Package 'MazamaSpatialUtils'

Description

Converts a vector of ISO 3166-1 alpha-2 codes to the corresponding English names.

Usage

codeToCountry(countryCodes)

Arguments

Value

A vector of English country names or NA.

Note

This function is deprecated as of MazamaSpatialUtils 0.8.7. Please use countryCodeToName instead.

Description

Converts a vector of ISO 3166-2 alpha-2 state codes to the corresponding English names.

Usage

codeToState(stateCodes, countryCodes = NULL, dataset = "NaturalEarthAdm1")

Arguments

Details

For this function to work, you must install and load the "NaturalEarthAdm1" dataset.

Value

A vector of English state names or NA.

See Also

convertNaturalEarthAdm1

Description

State codes for the 48 contiguous states +DC that make up the CONtinental US.

Usage

CONUS

Format

A vector with 49 elements

Description

Create a simple features dataframe for combined EEZ/country boundaries.

The full resolution file will be named "EEZCountries.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertEEZCountries()

Details

A world EEZ/countries shapefile is converted to a simple features dataframe with additional columns of data. To use this function, the file "EEZ_land_union_v3_202003.zip" must be downloaded into the user's spatial directory which is set with setSpatialDataDir(). The resulting file will be created in this same spatial data directory.

Value

Name of the datasetName being created.

Note

For polygons with overlapping claims of sovereignty, we arbitrarily assign the polygon to the country identified in the ISO_SOV1 field.

The source data is from Version 3 – 2020-03-17.

From the source documentation:

Geographic Information Systems have become indispensable tools in managing and displaying marine data and information. However, a unique georeferenced standard of marine place names and areas was not available, hampering several marine geographic applications, for example the linking of these locations to databases to integrate data. The purpose of Marine Regions is therefore to create a standard, relational list of geographic names, coupled with information and maps of the geographic location of these features. This will improve access and clarity of the different geographic, marine names such as seas, sandbanks, ridges and bays and display univocally the boundaries of marine biogeographic or managerial marine areas.

Marine Regions is an integration of the VLIMAR Gazetteer and the VLIZ Maritime Boundaries Geodatabase. The VLIMAR Gazetteer is a database with geographic, mainly marine names such as seas, sandbanks, seamounts, ridges, bays or even standard sampling stations used in marine research. The geographic cover of the VLIMAR gazetteer is global but initially focused on the Belgian Continental Shelf and the Scheldt Estuary and the Southern Bight of the North Sea. Gradually more regional and global geographic information was added to VLIMAR and combining this information with the Maritime Boundaries database, representing the Exclusive Economic Zone (EEZ) of the world, led to the creation of marineregions.org.

Marine Regions is managed by the Flanders Marine Institute. Funding for the creation of the VLIMAR gazetteer was provided initially through the EU Network of Excellence MarBEF, but also other European initiatives such as Lifewatch provide the necessary funding for the maintenance and management of Marine Regions.

Marine Regions depends on data and knowledge sharing from global, European, regional and national data providers and relevant experts. By setting up Collaboration Agreements, data providers will benefit from belonging to the Marine Regions partnership as they would get increased visibility, gain access to a variety of data analysis services which will benefit from integration of several distributed spatial datasetNames, as well as enjoying the benefit of the creation of stable unique identifiers. An example template of a Collaboration Agreement can be found here. Please contact [email protected] if your organisation is interested to explore this collaboration.

Citation: Flanders Marine Institute (2020). Union of the ESRI Country shapefile and the Exclusive Economic Zones (version 3). Available online at https://www.marineregions.org/. https://doi.org/10.14284/403 #'

References

https://www.marineregions.org/sources.php#unioneezcountry

See Also

setSpatialDataDir

Description

Returns a simple features data frame for EPA Regions

The full resolution file will be named "EPA_Regions.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertEPARegions()

Details

An EPA region boundary shapefile is converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

The source data is from 2022.

Value

Name of the datasetName being created.

Note

From the source documentation:

This datasetName represents delineated EPA Region boundaries. EPA has ten regional offices across the country, each of which is responsible for several states and in some cases, territories or special environmental programs.

This Shared Enterprise Geodata and Services (SEGS)datasetName was created by U.S. EPA using 2011 TIGER/Line state boundaries from the U.S. Census Bureau. The core mission of SEGS is to provide a single point of ownership for geospatial datasetNames that are national in extent and of general use to all EPA users and to make those datasetNames available through channels that best meet user needs.

References

https://hub.arcgis.com/datasets/geoplatform::epa-regions

Description

Create a simple features data frame for Geographic Area Coordination Centers (GACCs). These are regions defined by the National Interagency Fire Center (NIFC).

The full resolution file will be named "GACC.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertGACC()

Details

A GACC shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

The source data is from 2020.

