Special Issue "Advances in Spatio-Temporal Data Analysis and Mining"
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (1 October 2015)
Prof. Yaolin Liu
School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 430079, China
Prof. Phaedon Kyriakidis
With the recent rapid developments in mobile positioning technologies and the advances in massive dynamic data handling, spatio-temporal data analysis and data mining are witnessing significant growth in both research and development. New methods and trends in mobility data management, geographic knowledge discovery, semantic modeling, interoperability, open and linked-data, big data analytics, and geo-visualization – to name a few – have drastically changed the way data are collected, modeled, managed, analyzed, shared, and mapped. In addition, the expectations of data consumers (users) are becoming higher and higher. This Special Issue seeks original research contributions in all aspects of spatio-temporal data analysis and data mining. The scope of submission encompasses, but is not limited to, the following themes:
- Knowledge discovery in spatio-temporal data
- Data Mining and Privacy of Mobility Data
- Geospatial Analytics for Big Spatio-Temporal Data, including relevant advances in geostatistics
- Geospatial Web Services for Spatio-temporal Data
- Geospatial Semantics and Linked Spatio-temporal Data
- Visualization and Mapping of Spatio-Temporal Data
Abstracts Due: 01/May/2015
Full Papers Due: 31/May/2015
Decisions to Authors: 30/June/2015
Final Papers Due: 31/July/2015
Prof. Emmanuel Stefanakis
Prof. Yaolin Liu
Prof. Phaedon Kyriakidis
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 900 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.
- spatial analysis models and methods, and GIS modeling
- knowledge discovery from spatial databases
- semantically enriched data analysis and mining
- spatial analysis, spatial statistics, and data mining applications
- spatio-temporal data analysis and mining
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Shashi Shekhar, Zhe Jiang
Affiliation: Department of Computer Science & Engineering, University of Minnesota
Abstract: Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data science studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial and spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatial and spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data science, focusing on several major tasks: spatiotemporal outlier detection, colocation pattern mining and its spatiotemporal variants, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data science and provides a comprehensive coverage of computational approaches for various pattern families. We also list popular software tools for spatiotemporal data analysis. We conclude with a look at future research needs.