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4.38 out of 5
4.38
394 reviews on Udemy

[Intermediate] Spatial Data Analysis with R, QGIS & More

Become an Open source GIS Guru and Tackle Spatial Data Analysis Using R, QGIS, GRASS & GOOGLE EARTH
Instructor:
Minerva Singh
4,482 students enrolled
English [Auto]
Carry out the most common spatial data analysis and GIS tasks using free software tools
Perform advanced spatial data analysis and mapping using both R and QGIS
Develop robust map-making skills including harnessing the power of Google Earth.
Get started with using the powerful, freeware tool GRASS GIS for some spatial data analysis tasks
Stop spending money on paid-for GIS software tools
Have a solid foundation to learn advanced GIS tasks
Gain experience in working with a variety of different spatial data and gain hands-on expertise

PRACTICAL TRAINING WITH REAL SPATIAL DATA FROM DIFFERENT SOURCES.

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DEVELOP MAD GIS SKILLS AND PERFORM SPATIAL DATA ANALYSIS USING FREE KICKASS TOOLS SUCH AS QGIS, R, GRASS AND GOOGLE EARTH.

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This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS.

This course takes a completely practical approach to spatial data analysis and mapping- Each lecture will teach you a practical application/processing technique which you can apply easily.

The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences.  

The underlying motivation for the course is to ensure you can put spatial data analysis into practice today and develop sound GIS analysis skills. You’ll be able to start analyzing spatial data for your own projects, and IMPRESS YOUR FUTURE EMPLOYERS with examples of your PRACTICAL spatial data analysis abilities. This course is different from other training resources. Each lecture seeks to enhance your GIS skills in a demonstrable and tangible manner and provide you with practically implementable GIS solutions.

This is an intermediate course in spatial data analysis, i.e. we will build on on basic spatial data analysis tasks (such as those covered in the beginner version course: Core Spatial Data Analysis: Introductory GIS with R and QGIS) and teach users how to practically implement more complex GIS tasks such as interpolation, mapping spatial data, geo-referencing and detailed vector processing. Additionally you will be introduced to preliminary geo-statistics and mapping/visualizing spatial data.

This course covers complex GIS techniques, and by completing this course, you will be implementing these PRACTICALLY in freely-available software, thus making you MORE ATTRACTIVE TO EMPLOYERS.  

It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts pertaining to the different spatial data analysis techniques demonstrated in the course. However, majority of the course will focus on working with real spatial data from different sources. After each video you will learn how to practically implement a new concept or technique in the different softwares used for the course.

During the course of my research I have discovered that R is a powerful tool for collating and analyzing spatial data acquired from different sources.  Proficiency in spatial data analysis in R and QGIS has helped me publish more peer reviewed papers faster. Feel free to check out my profile on ResearchGate.

FREE BONUS: You will have access to all the data used in the course, along with the R code files. You will also have access to future lectures, resources and R code files. Enroll in the course today & take advantage of this special bonus!

I don’t have to remind you that we have a RISK-FREE GUARANTEE in the case of you not being satisfied with the course. Take action now!


INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

1
Introduction: Welcome to the Course
2
Brief Introduction to the Concepts of Spatial Data Analysis

In this lecture students will be briefly introduced to the concepts pertaining to spatial data analysis such as coordinate reference systems and the different spatial data that will be used in the course. 

3
Get Started with R and QGIS
4
Get Started with GRASS

This lecture will show how to configure GRASS to read in our own data. The demonstration data are in folder "Lecture_4-grass_eg1"

5
Conclusion to Section 1
6
Introduction Quiz

This quiz will test the introductory concepts relating to spatial data and the software tools that we will use in this course 

MAPPING THE VISUALIZATION & DISTRIBUTION OF SPATIAL DATA: Shapefile Analysis

1
Introduction to shapefiles and exploring their properties in QGIS

This lecture presents a brief overview of what shapefiles are and their attribute table is. Further I briefly demonstrate how to modify the basic properties of shapefiles to improve their appearance. The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

2
Visualize Shapefiles Using Qualitative Attributes in QGIS

In this section we will see how shapefiles can be rendered and visualized using qualitative attributes. We will focus on the world map and display the different continents in there as a way of making the world map more intuitive.  The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

3
Visualize Shapefiles Using Quantitative Attributes in QGIS

In this section we will see how shapefiles can be rendered and visualized using quantitative attributes. We will focus on the world map and use the country areas in there as a way of making the world map more intuitive. The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