Value

Name of the datasetName being created.

Note

From the source documentation:

Although the primary mission of the GACC is logistical coordination, the Center also has support programs in Predictive Services, Intelligence, and in several Center's Fire Information. Predictive Services consists primarily of professional meteorologists who monitor weather and fuel conditions, conduct briefings, produce fire weather related products, liaison with the National Weather Service, and oversee all aspects of the Remote Automated Weather System (RAWS). The Intelligence Section is primarily responsible for collecting and disseminating wildland fire and prescribed fire activity information, monitoring the status of national firefighting resources, maintaining year-to-date and historical fire occurrence data, and managing the Sit Report and ICS-209 programs.

In some GACCs, the Predictive Services and Intelligence sections work as one unit called the Predictive Services Group. The Predictive Services and Intelligence Sections, whether separated or combined, work collaboratively producing Weekly, Monthly, and Seasonal Fire Weather/Fire Danger Outlooks. Each Coordination Center provides additional support to their respective geographic area's wildland fire community through training, workshops, special projects, and other tasks. Except for dispatch of air tankers and lead planes based outside the dispatch center responsibility the fire is located in, the GACC does not have initial-attack dispatch responsibilities. The United States and Alaska are divided into 11 Geographic Areas for the purpose of incident management and mobilization of resources (people, aircraft, ground equipment). Within each Area, an interagency Geographic Area Coordinating Group (GACG), made up of Fire Directors from each of the Federal and State land management agencies from within the Area, is established.

Working collaboratively, the GACG's mission is to provide leadership and support not only for wildland fire emergencies, but to other emergency incidents (i.e. earthquakes, floods, hurricanes, tornadoes, etc), as necessary. Authority for establishment of the GACG is through departmental policy and interagency agreements. Additional agreements are established with cooperators and other organizations in order to facilitate efficient fire management activities within and adjacent to the Area. A cost-effective sharing of resources among public agencies is a key component of the GACG mission and is expected by the public, Congress, and States. All agencies and geographic areas work together under the auspices and direction of the National Interagency Fire Center (NIFC). The Geographic Area Coordination Centers (GACC) is a result of an interagency agreement established by the respective Geographic Area Coordinating Group. The primary mission of the GACC is to serve Federal and State wildland fire agencies through logistical coordination and mobilization of resources (people, aircraft, ground equipment) throughout the geographical area, and with other geographic areas, as necessary. This is generally done through coordinating the movement of resources between the many Dispatch Centers within the geographic area and, as necessary, with the National Interagency Coordination Center (NICC) when resources are unavailable within the Area or when mobilization support is needed in other geographic areas. As you survey each GACC website, it will become obvious they are technical in design and are primarily for use by local and geographic area wildland fire managers and firefighters. For the general public, the GACC website may not meet your needs. If this is the case, please check out the National Fire News website, provided by the National Interagency Fire Center, and the InciWeb website, provided as a guide to large fire incidents throughout the United States.

The National Wildfire Coordinating Group (NWCG) makes no claims, promises, or guarantees about the accuracy, completeness, or adequacy of the content; and expressly disclaims liability for errors and omissions. No warranty of any kind, implied, expressed or statutory is given with respect to the contents.

References

https://hub.arcgis.com/datasets/nifc::national-gacc-boundaries

See Also

setSpatialDataDir

Description

Raw shapefiles are read in using st_read. Spatial data are reprojected onto a standard projection (https://epsg.io/4269) before being returned.

If shapefiles have no projection information they are assumed to 'geographic' coordinates .

Usage

convertLayer(dsn = NULL, layer)

Arguments

Value

An object of class sf.

Description

Returns a simple features data frame for top level administrative divisions.

The full resolution file will be named "NaturalEarthAdm0.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Returns a simple features data frame for 1st level administrative divisions

The full resolution file will be named "NaturalEarthAdm1.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertNaturalEarthAdm1()

convertNaturalEarthAdm1()

Details

A country border shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir(). The resulting file will be created in this same spatial data directory.

A state border shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir(). The resulting file will be created in this same spatial data directory.

Within the MazamaSpatialUtils package the phrase 'state' refers to administrative divisions beneath the level of the country or nation. This makes sense in the United 'States'. In other countries this level is known as 'province', 'territory' or some other term.

References

https://www.naturalearthdata.com

https://www.naturalearthdata.com

Description

Create a simple features data frame for world timezones.

The full resolution file will be named "OSMTimezones.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertOSMTimezones()

Details

A world timezone shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

There are 2 timezones which have polygons but the associated rows in the dataframe have no data. These timezones also have no countryCode assigned. We hope to rectify this in a future release. These are the missing timezones:

> OSMTimezones$timezone[is.na(OSMTimezones$countryCode)]
 [1] "America/Nuuk"  "Asia/Qostanay"

Value

Name of the datasetName being created.

