A conceptual data modeling framework with four levels of abstraction for environmental information

Lyme disease is the most common vector-borne disease in North America. Predicting Lyme disease incidence is a key component of public health preparedness. Previously, we demonstrated that the volume of data searches on Google Trends for terms related to Lyme disease, such as "Lyme" and "tick bite", can be used as a tool to predict monthly human Lyme disease incidence at the state level. The objective of this project was to build upon our previous work by adding environmental and canine data to our predictive models for the prediction of state-level human and canine Lyme disease incidence. Human data were acquired from state health departments. Canine data were acquired from IDEXX Laboratories. We hypothesized that incorporating a One Health approach with human, animal, and environmental data would improve the predictive ability of the models. The One Health model performed significantly better (Mean Absolute Error [MAE] = 12.1) in predicting human disease incidence in 6 out of 16 states compared to the environmental data model (MAE = 16.5), human search terms model (MAE = 21.4), canine data (search terms + case count) model (MAE = 31.1), and the canine case data model (MAE = 32.0). For canine Lyme disease incidence, the One Health model performed worse (MAE = 330.5) compared to the canine search data model (MAE = 282.3), the human data (search terms + cases) model (MAE = 248.4), and the environmental data (MAE = 221.5) model. However, even the best-performing models had large prediction errors, which limit practical utility. Future studies should incorporate alternative data streams, such as electronic health records and insurance claims, to test predictive ability.

Semantic mediation of observation datasets through Sensor Observation Services

The World-Wide Sensor Web is generating tremendous amount of real-time sensor data streams and will enable scientists to observe phenomena that are previously unobservable. As the concept of sensor web is to connect all the sensors and their data to achieve shared goals, improving the openness and accessibility of sensor data is important. The Open Geospatial Consortium Sensor Observation Service (SOS) defines standard web service protocols for sharing sensor data online in an interoperable manner. However, SOS has a relatively weak ecosystem, which makes it difficult to build and consume, and it only supports predefined queries. On the other hand, the OASIS1 Open Data Protocol (OData) has a strong ecosystem and flexible query functions. But the soft-typing approach of OData requires it to have a commonly agreed data model to be interoperable. As we argue that the two standards can benefit from each other, we propose a sensor data mediator solution and define an SOS entity data model for OData (SOS-OData) to bridge these two standards. Our prototype demonstrates that the proposed system can convert between existing SOS and SOS-OData services. As a result, we not only can consume SOS data with the flexible OData query functions, but can also easily build an SOS-compliant service with the strong OData ecosystem. We argue that the bridging between these two standards would consequently lead us to the vision of open data for sensor web.

Soil moisture plays a significant role in both water and energy cycles. It is important to manage and analyze in situ sensor observations of soil moisture due to its impacts on agricultural and hydrological processes. Google Fusion Tables (GFT) is a cloud computing database that provides a service on the Web for data management and integration. Using GFT for managing soil moisture sensor observations, it is possible to take advantages of GFT for collaborative management, on-the-fly visualization, and flexible integration and analysis. The Open Geospatial Consortium (OGC) sensor observation service (SOS) can provide real-time or near-real-time observations in an interoperable way. Combing SOS and GFT together can take the best of both. The paper investigates how GFT could be employed for managing, visualizing, and analyzing soil moisture sensor observations. It describes the design and implementation of a cloud-based SOS for managing soil moisture data using cloud computing databases. By storing sensor observations in GFT, the SOS service is scalable, and observations can be visualized and analyzed on demand. Challenges and approaches on the integration of GFT and SOS are discussed. A prototype service on sharing and managing soil moisture sensor observations is developed to demonstrate the applicability of the approach.

