A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics
Abstract
:1. Introduction
2. Background and Related Work
2.1. Ontology-Based Geospatial Data Integration
2.2. Geovisual Analytics
2.3. Sensor Data Analysis
3. GOdIVA: A Framework Unifying Ontology-based Geodata Integration and Visual Analytics
3.1. Ontology-Based Geodata Integration Module
3.2. Geovisual Analytics Module
4. Case Study
4.1. Test Area and Data
4.2. Ontology-Based Data Integration
4.2.1. Ontology
- We have created two classes :WeatherStation and :TrafficStation as subclasses of both sosa:Platform and geo:Feature.
- We have created five subclasses of sosa:Sensor, e.g.,:MinTemperatureSensor and :TrafficSpeedSensor.
- We have created five instances of the sosa:ObservableProperty class, e.g., <minTemperature> and <trafficSpeed>.
- We have introduced a class :GridCell extending geo:Feature to represent a seamless partition of a geographic area. Then we create the :Interpolator class as a subclass of ssn:System, whose instance is hosted on a :GridCell platform and interpolates instances of :Observation.
4.2.2. Mapping
<traffic_station/3> a sosa:Platform . <traffic_volume_sensor/{station_code}/dailyTrafficVolume> a sosa:Sensor . |
4.2.3. Query
4.3. Geovisual Analytics
- A data access and analysis view (upper left). This view lists the core concepts as information items, which connects the ontology model and SPARQL. Users can click/check the intended features to formulate a query to access data. The design of this view is basically according to the core vocabularies in the ontology, including stations, sensors, and observable properties. A time window is added to select data in a certain time slot. In addition, we add one functionality to allow the visual exploration of the correlations between weather and traffic data. At the moment, the view is hand-crafted, but we plan to automatically generate it in the future according to the ontology.
- A SPARQL query view (bottom left). This view is linked to the data access view. When the query is formulated and issued to the SPARQL endpoint, it draws a network graph of the SPARQL query, showing directly the basic graph patterns of the query. It allows an intuitive perception of the involved concepts and their complicated relations.
- A map view (upper right). The map view is linked with the data access view and the statistical view. It is designed to show the spatial distribution of queried objects, for instance, the locations of all the meteo-stations, and the precipitation distribution. In addition, users can interactively select a feature on the map to investigate its characteristics in the linked statistical view.
- A statistical result view (bottom right). It is linked to the data access view and the map view, and is designed to show relevant statistics of the selected feature on the map in the selected time period. We have designed three tabs, respectively showing the basic information of the selected feature (e.g., traffic station ID, and the min and max traffic volume at this station), the time series of the observations, and the correlation coefficients of the weather and traffic at this station.
- Network visualization. The visualization consists of nodes and edges and is especially suitable to visualize the complicated objects and relations involved in SPARQL queries. Figure 6 shows the query graph after selecting the traffic station with the ID of 3 to retrieve all the relevant information of this station, in which blue-filled nodes are used to represent IRIs and literals, and unfilled ones are variables.
- Dot maps and Heat maps. Cartographic techniques are effective in conveying spatiotemporal patterns. We use dot maps to represent the distribution of the sensor and station locations, and heat maps to show the distribution surfaces of the continuous phenomena, e.g., precipitation and temperature.
- 2-D scatter and line plots. They are designed mainly to reveal the temporal patterns of the observations. The scatter plots can show the individual variable values of each day, while the line plots show the temporal trend over a time period.
- Interactive correlation coefficient matrix. The matrix view can show the overview of the calculated coefficient results among multiple variables. It helps the users to find significant correlations. A bipolar color scheme from blue to red is applied to represent the correlations from negative to positive values. Furthermore, users can click a cell in the matrix to investigate the scatter plot of the two selected variables.
- Aggregate functions. The aggregate functions mainly calculate the min, max, and average values of each variable. For example, at each traffic station, users can get a list of the basic statistical values. These values, like daily traffic volume values, give users an overview of the traffic flow at the station.
- Correlation coefficient analysis. Spatial and temporal correlations of multivariates from different sources are important for finding interesting patterns and inferring potential events. As a demonstration, we implemented the Pearson correlation coefficient (For two datasets and , the Pearson correlation coefficient is .) and visualize the coefficients as a matrix.
4.4. Analysis
4.5. Preliminary Studies
4.5.1. Exploring Effectiveness
4.5.2. Feedback
- IDEE: Data Integration for Energy Efficiency (https://ideenergy.eu/) is a 3-year project supported by European Regional Development Fund (ERDF). The aim of the IDEE project is to develop a technological infrastructure based on semantic technologies for the integration of data concerning buildings, with an emphasis on the energy related data, and to provide techniques and tools for the visualization and analysis of such data. The consortium consists of unibz (geodata integration solution provider), Alperia (energy consumption data provider), and R3 GIS (GIS infrastructure provider), and has the city of Merano as the main use-case partner providing both requirements and data about the city.