Note

From the source documentation:

This project aims to stay up-to-date with all of the currently valid timezones that are defined in the timezone database. This project also will attempt to provide the most accurate possible boundaries of timezones according to community input.

The underlying data is downloaded from OpenStreetMap via the overpass turbo API. Various boundaries are assembled together to produce each zone with various geographic operations. In numerous edge cases arbitrary boundaries get created in various zones which are noted in the timezones.json file.

To maintain consistency with the timezone database, this project will only create a new release after the timezone database creates a new release. If there are no new timezones created or deleted in a timezone database release, then this project will only create a release if there have been changes performed to the boundary definitions of an existing zone within this project.

References

https://github.com/evansiroky/timezone-boundary-builder

Description

Returns a simple features data frame for world divisions

The full resolution file will be named "TMWorldBorders.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertTMWorldBorders()

Details

A world borders shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file is created in the spatial data directory which is set with setSpatialDataDir().

Value

Name of the datasetName being created.

References

https://thematicmapping.org/

Description

Returns a simple features data frame for US CBSAs

The full resolution file will be named "USCensusCBSA.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertUSCensusCBSA()

Details

A US Core Based Statistical Areas (CBSA) shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

Value

Name of the datasetName being created.

Note

From the source documentation:

Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urbanized areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban clusters of at least 10,000 population but less than 50,000 population.

The CBSA boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2020.

References

https://www2.census.gov/geo/tiger/TIGER2021/CBSA/

See Also

setSpatialDataDir

getUSCounty

Description

Returns a simple features data frame for US Congressional Districts for the 116th US House of Representatives.

The full resolution file will be named "USCensus116thCongress.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertUSCensusCongress()

Details

A US congressional district shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

#' The source data is from 2021.

Value

Name of the datasetName being created.

Note

From the source documentation:

Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 116th Congress is seated from January 2019 to 2021. The cartographic boundary files for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts are provided to the Census Bureau by the states by May 1, 2018.

You can join this file with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file.

References

https://www2.census.gov/geo/tiger/GENZ2021/

See Also

setSpatialDataDir

Description

Create a simple features data frame for US counties.

The full resolution file will be named "USCensusCounties.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertUSCensusCounties()

Details

A US county borders shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir.

The source data is from 2021.

Value

Name of the datasetName being created.

Note

From the source documentation:

The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

You can join this file with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file.

References

https://www2.census.gov/geo/tiger/GENZ2021/

Description

Create a simple features dataframe for US states

The full resolution file will be named "USCensusStates". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertUSCensusStates()

Details

A US state borders shapefile is downloaded and converted to a simple features dataframe with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir.

The source data is from 2021.

Value

Name of the datasetName being created.

Note

From the source documentation:

Cartographic Boundary Files

The cartographic boundary files are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. To improve the appearance of shapes, areas are represented with fewer vertices than detailed TIGER/Line equivalents. Some small holes or discontiguous parts of areas are not included in generalized files. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some offshore areas may be excluded from the generalized files.

Limitations

Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.

"Island Areas" are removed in the MazamaSpatialUtils version.

These files were specifically created to support small-scale thematic mapping. To improve the appearance of shapes at small scales, areas are represented with fewer vertices than detailed TIGER/Line Shapefiles. Cartographic boundary files take up less disk space than their ungeneralized counterparts. Cartographic boundary files take less time to render on screen than TIGER/Line Shapefiles. You can join this file with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file. If detailed boundaries are required, please use the TIGER/Line Shapefiles instead of the generalized cartographic boundary files.

References

https://www2.census.gov/geo/tiger/GENZ2021/

Description

Create a simple features data frame for USGS watershed boundaries

Usage

convertWBDHUC(
  gdbDir = "~/Data/WBD/WBD_National_GDB.gdb",
  level = 2,
  simplify = TRUE
)

Arguments

Details

A USGS Watershed Boundary Dataset geodatabase is converted to a simple features data frame with additional columns of data. To use this function, the WBD geodatabase must be downloaded into a directory which is identified with gdbDir. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

The full WBD datasetName can be downloaded from the USGS with the following command:

curl https://prd-tnm.s3.amazonaws.com/StagedProducts/Hydrography/WBD/National/GDB/WBD_National_GDB.zip -O

The source data was downloaded on 2023-03-23.

Value

Name of the datasetName being created.

