Package 'MazamaLocationUtils'

Description

This package maintains an internal set of API keys which users can set using setAPIKey(). These keys will be remembered for the duration of an R session. This functionality provides an abstraction layer in dependent packages so that data access functions can test for and access specific API keys with generic code.

Format

Character strings.

Details

The following functions help with the management of API keys:

getAPIKey() – Returns the API key associated with a web service. If provider == NULL a list is returned containing all recognized API keys.

setAPIKey() – Sets the API key associated with a web service. Silently returns previous value of the API key.

showAPIKeys() – Returns a list of all currently set API keys.

Description

Distance clustering is used to identify unique deployments of a sensor in an environmental monitoring field study. GPS-reported locations can be jittery and result in a sensor self-reporting from a cluster of nearby locations. Clustering helps resolve this by assigning a single location to the cluster.

Standard kmeans clustering does not work well when clusters can have widely differing numbers of members. A much better result is acheived with the Partitioning Around Medoids method available in cluster::pam().

The value of clusterDiameter is compared with the output of cluster::pam(...)$clusinfo[,'av_diss'] to determine the number of clusters.

Usage

clusterByDistance(
  tbl,
  clusterDiameter = 1000,
  lonVar = "longitude",
  latVar = "latitude",
  maxClusters = 50
)

Arguments

Value

Input tibble with additional columns: clusterLon, clusterLat, clusterID.

Note

In most applications, the table_addClustering function should be used as it implements two-stage clustering using clusterbyDistance().

References

When k-means clustering fails

See Also

table_removeRecord

Examples

library(MazamaLocationUtils)

# Fremont, Seattle 47.6504, -122.3509
# Magnolia, Seattle 47.6403, -122.3997
# Downtown Seattle 47.6055, -122.3370

fremont_x <- jitter(rep(-122.3509, 10), .0005)
fremont_y <- jitter(rep(47.6504, 10), .0005)

magnolia_x <- jitter(rep(-122.3997, 8), .0005)
magnolia_y <- jitter(rep(47.6403, 8), .0005)

downtown_x <- jitter(rep(-122.3370, 3), .0005)
downtown_y <- jitter(rep(47.6055, 3), .0005)

# Apply clustering
tbl <-
  dplyr::tibble(
    longitude = c(fremont_x, magnolia_x, downtown_x),
    latitude = c(fremont_y, magnolia_y, downtown_y)
  ) %>%
  clusterByDistance(
    clusterDiameter = 1000
  )

plot(tbl$longitude, tbl$latitude, pch = tbl$clusterID)

Description

Character string identifiers of the minimum set of fields required for a table to be considered a valid "known locations" table.

coreMetadataNames <- c(
  "locationID",           # from MazamaLocationUtils::location_createID()
  "locationName",         # from MazamaLocationUtils::location_initialize()
  "longitude",            # user supplied
  "latitude",             # user supplied
  "elevation",            # from MazamaLocationUtils::getSingleElevation_USGS()
  "countryCode",          # from MazamaSpatialUtils::getCountryCode()
  "stateCode",            # from MazamaSpatialUtils::getStateCode()
  "countyName",           # from MazamaSpatialUtils::getUSCounty()
  "timezone",             # from MazamaSpatialUtils::getTimezone()
  "houseNumber",          # from MazamaLocationUtils::getSingleAddress_Photon()
  "street",               # from MazamaLocationUtils::getSingleAddress_Photon()
  "city",                 # from MazamaLocationUtils::getSingleAddress_Photon()
  "postalCode"            # from MazamaLocationUtils::getSingleAddress_Photon()
)

Usage

coreMetadataNames

Format

A vector with 3 elements

Details

coreMetadataNames

Description

Returns the directory path where known location data tables are located.

Usage

getLocationDataDir()

Value

Absolute path string.

See Also

LocationDataDir

setLocationDataDir

Description

The id_monitor_500 dataset provides a set of known locations associated with Idaho state air quality monitors. This dataset was generated on 2023-10-24 by running:

library(AirMonitor)
library(MazamaLocationUtils)

initializeMazamaSpatialUtils()
setLocationDataDir("./data")

monitor <- monitor_loadLatest() %>% monitor_filter(stateCode == "ID")
lons <- monitor$meta$longitude
lats <- monitor$meta$latitude

table_initialize() %>%
  table_addLocation(
    lons, lats,
    distanceThreshold = 500,
    elevationService = "usgs",
    addressService = "photon"
  ) %>%
  table_save("id_monitors_500")

Usage

id_monitors_500

Format

A tibble with 30 rows and 13 columns of data.

See Also

or_monitors_500

wa_monitors_500

Description

Convenience function that wraps:

  MazamaSpatialUtils::setSpatialDataDir("~/Data/Spatial")
  MazamaSpatialUtils::loadSpatialData("EEZCountries.rda")
.  MazamaSpatialUtils::loadSpatialData("OSMTimezones.rda")
  MazamaSpatialUtils::loadSpatialData("NaturalEarthAdm1.rda")
  MazamaSpatialUtils::loadSpatialData("USCensusCounties.rda")

If spatial data has not yet been installed, an error is returned with an extended message detailing how to install the appropriate data.

