# Introduction to MazamaSpatialUtils

#### Mazama Science

#### 2019-09-28

## Background

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 de facto 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 timezones, the de facto standard is the set of Olson timezones 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:

- download spatial data in shapefile format into a standard directory
- convert shapefile data into a sp SpatialPolygonsDataFrame
- modify the dataframe in the @data slot so that it adheres to package internal standards

Other datasets can be added following the same procedure.

The ‘package internal standards’ are very simple.

1. Every spatial dataset must contain the following columns:

- polygonID – unique identifier for each polygon
- countryCode – country at centroid of polygon (ISO 3166-1 alpha-2)

2. Spatial datasets with timezone data must contain the following column:

- timezone – Olson timezone

3. Spatial datasets at scales smaller than the nation-state should contain the following column:

- stateCode – ‘state’ at centroid of polygon (ISO 3166-2 alpha-2)

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 timezone associated with a set of locations.

## Functionality

The core functionality for which this package was developed is determining spatial information associated with a set of locations.

Current functionality includes the following:

- `getCountry~(lon,lat,...)` – returns names, ISO codes and other country-level data specified lo cations
- `getState~(lon,lat,...)` – returns names, ISO codes and other state-level at specified locations
- `getUSCounty~(lon,lat,...)` – returns names and other county-level data at specified locations
- `getTimezones(lon,lat,...)` – returns Olson timezones and other data at specified locations
- `getHUC~(lon,lat,...)` – returns USGS Hydrologic Unit Codes and other data at specified locations

A generic `getSpatialData(lon,lat,...)` returns a dataframe whose rows are associated with specified locations. This function can be used with newly converted SpatialPolygonsDataFrames.

For those working with geo-located data, the ability to enhance location metadata with this information can be extremely helpful.

## Standard Datasets and Setup

When using MazamaSpatialUtils, always run `setSpatialDataDir(<spatial_data_directory>)` first. This sets the directory where spatial data will be installed and loaded from.

MazamaSpatialUtils has three built-in datasets:

- `SimpleCountries` – country outlines
- `SimpleCountriesEEZ` – country outlines including Exclusive Economic Zones over water
- `SimpleTimezones` – timezones

Version 0.5.x of the package is built around the three built-in datasets and several other core datasets that may be installed including:

- `2 Mb EEZCountries.RData` – country boundaries including Exclusive Economic Zones
- `15 Mb NaturalEarthAdm1.RData` – state/province/oblast level boundaries
- `61 Mb OSMTimezones.RData` – Open Street Map high resolution time zones
- `4 Mb TMWorldBorders.RData` – high resolution country level boundaries
- `48 Mb TerrestrialEcoregions.RData` – terrestrial eco-regions
- `7 Mb USCensus115thCongress.RData` – US congressional districts
- `2 Mb USCensusCounties.RData` – US county level boundaries
- `3 Mb USCensusStates.RData` – US state level boundaries
- `3 Mb USIndianLands.RData` – US tribal boundaries
- `16 Mb WorldTimezones.RData` – high resolution timezones

Install these with:

    setSpatialDataDir('~/Data/Spatial')
    installSpatialData()

This may take some time to download and untar the 170 Mb file.

Once datasets have been installed, `loadSpatialData()` can be used load datasets found in the `SpatialDataDir` that match a particular pattern, e.g:

    loadSpatialData('NaturalEarthAdm1')
    loadSpatialData('USCensus')

Additional watershed data from the Watershed Boundary Dataset are quite large and can be downloaded separately from http://mazamascience.com/RData/Spatial.

- `38 Mb WBDHU2.RData` – USGS level 2 watersheds
- `107 Mb WBDHU4.RData` – USGS level 4 watersheds
- `136 Mb WBDHU6.RData` – USGS level 6 watersheds
- `294 Mb WBDHU8.RData` – USGS level 8 watersheds
- `768 Mb WBDHU10.RData` – USGS level 10 watersheds
- `1.4 Gb WBDHU12.RData` – USGS level 12 watersheds

## `getCountry()` and `getCountryCode()`

These two functions are used for assigning countries to one or many locations. `getCountry()` returns English country names and `getCountryCode()` returns the ISO-3166 two character country code. Both functions can be passed `allData = TRUE` which returns a dataframe with more information on the countries. You can also specify `countryCodes = c(<codes>)` to speed up searching by restricting the search to polygons associated within those countries.

