SensorThings There is a service focused on data streaming. Metadata of well itself can be incorporated into SensorThings model or within CKANlarge diversity in the amount and type of water data collected across the state and needed to inform water-related decisions. Data could be about surface water conditions (e.g., evaporation, soil moisture, precipitation, ecosystem health, and location of reservoirs/rivers/conveyances); ground water conditions (e.g., groundwater levels, locations and depths of wells, aquifer parameters, and geological formations); and related management processes (e.g., permits for use (type and quantity), measured diversions/extractions, amount in storage, water returns/injections, and water quality). Towards this end, the NM Water Data Catalog organizes datasets into 9 groups, spanning climate, ecosystems, energy, infrastructure, hazards as well as related water topics. Any single dataset can be tagged with one or more of these groups.
Generally, every agency has their own internal practices and standards but we require a common understanding to ensure data is being accurately represented within the central platform. Water datasets can further be classified broadly or specific to a particular use (e.g., water used for irrigation). For the purposes of organizing standards development webpages/activities, we group water datasets into two types, one focused on water quantity (e.g., groundwater levels, water use, ...) and another on water quality (e.g., chemical, biological, …) (see subpages on the left). Within each of these pages, we adopted a SensorThings (ST) data model to develop a common “language” around the different types of water data.
ST is a service focused on data streaming that supports integration of metadata within the data structure. ST organizes data into “Things” each of which has a particular location and associated datastreams that captured one or more observations from sensors that capture features of interest. This general framework allows for a flexible approach to standardize data across diverse types of datasets, ultimately enabling for data to be better described, easy to use, and discoverable. This is due to the inclusion of relevant metadata to make the data more complete and allowing for more convenient ways to query the data using ST APIs.
The basic elements of SensorThings are are:
Location - physical location of the observations as a geoJSON feature. Can be a point or polygon. Captured in a geoJSON format.
Thing - A a collection of one or more sensors/an abstract description of what is being measured e.g Water Levels
Sensor - The sensor the device or entity doing the measurement
Datastream - A a group of observations of an observed property by a sensor for a thing at a given location
ObservedProperty - Description description of what is being observed
Observations - The the actual time vs intensity data
Overview from Kyle presented on 12/8/2020Kyle Onda (Unlicensed) from the Internet of Water provided us an overview of the ST framework on . A copy of his slides are available for reference:
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The following subpages capture details about how the ST framework can be mapped onto our NM-specific datasets to improve standard referencing and use of diverse water data. For the purposes of organizing these reference tables, we use a primary distinction between water quantity vs. water quality for categorizing related datasets. For all these datasets, both relevant information (i.e., specific measurements) as well as associated metadata (i.e., describes and gives information about the specific measurements) are discussed.