Descripción del Curso

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.

About The Author    

Jane Wang is a GIS Developer  and has a Masters Degree in Geospatial Sciences with experiences in  research, teaching, data analysis, and visualizations. She has 6 years'  academic and 3 years' professional experience working with geospatial  products and communicating scientific information in appealing visual  outputs using R, GIS, Python, and Adobe Suite. She has been given awards  for her creative and novel geospatial work in international conferences  and is also an active member of the R community. She has actively  communicated with and taught a wide-ranging audience. 

Detalles del Curso
en
en
Packt Publishing
Ritmo propio
Intermedio
3 horas
Detalles del Curso
en
en
Packt Publishing
Ritmo propio
Intermedio
3 horas
Descripción del Curso

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.

About The Author    

Jane Wang is a GIS Developer  and has a Masters Degree in Geospatial Sciences with experiences in  research, teaching, data analysis, and visualizations. She has 6 years'  academic and 3 years' professional experience working with geospatial  products and communicating scientific information in appealing visual  outputs using R, GIS, Python, and Adobe Suite. She has been given awards  for her creative and novel geospatial work in international conferences  and is also an active member of the R community. She has actively  communicated with and taught a wide-ranging audience.