Note

From the source documentation:

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. It defines the areal extent of surface water drainage to a point except in coastal or lake front areas where there could be multiple outlets as stated by the "Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD)" "Standard" (https://pubs.usgs.gov/tm/11/a3/). Watershed boundaries are determined solely upon science-based hydrologic principles, not favoring any administrative boundaries or special projects, nor particular program or agency. This dataset represents the hydrologic unit boundaries to the 12-digit (6th level) for the entire United States. Some areas may also include additional subdivisions representing the 14- and 16-digit hydrologic unit (HU). At a minimum, the HUs are delineated at 1:24,000-scale in the conterminous United States, 1:25,000-scale in Hawaii, Pacific basin and the Caribbean, and 1:63,360-scale in Alaska, meeting the National Map Accuracy Standards (NMAS). Higher resolution boundaries are being developed where partners and data exist and will be incorporated back into the WBD. WBD data are delivered as a dataset of polygons and corresponding lines that define the boundary of the polygon. WBD polygon attributes include hydrologic unit codes (HUC), size (in the form of acres and square kilometers), name, downstream hydrologic unit code, type of watershed, non-contributing areas, and flow modifications. The HUC describes where the unit is in the country and the level of the unit. WBD line attributes contain the highest level of hydrologic unit for each boundary, line source information and flow modifications.

The intent of defining Hydrologic Units (HU) within the Watershed Boundary Dataset is to establish a base-line drainage boundary framework, accounting for all land and surface areas. Hydrologic units are intended to be used as a tool for water-resource management and planning activities particularly for site-specific and localized studies requiring a level of detail provided by large-scale map information. The WBD complements the National Hydrography Dataset (NHD) and supports numerous programmatic missions and activities including: watershed management, rehabilitation and enhancement, aquatic species conservation strategies, flood plain management and flood prevention, water-quality initiatives and programs, dam safety programs, fire assessment and management, resource inventory and assessment, water data analysis and water census.

References

https://www.usgs.gov/national-hydrography/watershed-boundary-dataset

See Also

setSpatialDataDir

Description

Create a simple features data frame for NWS weather forecast zones.

The full resolution file will be named "WeatherZones.rda". In addition, "_05", _02" and "_01" versions of the file will be created that that are simplified to 5%, 2% and 1%. Simplified versions will greatly improve the speed of both searching and plotting.

Usage

convertWeatherZones()

Details

A weather forecast zone shapefile is downloaded and converted to a simple features data frame with additional columns of data. The resulting file will be created in the spatial data directory which is set with setSpatialDataDir().

The source data is from 2022-09-13.

Value

Name of the datasetName being created.

Note

Records with a duplicated zoneID column (typically representing coastal land and its watery inlets separately) are combined so that zoneID becomes a unique identifier.

From the source documentation:

The NWS issues forecasts and some watches and warnings for public zones which usually are the same as counties but in many cases are subsets of counties. Counties are subset into zones to allow for more accurate forecasts because of the differences in weather within a county due to such things as elevation or proximity to large bodies of water.

References

https://www.weather.gov/gis/PublicZones

Description

Returns a dataframe version of the Wikipedia timezone table with the following columns:

• timezone – Olson timezone

• UTC_offset – hours between local timezone and UTC

• UTC_DST_offset – hours between local timezone daylight savings and UTC

• countryCode – ISO 3166-2 country code

• longitude – longitude of the Olson timezone city

• latitude – latitude of the Olson timezone city

• status – either 'Canonical', 'Alias' or 'Deprecated'

• notes – typically specifying the target of an 'Alias'

Usage

convertWikipediaTimezoneTable()

Details

Older named timezones from the table which are linked to more modern equivalents are not included in the returned dataframe.

Value

Dataframe with 388 rows and 10 columns.

References

https://en.wikipedia.org/wiki/List_of_tz_database_time_zones

Description

Converts a vector of country names or codes from one system to another returning NA where no match is found.

Usage

countryCodeToName(countryCode = NULL)

countryCodeToFIPS(countryCode = NULL)

countryFIPSToName(countryFIPS = NULL)

countryFIPSToCode(countryFIPS = NULL)

countryNameToCode(countryName = NULL)

countryNameToFIPS(countryName = NULL)

Arguments

Value

A vector of country names or codes.

Examples

library(MazamaSpatialUtils)

# FIPS codes are different!

countryNameToCode("Germany")
countryNameToFIPS("Germany")

countryCodeToName("CH")
countryFIPSToName("CH")

countryCodes <- sample(SimpleCountries$countryCode, 30)

data.frame(
  name = countryCodeToName(countryCodes),
  code = countryCodes,
  FIPS = countryCodeToFIPS(countryCodes)
)

Description

Converts a vector of English country names to the corresponding ISO 3166-1 alpha-2 codes.

Usage

countryToCode(countryNames)

Arguments

Value

A vector of ISO 3166-1 alpha-2 codes or NA.

Note

This function is deprecated as of MazamaSpatialUtils 0.8.7. Please use countryNameToCode instead.

Description

Aggregate shapes in a simple features dataframe. This is a convenience wrapper for ms_dissolve.

