，让学员亲身体验和测试并行处理流程。 **目标学员：** 准备好处理海量空间和非空间数据的GIS专业人士。
Did you know that you can leverage parallel processing using your desktop computer? If you are moving into a territory in your GIS career where the amount of data you are using prevents you from doing your job effectively, this course is for you. We'll focus on the best practices for using large data sources and the new offering by Esri and open source tools to parallelize geospatial tasks. Esri's GeoAnalytics Desktop tools and Postgres/PostGIS provide a parallel processing framework for GIS analysis using your existing PC. Most PCs today have 8 or more processing cores (CPUs). The use of Apache Spark in GeoAnalytics Desktop and the use of worker processes in Postgres turns your desktop PC system into a mini high-performance computing lab. The tools are so well integrated in ArcGIS Pro that they operate in the same way as other geoprocessing tools in ArcGIS. And, while Postgres requires a little more thought, the flexibility it offers provides really exceptional speed for handling large data analysis projects.While parallel processing tools exist, they may be severely ineffective unless you properly utilize the hardware, software, and data on your computer. This class will introduce you not only to the actual features in GeoAnalytics Desktop and Postgres, but also some of the best practices when working with hardware, software, and data. Some of the topics we'll address include:hardware considerations for working with large spatial data.classes of databases to store large spatial data.working with different coordinate systems with large spatial data.indexing strategies for improving the speed of database searches.formatting GIS data to improve spatial analysis.You will have an opportunity to not only learn about the theoretical topics of large spatial data analysis, but you'll perform hands on activities to test the processes yourself. This is the perfect course to get you ready for working with large amounts of spatial and non-spatial data.