Usage

initializeMazamaSpatialUtils(spatialDataDir = "~/Data/Spatial")

Arguments

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Set up directory for spatial data
  spatialDataDir <- tempdir() # typically "~/Data/Spatial"
  MazamaSpatialUtils::setSpatialDataDir(spatialDataDir)

  exists("NaturalEarthAdm1")
  initializeMazamaSpatialUtils(spatialDataDir)
  exists("NaturalEarthAdm1")
  class(NaturalEarthAdm1)
  
}, silent = FALSE)

Description

A unique locationID is created for each incoming longitude and latitude.

See MazamaCoreUtils::createLocationID for details.

At precision = 10, this results in a maximum error of 0.6 meters which is more than precise enough for environmental monitoring studies making use of this package.

An excellent way to become familiar with geohash is through the GeoHash Explorer.

Usage

location_createID(
  longitude = NULL,
  latitude = NULL,
  algorithm = c("geohash", "digest"),
  precision = 10
)

Arguments

Value

Vector of character locationIDs.

Note

The "digest" algorithm is deprecated but provided for backwards compatibility with databases that were built using locationIDs generated with this algorithm.

References

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

https://www.johndcook.com/blog/2017/01/10/probability-of-secure-hash-collisions/

Examples

library(MazamaLocationUtils)

# Wenatchee
lon <- -120.325278
lat <- 47.423333
locationID <- location_createID(lon, lat)
print(locationID)

location_createID(lon, lat, algorithm = "geohash")
location_createID(lon, lat, algorithm = "geohash", precision = 7)

Description

The FCC Block API is used get census block, county, and state FIPS associated with the longitude and latitude. The following list of data is returned:

• stateCode

• countyName

• censusBlock

The data from this function should be considered to be the gold standard for state and county. i.e. this information could and should be used to override information we get elsewhere.

Usage

location_getCensusBlock(
  longitude = NULL,
  latitude = NULL,
  censusYear = 2010,
  verbose = TRUE
)

Arguments

Value

List of census block/county/state data.

References

https://geo.fcc.gov/api/census/

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Wenatchee
  lon <- -120.325278
  lat <- 47.423333

  censusList <- location_getCensusBlock(lon, lat)
  str(censusList)
  
}, silent = FALSE)

Description

The OpenCage reverse geocoding service is used to obtain all available information for a specific location.

The data from OpenCage should be considered to be the gold standard for address information could and should be used to override information we get elsewhere.

Usage

location_getOpenCageInfo(longitude = NULL, latitude = NULL, verbose = FALSE)

Arguments

Value

Single-row tibble with OpenCage information.

Note

The OpenCage service requires an API key which can be obtained from their web site. This API key must be set as an environment variable with:

Sys.setenv("OPENCAGE_KEY" = "YOUR_PERSONAL_API_KEY")

The OpenCage "free trial" level allows for 1 request/sec and a maximum of 2500 requests per day.

References

https://opencagedata.com

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Wenatchee
  lon <- -120.325278
  lat <- 47.423333

  Sys.setenv("OPENCAGE_KEY" = "YOUR_PERSONAL_API_KEY")

  openCageTbl <- location_getOpenCageInfo(lon, lat)
  dplyr::glimpse(openCageTbl)
  
}, silent = FALSE)

Description

The Photon API is used get address data associated with the longitude and latitude. The following list of data is returned:

• houseNumber

• street

• city

• stateCode

• stateName

• postalCode

• countryCode

• countryName

The function makes an effort to convert both state and country Name into Code with codes defaulting to NA. Both Name and Code are returned so that improvements can be made in the conversion algorithm.

Usage

location_getSingleAddress_Photon(
  longitude = NULL,
  latitude = NULL,
  baseUrl = "https://photon.komoot.io/reverse",
  verbose = TRUE
)

Arguments

Value

List of address components.

References

https://photon.komoot.io

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Set up standard directories and spatial data
  spatialDataDir <- tempdir() # typically "~/Data/Spatial"
  initializeMazamaSpatialUtils(spatialDataDir)

  # Wenatchee
  lon <- -120.325278
  lat <- 47.423333

  addressList <- location_getSingleAddress_Photon(lon, lat)
  str(addressList)
  
}, silent = FALSE)

Description

Texas A&M APIs are used to determine the address associated with the longitude and latitude.

Usage

location_getSingleAddress_TexasAM(
  longitude = NULL,
  latitude = NULL,
  apiKey = NULL,
  verbose = TRUE
)

Arguments

Value

Numeric elevation value.

References

https://geoservices.tamu.edu/Services/ReverseGeocoding/WebService/v04_01/HTTP.aspx

Examples

## Not run: 
library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Wenatchee
  longitude <- -122.47
  latitude <- 47.47
  apiKey <- YOUR_PERSONAL_API_KEY

  location_getSingleAddress_TexasAM(longitude, latitude, apiKey)
  
}, silent = FALSE)

## End(Not run)

Description

USGS APIs are used to determine the elevation in meters associated with the longitude and latitude.