4
Introduction to Shapefile Mapping in R

In this lecture the students will be exposed to basic concepts of mapping shapefile data and mapping shapefile attributes in R using spplot function.  The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

5
More Shapefile Mapping in R

This lecture will demonstrate how to build choropleth maps using shapefiles in R. The students will also be introduced to the R package, GISTools for choropleth mapping.  The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

6
Spatial Data Mapping with ggplot2 and Google Earth in R

In this lecture, students will learn how to use Google Earth data and display their own spatial data on Google Earth base layers. The data for this lecture are in folder "Lecture_11-ggplot_GE_R"

7
Conclusion to Section 2
8
Spatial Data Visualization Quiz

VECTOR GEO-PROCESSING: Detailed Analysis & Processing of Shapefile Data

1
Spatial joins in QGIS

In this section I will demonstrate how to add data from a CSV file to a shapefile using a spatial join. Spatial joins work by combining data for common attributes in CSV and the shapefile. The data for this lecture are in folder "Lecture_13-JpnPop_joinR". 

2
Spatial joins in R

In this lecture we will see how to carry out the spatial joining demonstrated in the previous lecture in QGIS using R. The data for this lecture are in folder "Lecture_13-JpnPop_joinR". 

3
Basic Shapefile statistics in QGIS & R

In this lecture I will demonstrate how to compute basic descriptive statistics from a shapefile using R.  The data used for the lecture demonstration are present in the folder "Lecture_6-countries_shp"

4
Add Buffer Areas to Shapefiles in QGIS

In this lecture the students will learn how to add a user defined buffer to a polygon or a polyline. The data used in this lecture are present in the folder "Lecture_16-buffer_vector_data". 

5
Add Buffer Areas to Shapefiles in R

In this lecture the students will learn how to add a user defined buffer to a polygon or a polyline. The data used in this lecture are present in the folder "Lecture_17-mynamar_intersecn". 

6
Create Outer Buffers in R and QGIS

This lecture demonstrates how to make an outer buffer/boundary in both R and QGIS. The data for this lecture are in folder "Lecture_18-outer_buffer".

7
Data used in Clip, Union and Intersection in QGIS

A brief description of the data used for lectures 20--23

8
Union of Two Shapefiles in QGIS

In this lecture the students will learn how to carry out the union between 2 shapefiles in QGIS. The data used in this lecture are present in the folder "Lecture_17-mynamar_intersecn". 

9
Clip Vector Data in QGIS

In this lecture the students will learn how to clip a shapefile in QGIS. The data used in this lecture are present in the folder "Lecture_17-mynamar_intersecn". 

10
Intersection of two vectors in QGIS

In this lecture the students will learn how to intersect 2 shapefiles in QGIS. The data used in this lecture are present in the folder "Lecture_17-mynamar_intersecn". 

11
Clipping and Intersection operations in R

In this lecture I will show how to carry out intersection between two shapefiles and clip the bigger shapefile using the smaller shapefile as a cookie-cutter in R. The data used in this lecture are present in the folder "Lecture_17-mynamar_intersecn". 

12
Conclusion to Section 3
13
Vector Geo-Processing Quiz

This quiz is designed to test the ability of the students to carry out analysis of shapefile data

POINT PATTERN ANALYSIS: Analyze & Map XY Point Spatial Data

1
Heat maps in QGIS

In this lecture I will demonstrate how to make a heat map from point/XY data in QGIS and visually display the distribution and concentration of attributes using QGIS. The data used in this lecture are present in the folder "Lecture_25-Heatmap". 

2
Map Spatial Distribution of Point Data-Brief Introduction

A brief introduction to the theory behind Kernel Density Estimation (KDE)

3
Map Spatial Distribution of Point Data in R

In this lecture I will demonstrate how to use geographical point data to map the distribution and clustering of an attribute using Kernel Density Estimates in R. A brief introduction to the package spatstat (used for analyzing point/XY data) has been provided. The data used in this lecture are present in the folder "Lecture_27-uk_plaque". 

4
Plot a Heatmap on Google Earth using R

In this lecture, the students will learn how to plot heat maps to show the spatial distribution and concentration of point data on Google Earth in R. The data for this lecture are in "Lecture_27-uk_plaque".

5
Brief Introduction to the Concepts of Interpolation
6
Interpolating point data in QGIS

In this lecture, the students will learn how to carry out interpolation of point data in QGIS. The data for this lecture are in "Lecture_30-aust_elev".