These functions use the package-internal `SimpleCountries` dataset which can be used without loading any additional datasets.

In this example we’ll find which countries a vector of points fall in.

    library(MazamaSpatialUtils)

    ## Loading required package: sp

    lon <- c(-122.3, -73.5, 21.1, 2.5)
    lat <- c(47.5, 40.75, 52.1, 48.5)

    # Get countries/codes associated with locations
    getCountry(lon, lat)

    ## [1] "United States" "United States" "Poland"        "France"

    getCountryCode(lon, lat)

    ## [1] "US" "US" "PL" "FR"

    # Review all available data
    getCountry(lon, lat, allData=TRUE)

    ##   objectID ISO3   countryName changes countryCode polygonID
    ## 1      232  USA United States    <NA>          US       232
    ## 2      232  USA United States    <NA>          US       232
    ## 3      181  POL        Poland    <NA>          PL       181
    ## 4       87  FRA        France 2014-10          FR        87

## `getState()` and `getStateCode()`

Similar to above, these functions return state names and ISO 3166 code. They also take the same arguments. Adding the `countryCodes` argument is more important for `getState()` and `getStateCode()` because the `NaturalEarthAdm1` dataset is fairly large. Lets use the same `lat` and `lon` variables as above and assign states to those locations.

These functions require installation of the large `NaturalEarthAdm1` dataset which is not distributed with the package.

(The next block of code is not evaluated in the vignette.)

    # Load states dataset if you haven't already
    loadSpatialData('NaturalEarthAdm1')

    # Get country codes associated with locations
    countryCodes <- getCountryCode(lon, lat)

    # Pass the countryCodes as an argument to speed everything up
    getState(lon, lat, countryCodes = countryCodes)
    getStateCode(lon, lat, countryCodes = countryCodes)

    # This is a very detailed dataset so we'll grab a few important columns
    states <- getState(lon, lat, allData=TRUE, countryCodes = countryCodes)
    states[c('countryCode', 'stateCode', 'stateName')]

## `getTimezone()`

Returns the Olsen Timezone where the given points are located. Arguments are the same as the previous functions. `allData=TRUE` will return other useful information such as the UTC Offset.

These functions use the package-internal `SimpleTimezones` dataset which can be used without loading any additional datasets.

    # Find the timezones the points are in
    getTimezone(lon, lat)

    ## [1] "America/Los_Angeles" "America/New_York"    "Europe/Warsaw"
    ## [4] "Europe/Paris"

    # Get country codes associated with locations
    countryCodes <- getCountryCode(lon, lat)

    # Pass the countryCodes as an argument to potentially speed things up
    getTimezone(lon, lat, countryCodes = countryCodes)

    ## [1] "America/Los_Angeles" "America/New_York"    "Europe/Warsaw"
    ## [4] "Europe/Paris"

    # Review all available data
    getTimezone(lon, lat, allData=TRUE, countryCodes = countryCodes)

    ##              timezone UTC_offset UTC_DST_offset countryCode   longitude
    ## 1 America/Los_Angeles         -8             -7          US -118.242778
    ## 2    America/New_York         -5             -4          US  -74.006389
    ## 3       Europe/Warsaw          1              2          PL   21.000000
    ## 4        Europe/Paris          1              2          FR    2.333333
    ##   latitude           polygonID
    ## 1 34.05083 America/Los_Angeles
    ## 2 40.71167    America/New_York
    ## 3 52.25417       Europe/Warsaw
    ## 4 48.88111        Europe/Paris

## `getUSCounty()`

Returns the US County which name pairs of coordinates fall in. The arguments are similar as above except that `stateCodes=c()` is used instead of `countryCodes=c()` since this dataset is US specific.