- Open Data Hub-Virtual Knowledge Graph is a joint project between NOI techpark and Ontopic (http://ontopic.biz/) to extend the South Tyrolean OpenDataHub (https://opendatahub.bz.it/) with a Knowledge Graph interface (https://sparql.opendatahub.bz.it/). The first phase to integrate tourism data (e.g., about hotels and events) is already completed, and a second phase with the aim of integrating traffic data has started. In addition, following the principle of GOdIVA, we have created a Web Component (https://webcomponents.opendatahub.bz.it/webcomponent/567cb2e2-3e5d-421a-bf85-b8ecc500aab9), which can be embedded into any web page like a standard HTML tag, to visualize SPARQL query results in different ways, including customized maps.
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Dataset | Description | Format | Spatial | Temporal | #Entries | Source |
---|---|---|---|---|---|---|
municipality | polygons, names (de/it), etc. | .shp | √ | - | 116 | ODP |
meteo stations | code, name, location, etc. | .json | √ | - | 84 | ODP |
meteo sensors | amounted station, sensor type (e.g., air temperature, precipitation) | .json | - | - | 584 | ODP |
meteo measurements | 1981–2017, daily min-, max-temperature, precipitation | .xls | - | √ | 388,680 | ODP |
traffic counters | code, name, location, etc. | .shp | √ | - | 75 | ODP |
traffic volume | daily average traffic volume in 2017 | .xls | - | √ | 23,381 | ASTAT |
traffic speed | daily average traffic speed in 2017 | .xls | - | √ | 23,950 | ASTAT |
Mapping Assertion | Sample Data in the Database | Generated RDF Triples |
---|---|---|
M_traffic_station_info: <traffic_station /{trst_inter}> a : TrafficStation; :hasID {trst_inter}; rdfs:label {trst_place}@it , {trst_pla00}@de; sosa:hosts <traffic_volume_sensor /{trst_inter}/ dailyTrafficVolume>, <traffic_speed_sensor /{trst_inter}/ dailyTrafficSpeed>. ← SELECT trst_inter , trst_place , trst_pla00 FROM traffic_counters | (3, ‘Pineta di Laives’, ‘Steinmannwald’) | <traffic_station/3> a :TrafficStation ; :hasID ‘3’ ; rdfs:label "Pineta di Laives"@it , "Steinmannwald"@de ; sosa:hosts <traffic_volume_sensor/3/dailyTrafficVolume>, <traffic_speed_sensor/3/dailyTrafficSpeed>. |
M_traffic_station_geom : <traffic_station/{trst_inter}> geo:defaultGeometry <traffic_station_geom/{trst_inter}>. <traffic_station_geom/{trst_inter}> a sf:Point; geo:asWKT {wkt }^^geo:wktLiteral . ← SELECT trst_inter , ST_AsText (geom) AS wkt FROM traffic_counters | (3, ‘POINT (680089.9 5146685.9)’) | <traffic_station/3> geo:defaultGeometry <traffic_station_geom/3>. <traffic_station_geom/3> a sf:Point ; geo:asWKT "POINT (680089.9 5146685.9)"^^geo:wktLiteral. |
M_sensor_traffic_volume : <traffic_volume_sensor/{station_code}/dailyTrafficVolume> a :TrafficVolumeSensor; sosa madeObservation <obs_traffic_volume/{station_code}/{date}>. ← SELECT station_code , date FROM traffic_volume | (3, ‘2017-01-01’) | <traffic_volume_sensor/3/dailyTrafficVolume> a :TrafficVolumeSensor; sosa:madeObservation <obs_traffic_volume /3/2017-01-01>. |
M_observation_traffic_volume : <obs_traffic_volume/{station_code}/{date}> a sosa:Observation; sosa:observedProperty <dailyTrafficVolume> ; sosa:hasSimpleResult {daily_volume}; sosa:resultTime {date}. ← SELECT station_code , date , daily_volume FROM traffic_volume | (3, ‘2017-01-01’ ,11771) | <obs_traffic_volume/3/2017-01-01> a sosa:Observation. sosa:observedProperty <dailyTrafficVolume>; sosa:hasSimpleResult 11771 ; sosa:resultTime "2017-01-01". |
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Ding, L.; Xiao, G.; Calvanese, D.; Meng, L. A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics. ISPRS Int. J. Geo-Inf. 2020, 9, 474. https://doi.org/10.3390/ijgi9080474
Ding L, Xiao G, Calvanese D, Meng L. A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics. ISPRS International Journal of Geo-Information. 2020; 9(8):474. https://doi.org/10.3390/ijgi9080474
Chicago/Turabian StyleDing, Linfang, Guohui Xiao, Diego Calvanese, and Liqiu Meng. 2020. "A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics" ISPRS International Journal of Geo-Information 9, no. 8: 474. https://doi.org/10.3390/ijgi9080474
APA StyleDing, L., Xiao, G., Calvanese, D., & Meng, L. (2020). A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics. ISPRS International Journal of Geo-Information, 9(8), 474. https://doi.org/10.3390/ijgi9080474