Real-time GIS data model and sensor web service platform for environmental data management
© Gong et al.; licensee BioMed Central. 2015
Received: 17 November 2014
Accepted: 28 December 2014
Published: 9 January 2015
Effective environmental data management is meaningful for human health. In the past, environmental data management involved developing a specific environmental data management system, but this method often lacks real-time data retrieving and sharing/interoperating capability. With the development of information technology, a Geospatial Service Web method is proposed that can be employed for environmental data management. The purpose of this study is to determine a method to realize environmental data management under the Geospatial Service Web framework.
A real-time GIS (Geographic Information System) data model and a Sensor Web service platform to realize environmental data management under the Geospatial Service Web framework are proposed in this study. The real-time GIS data model manages real-time data. The Sensor Web service platform is applied to support the realization of the real-time GIS data model based on the Sensor Web technologies.
To support the realization of the proposed real-time GIS data model, a Sensor Web service platform is implemented. Real-time environmental data, such as meteorological data, air quality data, soil moisture data, soil temperature data, and landslide data, are managed in the Sensor Web service platform. In addition, two use cases of real-time air quality monitoring and real-time soil moisture monitoring based on the real-time GIS data model in the Sensor Web service platform are realized and demonstrated. The total time efficiency of the two experiments is 3.7 s and 9.2 s.
The experimental results show that the method integrating real-time GIS data model and Sensor Web Service Platform is an effective way to manage environmental data under the Geospatial Service Web framework.
Environmental data are some of the most critical information sources for evaluating, preventing, and alleviating the adverse effects of the environment on human health. Effective environmental data management plays an important role in retrieving and applying environmental data. In the past, environmental data management involved developing a particular application and isolated environmental data management system [1–5]. With the development of sensor technology, sensors have become smaller, cheaper, more intelligent, and more power-efficient . A large number of sensors are deployed for environmental monitoring, and plenty of real-time spatiotemporal environmental data are generated. Static geographic information handling extends to dynamic real-time data handling , and environmental data management systems heavily rely on integrating and consolidating heterogeneous sensor data streams . However, many of the particular application and isolated environmental data management systems cannot meet the requirements of managing real-time data.
Recently, with the development of information technologies such as Web services and interoperable services, a Geospatial Service Web (GSW) has been proposed in the geospatial community. GSW is a virtual geospatial infrastructure based on the Internet, and it integrates various geospatial-related resources such as sensor resources, data resources, processing resources, information resources, knowledge resources, computing resources, network resources, and storage resources to manage data, extract information, and obtain knowledge in the geospatial community domain . GSW unifies the functions of a geospatial acquisition system, data transformation system, distributed spatial data collection, high-capability server system, large volume storage system, remote sensing, and a geographic information system (GIS), where the functions are implemented by Web services and communicated through the standardized protocols of the Internet. The mission of GSW includes the following: 1) acquire global spatial data for all seasons, all days, and all directions using all kinds of sensors on satellite, aircraft, and ground surface; 2) chain the whole process seamlessly from sensors to application services using unified information networks, including satellite communication, data relay network, and wired or wireless computer communication networks; 3) register sensors, computing resources, storage resources, internet resources, manipulate software and spatial data on the internet, and process spatial data online quantitatively, automatically, intelligently, and in real time; and 4) provide geospatial services, compose virtual service chains and transmit user-required information in the most effective and efficient ways. Using GSW for real-time environmental data manage will help describe, organize, manage, manipulate, interchange, search, and release environmental data in a unified framework.
Currently, the GSW is a conceptual framework. It will be a long-term task to realize the blueprint . An urgent task for GSW is developing a real-time GIS data model to manage real-time data. To date, GIS data models have evolved from static GIS data models, to temporal GIS data models, and then to real-time GIS data models . A static GIS data model manages spatial data, describes spatial relationships, and expresses the distributions of geospatial objects. Based on the static GIS data model, a temporal GIS data model adds the description information of time. The temporal GIS data model represents the distributions of geographic objects and the change process of these objects with time. The temporal GIS data model can be divided into three phases according to the significance of time in a model: 1) temporal snapshots phase: focusing on recording an entity’s snapshots in their temporal changes. Typical data models include the space-time cube model [10, 11], sequential snapshots model [12, 13], discrete grid cell list model [14, 15], base state with amendments [13, 15], and the space-time composite model [16–18]. This type of data model is primarily used for recording the state changes of the entity itself with time to store and retrieve spatial characteristics and special features of an object. 2) object change phase: focusing on the changed relationship of an object before and after its change. Some famous data models include the object-oriented spatiotemporal data model [19–21], feature-based spatiotemporal date model , and process-oriented spatiotemporal data model [23, 24]. The data models record the change states of the entity itself and also describe the changed relationship of the states to express the spatiotemporal changed relationship between geographic objects. 3) events and action phase: focusing on describing the semantic relations of an entity’s changes. Some well-known data models include the event-based spatiotemporal data model , graph-based spatiotemporal data model , and spatiotemporal three-domain model . Compared with the data models in the object change phase, a benefit of the data models in the events and action phase is implying the reason for the spatiotemporal change of a geographic object state. This helps to express the interactive relationship between geographic objects, or a geographic object with an external environment. The temporal GIS data models are primarily used to express the geographic object changes through time, store masses of history data, and maintain their relationships. However, most of them are employed to represent the object changing from one kind of state to another. They are often not effective for storing and retrieving real-time spatial data from various sensors and moving objects, lacking of real-time capability to meet the increasing demand for time-sensitive applications. The real-time GIS data model is developed from the temporal GIS data model and emphasizes the time efficiency of data management [26, 27]. Currently, the real-time GIS data model is still in an immature stage and needs further study.
