R is particularly powerful for spatial statistical analysis and quantitative researchers in particular may find R more useful than GIS desktop applications. As data becomes more geographical, there is a growing necessity to make spatial data more accessible and easy to process. While there are plenty of tools out there that can make your life much easier when processing spatial data (e.g. QGIS and ArcMap) using R to conduct spatial analysis can be just as easy.
As an Oceanography, one key parameter that need to get right is the bathymetry. Bathymetry is the science of determining the topography of the seafloor. Bathymetry data is used to generate navigational charts, seafloor profile, biological oceanography, beach erosion, sea-level rise, etc. There prenty of bathymetry data and one of the them is the GEBCO Gridded Bathymetry Data.
The General bathymetric Chart of the Oceans (GEBCO) consists of an international group of experts in ocean mapping.
OpenStreetMap (OSM) is a collaborative project to create a free editable geographic database of the world. The geodata underlying the maps is considered the primary output of the project (Wikipedia contributors 2021). OpenStreetMap was born in 2004 in the UK, at a time when map data sources were controlled by private and governmental players. They were expensive and highly restrictive which made them accessible only by large companies.
Data is the most important asset. It validates a research story and a conclusions; it provides a platform of confidence for other researchers who might continue your work; and it is a resource that can be used by researchers in other fields to undertake new work, perhaps completely unrelated to your own research interests. Well-organised data that is accessible to the research community can continue to provide extended benefit and value long after your projects have been completed.
Word clouds Word clouds visualize word frequencies of either single corpus or different corpora. Although word clouds are rarely used in academic publications, they are a common way to display language data and the topics of texts - which may be thought of as their semantic content. To exemplify how to use word clouds, we are going to have a look at the State of Environment issued in 2019 by the department of environment of the vice president’s office.
