空间数据分析与可视化 MasterTrack™ 计划

Spatial Data Analysis and Visualization MasterTrack™ Program

Solve real-world problems using geographic information systems with ArcGIS Pro and Python programming language with a top 5 public university in the U.S.

1130 次查看
加州大学戴维斯分校
Coursera
  • 完成时间大约为 6 个月
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Conduct spatial analysis and modeling with Python

Use Python to customize script and geoprocessing tools as part of your overall geodatabase administration

Utilize remote sensing imagery and hyperspectral imagery to explore images in greater detail

Produce attractive, easy-to-understand maps and geospatial models

Make informed decisions about spatial information design and mapping and follow through with them in ArcGIS Pro

课程概况

Learn how to analyze and visualize spatial data as a data scientist.

Industry areas that rely on skills in spatial data analysis, remote sensing, cartography and data visualization are growing at accelerated rates. Through this graduate-level online certificate program, you’ll gain the skills and knowledge you need in GIS, using ArcGIS Pro, and Python programming language to advance in your career.

Through real-world projects designed by UC Davis faculty, offered in partnership with ArcGIS developer Esri, you’ll learn how to apply data analysis and visualization techniques that are vital to solving business problems. You’ll also benefit from graded feedback from instructors and engage in live sessions with a group of high caliber peers.

By committing 8-10 hours of online study per week for six months, you will earn a Certificate of Completion in Spatial Data Analysis and Visualization from UC Davis and have the credentials in spatial decision support systems and cartography to help you land the job you want.

包含课程

Spatial Analysis with Data Handling

This course will allow you to take your GIS skills to the next level, all while working in ArcGIS Pro. Topics will cover a wide range of applications and concepts. You will use Survey123, to populate, analyze and present data results surveyed from fellow cohorts to design and implement your own surveys using ArcGIS Collector, to gain hands-on experience in data visualization.

The course will also explore the utility of using applications to establish topography rules to streamline and create accurate data. Using data from the United States Census, you will explore the value that this information can have on environmental justice and public health issues. Topographic topics will include derivatives of Digital Elevation Models, with various methods for display and analysis, as well as 2D and 3D representation, complete with the generation and export of a fly-through animation video. You will also use Model Builder and Python to build tools to automate repeated tasks, enable others to make changes in the workflow, or evaluate the results.

Introduction to Remote Sensing

In this course, you will continue to hone your skills working with concepts of remotely-sensed data through the use of Image Analyst Tools and Raster Functions. You’ll develop the fundamental skills of georegistration and mosaicking, and assemble a time series, from which you can ascertain and quantify the extent, intensity and recovery of wildfire regions in California.

You will also be introduced to hyperspectral imagery, and use ArcGIS Pro to explore the relationships between data in various parts of the EM spectrum. Next, you will visit LiDAR data, and fuse this information with other types of imagery in order to assess forest structure. A highlight of the Remote Sensing course in this MasterTrack will feature a lecture by Dr. Susan Ustin, who will outline some of the technologies on the brink of release, or in the near future.

Cartography

Well-designed maps can inform and inspire in websites, presentations, and in print. In this course, you will learn data visualization techniques to create high-quality cartographic products with ArcGIS Pro software.

The goal is to improve your ability to convey messages with maps by identifying your audience and medium, assessing and managing data, and using design principles to craft an engaging final product. Lab exercises include creation of reference, thematic, and interactive or animated maps.

This course presents the process of map making from the design perspective, focusing on communication effectiveness.

面向人群

需要空间数据分析,遥感,制图和数据可视化的行业领域的专业人员

课程项目

Spatial Data Handling with Python

You will explore the basics of spatial data types and their representation in Python. You manipulate spatial data and use vector and raster type data to become proficient in reading, subsetting, spatial queries and transformations.

WHAT YOU WILL LEARN

Reading and writing spatial data into Python
Extracting raster values with vector objects
Manipulating vector data in Python (union, intersect, aggregate, etc.)
Manipulating raster data in Python (crop, mask, reclassify, etc.)
Spatial data concepts (vector, raster, coordinate reference systems)

Analyzing Spatial Data

You’ll use spatial statistics to solve three exercises throughout the course: point pattern analysis (with examples from ecology and health), regression with spatial data (with examples from the social sciences), and spatio-temporal data (with examples from the environmental sciences).

WHAT YOU WILL LEARN

Basic statistical concepts of spatial data, such as autocorrelation and scale
Exploration and statistical modeling with spatio-temporal data
Global and local measures of spatial autocorrelation
Global and local regression with spatial data
Point pattern analysis

Multimodal Map Making (Team Project)

Design a beautiful and complex map that clearly communicates a message to your target audience. In this final project, you’ll work with a team to transform raw data into an engaging poster, print map, and PowerPoint slide that demonstrates your ability to convey important concepts in an engaging presentation format.

WHAT YOU WILL LEARN

Visual data hierarchy and color correspondence
Design features like a legend, north arrow, scale bar, credits for data sources, author list, and other relevant supporting elements
Settings that make the map appealing and viewer-friendly (alignment, contrast, repetition, balance, space)

预备知识

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