Note: The conversion factor for meters to feet is 3.28084.

Usage

location_getSingleElevation_USGS(
  longitude = NULL,
  latitude = NULL,
  verbose = TRUE
)

Arguments

Value

Numeric elevation value.

References

https://epqs.nationalmap.gov/v1/docs

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Wenatchee
  longitude <- -120.325278
  latitude <- 47.423333

  location_getSingleElevation_USGS(longitude, latitude)
  
}, silent = FALSE)

Description

Creates a known location record with the following columns of core metadata:

• locationID

• locationName

• longitude

• latitude

• elevation

• countryCode

• stateCode

• countyName

• timezone

• houseNumber

• street

• city

• postalCode

Usage

location_initialize(
  longitude = NULL,
  latitude = NULL,
  stateDataset = "NaturalEarthAdm1",
  elevationService = NULL,
  addressService = NULL,
  precision = 10,
  verbose = TRUE
)

Arguments

Value

Tibble with a single new known location.

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Set up standard directories and spatial data
  spatialDataDir <- tempdir() # typically "~/Data/Spatial"
  initializeMazamaSpatialUtils(spatialDataDir)

  # Wenatchee
  lon <- -120.325278
  lat <- 47.423333
  
  locationRecord <- location_initialize(lon, lat)
  str(locationRecord)
  
}, silent = FALSE)

Description

This package maintains an internal directory path which users can set using setLocationDataDir(). All package functions use this directory whenever known location tables are accessed.

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

Format

Absolute path string.

See Also

getLocationDataDir

setLocationDataDir

Description

A suite of utility functions for discovering and managing metadata associated with sets of spatially unique "known locations".

This package is intended to be used in support of data management activities associated with fixed locations in space. The motivating fields include both air and water quality monitoring where fixed sensors report at regular time intervals.

Details

When working with environmental monitoring time series, one of the first things you have to do is create unique identifiers for each individual time series. In an ideal world, each environmental time series would have both a locationID and a deviceID that uniquely identify the specific instrument making measurements and the physical location where measurements are made. A unique timeseriesID could be produced as locationID_deviceID. Metadata associated with each timeseriesID would contain basic information needed for downstream analysis including at least:

  timeseriesID, locationID, deviceID, longitude, latitude, ...

• An extended time series for an occasionally re-positioned sensor would group by deviceID.

• Multiple sensors placed at a single location could be be grouped by locationID.

• Maps would be created using longitude, latitude.

• Time series would be accessed from a secondary data table with timeseriesID.

Unfortunately, we are rarely supplied with a truly unique and truly spatial locationID. Instead we often use deviceID or an associated non-spatial identifier as a stand-in for locationID.

Complications we have seen include:

• GPS-reported longitude and latitude can have jitter in the fourth or fifth decimal place making it challenging to use them to create a unique locationID.

• Sensors are sometimes re-positioned in what the scientist considers the "same location".

• Data for a single sensor goes through different processing pipelines using different identifiers and is later brought together as two separate time series.

• The spatial scale of what constitutes a "single location" depends on the instrumentation and scientific question being asked.

• Deriving location-based metadata from spatial datasets is computationally intensive unless saved and identified with a unique locationID.

• Automated searches for spatial metadata occasionally produce incorrect results because of the non-infinite resolution of spatial datasets.

This package attempts to address all of these issues by maintaining a table of known locations for which CPU intensive spatial data calculations have already been performed. While requests to add new locations to the table may take some time, searches for spatial metadata associated with existing locations are simple lookups.

Working in this manner will solve the problems initially mentioned but also provides further useful functionality.

• Administrators can correct entries in the collectionName table. (e.g. locations in river bends that even high resolution spatial datasets mis-assign)

• Additional, non-automatable metadata can be added to collectionName. (e.g. commonly used location names within a community of practice)

• Different field campaigns can have separate collectionName tables.

• .csv or .rda versions of well populated tables can be downloaded from a URL and used locally, giving scientists working with known locations instant access to spatial data that otherwise requires special skills, large datasets and lots of compute cycles.

Description

The or_monitor_500 dataset provides a set of known locations associated with Oregon state air quality monitors. This dataset was generated on 2023-10-24 by running:

library(AirMonitor)
library(MazamaLocationUtils)

initializeMazamaSpatialUtils()
setLocationDataDir("./data")

monitor <- monitor_loadLatest() %>% monitor_filter(stateCode == "OR")
lons <- monitor$meta$longitude
lats <- monitor$meta$latitude

table_initialize() %>%
  table_addLocation(
    lons, lats, 
      distanceThreshold = 500,
      elevationService = "usgs",
      addressService = "photon"
    ) %>%
  table_save("or_monitors_500")

Usage

or_monitors_500

Format

A tibble with 64 rows and 13 columns of data.

See Also

id_monitors_500

wa_monitors_500

Description

Sets the data directory where known location data tables are located. If the directory does not exist, it will be created.

Usage

setLocationDataDir(dataDir)

Arguments

Value

Silently returns previous value of the data directory.

