Spatial Stream Processing

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 4521

Special Issue Editor

Special Issue Information

Dear Colleagues,


Over the past four decades, the field of geographic information science has developed an impressive arsenal of techniques, technologies, and tools for capturing, storing, managing, processing, and using spatial information (de Smith et al. 2009). These tools take some data as inputs, and perform a variety of spatial operators (or computational processes) on that data to produce new data as result. Such spatial operators can be vector-based (geoprocessing) or raster-based (map algebra). Several vector-based and raster-based spatial operators may be arranged together, in the form of geospatial workflows, to produce desired results. Research work (e.g. Granell et al., 2010; Granell, 2014; Bernard et al., 2014; Yue et al., 2016) has made substantial progress over the past years to go well beyond desktop-based environments to bring geospatial workflows to the cloud and distributed computing environments, contributing to the emerging field of the Geoprocessing Web (Zhao et al., 2012; Hofer et al., 2018).


Geospatial web-based workflows are characterized by a query-driven, pull-based processing model: Dynamic “queries” are specified (i.e., spatial operators and/or geospatial services) and processed once over relatively static input data. In other words, all input data must be available before running a geospatial workflow. Spatial operators and algorithms devised since the late 1980s were designed to address the “all-data-available-before-process” processing model. To this regard, leading researchers recently called for an entirely new brand of geospatial algorithms and techniques to analyze and process real-time data streams (Batty et al., 2016; Jiang et al., 2015; Miller & Goodchild, 2014; Goodchild, 2016; Li et al., 2016). To put it simply, the world today is real time; what served in the past, and still serves for traditional, non-real-time scenarios, does not fit well with scenarios that handle data streams, i.e., real-time, time-varying sequences of data items or tuples.


Contrary to traditional geospatial workflows, stream processing is characterized by a data-driven, push-based processing model, in that static “queries” are processed continuously over transient, dynamic, frequently changing data. That is, analytical operators are defined once and run continuously over input data streams to put results into output data streams; what really changes are the input and output streams. State-of-the-art research in stream processing is ongoing, but is in its infancy when it comes to dealing with the spatial and spatiotemporal dimensions of data streams, due partly to (Galic, 2016): 


  • The inherent complexity of spatial data and spatiotemporal relationship makes traditional data stream techniques less useful for efficiently processing spatiotemporal data streams. 

  • Existing spatial operators, geoprocessing algorithms and spatial analytical techniques were initially conceived, designed and implemented following the query-driven, pull-based processing model in mind (i.e., “all-data-available-before-process”), which limits the usefulness of such well-known spatial operators for stream processing.


This Special Issue encourages the submission of both basic research papers and application-oriented contributions in the area of spatial stream processing. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics where spatial (and/or spatiotemporal) data streaming and spatial stream computing are central, including, but not limited to, the following:

  • Spatio-temporal stream systems

  • Novel streaming algorithms for spatial data

  • Location-aware stream systems

  • Real-time spatial data visualization

  • Participatory spatiotemporal data streams and Volunteered Geographic Information (VGI)

  • The use of stream processing in urban oriented applications such as traffic management, mobility, etc. 


de Smith, M., Longley, P., & Goodchild, M. (2009). Geospatial analysis - A comprehensive guide (3rd ed.). The Winchelsea Press 

Granell C., Díaz, L., & Gould, M. (2010). Service-oriented applications for environmental models: reusable geospatial services. Environmental Modelling and Software, 25(2), 182-198 

Granell, C. (2014). Robust Workflow Systems + Flexible Geoprocessing Services = Geo-enabled Model Web? In: Geographical Information Systems: Trends and Technologies. Boca Raton: CRC Press, pp. 172-204 

Bernard, L., Mäs, S., Müller, M., Henzen, C., & Brauner, J. (2014). Scientific geodata infrastructures: challenges, approaches and directions. International Journal of Digital Earth, 7(7), 613-633 

Yue, S., Chen, M., Wen, Y., & Lu, G. (2016). Service-oriented model-encapsulation strategy for sharing and integrating heterogeneous geo-analysis models in an open web environment. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 258-273

Moncrieff, S., Turdukulov, U., & Gulland, E-K. (2016) Integrating geo web services for a user driven exploratory analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 294-305 

Zhao, P., Foerster, T., & Yue, P. (2012). The Geoprocessing Web. Computers & Geosciences, 47, 3-12 

Hofer, B., Granell, C., & Bernard, L. (2018). Innovation in geoprocessing for a Digital Earth. International Journal of Digital Earth, 11(1), 3-6

Batty, M. (2016). Big Data and the City. Built Environment, 42(3), 321–337

Jiang, B. 2015. Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity. GeoJournal, 80(1), 1-13 

Miller, H. J., & Goodchild, M. F. (2014). Data-driven geography. GeoJournal, 80(4), 449-461 

Goodchild, M. F. (2016). GIS in the Era of Big Data. Cybergeo: European Journal of Geography. Retrieved from 

Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., … & Cheng, T. (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119-133 

Galic, Z. (2016). Spatio-Temporal Data Streams. Springer-Verlag New York

Dr. Carlos Granell Canut
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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26 pages, 6509 KiB  
A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams
ISPRS Int. J. Geo-Inf. 2018, 7(12), 475; - 11 Dec 2018
Cited by 10 | Viewed by 4036
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Spatial Stream Processing )
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