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.
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
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, ...
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
Complications we have seen include:
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
.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 known locations
files, each created with different locations and different distance thresholds
to address the needs of different scientific studies.
Working in this manner will solve the problems initially mentioned but also provides further useful functionality:
.rdaversions 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.
This project is supported with funding from the US Forest Service.
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