See Also

LocationDataDir

getLocationDataDir

Description

Clustering is used to identify unique deployments of a sensor in an environmental monitoring field study.

Sensors will be moved around from time to time, sometimes across the country and sometimes across the street. We would like to assign unique identifiers to each new "deployment" but not when the sensor is moved a short distance.

We use clustering to find an appropriate number of unique "deployments". The sensitivity of this algorithm can be adjused with the clusterDiameter argument.

Standard kmeans clustering does not work well when clusters can have widely differing numbers of members. A much better result is acheived with the Partitioning Around Medoids method available in cluster::pam().

The value of clusterRadius is compared with the output of cluster::pam(...)$clusinfo[,'av_diss'] to determine the number of clusters.

Usage

table_addClustering(
  tbl,
  clusterDiameter = 1000,
  lonVar = "longitude",
  latVar = "latitude",
  maxClusters = 50
)

Arguments

Value

Input tibble with additional columns: clusterLon, clusterLat.

Note

The table_addClustering() function implements two-stage clustering using clusterByDistance. If the first attempt at clustering produces clustered locations that are still too close to eachother, another round of clustering is performed using the results of the previous attempt. This two-stage approach seems to work well in. practice.

References

When k-means clustering fails

See Also

clusterByDistance

Examples

library(MazamaLocationUtils)

# Fremont, Seattle 47.6504, -122.3509
# Magnolia, Seattle 47.6403, -122.3997
# Downtown Seattle 47.6055, -122.3370

fremont_x <- jitter(rep(-122.3509, 10), .0005)
fremont_y <- jitter(rep(47.6504, 10), .0005)

magnolia_x <- jitter(rep(-122.3997, 8), .0005)
magnolia_y <- jitter(rep(47.6403, 8), .0005)

downtown_x <- jitter(rep(-122.3370, 3), .0005)
downtown_y <- jitter(rep(47.6055, 3), .0005)

# Apply clustering
tbl <-
  dplyr::tibble(
    longitude = c(fremont_x, magnolia_x, downtown_x),
    latitude = c(fremont_y, magnolia_y, downtown_y)
  ) %>%
  table_addClustering(
    clusterDiameter = 1000
  )

plot(tbl$longitude, tbl$latitude, pch = tbl$clusterID)

Description

A new metadata column is added to the locationTbl. For matching locationID records, the associated locationData is inserted. Otherwise, the new column will be initialized with NA.

Usage

table_addColumn(
  locationTbl = NULL,
  columnName = NULL,
  locationID = NULL,
  locationData = NULL,
  verbose = TRUE
)

Arguments

Value

Updated tibble of known locations.

See Also

table_removeColumn

table_updateColumn

Examples

library(MazamaLocationUtils)

# Starting table
locationTbl <- get(data("wa_monitors_500")) 
names(locationTbl)

# Add an empty column
locationTbl <-
  locationTbl %>%
  table_addColumn("AQSID")
  
names(locationTbl)

Description

An existing table will be amended to guarantee that it includes the following core metadata columns.

• locationID

• locationName

• longitude

• latitude

• elevation

• countryCode

• stateCode

• countyName

• timezone

• houseNumber

• street

• city

• postalCode

The longitude and latitude columns are required to exist in the incoming tibble but all others are optional.

If any of these core metadata columns are found, they will be retained.

The locationID will be generated (anew if already found) from existing longitude and latitude data.

Other core metadata columns will be filled with NA values of the proper type.

The result is a tibble with all of the core metadata columns. Theses columns must then be filled in to create a usable "known locations" table.

Usage

table_addCoreMetadata(locationTbl = NULL, precision = 10)

Arguments

Value

Tibble with the metadata columns required in a "known locations" table.

Note

No check is performed for overlapping locations. The returned tibble has the structure of a "known locations" table and is a good starting place for investigation. But further work is required to produce a valid table of "known locations" associated with a specific spatial scale.

Description

Incoming longitude and latitude values are compared against the incoming locationTbl to see if they are already within distanceThreshold meters of an existing entry. A new record is created for each location that is not already found in locationTbl.

Usage

table_addLocation(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL,
  stateDataset = "NaturalEarthAdm1",
  elevationService = NULL,
  addressService = NULL,
  verbose = TRUE
)

Arguments

Value

Updated tibble of known locations.

Note

This function is a vectorized version of table_addSingleLocation().

See Also

table_addSingleLocation

table_removeRecord

table_updateSingleRecord

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Set up standard directories and spatial data
  spatialDataDir <- tempdir() # typically "~/Data/Spatial"
  initializeMazamaSpatialUtils(spatialDataDir)

  locationTbl <- get(data("wa_monitors_500"))

  # Coulee City, WA
  lon <- -119.290904
  lat <- 47.611942

  locationTbl <- 
    locationTbl %>%
    table_addLocation(lon, lat, distanceThreshold = 500)
  
  dplyr::glimpse(locationTbl)
  
}, silent = FALSE)

Description

The OpenCage reverse geocoding service is used to update an existing table. Updated columns include:

• countryCode

• stateCode

• countyName

• timezone

• houseNumber

• street

• city

• postalCode

• address

When replaceExisting = TRUE, all existing address fields are discarded in favor of the OpenCage versions. To only fill in missing values in locationTbl, use replaceExisting = FALSE.