GIScience Research at the Thirty‐second Annual Esri International User Conference

Assessing the status and trends of water quality in inland water bodies requires access to reliable water quality monitoring data and associated metadata such as the monitoring locations, sampling methods, monitoring equipment and analytical methods. Many environmental agencies and research organizations collect water quality monitoring data, but unlike in other environmental domains and due to a lack of common best practices and standards, most organizations use their own data models, formats and controlled vocabularies to store and share these data. As a result, large-scale water quality analyses with a transboundary, continental or global scope require significant efforts to collect the necessary monitoring data from different sources and to harmonize the different data structures. Several international initiatives such as the UNEP Global Environment Monitoring System for Freshwater (GEMS/Water)1 or research activities such as the Global River Water Quality Archive (GRQA)2 have compiled global water quality datasets to facilitate large-scale hydrological studies, all facing the same challenges and often duplicating data processing efforts.Over the last 20 years, the observing community has developed data models and semantic ontologies such as the OGC Observations, Measurements, and Samples (OMS)3 standard or the OGC/W3C Semantic Sensor Network (SSN)4 ontology to describe observations and associated metadata. These form the basis of several standards for the exchange of hydrological observation data such as the WaterML 2.0 family of standards. However, water quality specific aspects such as the description of sampling activities and associated metadata have not yet been included in these water specific standards. To address this issue, several government agencies and research organizations have started a Water Quality Interoperability Experiment (WQIE) within the Open Geospatial Consortium (OGC) in 2022. Several use cases for the exchange of water quality monitoring data of physical and chemical parameters monitored in surface and groundwater bodies using in-situ (sensor) or ex-situ (laboratory) monitoring were developed and described as object diagrams in UML based on the OMS conceptual model. Based on this exercise, a physical data model was developed by extending the OGC SensorThingsAPI (STA)5 with a plugin for the open source FROST server6. Several WQIE participants deployed pilot instances of water quality enabled FROST servers, making their water quality data publicly available. A web client was developed to facilitate access to the various STA endpoints and to enable data visualisation7. This presentation will give an overview of the developments of the OGC Water Quality Interoperability Experiment, highlighting achievements, outstanding challenges and future development plans. 

We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers’ decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients’ convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.

Emerging developments in geographic information systems and distributed computing offer a roadmap towards an unprecedented spatial data infrastructure in the climate sciences. Key to this are the standards developments for digital geographic information being led by the International Organisation for Standardisation (ISO) technical committee on geographic information/geomatics (TC211) and the Open Geospatial Consortium (OGC). These, coupled with the evolution of standardised web services for applications on the internet by the World Wide Web Consortium (W3C), mean that opportunities for both new applications and increased interoperability exist. These are exemplified by the ability to construct ISO‐compliant data models that expose legacy data sources through OGC web services. This paper concentrates on the applicability of these standards to climate data by introducing some examples and outlining the challenges ahead. An abstract data model is developed, based on ISO standards, and applied to a range of climate data –both observational and modelled. An OGC Web Map Server interface is constructed for numerical weather prediction (NWP) data stored in legacy data files. A W3C web service for remotely accessing gridded climate data is illustrated. Challenges identified include the following: first, both the ISO and OGC specifications require extensions to support climate data. Secondly, OGC services need to fully comply with W3C web services, and support complex access control. Finally, to achieve real interoperability, broadly accepted community‐based semantic data models are required across the range of climate data types. These challenges are being actively pursued, and broad data interoperability for the climate sciences appears within reach. Copyright © 2005 Royal Meteorological Society.

Assessing the state of the art in Discrete Global Grid Systems: OGC criteria and present functionality

Conceptual modeling is a learning task essential to students of computer science, software engineering and related programs. Construed as a complex task, surprisingly little is known about the actual act of conceptual modeling, and about modeling difficulties learners experience. Combining complementary modes of observation of learners’ modeling processes, we study modeling difficulties encountered while performing a data modeling task. Using the concept of cognitive breakdown, we analyze audiovisual protocols of the individual modelers’ modeling processes, recordings of their interactions with the employed modeling software tool and survey data of modelers about their perception of encountered modeling difficulties. In an exploratory study and a follow-up study, we identify eight types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes and cardinalities. The identified types of modeling difficulties contribute to a better and more complete understanding of data modeling processes intended to inform design science research on modeling assistance for data modelers at different stages of their learning and mastering of conceptual data modeling.

