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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/gis-data
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课程名称：GIS数据格式、设计与质量 课程概述：本课程是地理信息系统（GIS）专业化的第二部分，深入探讨常见的数据类型（如光栅数据和矢量数据）、结构、质量及存储，分为四个为期一周的模块： 第一周：学习数据模型和格式，全面理解矢量数据和光栅概念，以及数据规模的影响与如何从网络服务加载图层。 第二周：通过使用矢量属性表、编写查询字符串、定义查询、添加和计算字段，创建矢量数据模型。您还将学习通过数字化过程创建新数据，并使用ArcGIS中的内置编辑工具。 第三周：了解GIS中常见的数据存储机制，包括地理数据库和 shapefile。学习如何在项目中选择这些存储格式，并优化其速度和大小。此外，您将首次接触光栅数据，使用数字高程模型并创建坡度和距离分析产品。 第四周：探索数据集并评估其质量和不确定性。您还将学习如何将地图和数据带到互联网，并快速使用ArcGIS Online创建网络地图。 您可以将《GIS数据格式、设计与质量》作为独立课程学习，也可以作为GIS专业化的一部分。建议在学习本课程之前具备完成第一门课程《GIS基础》的相应经验。完成此专业化的第二门课程后，您将获得成功完成整个项目所需的技能。 课程大纲： 1. 课程概述与数据模型和格式：涵盖矢量和光栅数据模型的主要概念，数据规模、设计数据表、使用矢量属性表，以及在关系数据库中更有效地分离和连接数据。 2. 创建和处理矢量数据：重点在于处理矢量数据，复习地理处理并介绍相交工具，涉及编写查询字符串、添加和计算字段、配置选择、编辑和创建要素类，以及数字化数据的所有相关知识。 3. 存储格式与光栅处理：选择特定目的的数据存储格式以及光栅操作工具。内容包括地理数据库设计及文件地理数据库、个人地理数据库、shapefile和SQLite数据库的考量，创建和处理光栅数据，空间分析、光栅的地理配准、光栅计算器以及区域统计。 4. 数据质量与创建网络地图：第一部分讲解不确定性和数据质量，包括影响矢量要素类中数据关系的拓扑学讲座。第八课，特邀讲师Megan Nguyen将介绍如何使用ArcGIS Online，与同事分享地图。
Name:Course Overview & Data Models and Formats
Description:This first module covers major concepts in vector and raster data models, scale, designing data tables, using vector attribute tables, and separating and joining data in order to use it more effectively in a relational database.
Name:Creating and Working with Vector Data
Description:This module is all about working with vector data. We'll review geoprocessing and introduce the intersect tool. This module also covers writing query strings to subset data, adding and calculating fields, configuring selections, editing and creating feature classes, and everything you need to know about digitizing data.
Name:Storage Formats and Working with Rasters
Description:This module covers choosing data storage formats for particular purposes as well as tools for working with rasters. In the first lesson, we'll discuss geodatabase design and go over considerations for file geodatabases, personal geodatabases, shapefiles, and SQLite databases. The second lesson covers creating and working with raster data. We'll talk about spatial analysis, georeferencing rasters, raster calculator, and using zonal statistics.
Name:Data Quality and Creating Web Maps
Description:The first half of this module goes over uncertainty and data quality, including a lecture on topology, which affects data relationships in your vector feature classes. In Lesson 8, guest lecturer Megan Nguyen will talk all about using ArcGIS Online, including sharing our maps with our colleagues.
In this course, the second in the Geographic Information Systems (GIS) Specialization, you will go in-depth with common data types (such as raster and vector data), structures, quality and storage during four week-long modules: Week 1: Learn about data models and formats, including a full understanding of vector data and raster concepts. You will also learn about the implications of a data’s scale and how to load layers from web services. Week 2: Create a vector data model by using vector attribute tables, writing query strings, defining queries, and adding and calculating fields. You'll also learn how to create new data through the process of digitizing and you'll use the built-in Editor tools in ArcGIS. Week 3: Learn about common data storage mechanisms within GIS, including geodatabases and shapefiles. Learn how to choose between them for your projects and how to optimize them for speed and size. You'll also work with rasters for the first time, using digital elevation models and creating slope and distance analysis products. Week 4: Explore datasets and assess them for quality and uncertainty. You will also learn how to bring your maps and data to the Internet and create web maps quickly with ArcGIS Online. Take GIS Data Formats, Design and Quality as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first course in this specialization, Fundamentals of GIS, before taking this course. By completing the second class in the Specialization you will gain the skills needed to succeed in the full program.