The OpenCage service returns a large number of fields, some of which may be useful. To add all OpenCage fields to a location table, use retainOpenCage = TRUE. This will append 78+ fields of information, each each named with a prefix of "opencage_".

Usage

table_addOpenCageInfo(
  locationTbl = NULL,
  replaceExisting = FALSE,
  retainOpenCage = FALSE,
  verbose = FALSE
)

Arguments

Value

Tibble of "known locations" enhanced with information from the OpenCage reverse geocoding service.

Note

The OpenCage service requires an API key which can be obtained from their web site. This API key must be set as an environment variable with:

Sys.setenv("OPENCAGE_KEY" = "<your api key>")

Parameters are set for use at the OpenCage "free trial" level which allows for 1 request/sec and a maximum of 2500 requests per day.

Because of the 1 request/sec default, it is recommended that table_addOpenCageInfo() only be used in an interactive session when updating a table with a large number of records.

References

https://opencagedata.com

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  myTbl <- id_monitors_500[1:3,]
  myTbl$countryCode[1] <- NA
  myTbl$countryCode[2] <- "WRONG"
  myTbl$countyName[3] <- "WRONG"
  myTbl$timezone <- NA

  dplyr::glimpse(myTbl)

  Sys.setenv("OPENCAGE_KEY" = "<YOUR_KEY>")

  table_addOpenCageInfo(myTbl) %>% 
    dplyr::glimpse()

  table_addOpenCageInfo(myTbl, replaceExisting = TRUE) %>% 
    dplyr::glimpse()

  table_addOpenCageInfo(myTbl, replaceExisting = TRUE, retainOpenCage = TRUE) %>% 
    dplyr::glimpse()
  
}, silent = FALSE)

Description

Incoming longitude and latitude values are compared against the incoming locationTbl to see if they are already within distanceThreshold meters of an existing entry. A new record is created for if the location is not already found in locationTbl.

Usage

table_addSingleLocation(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL,
  stateDataset = "NaturalEarthAdm1",
  elevationService = NULL,
  addressService = NULL,
  verbose = TRUE
)

Arguments

Value

Updated tibble of known locations.

See Also

table_addLocation

table_removeRecord

table_updateSingleRecord

Examples

library(MazamaLocationUtils)

# Fail gracefully if any resources are not available
try({

  # Set up standard directories and spatial data
  spatialDataDir <- tempdir() # typically "~/Data/Spatial"
  initializeMazamaSpatialUtils(spatialDataDir)

  locationTbl <- get(data("wa_monitors_500"))
  
  nrow(locationTbl)

  # Coulee City, WA
  lon <- -119.290904
  lat <- 47.611942

  locationTbl <- 
    locationTbl %>%
    table_addSingleLocation(lon, lat, distanceThreshold = 500)
    
  nrow(locationTbl)
  
}, silent = FALSE)

Description

Returns a tibble of the known locations from locationTbl that are within distanceThreshold meters of the target location specified by longitude and latitude.

Usage

table_filterByDistance(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Tibble of known locations.

Note

Only a single target location is allowed.

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

# Too small a distanceThreshold will not find a match
locationTbl %>%
  table_filterByDistance(
    longitude = -117.3647, 
    latitude = 47.6725, 
    distanceThreshold = 10
  ) %>% 
  dplyr::glimpse()

# Expanding the distanceThreshold will find several
locationTbl %>%
  table_filterByDistance(
    longitude = -117.3647, 
    latitude = 47.6725, 
    distanceThreshold = 10000
  ) %>%
  dplyr::glimpse()

Description

Calculate distances between all locations within a known locations table and return a tibble with the row indices and separation distances of those records separated by less than distanceThreshold meters. Records are returned in order of distance.

It is useful when working with new metadata tables to identify adjacent locations early on so that decisions can be made about the appropriateness of the specified distanceThreshold.

Usage

table_findAdjacentDistances(
  locationTbl = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Tibble of row indices and distances for those locations separated by less than distanceThreshold meters.

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

Examples

library(MazamaLocationUtils)

meta <- wa_airfire_meta

# Any locations closer than 2 km?
table_findAdjacentDistances(meta, distanceThreshold = 2000)

# How about 4 km?
table_findAdjacentDistances(meta, distanceThreshold = 4000)

Description

Calculate distances between all locations within a known locations table and return a tibble containing all records that have an adjacent location separated by less than distanceThreshold meters. The return tibble is ordered by separation distance.

It is useful when working with new metadata tables to identify adjacent locations early on so that decisions can be made about the appropriateness of the specified distanceThreshold.

Usage

table_findAdjacentLocations(
  locationTbl = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Tibble of known locations separated by less than distanceThreshold meters.

