Nothing
## ----echo=FALSE---------------------------------------------------------------
knitr::opts_chunk$set(fig.width = 7, fig.height = 5)
## ----load_data----------------------------------------------------------------
wa <- get(data("wa_airfire_meta", package = "MazamaLocationUtils"))
names(wa)
## ----load_data_hidden, eval = TRUE, echo = FALSE------------------------------
library(MazamaLocationUtils)
wa_monitors_500 <-
get(data("wa_monitors_500", package = "MazamaLocationUtils")) %>%
dplyr::mutate(elevation = as.numeric(NA))
## ----create_table, eval = FALSE, echo = TRUE----------------------------------
# library(MazamaLocationUtils)
#
# # Initialize with standard directories
# initializeMazamaSpatialUtils()
# setLocationDataDir("./data")
#
# wa_monitors_500 <-
# table_initialize() %>%
# table_addLocation(wa$longitude, wa$latitude, distanceThreshold = 500)
## ----basic_columns------------------------------------------------------------
dplyr::glimpse(wa_monitors_500, width = 75)
## ----import_colmns------------------------------------------------------------
# Use a subset of the wa metadata
wa_indices <- seq(5,65,5)
wa_sub <- wa[wa_indices,]
# Use a generic name for the location table
locationTbl <- wa_monitors_500
# Find the location IDs associated with our subset
locationID <- table_getLocationID(
locationTbl,
longitude = wa_sub$longitude,
latitude = wa_sub$latitude,
distanceThreshold = 500
)
# Now add the "AQSID" column for our subset of locations
locationData <- wa_sub$AQSID
locationTbl <- table_updateColumn(
locationTbl,
columnName = "AQSID",
locationID = locationID,
locationData = locationData
)
# Lets see how we did
locationTbl_indices <- table_getRecordIndex(locationTbl, locationID)
locationTbl[locationTbl_indices, c("city", "AQSID")]
## ----new_locations------------------------------------------------------------
# Create new locations near our known locations
lons <- jitter(wa_sub$longitude)
lats <- jitter(wa_sub$latitude)
# Any known locations within 50 meters?
table_getNearestLocation(
wa_monitors_500,
longitude = lons,
latitude = lats,
distanceThreshold = 50
) %>% dplyr::pull(city)
# Any known locations within 250 meters
table_getNearestLocation(
wa_monitors_500,
longitude = lons,
latitude = lats,
distanceThreshold = 250
) %>% dplyr::pull(city)
# How about 5000 meters?
table_getNearestLocation(
wa_monitors_500,
longitude = lons,
latitude = lats,
distanceThreshold = 5000
) %>% dplyr::pull(city)
## ----MSU_setup, echo = TRUE, eval = FALSE-------------------------------------
# library(MazamaSpatialUtils)
# setSpatialDataDir("~/Data/Spatial")
#
# installSpatialData("EEZCountries")
# installSpatialData("OSMTimezones")
# installSpatialData("NaturalEarthAdm1")
# installSpatialData("USCensusCounties")
## ----standard_setup, echo = TRUE, eval = FALSE--------------------------------
# MazamaSpatialUtils::setSpatialDataDir("~/Data/Spatial")
#
# MazamaSpatialUtils::loadSpatialData("EEZCountries.rda")
# MazamaSpatialUtils::loadSpatialData("OSMTimezones.rda")
# MazamaSpatialUtils::loadSpatialData("NaturalEarthAdm1.rda")
# MazamaSpatialUtils::loadSpatialData("USCensusCounties.rda")
## ----easy_setup, echo = TRUE, eval = FALSE------------------------------------
# library(MazamaLocationUtils)
# initializeMazamaSpatialData()
# setLocationDataDir("~/Data/KnownLocations")
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