7
Interpolating point data in R-Thin Spline Interpolation

Students will be able to carry out thin spline interpolation on point data to produce a raster surface. The data for this lecture are in "Lecture_31-interpolation_r".

8
Interpolating point data in R-Inverse Distance Weighting(IDW)

This lecture demonstrates how to carry out the IDW interpolation of point data in R. Students will be able to carry out thin spline interpolation on point data to produce a raster surface. The data for this lecture are in "Lecture_31-interpolation_r".

9
Interpolating point data in R-Kriging

This lecture briefly demonstrates how to carry out kriging in R. Students will be able to carry out thin spline interpolation on point data to produce a raster surface. The data for this lecture are in "Lecture_31-interpolation_r".

10
Interpolating point data in GRASS

This lecture demonstrates how to use GRASS to implement some interpolation techniques on point data. The data for this lecture are in "Lecture_32-interpolation_grass1".

11
Conclusion to Section 4
12
Point Pattern Analysis Quiz

This quiz seeks to test the understanding of carrying out point patterns analysis of spatial data

RASTER DATA PROCESSING: Map and Analyze Image Data

1
Display Raster Data in QGIS

I will demonstrate how to display raster data in QGIS and how to use Properties to enhance the rendering and visualization of these data.  The data for this lecture are in "Lecture_37-digital elevation model_easia".

2
Display Raster Data in R

This lecture will demonstrate how to display raster data in R. A brief introduction the package rasterVis which is used for visualizing raster data in R will be provided. The data for this lecture are in "Lecture_37-digital elevation model_easia".

3
Zonal Statistics in QGIS

This lecture will demonstrate how to extract raster statistics for a given set of shapefile polygons. The data for this lecture are in "Lecture_38-zonal_stats".

4
Merge Rasters in QGIS

In this lecture I will demonstrate how to merge and stitch together non-overlapping rasters in QGIS . The data for these lectures are in folder "Lecture_39-raster_merging"

5
Merge Rasters in R

This lecture demonstrates how we can merge adjacent, non-overlapping rasters in R.  The data for these lectures are in folder "Lecture_39-raster_merging"

6
Clip a Raster Using a Shapefile in QGIS & R

Briefly demonstrate how to clip a raster to desired boundary using a shapefile as cookie-cutter in R and QGIS. The data for this lecture are in "Lecture_37-digital elevation model_easia".

7
Clip a Raster Using a Shapefile in GRASS GIS

Briefly demonstrate how to clip a raster to desired boundary using a shapefile as cookie-cutter in GRASS. The data for this lecture are in "Lecture_42-clipRasters_grass".

8
Terrain analysis in GRASS GIS

In this lecture, I will demonstrate how to carry out basic terrain analysis calculations on DEMs using GRASS. The data for this lecture are in "Lecture_37-digital elevation model_easia".

9
Geo-referencing in QGIS

In this lecture I will show you how to geo-reference image data using QGIS. I will show how to add coordinate information both manually and using a Google Earth base layer map. The data for this lecture are in folder "Lecture_44-georeferencing_qgis"

10
Conclusion to Section 5
11
Raster Processing Quiz

A brief quiz pertaining to processing of raster data

OTHER (SLIGHTLY) ADVANCED GIS TASKS

1
Rationale For This Section
2
Graphical Modeler in QGIS: Automating Analysis with Processing Models

This lecture shows how simple GIS tasks can be automated as a part of a workflow in QGIS. The data for this lecture are in "Lecture_37-digital elevation model_easia".

3
Multi-Criteria Decision Making/Suitability Analysis-Theory
4
Multi-Criteria Decision Making/Suitability Analysis in QGIS

This lecture will show how to implement the AHP process on raster data in QGIS. The data for this lecture are in "Lecture_49-suitability analysis_MCDM".

5
Web Mapping in QGIS- Brief Introduction

This lecture will show how to build a basic interactive webmap in QGIS. The data for this lecture are in "Lecture_50-webmap_qgis".

6
Web Mapping in R- Build a Basic Interactive Map

This lecture shows how the student can build interactive web maps using their own spatial data in R. An introduction to the leaflet package is provided. The data for this lecture are in "Lecture_50-webmap_qgis".

7
CONCLUSION

Additional Material

1
Work With R's Inbuilt Geospatial Data
2
Use ggplot2 to visualize geographic data
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5 hours on-demand video
2 articles
Certificate of Completion

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