[Intermediate] Spatial Data Analysis with R, QGIS & More
- 4.5 hours on-demand video
- 2 articles
- 22 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Get your team access to 4,000+ top Udemy courses anytime, anywhere.Try Udemy for Business
- 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
- Interest in spatial data analysis, mapping and GIS
- Basic knowledge of manipulating data using QGIS & R
- Basic understanding of different spatial data types & projections
- The course will be demonstrated using a Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.
- Have the latest versions of GRASS GIS and Google Earth installed on their computers
- Most of the R-based analysis will be demonstrated in R Studio (but can be carried out in either R or R Studio)
PRACTICAL TRAINING WITH REAL SPATIAL DATA FROM DIFFERENT SOURCES.
DEVELOP MAD GIS SKILLS AND PERFORM SPATIAL DATA ANALYSIS USING FREE KICKASS TOOLS SUCH AS QGIS, R, GRASS AND GOOGLE EARTH.
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!
- People who have a basic understanding of spatial data analysis and want to learn more
- Students interested in building up on skills acquired through my previous course Core Spatial Data Analysis: Introductory GIS with R and QGIS
- Conservation managers
- GIS Technicians
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.
This lecture will show how to configure GRASS to read in our own data. The demonstration data are in folder "Lecture_4-grass_eg1"
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"
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"
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"
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"
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".
A brief description of the data used for lectures 20--23
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".
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".
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".
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".