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MazamaLocationUtils

Utility functions for discovering and managing metadata associated 
with spatially unique "known locations". Applications include all 
fields of environmental monitoring (e.g. air and water quality) where 
data are collected at stationary sites.

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 an occasionally repositioned 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 re-positioned 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 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 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 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 save multiple collections files, each created with different locations and/or different distance thresholds to address the needs of different scientific studies.

Immediate Advantages

Working in this manner solves 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 and analysts working with known locations instant access to location-specific spatial metadata data that otherwise requires special software and skills, large datasets and many compute cycles to generate.

Development of this R package has been supported with funding from the following institutions:

• USFS AirFire Research Team

Questions regarding further development of the package should be directed to jonathan.callahan@dri.edu.