--- title: "Introduction to MazamaLocationUtils" author: "Jonathan Callahan" date: "2023-10-24" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to MazamaLocationUtils} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo=FALSE} knitr::opts_chunk$set(fig.width = 7, fig.height = 5) ``` # Background This package is intended for use in data management activities associated with fixed locations in space. The motivating fields include air and water quality monitoring where fixed sensors report at regular time intervals. 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 a mobile 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 measurements would be accessed from a secondary `data` table with `timeseriesID` column names. 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 _repositioned_ in what the scientist considers the "same location". * Data from a single sensor goes through different processing pipelines using different identifiers and is later brought together as two separate timeseries. * 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 and must be corrected by hand. # Functionality A solution to all these problems is possible if we store spatial metadata in simple tables in a standard directory. These tables will be referred to as _collections_. Location lookups can be performed with geodesic distance calculations where a longitude-latitude pair is assigned to a pre-existing _known location_ if it is within `distanceThreshold` meters of that location. These lookups will be extremely fast. If no previously _known location_ is found, the relatively slow (seconds) creation of a new _known location_ metadata record can be performed and then added to the growing collection. For collections of stationary environmental monitors that only number in the thousands, this entire _collection_ can be stored as either a `.rda` or `.csv` file and will be under a megabyte in size making it fast to load. This small size also makes it possible to store multiple _known locations_ files, each created with different locations and different distance thresholds to address the needs of different scientific studies. # Example Usage The package comes with some example _known locations_ tables. Lets take some metadata we have for air quality monitors in Washington state and create a _known locations_ table for them. ```{r load_data} wa <- get(data("wa_airfire_meta", package = "MazamaLocationUtils")) names(wa) ``` ```{r 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)) ``` ## Creating a Known Locations table We can create a _known locations_ table for them with a minimum 500 meter separation between distinct locations. _(NOTE: This will take some time to performa all the spatial queries.)_ To speed things up, we call `table_addLocation()` with defaults: `elevationService = NULL, addressService = NULL`. This avoids these slow web service requests and results in a table with `NA` for these columns. ```{r 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$latitude, distanceThreshold = 500) ``` At this point, our _known locations_ table contains only automatically generated spatial metadata. ```{r basic_columns} dplyr::glimpse(wa_monitors_500, width = 75) ``` ## Merging external metadata Perhaps we would like to import some of the original metadata into our new table. This is a very common use case where non-spatial metadata like uniform identifiers or owner information for a monitor can be added. Just to make it interesting, let's assume that our _known locations_ table is already large and we are only providing additional metadata for a subset of the records. ```{r import_colmns} # Use a subset of the wa metadata wa_indices <- seq(5,65,5) wa_sub <- wa[wa_indices,] # Use a generic name for the location table locationTbl <- wa_monitors_500 # Find the location IDs associated with our subset locationID <- table_getLocationID( locationTbl, longitude = wa_sub$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")] ``` Very nice. We have added `AQSID` to our known locations table for a more detailed description of each monitors' location. ## Finding known locations The whole point of a known locations table is to speed up access to spatial and other metadata. Here's how we can use it with a set of longitudes and latitudes that are not currently in our table. ```{r 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) ``` # Standard Setup Before using **MazamaLocationUtils** you must first install **MazamaSpatialUtils** and then install core spatial data with: ```{r MSU_setup, echo = TRUE, eval = FALSE} library(MazamaSpatialUtils) setSpatialDataDir("~/Data/Spatial") installSpatialData("EEZCountries") installSpatialData("OSMTimezones") installSpatialData("NaturalEarthAdm1") installSpatialData("USCensusCounties") ``` The `initializeMazamaSpatialData()` function by default assumes spatial data are installed in the standard location and is just a wrapper for: ```{r 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") ``` Once the required datasets have been installed, the easiest way to set things up each session is with: ```{r easy_setup, echo = TRUE, eval = FALSE} library(MazamaLocationUtils) initializeMazamaSpatialData() setLocationDataDir("~/Data/KnownLocations") ``` Every time you `table_save()` your location table, a backup will be created so you can experiment without losing your work. File sizes are pretty tiny so you don't have to worry about filling up your disk. ---- Best wishes for well organized spatial metadata!