A suite of conversion functions to create internally standardized
spatial polygons data frames. Utility functions use these data sets to
return values such as country, state, time zone, watershed, etc. associated
with a set of longitude/latitude pairs. (They also make cool maps.)The MazamaSpatialUtils package was created by MazamaScience to regularize our work with spatial data. The sp, rgdal and maptools packages have made it much easier to work with spatial data found in shapefiles. Many sources of shapefile data are available and can be used to make beautiful maps in R. Unfortunately, the data attached to these datasets, even when fairly complete, often lacks standardized identifiers such as the ISO 3166-1 alpha-2 encodings for countries. Maddeningly, even when these ISO codes are used, the dataframe column in which they are stored does not have a standardized name. It may be called ISO or ISO2 or alpha or COUNTRY or any of a dozen other names we have seen.
While many mapping packages provide ‘natural’ naming of countries, those who wish to develop operational, GIS-like systems need something that is both standardized and language-independent. The ISO 3166-1 alpha-2 encodings have emerged as the defacto standard for this sort of work. In similar fashion, ISO 3166-2 alpha-2 encodings are available for the next administrative level down – state/province/oblast, etc.. For time zones, the defacto standard is the set of Olson time zones used in all UNIX systems.
The main goal of this package is to create an internally standardized set of spatial data that we can use in various projects. Along with three built-in datasets, this package provides convert~() functions for other spatial datasets that we currently use. These convert functions all follow the same recipe:
@data slot so that it adheres to package internal standardsOther datasets can be added following the same procedure.
The ‘package internal standards’ are very simple.
If other columns contain these data, those columns must be renamed or duplicated with the internally standardized name. This simple level of consistency makes it possible to generate maps for any data that is ISO encoded. It also makes it possible to create functions that return the country, state or time zone associated with a set of locations.
This package is designed to be used with R (>= 3.1.0) and RStudio so make sure you have those installed first.
Users can use the devtools package to install the latest version of the package which may have new features that are not yet available on CRAN:
devtools::install_github('mazamascience/MazamaSpatialUtils', build_vignettes=TRUE)The package comes with the following simplified spatial spatial datasets:
* 276K data/SimpleCountries.RData
* 2.1M data/SimpleCountriesEEZ.RData
* 1.1M data/SimpleTimezones.RDataThese datasets allow you to work with low-resolution country outlines and time zones.
Additional datasets are available at http://data.mazamascience.com/MazamaSpatialUtils/Spatial/ and can be loaded with the following commands:
# Create a location where large spatial datasets will be stored
dir.create('~/Data/Spatial', recursive = TRUE)
# Tell the package about this location
setSpatialDataDir('~/Data/Spatial')
# Install core spatial data
installSpatialData()Datasets included in the core set include:
* 2.1M EEZCountries.RData
* 15M NaturalEarthAdm1.RData
* 61M OSMTimezones.RData
* 3.0M OSMTimezones_05.RData
* 3.6M TMWorldBorders.RData
* 48MTerrestrialEcoregions.RData
* 3.5M TerrestrialEcoregions_05.RData
* 7.5M USCensus115thCongress.RData
* 17M USCensusCounties.RData
* 4.6M USCensusStates.RData
* 1.2M USIndianLands.RData
* 17M WorldTimezones.RDataFurther details about each dataset are provided in the associated convert~() function. Datasets appearing with, e.g., _05 are simplified datasets whose polygons retain only 5% of the vertices of the original .
Mazama Science regularly generates new datasets that adhere to package standards. These can be download manually from http://data.mazamascience.com/MazamaSpatialUtils/Spatial/. As of Jan 10, 2019, the full list of available datasets includes:
* 24K CA_AirBasins_01.RData
* 44K CA_AirBasins_02.RData
* 100K CA_AirBasins_05.RData
* 2.1M CA_AirBasins.RData
* 2.2M EEZCountries.RData
* 404K GACC_05.RData
* 7.0M GACC.RData
* 15M NaturalEarthAdm1.RData
* 3.1M OSMTimezones_05.RData
* 62M OSMTimezones.RData
* 3.6M TerrestrialEcoregions_05.RData
* 49M TerrestrialEcoregions.RData
* 3.7M TMWorldBorders.RData
* 7.6M USCensus115thCongress.RData
* 564K USCensusCBSA_01.RData
* 944K USCensusCBSA_02.RData
* 2.0M USCensusCBSA_05.RData
* 34M USCensusCBSA.RData
* 2.3M USCensusCounties.RData
* 3.5M USCensusStates.RData
* 1.2M USIndianLands.RData
* 769M WBDHU10.RData
* 1.5G WBDHU12.RData
* 424K WBDHU2_01.RData
* 840K WBDHU2_02.RData
* 38M WBDHU2.RData
* 1.1M WBDHU4_01.RData
* 2.2M WBDHU4_02.RData
* 108M WBDHU4.RData
* 1.4M WBDHU6_01.RData
* 2.8M WBDHU6_02.RData
* 137M WBDHU6.RData
* 295M WBDHU8.RData
* 18M WorldTimezones.RDataThe package vignette ‘Introduction to MazamaSpatialUtils’ has numerous examples.
There are three demos associated with the package:
demo(package = 'MazamaSpatialUtils')There is also an exampe R Shiny app which uses the WBDHU# datasets combines two large datasets:
The app allows you to aggregate point location data by watershed to create summary values associated with each watershed. It also demonstrates the need to enable caching in a shiny app when plots take a long time to generate.
library(MazamaSpatialUtils)
setSpatialDataDir('~/Data/Spatial')
runExample()Instructions for installing the javascript mapshaper utility and using it to simplify large shapefiles are found in the localMapshaper/ directory.
This project is supported by Mazama Science.