(The next block of code is not evaluated in the vignette.)

    # Load counties dataset if you haven't already
    loadSpatialData("USCensusCounties")

    # New dataset of points only in the US
    stateCodes <- getStateCode(lon,lat)

    # Optionally pass the stateCodes as an argument to speed everything up
    getUSCounty(lon, lat, stateCodes=stateCodes)
    getUSCounty(lon, lat, allData=TRUE, stateCodes=stateCodes)

## Timezone Map

While identifying the states, countries and timezones associated with a set of locations is important, we can also generate some quick eye candy with these datasets. Let’s color the timezones by the data variable ‘UTC_offset’

    library(sp)         # For spatial plotting

    # Assign timezones polygons an index based on UTC_offset
    colorIndices <- .bincode(SimpleTimezones@data$UTC_offset, breaks=seq(-12.5,12.5,1))

    # Color our timezones by UTC_offset
    plot(SimpleTimezones, col=rainbow(25)[colorIndices])
    title(line=0,'Timezone Offsets from UTC')

## Working with ISO 3166-1 Encoded Data

On of the main reasons for ensuring that our spatial datasets use ISO encoding is that it makes it easy to generate plots with any datasets that use that encoding. Here is a slightly more involved example using Energy data from the British Petroleum Statistical Review that has been ISO-encoded.

    library(sp)         # For spatial plotting

    # Read in ISO-encoded oil production and consumption data
    prod <- read.csv(url('http://mazamascience.com/OilExport/BP_2016_oil_production_bbl.csv'),
                     skip=6, stringsAsFactors=FALSE, na.strings='na')
    cons <- read.csv(url('http://mazamascience.com/OilExport/BP_2016_oil_consumption_bbl.csv'),
                     skip=6, stringsAsFactors=FALSE, na.strings='na')

    # Only work with ISO-encoded columns of data
    prodCountryCodes <- names(prod)[ stringr::str_length(names(prod)) == 2 ]
    consCountryCodes <- names(cons)[ stringr::str_length(names(cons)) == 2 ]

    # Use the last row (most recent data)
    lastRow <- nrow(prod)
    year <- prod$YEAR[lastRow]

    # Neither dataframe contains all countries so create four categories based on the
    # amount of information we have:  netExporters, netImporters, exportOnly, importOnly
    sharedCountryCodes <- intersect(prodCountryCodes,consCountryCodes)
    net <- prod[lastRow, sharedCountryCodes] - cons[lastRow, sharedCountryCodes]

    # Find codes associated with each category
    netExportCodes <- sharedCountryCodes[net > 0]
    netImportCodes <- sharedCountryCodes[net <= 0]
    exportOnlyCodes <- setdiff(prodCountryCodes,consCountryCodes)
    importOnlyCodes <- setdiff(consCountryCodes,prodCountryCodes)

    # Create a logical 'mask' associated with each category
    netExportMask <- SimpleCountries@data$countryCode %in% netExportCodes
    netImportMask <- SimpleCountries@data$countryCode %in% netImportCodes
    onlyExportMask <- SimpleCountries@data$countryCode %in% exportOnlyCodes
    onlyImportMask <- SimpleCountries@data$countryCode %in% importOnlyCodes

    color_export = '#40CC90'
    color_import = '#EE5555'
    color_missing = 'gray90'

    # Base plot (without Antarctica)
    notAQ <- SimpleCountries@data$countryCode != 'AQ'
    plot(SimpleCountries[notAQ,],col=color_missing)

    plot(SimpleCountries[netExportMask,],col=color_export,add=TRUE)
    plot(SimpleCountries[onlyExportMask,],col=color_export,add=TRUE)
    plot(SimpleCountries[netImportMask,],col=color_import,add=TRUE)
    plot(SimpleCountries[onlyImportMask,],col=color_import,add=TRUE)

    legend('bottomleft',legend=c('Net Exporters','Net Importers'),fill=c(color_export,color_import))
    title(line=0,paste('World Crude Oil in',year))