---
title: "Introduction to MazamaSpatialUtils"
author: "Mazama Science"
date: "Nov 04, 2022"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to MazamaSpatialUtils}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, echo=FALSE}
knitr::opts_chunk$set(fig.width = 7, fig.height = 5)
```

## Background

The **MazamaSpatialUtils** package was created to regularize 
work with spatial data. 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 time zones, the _de facto_ 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 can be used in various projects. Along with three built-in
datasets, this package provides `convert~()` functions for other spatial datasets
of interest. These convert functions all follow the same recipe:

 * download spatial data into a standard directory
 * convert spatial data into a **sf** simple features data frame
 * modify the dataframe 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 time zone 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 time zone 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~(longitude, latitude, ...)` -- returns names, ISO codes and other country-level data at specified locations
* `getState~(longitude, latitude, ...)` -- returns names, ISO codes and other state-level at specified locations
* `getTimezone(longitude, latitude, ...)` -- returns Olson time zones and other data at specified locations
* `getUSCounty(longitude, latitude, ...)` -- returns names and other county-level data at specified locations

A generic `getSpatialData(longitude, latitude, ...)` returns a dataframe whose 
rows are associated with specified locations. This function can be used with 
newly converted simple features data frames.

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 from which it will be loaded. 
This can be a directory on a user's personal computer or perhaps a remotely 
mounted disk if huge spatial datasets are going to be used.

**MazamaSpatialUtils** has three built-in spatial datasets:

* `SimpleCountries` -- country outlines
* `SimpleCountriesEEZ` -- country outlines including Exclusive Economic Zones over water
* `SimpleTimezones` -- time zones

Version 0.8 of the package is built around the three built-in datasets and several other core 
datasets that may be installed including: 

 * ` 20 MB EEZCountries` -- country boundaries including Exclusive Economic Zones
 * `  5 MB EPARegions` -- US EPA region boundaries
 * `  7 MB GACC` -- Geographic Area Coordination Center (GACC) boundaries
 * `  5 MB NaturalEarthAdm0` -- country level boundaries	 
 * ` 14 MB NaturalEarthAdm1` -- state/province/oblast level boundaries	 
 * ` 99 MB OSMTimezones` -- OpenStreetMap time zones
 * `  3 MB TMWorldBorders` -- high resolution country level boundaries
 * `  7 MB USCensus116thCongress` -- 2019 US congressional districts
 * ` 34 MB USCensusCBSA` -- US Core Based Statistical Areas
 * ` 12 MB USCensusCounties` -- US county level boundaries
 * `  3 MB USCensusStates` -- US state level boundaries
 * ` 23 MB WeatherZones` -- US NWS public weather forecast zones
 
Install these one at a time with:

```{r setSpatialData, eval = FALSE}
setSpatialDataDir('~/Data/Spatial_0.8')
installSpatialData("<datasetName>")
```

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

```{r loadSpatialData, eval = FALSE}
loadSpatialData('USCensusStates')
loadSpatialData('USCensusCounties')
```

## `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 speedup 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 the countries underneath a vector of points: 

```{r getCountry}
library(MazamaSpatialUtils)

longitude <- c(-122.3, -73.5, 21.1, 2.5)
latitude <- c(47.5, 40.75, 52.1, 48.5)

# Get countries/codes associated with locations
getCountry(longitude, latitude)
getCountryCode(longitude, latitude)

# Review all available data
getCountry(longitude, latitude, allData = TRUE)
```

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

Similar to above, these functions return state names and ISO 3166 codes. They 
also take the same arguments. Adding the `countryCodes` argument is more 
important for `getState()` and `getStateCode()` because the `NaturalEarthAdm1` 
dataset is fairly large.

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.*)

```{r getCodes, eval=FALSE}
# Load states dataset if you haven't already
loadSpatialData('NaturalEarthAdm1')

# Get country codes associated with locations
countryCodes <- getCountryCode(longitude, latitude)

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

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

## `getTimezone()`

Returns the Olsen time zone 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.

```{r getTimezone}
# Find the time zones the points are in
getTimezone(longitude, latitude)

# Get country codes associated with locations
countryCodes <- getCountryCode(longitude, latitude)

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

# Review all available data
getTimezone(longitude, latitude, allData = TRUE, countryCodes = countryCodes)
```

## `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.*)

```{r getUSCounty, eval=FALSE}
# Load counties dataset if you haven't already
loadSpatialData("USCensusCounties")

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

# Optionally pass the stateCodes as an argument to speed everything up
getUSCounty(longitude, latitude, stateCodes = stateCodes)
getUSCounty(longitude, latitude, allData = TRUE, stateCodes = stateCodes)
```

## Timezone Map

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

```{r timezoneMap}
# Assign time zones polygons an index based on UTC_offset
colorIndices <- .bincode(SimpleTimezones$UTC_STD_offset, breaks = seq(-12.5,12.5,1))

# Color our time zones by UTC_offset

plot(SimpleTimezones$geometry, 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.

```{r netExport}
library(sf)         # 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$countryCode %in% netExportCodes
netImportMask <- SimpleCountries$countryCode %in% netImportCodes
onlyExportMask <- SimpleCountries$countryCode %in% exportOnlyCodes
onlyImportMask <- SimpleCountries$countryCode %in% importOnlyCodes

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

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

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