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.
In the post titled Access, Download, Process and VIsualize sea surface height and geostrophic current from AVISO in R posted in my blog on Monday, Apr 15, 2019, I explained how we can download the satellite data like sea surface height from AVISO in R. I illustrate in detail getting the data using xtractomatic package (Mendelssohn 2018). Though xtractomatic package provide functions that allows us to get access to the ERDDAP server and get the data, but one big challenge is that the data comes is array and need an expensive computation process, especially if you deal with gridded data for a long term time series.
In the previous post I illustrated a simple way to do Principal Component Analysis in R. I simply used the output results from prcomp() function of R base. But, I constantly find hard to the untidy output that prcomp generates and wished to get a tidy result. In this post I will illustrate the approaches that I was inspired by Claus Wilke in the post PCA tidyverse style.
Principal Component Analysis (PCA) Principal Component Analysis (PCA) is widely used to explore data. This technique allows you visualize and understand how variables in the dataset varies. Therefore, PCA is particularly helpful where the dataset contain many variables.This is a method of unsupervised learning that allows you to better understand the variability in the data set and how different variables are related.
The Components in PCA are the underlying structure in the data.
I was looking for bathymetry dataset for Lake Victoria online and I came across this link. It stores several products of the bathymetry data of the Lake Victoria. Among them products is the gridded TIFF file. This dataset was created by a team from Harvard University in 2017 (Hamilton et al. 2016). They used over 4.2 million points collected over 100-years of surveys. The point data was obtained from an Admiral Bathymetry map and points collected in the field.