Assessing the status and trends of the environment requires the collection, management and publication of spatially referred observations. Since many years, Long-Term Ecological Research sites in Europe collect ecological data, resulting in long-term data series. Nowadays, advanced software tools can enable discovering, accessing, and distributing collected data in a user friendly way. Based on Open Geospatial Consortium (OGC) standards, Web based Geographic Information System allows access to interoperable distributed repositories of observations. The present paper gives an overview of the methods and solutions proposed and tested in the LIFE+ project EnvEurope for the community of researchers of the Long-Term Ecosystem Research (LTER) Network in Europe. These solutions consist in a straightforward online environment for metadata management and discovery, shaped on the ecological community and its practices; components of a Spatial Data Infrastructure including both a network of repositories deploying observations via OGC SOS (Sensor Observation Service) services and Web user interfaces to access and visualize them. The success of the presented approach is linked to the development and availability of easy to define, ready to use tools, enabling site managers to create their own repositories and services.

This work demonstrates a low-cost, miniature data repository proof-of-concept. Such a system needs to be resilient to power and network failures, and expose adequate processing power for persistent, long-term storage. Additional services are required for interoperable data sharing and visualization. We designed and implemented a software tool called Airchive to run on a Raspberry Pi, in order to assemble a data repository for archiving and openly sharing timeseries data. Airchive employs a relational database for storing data and implements two standards for sharing data (namely the Sensor Observation Service by the Open Geospatial Consortium and the Protocol for Metadata Harvesting by the Open Archives Initiative). The system is demonstrated in a realistic indoor air pollution data acquisition scenario in a four-month experiment evaluating its autonomy and robustness under power and network disruptions. A stress test was also conducted to evaluate its performance against concurrent client requests.

Using "the crowd" as a means to collect observations within a certain area is currently explored in various fields. The European Commission funds a set of research and development projects, all flagged as "citizen observatories" projects. The goal is to provide best practices on data modeling and processing for citizen observatories. The best practices help create and maintain crowdsourcing systems in an effective and secure way. The Open Geospatial Consortium (OGC) has developed a suite of standards to describe, access, and process crowdsourcing data in an interoperable way. Concentrating on Biosphere Reserves in Wales, Germany and Greece, the main aim is to create a testbed environment, which will enable citizens living within Biosphere Reserves to collect environmental data using mobile devices. Information of use for policy formation and delivery is generated by quality controlling the crowdsourced data and aggregating with Spatial Data Infrastructure (SDI) reference data from authoritative sources. This paper describes how the Sensor Web Enablement suite of standards together with other OGC standards facilitates the setup efficient SDIs that integrate remote sensing data with in-situ crowd sourced data.

Monitoring the environment is critical for scientists to understand the environmental dynamics. However, the traditional monitoring systems such as sensor networks are usually labor-intensive and complicated to deploy. As the concept of citizen sensing has been proposed to include volunteers into the environmental monitoring systems, a key to realize the citizen sensing vision is to empower citizens with the low-cost and easy-to-use sensing devices. In this paper, we focus on two technologies that have the potential to realize the citizen sensing vision: the Internet of Things (IoT) and the world-wide sensor web. In order to address the issues from the decentralized and heterogeneous nature of IoT devices and sensors, we propose the TinySOS service, a tiny web server hosting a light-weight profile of Open Geospatial Consortium (OGC) Sensor Observation Service (SOS). By hosting open standard sensor web services on the IoT devices, not only the devices become self-describable, self-contained, and interoperable, but also the collected observations are accessible via the Internet as soon as they are measured. In this case, the sensor web can provide real-time sensor data streams in a much higher spatial and temporal resolution, which consequently allows scientists to observe phenomena that were previously unobservable.