International Standard “OGC Moving Features” to address “4Vs” on locational BigData

Asahara, Akinori; Hayashi, Hideki; Ishimaru, Nobuhiro; Shibasaki, Ryosuke; Kanasugi, Hiroshi

doi:10.1109/bigdata.2015.7363975

Cited by 2 publications

(5 citation statements)

References 28 publications

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“…This standard supports point (0-dimensional), linear (1-dimensional), areal (2-dimensional), and volumetric (3-dimensional) representations, enabling the modeling of object trajectories and interpolated positions. Researchers (Asahara et al, 2015a) also found that the OGC Moving Features model can be effectively used to integrate many types of location data and applications. It facilitates sophisticated analysis of movement patterns, with the flexibility to incorporate both location-and time-based queries.…”

Section: Ogc Api Moving Featuresmentioning

confidence: 99%

“…The OGC Moving Features API built upon a foundational data model, offers various encoding mechanisms for their representation. These include XML/GML (Asahara et al, 2019) and JSON (Kim and Ishimaru, 2020) for comprehensive encoding, and simpler CSV (Asahara et al, 2015b). While these encodings share the same standard and data model, they exhibit not-able discrepancies that significantly impact the types of information that can be modeled.…”

Section: Ogc Api Moving Featuresmentioning

confidence: 99%

“…The emergence of smart cities and intelligent transportation systems (ITS) underscores the critical need for efficient management and analysis of dynamic geospatial data in tracking moving objects such as vehicles and pedestrians within urban environments. This demand entails real-time location updates alongside the integration of various sensor data to fully harness the capabilities of applications designed for disaster management, traffic services, security, navigation (robotic, aviation, maritime), and wildlife conservation (Richter et al, 2020, Asahara et al, 2015a. Acknowledging these requirements, the Open Geospatial Consortium (OGC), a leading entity in developing open geospatial standards, acknowledged the need to formulate open standards for incorporating dynamic geo-datasets and web-based frameworks, thus enhancing the precision of spatial decision-making procedures.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API

Santhanavanich,

Padsala,

Betz

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems.

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Section: Ogc Api Moving Featuresmentioning

confidence: 99%

Section: Ogc Api Moving Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API

Santhanavanich,

Padsala,

Betz

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The moving feature is adopted from the ISO 19141:2008 standard and its OGC implementation. A moving feature is defined as "a feature whose location changes over time" [35]. A description for this scenario is one of a shipping vessel moving through the ocean.…”

Section: Observations Of Attributes Of a Moving Featurementioning

confidence: 99%

“…The components of the geoStreamViewer application are illustrated in Figure 10. Raw data arrives from external data streams and is transmitted to the geospatial stream processing library SwordFish [35]. The SwordFish library is used for geospatial processing of the data to discover known patterns and to perform aggregations of the data to avoid streaming data to the front end that is not required.…”

Section: Platform Implementation Of Geostreamviewermentioning

confidence: 99%

A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

Sibolla

Coetzee

Zyl

2018

IJGI

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Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.

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International Standard “OGC Moving Features” to address “4Vs” on locational BigData

Cited by 2 publications

References 28 publications

Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API

Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API

A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

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