The objective of this study is to propose a method to manage real-time environmental data. This method is based on a novel real-time GIS data model and the model’s implementation called the Sensor Web Service Platform with Sensor Web technologies . The proposed real-time GIS data model in this study collects real-time data from various types of sensors and represents the relationships between data such as geographic objects, states, events, processes, sensors, and observations. The model also supports the dynamic simulation of spatiotemporal processes from real-time GIS data. The real-time GIS data model represents further progress for static and temporal GIS data models.
This section demonstrates the proposed real-time GIS data model and one method of implementation, called the Sensor Web Service Platform.
Real-time GIS data model
The real-time GIS data model is actually a spatiotemporal data model for real-time GIS. Real-time GIS is an important new research domain, transforming the study of historical changed data to real-time data in GIS . Compared with traditional GIS, real-time GIS has strict time controls and restraints; therefore, all actions will be performed in a very short and acceptable time. The data model is the core of GIS; an appropriate data model plays a decisive role in constructing a GIS application. The primary task of a spatiotemporal data model is the organization and management of spatiotemporal data, as well as analysis and expression of the content and relationships of spatiotemporal change. This study draws lessons from existing spatiotemporal data models, analyses the evolutive mechanism of a spatiotemporal process, studies the retrieval of real-time observation data derived from sensors and mobile targets, and then develops a real-time GIS data model.
Sensor (Sensor): Various sensors containing space-borne, air-borne, and ground sensors.
Observation (Observation): The behaviour of observable attributes from various sensors provides observational data for the model.
Geographic Object (Geo-Object): Either physical entities or social phenomenon formed naturally or artificially, expressed with clear boundaries or not, as the objects of GIS research in the real world.
Object (Object): Single entity in the real world; a Geo-Object can contain one or multiple objects.
Spatiotemporal Process (StProcess): The Spatiotemporal Process is a periodized change process of a complex geographic phenomenon in a timeline, and the processes refer to a series of Geo-Objects and their interactions.
Simulation (Simulation): Simulation is the imitation of the operation of a real-world process or system over time.
Event (Event): An event is an occurrence of the Geo-Object change, and is the reason for the change of Geo-Objects.
State (State): A snapshot of a geographic object at a point of time in the change process.
Change Function (ChFunction): In the time of research, the correspondence between an instant and the values of geospatial and thematic properties. This function can be derived from industry, scientific computing, and relevant experience.
A geographic object consists of three basic indivisible features: time, space, and thematic attributes [15, 30]. A geographic object contains both unchangeable attributes and time-varying attributes. Time-varying attributes are associated with state sequences. The time-varying attributes may be different at different states.
A sensor is a special geo-object that contains self-parameters and observations. The sensor, described by its metadata, is a tool to observe the spatial attributes and the thematic attributes of geographic objects; therefore, a sensor is the primary means of obtaining the changed information of a geographic object. One sensor may observe many geo-objects; meanwhile, a geo-object can be observed by many sensors. The wide use of sensors has brought revolutionary changes to data acquisition by improving the accuracy, speed, timely perception, and timely transmission of spatiotemporal data. This change has resulted in the generation of a large volume of data, such as spatiotemporal data, thematic attribute data, image data, and video stream data. This information, which may be remote sensing image collected by a remote sensor, physical or chemistry parameters collected by an in-situ sensor, or only position information acquired by a Global Navigation Satellite System, is recorded in a series of observations along with the time.
Complex spatiotemporal changes in geographical phenomena refer to three core things: spatiotemporal processes, geographic objects, and events. A real-time GIS data model should not only be able to express and manage real-time sensor observation data, but also should express and manage spatiotemporal process. To support the spatiotemporal process, it should reveal the relationships between geographic objects, events, and spatiotemporal processes. An event is an occurrence whereby a geo-object changes in a spatiotemporal process [13, 31]. A geo-object generates an event, and an event drives the change of a geo-object from one state to another with the change function. If the relationships between the geo-object, event, function, and spatiotemporal process are known, the state information can also be simulated by spatial-temporal processes with events with respect to the prior state and change function. The data model must handle all elements and establish their relationships, as in Figure 1.
