# MazamaLocationUtils v0.1.6

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## Manage Spatial Metadata for Known Locations

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

# MazamaLocationUtils

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


## Background

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.

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 sensorID that uniquely identify the spatial location and specific instrument making measurements. A unique timeseriesID could be produced as locationID_sensorID. Location metadata associated with each time series would contain basic information needed for downstream analysis including at least:

timeseriesID, locationID, sensorID, longitude, latitude, ...

• Multiple sensors placed at a location could be be grouped by locationID.
• An extended time series for a mobile sensor would group by sensorID.
• 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 sensorID or an associated non-spatial identifier as a standin 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 for a single sensor goes through different processing pipelines using different identifiers and is later brought together as two separate timeseries.
• The radius 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.

## A Solution

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 location is assigned to a pre-existing known location if it is within radius meters. These 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 (i.e. "database") 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 location files, each created with different locations and different radii to address the needs of different scientific studies.

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

• Administrators can correct entries in an individual collection. (e.g. locations in river bends that even high resolution spatial datasets mis-assign)
• Additional, non-automatable metadata can be added to a collection. (e.g. commonly used location names within a community of practice)
• Different field campaigns can maintain separate collections.
• .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 many compute cycles to generate.

This project is supported by Mazama Science.