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. Metadata associated with each timeseries would contain basic information needed for downstream analysis including at least:

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

• Multiple sensors placed at a single location could be associated based on their shared locationID.
• An extended time series for a single, potentially redeployed sensor would be associated with a single sensorID.
• Maps could be created using longitude, latitude fields.
• Time series data for a sensor 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 sensorID 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 for 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 manually corrected.

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 distanceThreshold 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, an entire collection can be stored as either an .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 distance thresholds to address the needs of different instruments or scientific studies.

Immediate Advantages

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 and analysts working with known locations instant access to location-specifc spatial metadata data that otherwise requires special skills, large datasets and many compute cycles to generate.

This project is supported by Mazama Science.