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

Examples

library(MazamaLocationUtils)

meta <- wa_airfire_meta

# Any locations closer than 2 km?
meta %>%
  table_findAdjacentLocations(distanceThreshold = 2000) %>%
  dplyr::select(AQSID, timezone)

# How about 4 km?
meta %>%
  table_findAdjacentLocations(distanceThreshold = 4000) %>%
  dplyr::select(AQSID, timezone)

Description

Returns a tibble with the same number of rows as locationTbl containing the distance and direction from the target location specified by longitude and latitude to each known location found in locationTbl.

Usage

table_getDistanceFromTarget(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Tibble of distances in meters and cardinal directions from a target location.

Note

Only a single target location is allowed.

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

locationTbl %>%
  table_getDistanceFromTarget(
    longitude = -117.3647, 
    latitude = 47.6725
  ) %>% 
  dplyr::glimpse()

Description

Returns a vector of locationIDs for the known locations that each incoming location will be assigned to within the given. If more than one known location exists within the given distanceThreshold, the closest will be assigned. NA will be returned for each incoming that cannot be assigned to a known location in locationTbl.

Usage

table_getLocationID(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Vector of known locationIDs.

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

Examples

locationTbl <- get(data("wa_monitors_500"))

# Wenatchee
lon <- -120.325278
lat <- 47.423333

# Too small a distanceThreshold will not find a match
table_getLocationID(locationTbl, lon, lat, distanceThreshold = 50)

# Expanding the distanceThreshold will find one
table_getLocationID(locationTbl, lon, lat, distanceThreshold = 5000)

Description

Returns distances between target locations and the closest location found in locationTbl (if any). Target locations are specified with longitude and latitude.

For each target location, only a single distance to the closest known location is returned. If no known location is found within distanceThreshold, the distance associated with that target location will be NA. The length and order of resulting distances will match the order of the incoming target locations.

Usage

table_getNearestDistance(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Vector of closest distances between target locations and known locations.

Use Case

You may have a set of locations of interest for which you want to assess whether any monitoring locations are nearby. In this case, the locations of interest will provide longitude and latitude while locationTbl will be the known location table associated with the monitoring locations.

The resulting vector of distances will tell you the distance, for each target location, to the nearst monitoring location.

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

See geodist for details.

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

# Wenatchee
lon <- -120.325278
lat <- 47.423333

# Too small a distanceThreshold will not find a match
table_getNearestDistance(locationTbl, lon, lat, distanceThreshold = 50)

# Expanding the distanceThreshold will find one
table_getNearestDistance(locationTbl, lon, lat, distanceThreshold = 5000)

Description

Returns a tibble of the known locations from locationTbl that are closest to the vector of target locations specified by longitude and latitude. Only a single known location is returned for each incoming target location. If no known location is found for a particular incoming location, that record in the tibble will contain all NA.

Usage

table_getNearestLocation(
  locationTbl = NULL,
  longitude = NULL,
  latitude = NULL,
  distanceThreshold = NULL
)

Arguments

Value

Tibble of known locations.

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

# Wenatchee
lon <- -120.325278
lat <- 47.423333

# Too small a distanceThreshold will not find a match
table_getNearestLocation(locationTbl, lon, lat, distanceThreshold = 50) %>% str()

# Expanding the distanceThreshold will find one
table_getNearestLocation(locationTbl, lon, lat, distanceThreshold = 5000) %>% str()

Description

Returns a vector of locationTbl row indexes for the locations associated with each locationID.

Usage

table_getRecordIndex(locationTbl = NULL, locationID = NULL, verbose = TRUE)

Arguments

Value

Vector of locationTbl row indexes.

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

# Wenatchee
lon <- -120.325278
lat <- 47.423333

# Get the locationID first
locationID <- table_getLocationID(locationTbl, lon, lat, distanceThreshold = 5000)

# Now find the row associated with this ID
recordIndex <- table_getRecordIndex(locationTbl, locationID)

str(locationTbl[recordIndex,])

Description

Creates an empty known location tibble with the following columns of core metadata:

• locationID

• locationName

• longitude

• latitude

• elevation

• countryCode

• stateCode

• countyName

• timezone

• houseNumber

• street

• city

• postalCode

Usage

table_initialize()

Value

Empty known location tibble with the specified metadata columns.

Examples

library(MazamaLocationUtils)

# Create an empty Tbl
emptyTbl <- table_initialize()
dplyr::glimpse(emptyTbl)

Description

An existing table may have much of the data that is needed for a known location table. This function accepts an incoming table and searches for required columns:

• locationID

• locationName

• longitude

• latitude

• elevation

• countryCode

• stateCode

• countyName

• timezone

• houseNumber

• street

• city

• postalCode

The longitude and latitude columns are required but all others are optional.

If any of these optional columns are found, they will be used and the often slow and sometimes slightly inaccurate steps to generate that information will be skipped for locations that have non-missing data. Any additional columns of information that are not part of the required core metadata will be retained.

This method skips the assignment of columns like elevation and all address related fields that require web service requests.

Compared to initializing a brand new table and populating it one record at a time, this is a much faster way of creating a known location table from a pre-existing table of metadata.