Usage

dissolve(SFDF, field = NULL, sum_fields = NULL, copy_fields = NULL, ...)

Arguments

Value

A simple features dataframe with aggregated shapes.

Examples

regions <- dissolve(SimpleCountries, field = "UN_region", sum_fields = "area")
plot(regions)
dplyr::glimpse(regions)

Description

Uses spatial comparison to determine which country polygons the locations fall into and returns the country name for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getCountry(
  longitude = NULL,
  latitude = NULL,
  datasetName = "SimpleCountriesEEZ",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of English language country names.

References

http://www.naturalearthdata.com/downloads/10m-cultural-vectors/

See Also

SimpleCountries

getSpatialData

Examples

library(MazamaSpatialUtils)

longitude <- seq(0, 50)
latitude <- seq(0, 50)

getCountry(longitude, latitude)

Description

Uses spatial comparison to determine which country polygons the locations fall into and returns the country code strings for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getCountryCode(
  longitude = NULL,
  latitude = NULL,
  datasetName = "SimpleCountriesEEZ",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of ISO-3166-1 alpha-2 country codes.

References

http://www.naturalearthdata.com/downloads/10m-cultural-vectors/

See Also

SimpleCountries

getSpatialData

Examples

library(MazamaSpatialUtils)

longitude <- seq(0, 50)
latitude <- seq(0, 50)

getCountryCode(longitude, latitude)

Description

Uses spatial comparison to determine which country polygons the locations fall into and returns the country name for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getCountryName(
  longitude = NULL,
  latitude = NULL,
  datasetName = "SimpleCountriesEEZ",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of English language country names.

See Also

SimpleCountries

getSpatialData

Examples

library(MazamaSpatialUtils)

longitude <- seq(0, 50)
latitude <- seq(0, 50)

getCountryName(longitude, latitude)

Description

Uses spatial comparison to determine which HUC polygons the locations fall into and returns the HUC identifier strings for those polygons.

Specification of HUCs limits spatial searching to the specified HUCs and greatly improves performance. For instance, if searching for level 10 HUCs in the Upper Columbia basin, it would make sense to first search WBDHU4_01 to learn that the level 4 HUC is 1702. Then you can greatly improve search times for higher level HUCs by specifying: HUCs = c("1702").

If allData = TRUE, additional data is returned.

Usage

getHUC(
  longitude = NULL,
  latitude = NULL,
  dataset = NULL,
  HUCs = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of HUC identifiers.

See Also

getSpatialData

Description

Uses spatial comparison to determine which HUC polygons the locations fall into and returns the HUC names for those polygons.

Specification of HUCs limits spatial searching to the specified HUCs and greatly improves performance. For instance, if searching for level 10 HUCs in the Upper Columbia basin, it would make sense to first search WBDHU4_01 to learn that the level 4 HUC is 1702. Then you can greatly improve search times for higher level HUCs by specifying: HUCs = c("1702").

If allData = TRUE, additional data is returned.

Usage

getHUCName(
  longitude = NULL,
  latitude = NULL,
  dataset = NULL,
  HUCs = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of HUC names.

See Also

getSpatialData

Description

Extracts the the vector of unique polygon identifiers from SFDF.

This function is useful when writing code to aggregate data by polygon and calculate per-polygon statistics. Each unique simple features data frame will have a different set of data columns but each is guaranteed to have a column named polygonID that uniquely identifies each polygon.

This allows us to write code that aggregates by polygon without having to know whether the polygons represent, countries, timezones or HUCs, etc.

Usage

getPolygonID(SFDF)

Arguments

Value

Vector of polygon identifiers.

Description

All locations are first converted to SpatialPoints objects. The st_intersects function is then used to determine which polygon from SFDF each location falls in. The dataframe row associated with each polygon is then associated with each location.

Usage

getSpatialData(
  longitude = NULL,
  latitude = NULL,
  SFDF = NULL,
  useBuffering = FALSE,
  verbose = FALSE
)

Arguments

Details

For coastal locations, locations may lie just outside the boundaries of an individual polygon, especially if it is of low resolution. To account for this any location that remains unassociated after the first pass is checked to seee if it is with a specific distance of any polygon. The set of distances is gradually increased until a polygon is reached or the maximum distances is encountered. Distances include: 1km, 2km, 5km, 10km, 20km, 50km, 100km, 200km. If a location is more than 200km away from any polygon, a data frame record with all NAs is returned for that location.

Missing or invalid values in the incoming longitude or latitude vectors result in records with all NAs at those positions in the returned data frame.

Value

Dataframe of data.

Description

Returns the package data directory where spatial data is located.

Usage

getSpatialDataDir()

Value

Absolute path string.

