Special Issue "Spatial Databases: Design, Management, and Knowledge Discovery"

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

Deadline for manuscript submissions: 30 November 2019

Special Issue Editor

Guest Editor
Dr. Andreas Züfle

George Mason University, Department of Geography and Geoinformation Science, Fairfax, United States
Website | E-Mail
Interests: spatial data science, geographic information systems; data mining and machine learning, spatial index structures and efficient algorithms, uncertain data, geospatial simulation, location-based social networks

Special Issue Information

Dear Colleagues,

The recent explosion in the amount of spatial data calls for specialized systems to manage, search, and mine very large sets of spatial and spatio-temporal data.

This data explosion is facilitated by the vast proliferation of devices such as smartphones, traffic cameras, space telescopes, and Earth observation satellites. For example, NASA’s Earth Observing System Data and Information System (EOSDIS) adds more than 6 TB of data to its archives every day and makes it available to scientists and researchers around the world. As another example, millions of geo-tagged tweets become available on the Twitter API every day.

To handle this data deluge, specialized systems are required to store this data, to make this data actionable for knowledge extraction, and to develop data-driven geo-information systems.

This Special Issue is dedicated to giving an overview of state-of-the-art spatial and spatio-temporal data management, as well as to exploring future trends of concepts, methods, implementations, validations, and applications. We call for original papers from researchers around the world that focus on topics including, but not limited to, the following:

  • Big Spatial Data
  • Computational Geometry
  • Crowdsourcing Spatial Data
  • Distributed and Parallel Algorithms
  • Earth Observation Data Management
  • Efficient Algorithms for GIS
  • Geographic Information Systems
  • Geospatial Information Retrieval
  • Indoor Space
  • Moving Objects Databases
  • Parallel and Distributed Spatial Databases
  • Privacy, Security, and Integrity in Spatial Databases
  • Real Applications and Systems
  • Recommendation Systems
  • Spatial and Spatio-Temporal Data Acquisition
  • Spatial Data Mining and Knowledge Discovery
  • Spatial Database Design
  • Spatial (Road) Networks
  • Sensor Networks
  • Similarity Searching
  • Spatial Access Methods and Indexing
  • Spatial Data Streams
  • Spatial Database Design and Conceptual Modeling
  • Spatio-Temporal and Temporal Databases
  • Uncertain, Imprecise, and Probabilistic Data
  • Urban Analytics and Mobility
Dr. Andreas Züfle
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial databases
  • spatio-temporal data
  • big spatial data
  • data management
  • spatial data mining
  • spatial data science
  • geographic information systems
  • earth observation data
  • social media data

Published Papers (3 papers)

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Research

Open AccessArticle
NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space
ISPRS Int. J. Geo-Inf. 2019, 8(5), 218; https://doi.org/10.3390/ijgi8050218
Received: 27 March 2019 / Accepted: 5 May 2019 / Published: 8 May 2019
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Abstract
Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in [...] Read more.
Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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Open AccessArticle
Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field
ISPRS Int. J. Geo-Inf. 2019, 8(3), 151; https://doi.org/10.3390/ijgi8030151
Received: 8 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
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Abstract
In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible [...] Read more.
In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible k Nearest Neighbor (V2-kNN) query. Given the location of a user and his/her view field, a V2-kNN query finds data object p so that p is the nearest neighbor of and visible to the user, where visible means the data object is (1) not hidden by obstacles and (2) inside the view field of the user. Previous works on visible NN queries considered only one of these two factors, but not both. To the best of our knowledge, this work is the first to consider both the effect of obstacles and the restriction of the view field in finding the solutions. To support efficient processing of V2-kNN queries, a grid structure is used to index data objects and obstacles. Pruning heuristics are also designed so that only data objects and obstacles relevant to the final query result are accessed. A comprehensive experimental evaluation using both real and synthetic datasets is performed to verify the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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Open AccessArticle
A Novel Process-Oriented Graph Storage for Dynamic Geographic Phenomena
ISPRS Int. J. Geo-Inf. 2019, 8(2), 100; https://doi.org/10.3390/ijgi8020100
Received: 2 December 2018 / Revised: 1 February 2019 / Accepted: 5 February 2019 / Published: 25 February 2019
Cited by 1 | PDF Full-text (9117 KB) | HTML Full-text | XML Full-text
Abstract
There exists a sort of dynamic geographic phenomenon in the real world that has a property which is maintained from production through development to death. Using traditional storage units, e.g., point, line, and polygon, researchers face great challenges in exploring the spatial evolution [...] Read more.
There exists a sort of dynamic geographic phenomenon in the real world that has a property which is maintained from production through development to death. Using traditional storage units, e.g., point, line, and polygon, researchers face great challenges in exploring the spatial evolution of dynamic phenomena during their lifespan. Thus, this paper proposes a process-oriented two-tier graph model named PoTGM to store the dynamic geographic phenomena. The core ideas of PoTGM are as follows. 1) A dynamic geographic phenomenon is abstracted into a process with a property that is maintained from production through development to death. A process consists of evolution sequences which include instantaneous states. 2) PoTGM integrates a process graph and a sequence graph using a node–edge structure, in which there are four types of nodes, i.e., a process node, a sequence node, a state node, and a linked node, as well as two types of edges, i.e., an including edge and an evolution edge. 3) A node stores an object, i.e., a process object, a sequence object, or a state object, and an edge stores a relationship, i.e., an including or evolution relationship between two objects. Experiments on simulated datasets are used to demonstrate an at least one order of magnitude advantage of PoTGM in relation to relationship querying and to compare it with the Oracle spatial database. The applications on the sea surface temperature remote sensing products in the Pacific Ocean show that PoTGM can effectively explore marine objects as well as spatial evolution, and these behaviors may provide new references for global change research. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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