Sensor web service platform
A Sensor Web can obtain, access, manage, and process sensor data in a standardized way in real-time or near real-time [32–35]. Therefore, a Sensor Web Service Platform integrating Sensor Web technologies to provide the interfaces in GSW for registering, planning, and monitoring various space-borne, airborne, and ground sensors is adopted to support the realization of the real-time GIS data model.
The Sensor Web is an infrastructure providing a bridge between sensor resources (sensors and sensor systems) and their applications, where the infrastructure enables an interoperable usage of sensor resources by enabling their discovery, access, and tasking, as well as eventing and alerting in a standardized way . The Open Geospatial Consortium (OGC) Sensor Web Enablement defined the Sensor Web information model and interface model. The information model defines the encoding standards of sensor observations and sensor metadata, such as the Observations & Measurement  and the Sensor Model Languages (SensorML) . The interface model specifies the interfaces of the different Sensor Web services such as the Sensor Observation Service (SOS) , the Sensor Planning Service (SPS) , and the Sensor Event Service (SES) . The SOS provides a standardized interface to manage and retrieve metadata and observations from heterogeneous sensor systems. The SPS defines interfaces for queries that provide information about the capabilities of a sensor and how to task the sensor. The SES is an enhancement of the OGC Sensor Alert Service, and it provides operations to register sensors at the service application and let clients subscribe to observations available at the service.
To demonstrate the proposed method for environmental data management, a Sensor Web Service Platform was implemented that supported the realization of the real-time GIS data model. Two cases of environmental data management are shown for Wuhan city, China. One is real-time air quality monitoring, and the other is real-time soil moisture monitoring.
A prototype of sensor web service platform
Sensor Retrieval Module: retrieves motion sensors, in-situ sensors, and remote sensors according to the specified filter criteria, such as time, space, subject, and other constraints;
Sensor Observational Data Retrieval Module: provides access to various types of sensor observation data according to the specified filter conditions and then shows these observational data in Map World in different ways;
Sensor Control Module: controls in-situ sensors and video sensors, and provides feedback for sensor control based on these changes in sensor observational data or the method of accessing sensor observational data;
Sensor Planning Module: performs video sensor planning tasks and remote sensing satellite simulative planning tasks;
Thematic Map Module: generates thematic maps with observation data; the maps can reflect the overall situation in a specific area;
Sensor Registration Module: used to register sensors described by the SensorML format. Sensor Registration Module is used to register sensor described with SensorML format.
Currently, dozens of sensors and plenty of real-time environmental data are managed by the Sensor Web Service Platform with real-time GIS data models, such as meteorological data (wind speed, wind direction, sunshine duration, solar radiation, atmospheric pressure, air temperature, air humidity, rainfall), air quality data (air quality index (AQI), particulate matter smaller than 2.5 μm (PM2.5), respirable suspended particulate matter smaller than 10 μm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO)), soil moisture data, soil temperature data, and landslide data.
Real-time air quality monitoring
With the rapid economic growth and urbanization in Wuhan (the capital city of Hubei province in China), air-pollution events such as fog or haze strike Wuhan many times each year. Air quality affects people’s lives, as well as their health. Governments and citizens pay more attentions to air quality than ever before. A governmental agency named the Wuhan Environmental Monitoring Center has instituted some environmental monitoring stations and deployed many sensors in Wuhan to monitor SO2, NO2, PM10, CO, O3, and PM2.5 pollutants, as well as the air quality.
The AQI, a dimensionless index, is a quantitative description of the air quality status as one indicator to monitor air quality. The United States Environmental Protection Agency has released a guideline for standardizing AQI and individual AQI (IAQI; the AQI of individual pollutants) calculation methods and category descriptors to provide health guidelines for the public . The AQI calculation method is adopted by this study.
The parameters of the real-time GIS model for air quality monitoring
The real-time GIS data model
Parameter(s) in the experiment
The sensors monitor SO2, NO2, PM10, CO, O3, and PM2.5
The concentration data of SO2, NO2, PM10, CO, O3, and PM2.5 observed by the sensors
The AQI and the IAQIs of the SO2, NO2, PM10, CO, O3, and PM2.5 in Wuhan.