Usage

table_initializeExisting(
  locationTbl = NULL,
  stateDataset = "NaturalEarthAdm1",
  countryCodes = NULL,
  distanceThreshold = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap"),
  precision = 10,
  verbose = TRUE
)

Arguments

Value

Known location tibble with the specified metadata columns. Any locations whose circles (as defined by distanceThreshold) overlap will generate warning messages.

It is incumbent upon the user to address overlapping locations by one of:

1. reduce the distanceThreshold until no overlaps occur

2. assign one of the overlapping locations to the other location

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

Description

This function creates interactive maps that will be displayed in RStudio's 'Viewer' tab. The default setting of jitter will move locations randomly within an ~50 meter radius so that overlapping locations can be identified. Set jitter = 0 to see precise locations.

Usage

table_leaflet(
  locationTbl = NULL,
  maptype = c("terrain", "roadmap", "satellite", "toner"),
  extraVars = NULL,
  jitter = 5e-04,
  ...
)

Arguments

Details

The maptype argument is mapped onto leaflet "ProviderTile" names. Current mappings include:

• "roadmap" => "OpenStreetMap"

• "satellite" => "Esri.WorldImagery"

• "terrain" => "Esri.WorldTopoMap"

• "toner" => "Stamen.Toner"

If a character string not listed above is provided, it will be used as the underlying map tile if available. See https://leaflet-extras.github.io/leaflet-providers/ for a list of "provider tiles" to use as the background map.

Value

A leaflet "plot" object which, if not assigned, is rendered in Rstudio's 'Viewer' tab.

Examples

## Not run: 
library(MazamaLocationUtils)

# A table with all core metadata
table_leaflet(wa_monitors_500)
  
# A table missing some core metadata
table_leaflet(
  wa_airfire_meta,
  extraVars = c("stateCode", "countyName", "msaName")
)

# Customizing the map
table_leaflet(
  wa_airfire_meta,
  extraVars = c("stateCode", "countyName", "msaName"),
  radius = 6,
  color = "black",
  weight = 2,
  fillColor = "red",
  fillOpacity = 0.3
)

## End(Not run)

Description

This function adds a layer to an interactive map displayed in RStudio's 'Viewer' tab. The default setting of jitter will move locations randomly within an ~50 meter radius so that overlapping locations can be identified. Set jitter = 0 to see precise locations.

Usage

table_leafletAdd(
  map = NULL,
  locationTbl = NULL,
  extraVars = NULL,
  jitter = 5e-04,
  ...
)

Arguments

Value

A leaflet "plot" object which, if not assigned, is rendered in Rstudio's 'Viewer' tab.

Description

Load a tibble of known locations from the preferred directory.

The known location table must be named either <collectionName>.rda or <collectionName>.csv. If both are found, only <collectionName>.rda will be loaded to ensure that columns will have the proper type assigned.

Usage

table_load(collectionName = NULL)

Arguments

Value

Tibble of known locations.

See Also

setLocationDataDir

Examples

library(MazamaLocationUtils)

# Set the directory for saving location tables
setLocationDataDir(tempdir())

# Load an example table and check the dimensions
locationTbl <- get(data("wa_monitors_500"))
dim(locationTbl)

# Save it as "table_load_example"
table_save(locationTbl, "table_load_example")

# Load it and check the dimensions
my_table <- table_load("table_load_example")
dim(my_table)

# Check the locationDataDir
list.files(getLocationDataDir(), pattern = "table_load_example")

Description

Remove the column matching columnName. This function can be used in pipelines.

Usage

table_removeColumn(locationTbl = NULL, columnName = NULL, verbose = TRUE)

Arguments

Value

Updated tibble of known locations.

See Also

table_addColumn

table_removeColumn

Examples

library(MazamaLocationUtils)

# Starting table
locationTbl <- get(data("wa_monitors_500")) 
names(locationTbl)

# Add a new column
locationTbl <-
  locationTbl %>%
  table_addColumn("AQSID")
  
names(locationTbl)

# Now remove it
locationTbl <-
  locationTbl %>%
  table_removeColumn("AQSID")

names(locationTbl)

try({
  # Cannot remove "core" metadata
  locationTbl <-
    locationTbl %>%
    table_removeColumn("longitude")
}, silent = FALSE)

Description

Incoming locationID values are compared against the incoming locationTbl and any matches are removed.

Usage

table_removeRecord(locationTbl = NULL, locationID = NULL, verbose = TRUE)

Arguments

Value

Updated tibble of known locations.

See Also

table_addLocation

table_addSingleLocation

table_updateSingleRecord

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))
dim(locationTbl)

# Wenatchee
lon <- -120.325278
lat <- 47.423333

# Get the locationID first
locationID <- table_getLocationID(locationTbl, lon, lat, distanceThreshold = 500)

# Remove it
locationTbl <- table_removeRecord(locationTbl, locationID)
dim(locationTbl)

# Test
table_getLocationID(locationTbl, lon, lat, distanceThreshold = 500)

Description

Save a tibble of known locations to the preferred directory. If outputType is a vector, the known locations table will be saved to the preferred directory in multiple formats.