See Also

dataDir

setSpatialDataDir

Description

Uses spatial comparison to determine which 'state' polygons the locations fall into and returns the ISO 3166-2 2-character state code strings for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getState(
  longitude = NULL,
  latitude = NULL,
  datasetName = "NaturalEarthAdm1",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of English language state names.

See Also

getSpatialData

Examples

## Not run: 
library(MazamaSpatialUtils)
setSpatialData("~/Data/Spatial_0.8")

loadSpatialData("NaturalEarthAdm1")

longitude <- seq(-140, -90)
latitude <- seq(20, 70)
getState(longitude, latitude)

## End(Not run)

Description

Uses spatial comparison to determine which 'state' polygons the locations fall into and returns the ISO 3166 2-character state code strings for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getStateCode(
  longitude = NULL,
  latitude = NULL,
  datasetName = "NaturalEarthAdm1",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of ISO-3166-2 alpha-2 state codes.

See Also

getSpatialData

Examples

## Not run: 
library(MazamaSpatialUtils)
setSpatialData("~/Data/Spatial_0.8")

loadSpatialData("NaturalEarthAdm1")

longitude <- seq(-140, -90)
latitude <- seq(20, 70)
getStateCode(longitude, latitude)

## End(Not run)

Description

Uses spatial comparison to determine which 'state' polygons the locations fall into and returns the ISO 3166-2 2-character state code strings for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getStateName(
  longitude = NULL,
  latitude = NULL,
  datasetName = "NaturalEarthAdm1",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of English language state names.

See Also

getSpatialData

Examples

## Not run: 
library(MazamaSpatialUtils)
setSpatialData("~/Data/Spatial_0.8")

loadSpatialData("NaturalEarthAdm1")

longitude <- seq(-140, -90)
latitude <- seq(20, 70)
getStateName(longitude, latitude)

## End(Not run)

Description

Uses spatial comparison to determine which timezone polygons the locations fall into and returns the Olson timezone strings for those polygons.

Specification of countryCodes limits spatial searching to the specified countries and greatly improves performance.

If allData=TRUE, additional data is returned.

Usage

getTimezone(
  longitude = NULL,
  latitude = NULL,
  datasetName = "SimpleTimezones",
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of Olson timezones.

References

https://github.com/evansiroky/timezone-boundary-builder

Examples

library(MazamaSpatialUtils)

longitude <- seq(-120, -60, 5)
latitude <- seq(20, 80, 5)

getTimezone(longitude, latitude)

Description

Uses spatial comparison to determine which county polygons the locations fall into and returns the county name strings for those polygons.

Specification of stateCodes limits spatial searching to the specified states and greatly improves performance.

If allData = TRUE, additional data is returned.

Usage

getUSCounty(
  longitude = NULL,
  latitude = NULL,
  dataset = "USCensusCounties",
  stateCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector of English language county names.

References

http://www.naturalearthdata.com/downloads/10m-cultural-vectors/

See Also

getSpatialData

Examples

## Not run: 
library(MazamaSpatialUtils)
setSpatialDataDir("~/Data/Spatial_0.8")

loadSpatialData("USCensusCounties")

longitude <- seq(-140, -90)
latitude <- seq(20, 70)
getUSCounty(longitude, latitude)

## End(Not run)

Description

Uses spatial comparison to determine which polygons the locations fall into and returns the variable associated with those polygons.

If allData = TRUE, the entire dataframe is returned.

Usage

getVariable(
  longitude = NULL,
  latitude = NULL,
  dataset = NULL,
  variable = NULL,
  countryCodes = NULL,
  allData = FALSE,
  useBuffering = FALSE
)

Arguments

Value

Vector or dataframe.

See Also

getSpatialData

Description

Searches the directory set with setSpatialDataDir for locally installed spatial data and returns a list of dataset names that can be used with loadSpatialData.

If verbose = TRUE, a brief description is provided for each locally installed dataset.

Usage

installedSpatialData(verbose = TRUE)

Arguments

Value

Invisibly returns a vector of dataset names().

Description

Install spatial datasets found at url into the directory previously set with setSpatialDataDir().

If pattern = NULL (default), available datasets will be displalyed..

Usage

installSpatialData(
  dataset = NULL,
  urlBase = "http://data.mazamascience.com/MazamaSpatialUtils/Spatial_0.8"
)

Arguments

Value

If pattern = NULL a vector of dataset names.

Description

Converts a vector of ISO 3166-1 alpha-2 codes to the corresponding ISO 3166-1 alpha-3 codes.

Usage

iso2ToIso3(countryCodes)

Arguments

Value

A vector of ISO3 country codes

Description

Converts a vector of ISO 3166-1 alpha-3 codes to the corresponding ISO 3166-1 alpha-2 codes.

Usage

iso3ToIso2(countryCodes)

Arguments

Value

A vector of ISO2 country codes

Description

Load datasets found in the directory previously set with setSpatialDataDir(). Only files matching pattern will be loaded. By default, all .rda files are matched.