The process of simulating the observation
The category of air quality (“Good”, “Moderate”, “Unhealthy for Sensitive Groups”, “Unhealthy”, “Very Unhealthy”, “Hazardous” )
The hours during the period between 2014-09-08 14:00 to 2014-09-10 15:00
The real-time data used in this experiment come from the Wuhan Environmental Monitoring Center. The experimental time period was from 2014-09-08 14:00 to 2014-09-10 15:00. The sensors were registered for monitoring SO2, NO2, PM10, CO, O3, and PM2.5 into SOS, and then the real-time data of the sensors was inserted into SOS by the InsertObservation operation. With the natural real-time characteristics , the Sensor Web Service Platform enables the management of real-time observation data. Every hour, the SOS receives live records from the station, and the delay is 1.7 s. All the pollution data are managed by SOS. If any data are required, they are retrieved from SOS with the GetObservation operation. Information or knowledge from the real-time data is mined using the real-time GIS data model.
Real-time information is mined by the real-time GIS data model. The response time is about 2.0 s. From Figure 5, some observations can be made: 1) during the 24-h experimental period, the highest AQI was observed at 16:00, and the lowest at 22:00. During the 48 hours, the two highest AQI values are at approximately 16:00, while the lowest AQI is at 10:00, which is different from the result of the 24-hour observation. 2) During the 48 hours, the AQI values are between 100 and 200. This corresponds to a rating of “unhealthy for sensitive groups” (AQI from 101 to 150) or “unhealthy” (AQI from 151 to 200) . 3) Comparing the 24-hour AQI data and the 48-hour AQI data, nearly half of the AQI is less than 150 during the first 24 hours, while only one-sixth of the AQI is below 150 during the last 24 hours; thus, the air quality is steadily worsening.
Real-time soil moisture monitoring
The parameters of the real-time GIS model for real-time soil moisture monitoring
The real-time GIS data model
Parameter(s) in the experiment
The soil moisture sensors in Baoxie town
The values of the soil moisture observed the sensors
Soil moisture in Baoxie town experiment area
The process of simulating the observation
The degree of the soil drought
The hours during the period between 2014-07-05 to 2014-07-07
We proposed a method based on a novel real-time GIS data model and its realization called the Sensor Web Service Platform for real-time environmental data management. The model, the implementation, and the experiments reflect the real-time characteristic.
The real-time GIS model follows the Geospatial Service Web framework managing all types of geospatial resources from sensor to data, then to information, and finally to knowledge, with the process of obtaining, storing, and processing real-time data. The real-time GIS model is evolved from the temporal GIS model, but it places more emphasis on the time efficiency, having strict time restraints wherein all tasks must be performed in a very short amount of time. The temporal GIS model is always applied to record history data and their changes, while the real-time GIS model points to live data and concerns the current data. The process and causes associated with such real-time data as sensors, geo-objects, states, events, spatiotemporal processes, and functions are considered in the real-time GIS model.
In the two experiments, the proposed model and platform manage real-time environmental data. The air quality monitoring sensors, the soil moisture monitoring sensors, and their real-time observations are described with SensorML managed by the SOS and SPS. The sensors are in-situ sensors whose location is fixed during the observation. SensorML can describe both in-situ and mobile sensors  such as the GPS receiver sensor on moving taxis in the Sensor Web Service Platform  and a camera sensor on a car . The Sensor Web Service Platform obtains and provides real-time observation data using a standard service (see Figures 5 and 7). The two services monitor sensor resources and data resources, while the real-time GIS data model combines the observation and data processes to mine information and knowledge using the real-time data (see Figures 5 and 8). The air quality information from the pollutants is mined using the AQI method, while the soil moisture thematic map is constructed from the observed data using the IDWI method. The time from data collection to observation to server is 1.7 s. The request and visualization time of SOS is 2.0 s. The soil moisture mapping time is 7.5 s. Therefore, the total time efficiency is less than 10 s (1.7 + 7.5 = 9.2, 1.7 + 2.0 = 3.7). The time efficiency means it can meet the requirements of many types of environmental data applications, as the examples (air quality monitoring and soil moisture monitoring) in this study show. The two experiments show the application of the real-time GIS data model and Sensor Web Service Platform, and also use real-time data. Therefore, the real-time GIS data model and Sensor Web Service Platform are seamlessly integrated to manage real-time environmental data.
The main aim of this study was to propose a method integrating a real-time GIS data model and a Sensor Web Service Platform under a Geospatial Service Web framework for environmental data management. Two experiments, real-time air quality monitoring and real-time soil moisture monitoring in Wuhan, were performed. The experimental results show that using the proposed method to manage real-time environmental data is feasible and effective.
Future work will focus on analyzing the scientific problems associated with the two experimental results. As the objective of the experiments is to demonstrate the proposed model and platform under a GSW framework and their applications for environmental data management, the management processes are illustrated in two experiments, instead of an analysis of the implied meaning and reason for the results.
The research in this paper was funded by the National Basic Research Program of China (973 Program) (no. 2012CB719906), the National High Technology Research and Development Program of China (863 Program) (No. 2012AA121401, 2013AA122300), the National Natural Science Foundation of China (No. 41301441, 41023001), and China Postdoctoral Science Foundation funded project (no. 2014M562050).
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