Usage

table_save(
  locationTbl = NULL,
  collectionName = NULL,
  backup = TRUE,
  outputType = "rda"
)

Arguments

Details

Backup files are saved with "YYYY-mm-ddTHH:MM:SS"

Value

File path of saved file.

Examples

library(MazamaLocationUtils)

# Set the directory for saving location tables
setLocationDataDir(tempdir())

# Load an example table and check the dimensions
locationTbl <- get(data("wa_monitors_500"))
dim(locationTbl)

# Save it as "table_save_example"
table_save(locationTbl, "table_save_example")

# Add a column and save again 
locationTbl %>% 
  table_addColumn("my_column") %>% 
  table_save("table_save_example")
  
# Check the locationDataDir
list.files(getLocationDataDir(), pattern = "table_save_example")

Description

Updates records in a location table. Records are identified by locationID and the data found in locationData is used to replace any existing value in the columnName column. locationID and locationData must be of the same length. Any NA values in locationID will be ignored.

If columnName is not a named column within locationTbl, a new column will be created.

Usage

table_updateColumn(
  locationTbl = NULL,
  columnName = NULL,
  locationID = NULL,
  locationData = NULL,
  verbose = TRUE
)

Arguments

Value

Updated tibble of known locations.

See Also

table_addColumn

table_removeColumn

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))
wa <- get(data("wa_airfire_meta"))

# We will merge some metadata from wa into locationTbl

# Record indices for wa
wa_indices <- seq(5,65,5)
wa_sub <- wa[wa_indices,]

locationID <- 
  table_getLocationID(
    locationTbl, 
    wa_sub$longitude, 
    wa_sub$latitude, 
    distanceThreshold = 500
  )
  
locationData <- wa_sub$AQSID

locationTbl <- 
  table_updateColumn(locationTbl, "AQSID", locationID, locationData)

# Look at the data we attempted to merge
wa$AQSID[wa_indices]

# And two columns from the updated locationTbl
locationTbl_indices <- table_getRecordIndex(locationTbl, locationID)
locationTbl[locationTbl_indices, c("city", "AQSID")]

Description

Information in the locationList is used to replace existing information found in locationTbl. This function can be used for small tweaks to an existing locationTbl. Wholesale replacement of records should be performed with table_removeRecord() followed by table_addLocation().

Usage

table_updateSingleRecord(
  locationTbl = NULL,
  locationList = NULL,
  verbose = TRUE
)

Arguments

Value

Updated tibble of known locations.

See Also

table_addLocation

table_addSingleLocation

table_removeRecord

Examples

library(MazamaLocationUtils)

locationTbl <- get(data("wa_monitors_500"))

# Wenatchee
wenatcheeRecord <- 
  locationTbl %>% 
  dplyr::filter(city == "Wenatchee")

str(wenatcheeRecord)

wenatcheeID <- wenatcheeRecord$locationID

locationTbl <- table_updateSingleRecord(
  locationTbl,
  locationList = list(
    locationID = wenatcheeID,
    locationName = "Wenatchee-Fifth St"
  )
)

# Look at the new record
locationTbl %>% 
  dplyr::filter(city == "Wenatchee") %>%
  str()

Description

Ensures that the incoming table has numeric longitude and latitude columns.

Usage

validateLocationTbl(locationTbl = NULL, locationOnly = TRUE)

Arguments

Value

Invisibly returns TRUE if no error message has been generated.

Description

The MazamaSpatialUtils package mus be properly installed and initialized before using functions from the MazamaLocationUtils package. This function tests for this.

Usage

validateMazamaSpatialUtils()

Value

Invisibly returns TRUE if no error message has been generated.

Description

The wa_pwfsl_meta dataset provides a set of Washington state air quality monitor metadata used by the USFS AirFire group. This dataset was generated on 2023-10-24 by running:

library(AirMonitor)

wa_airfire_meta <-
  airnow_loadLatest() %>%
  monitor_filter(stateCode == "WA") %>%
  monitor_getMeta() %>%
  # On 2023-10-24, this metdata still uses zip instead of postalCode
  dplyr::rename(postalCode = zip) %>%
  # Remove internal fields
  dplyr::select(-dplyr::starts_with("airnow_"))

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

Usage

wa_airfire_meta

Format

A tibble with 92 rows and 29 columns of data.

Description

The wa_monitor_500 dataset provides a set of known locations associated with Washington state air quality monitors. This dataset was generated on 2023-10-24 by running:

library(AirMonitor)
library(MazamaLocationUtils)

initializeMazamaSpatialUtils()
setLocationDataDir("./data")

monitor <- monitor_loadLatest() %>% monitor_filter(stateCode == "WA")
lons <- monitor$meta$longitude
lats <- monitor$meta$latitude

table_initialize() %>%
  table_addLocation(
    lons, lats,
    distanceThreshold = 500,
    elevationService = "usgs",
    addressService = "photon"
  ) %>%
  table_save("wa_monitors_500")

Usage

wa_monitors_500

Format

A tibble with 78 rows and 13 columns of data.

See Also

id_monitors_500

or_monitors_500