Mapping raw data can lead to spurious spatial features. For example, regions can appear highly variable because of small sample sizes in spatial sub-units (as in the radon example) or small populations (as in the cancer example), and these apparently variable regions contain a disproportionate number of very high (or low) observed parameter values
Furthermore, maps really do make convenient look-up tables (what is the cancer rate, or mean radon level, in my county?). Unfortunately, even maps that are intended to be used only as look-up tables are almost sure to be used for identifying spatial features – we find it very hard to suppress this instinct ourselves
Kyrie Irving was trending on Twitter today because he believes the Earth is flat. I asked him about it. pic.twitter.com/ODe9aP9qmK
— Arash Markazi (@ArashMarkazi) February 18, 2017
Uh oh: Draymond is a Flat Earth/Kyrie Truther. pic.twitter.com/icJThyG7hx
— The Crossover (@TheCrossover) February 18, 2017
The Geospatial Data Abstraction Library (GDAL) is a C/C++ geospatial data format translation programming library and associated set of utility programs built using the library. GDAL is one of the jewels of the open source community, and I want to help you understand how to leverage its power to process spatial data.
“Here, you can take that, that’s the final map of the numbers,” Trump said, according to Reuters. “It’s pretty good, right? The red is obviously us.” 1
Use the geofacet package to build a map that depicts the important variables more than the spatial area.
install.packages("geofacet")
and the facet_geo()
function.