Core datastes available for the package include:

• TMWorldBorders – high resolution country polygons (higher resolution than SimpleCountries)

• NaturalEarthAdm1 – state/province polygons throughout the world

• USCensusCounties – county polygons in the United States

• WorldTimezones – high resolution timezone polygons (higher resolution than SimpleTimezones)

These can be installed with installSpatialData().

Usage

loadSpatialData(pattern = "*\\.rda")

Arguments

Value

Invisibly returns a vector of spatial dataset names loaded into the global environment.

See Also

setSpatialDataDir

installSpatialData

Description

Sets the package data directory where spatial data is located. If the directory does not exist, it will be created.

Usage

setSpatialDataDir(dataDir)

Arguments

Value

Silently returns previous value of data directory.

See Also

SpatialDataDir

getSpatialDataDir

Description

SimpleCountries is a simplified world borders dataset suitable for global maps and quick spatial searches. This dataset is distributed with the package and is can be used with getCountry(), getCountryCode() and getCountryName() when restricting searches to land-based locations.

Usage

SimpleCountries

Format

A simple features data frame with 246 records and 7 columns of data.

Details

This dataset is equivalent to TMWorldBordersSimple but with fewer columns of data.

See Also

convertTMWorldBordersSimple

This dataset was generated on 2022-11-04 by running:

library(MazamaSpatialUtils)
setSpatialDataDir("~/Data/Spatial_0.8")

convertTMWorldBorders()

loadSpatialData("NaturalEarthAdm0_05")

columnNames <- c("countryCode", "countryName", "ISO3", "FIPS",
                 "UN_region", "polygonID")
SimpleCountries <- NaturalEarthAdm0_05[, columnNames]
save(SimpleCountries, file = "data/SimpleCountries.rda")

Description

SimpleCountriesEEZ is a simplified world borders dataset with a 200 mile coastal buffer corresponding to Exclusive Economic Zones, suitable for quick spatial searches. This dataset is distributed with the package and is used by default in getCountry(), getCountryCode() and getCountryName().

Usage

SimpleCountriesEEZ

Format

A simple features data frame with 319 records and 5 columns of data.

Details

This dataset is equivalent to EEZCountries but with fewer columns of data.

See Also

convertEEZCountries

This dataset was generated on 2022-11-03 by running:

library(MazamaSpatialUtils)
setSpatialDataDir("~/Data/Spatial_0.8")

convertEEZCountries()

loadSpatialData("EEZCountries_05")

SimpleCountriesEEZ <- EEZCountries_05[,c("countryCode", "countryName", "polygonID")]
save(SimpleCountriesEEZ, file = "data/SimpleCountriesEEZ.rda")

Description

This dataset is used by default in the getTimezones() function and contains the following columns of data:

• timezone – Olson timezone

• UTC_offset – offset from UTC (hours)

• UTC_DST_offset – offset from UTC during daylight savings (hours)

• countryCode – ISO 3166-1 alpha-2 country code

• longitude – longitude of the timezone polygon centroid

• latitude – longitude of the timezone polygon centroid

• status – one of 'Canonical', 'Alias' or 'Deprecated'

• notes – typically specifying the target of an 'Alias'

• polygonID – unique identifier (= timezone)

This dataset was generated on 2022-11-03 by running:

library(MazamaSpatialUtils)
setSpatialDataDir("~/Data/Spatial_0.8")

convertOSMTimezones()

loadSpatialData("OSMTimezones_02")

SimpleTimezones <- OSMTimezones_02
save(SimpleTimezones, file = "data/SimpleTimezones.rda")

Usage

SimpleTimezones

Format

A simple features data frame with 423 records and 9 columns of data.

Description

Simplify a simple features dataframe. This is a convenience wrapper for ms_simplify

Usage

simplify(SFDF, keep = 0.05, ...)

Arguments

Value

A simplified simple features dataframe.

Examples

## Not run: 
library(MazamaSpatialUtils)
FR <-
  SimpleCountries %>%
  dplyr::filter(countryCode == "FR")
par(mfrow = c(3, 3), mar = c(1, 1, 3, 1))
for (i in 9:1) {
  keep <- 0.1 * i
  geom <-
    FR %>%
    simplify(keep) %>%
    sf::st_geometry()
  plot(geom, main=paste0("keep = ", keep))
}
layout(1)
par(mar = c(5,4,4,2)+.1)

## End(Not run)

Description

Creates and saves "_05", _02" and "_01" versions of SFDF that are simplified to 5%, 2% and 1%. .

Usage

simplifyAndSave(
  SFDF = NULL,
  datasetName = NULL,
  uniqueIdentifier = NULL,
  dataDir = getSpatialDataDir()
)

Arguments

Value

Writes data files to disk.

