English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 11m | 807 MB
Create professional and interactive geospatial and cartographic projects using an advanced free and open-source technology
Integrating geospatial data science and traditional cartographic methods is in demand for modern geospatial analysts. In an age of flourishing data products, having a working proficiency with QGIS and R is an added advantage to every analyst.
This course introduces you to the full workflow, ranging from acquiring data, data wrangling, and analysis to outputting and publishing visualization products. We touch on a variety of datasets (including remote-sensing data and techniques) and incorporate machine learning in QGIS analytical steps. We further investigate geospatial analysis using the most up-to-date R packages, such as ggplot2, raster, sf, Leaflet, and Shiny.
By the end of the course, you will be able to produce interactive maps and professional cartographic products, deploy them as a Shiny application, and critique a variety of end-results.
A step-by-step guide using realistic examples and illustrative slides to help you achieve a full workflow.
What You Will Learn
- Develop a Shiny application for geospatial data processing and visualizations using R and QGIS 3.4.
- Implement an efficient and reproducible workflow for geospatial analysis.
- Create interactive and professional mapping products and publish them on open applications.
- Conduct advanced geospatial analyses that address practical issues such as land cover using machine algorithms.
- Use modern and novel techniques to code with best practices.
- Utilize skills to serve a wide range of groups, including governmental organizations, Academia, consulting firms, and natural-resource industries.
- Critique a variety of geospatial data products and optimize your geospatial abilities to communicate your findings effectively
Introduction to Spatial Data and Operating Environment
1 Course Overview
2 Introduction to Spatial Data and GIS
3 Geospatial Data Science and the Need for Reproducible Workflow
4 Software Download and Setup
Data Preparation and Basic Plotting
5 Importing Data into QGIS
6 Importing Data into R
7 Turning Tables into Spatial Data
8 Basic Plotting and Visualization
9 Exporting Data
Geospatial Processing with Tidyverse and sf Package
10 Data Query and Acquisition from an API
11 Tidy Data and Analysis with Tidyverse Library
12 Statistical Summaries and Graphic Outputs
13 Making an Interactive Map with Leaflet Library
14 Map Specifics, Markers, Legends, and Scales
Geospatial Processing with Raster Package and Machine Learning
15 Landsat Data Query and Processing in QGIS
16 Supervised Classification – Machine Learning Logic
17 Supervised Classification – Landcover Analysis
18 Integrating R and QGIS Workflow
19 Presentation-Ready Map Product
Visualization and Application Deployment
20 Shiny Server – The Basics
21 Sharing the Map on Shiny
22 Reactive Values – Displaying Static Plots Upon Clicking on a Polygon
23 Deploying the Application
24 Advanced Customizations in Shiny
Extending Beyond the Map
25 Modern Cartographic Products
26 Novel Ways of Map Making – Creating a Dot Map
27 Data Types and Color Choices
28 Where to Find Spatial Data
29 OpenStreetMaps and Further Resources