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.
Introduction Maps are used in a variety of fields to express data in an appealing and interpretive way. Map making — the art of cartography — is an ancient skill that involves communication, intuition, and an element of creativity. Current solutions for creating maps usually involves GIS software, such as ArcGIS, QGIS, which allow to visually prepare a map.
On the other hand, R, a free and open-source software development environment (IDE) that is used for computing statistical data and graphic in a programmable language, for a long time has had a relatively simple mechanism, via the maps package, for making simple outlines of maps and plotting lat-long points and paths on them.
Time-series analysis aims to analyse and learn the temporal behaviour of datasets over a period. Examples include the investigation of long-term records of temperature , sea-level fluctuations, the effect of the El Niño/Southern Oscillation on tropical rainfall, and surface current influences on distribution of temperature and rainfall. Th e temporal pattern of a sequence of events in a time series data can be either random, clustered, cyclic, or chaotic.
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.
Introduction In this post, we explore the analysis of local spatial autocorrelation statistics, focusing on the concept and its most common implementation in the form of the Local Moran statistic. We explore how it can be utilized to discover hot spots and cold spots in the data, as well as spatial outliers. To illustrate these techniques, we will use the catch data from Deep Sea fishing authority.
Moran’s I Moran’s I statistic is arguably the most commonly used indicator of global spatial autocorrelation.