Description

This package maintains an internal directory location which users can set using setSpatialDataDir. All package functions use this directory whenever datasets are created or loaded.

The default setting when the package is loaded is getwd().

Format

Absolute path string.

See Also

getSpatialDataDir

setSpatialDataDir

Description

Converts a vector of state names to an ISO 3166-2 two character state codes.

Usage

stateToCode(stateNames, countryCodes = NULL, dataset = "NaturalEarthAdm1")

Arguments

Details

For this function to work, you must install and load the "NaturalEarthAdm1" dataset.

Value

A vector of ISO 3166-2 codes or NA.

See Also

convertNaturalEarthAdm1

Examples

## Not run: 
stateToCode("Washington")
stateToCode("Barcelona")
stateToCode("Shandong")

## End(Not run)

Description

Given vectors of longitudes, latitudes and values, this function will summarize given values by spatial polygon using the FUN and return a dataframe with polygon IDs and summary values.

Usage

summarizeByPolygon(
  longitude,
  latitude,
  value,
  SFDF,
  useBuffering = FALSE,
  FUN,
  varName = "summaryValue"
)

Arguments

Value

A dataframe with the same rows as 'SFDF' but containing only two columns: 'polygonID' and the summary value.

Note

This function has not been thoroughly tested and is included in the package for experimental use only.

Description

State codes for the 50 states +DC +PR (Puerto Rico).

Usage

US_52

Format

A vector with 52 elements

Description

US_countyCodes The following columns for US states and territories:

• stateCode – ISO 3166-2 alpha-2

• stateFIPS – 2-digit FIPS code

• countyName – English language county name

• countyFIPS – five-digit FIPS code (2-digit state and 3-digit county combined to create a unique identifier)

This dataset was generated on 2022-11-04 by running:

library(MazamaSpatialUtils)
setSpatialDataDir("~/Data/Spatial_0.8")
loadSpatialData("USCensusCounties_02")

US_countyCodes <-
  USCensusCounties_02 
  dplyr::select(stateCode, stateFIPS, countyName, countyFIPS)

US_countyCodes$geometry <- NULL

save(US_countyCodes, file = "data/US_countyCodes.rda")

Usage

US_countyCodes

Format

A dataframe with 3197 rows and 4 columns of data.

Description

Converts a vector of US county names or FIPS codes from one system to another returning NA where no match is found.

Usage

US_countyNameToFIPS(state = NULL, countyName = NULL)

US_countyFIPSToName(state = NULL, countyFIPS = NULL)

Arguments

Value

A vector of US county names or FIPS codes.

Examples

library(MazamaSpatialUtils)

US_countyNameToFIPS("Washington", "King")

# If a single state is provided, it will be recycled
US_countyNameToFIPS("Washington", c("King", "Okanogan"))

# Normally, equal length vectors are provided
US_countyNameToFIPS(c("WA", "WA"), c("King", "Okanogan"))

# You cannot mix codes!
US_countyNameToFIPS(c("WA", "Washington"), c("King", "Okanogan"))

# No 'Okanogan' county in Texas
US_countyNameToFIPS(c("WA", "TX"), c("King", "Okanogan"))

# But there is a 'King' county in Texas
US_countyNameToFIPS(c("TX", "WA"), c("King", "Okanogan"))
US_countyNameToFIPS(c("TX", "WA"), c("King", "King"))

# The US_countyFIPSToName() function is included for symmetry but a
# more typical usage of a 5-digit county FIPS would be to extract it from
# the US_countyCodes package dataset:

US_countyCodes %>% dplyr::filter(countyFIPS == 53033)

Description

US_stateCodes the following columns for US states and territories:

• stateName – English language state name

• stateCode – ISO 3166-2 alpha-2

• stateFIPS – two-digit FIPS code

This dataset was generated on 2020-06-11 by running:

Usage

US_stateCodes

Format

A dataframe with 52 rows and 3 columns of data.

Description

Converts a vector of US state names or codes from one system to another returning NA where no match is found.

Usage

US_stateCodeToName(stateCode = NULL)

US_stateCodeToFIPS(stateCode = NULL)

US_stateFIPSToName(stateFIPS = NULL)

US_stateFIPSToCode(stateFIPS = NULL)

US_stateNameToCode(stateName = NULL)

US_stateNameToFIPS(stateName = NULL)

Arguments

Value

A vector of US state names or codes.

Examples

library(MazamaSpatialUtils)

US_stateNameToCode("Washington")
US_stateNameToFIPS("Washington")

postalCodes <- sample(US_stateCodes$stateCode, 30)

data.frame(
  name = US_stateCodeToName(postalCodes),
  code = postalCodes,
  FIPS = US_stateCodeToFIPS(postalCodes)
)