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Advances in Spatio-Temporal Data Analysis and Mining
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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) | Viewed by 62255

Special Issue Editors

Prof. Emmanuel Stefanakis

E-Mail Website
Guest Editor
Dept. of Geodesy and Geomatics Engineering, University of New Brunswick, 15 Dineen Drive, PO Box 4400, Fredericton, NB E3B 5A3, Canada
Interests: geospatial data science
Prof. Yaolin Liu

E-Mail
Guest Editor
School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 430079, China
Interests: geographic information science and system; multi-scale representation and generalization; geographic data mining and spatial analysis; land information system; land evaluation modelling and spatial decision-making
Prof. Phaedon Kyriakidis

E-Mail Website
Guest Editor
1. Professor, Department of Civil Engineering and Geomatics, Cyprus University of Technology, P.O.Box 50329, 3603 Lemesos, Cyprus
2. Adjunct Professor, Department of Geography, University of California Santa Barbara, Ellison Hall 1832, Santa Barbara, CA 93106-4060, USA
Interests: geostatistics; geocomputation; geoinformation

Special Issue Information

Dear Colleagues,

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

Important Dates:

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
Guest Editors

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 submissions that pass pre-check are 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 1900 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 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

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Published Papers (5 papers)

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Research

15 pages, 652 KiB  
Article
Finding Causes of Irregular Headways Integrating Data Mining and AHP
by Shi An, Xinming Zhang and Jian Wang
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2604-2618; https://doi.org/10.3390/ijgi4042604 - 24 Nov 2015
Cited by 7 | Viewed by 4506
Abstract
Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway [...] Read more.
Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway performance and proposes a statistical method to identify the abnormal headways. Association mining is used to dig deeper and recognize six causes of bus bunching. The AHP, embedded data analysis, is applied to determine the weight of each cause in the case of that these causes are combined with each other constantly. Results show that the front bus has a greater effect on bus bunching than the following bus, and the traffic condition is the most critical factor affecting bus headway. Full article
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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23 pages, 1054 KiB  
Article
Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China
by Dong Liang, Yan Zuo, Linsheng Huang, Jinling Zhao, Ling Teng and Fan Yang
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2519-2541; https://doi.org/10.3390/ijgi4042519 - 16 Nov 2015
Cited by 98 | Viewed by 11579
Abstract
Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The [...] Read more.
Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The “woodland” LAI/FPAR is the worst, with a spatial similarity (O) of 58.17% due to the misclassification between “woodland” and “others”. The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales. Full article
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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33 pages, 322 KiB  
Article
Spatiotemporal Data Mining: A Computational Perspective
by Shashi Shekhar, Zhe Jiang, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Venkata M. V. Gunturi and Xun Zhou
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2306-2338; https://doi.org/10.3390/ijgi4042306 - 28 Oct 2015
Cited by 165 | Viewed by 21966
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 mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal [...] Read more.
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 mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large 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 spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, 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 mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs. Full article
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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26 pages, 18434 KiB  
Article
Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction
by Manuel Stein, Johannes Häußler, Dominik Jäckle, Halldór Janetzko, Tobias Schreck and Daniel A. Keim
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2159-2184; https://doi.org/10.3390/ijgi4042159 - 20 Oct 2015
Cited by 32 | Viewed by 9791
Abstract
With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. [...] Read more.
With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts. Full article
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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30 pages, 1124 KiB  
Article
Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis
by Andrew Crooks, Arie Croitoru, Xu Lu, Sarah Wise, John M. Irvine and Anthony Stefanidis
ISPRS Int. J. Geo-Inf. 2015, 4(3), 1627-1656; https://doi.org/10.3390/ijgi4031627 - 2 Sep 2015
Cited by 34 | Viewed by 12598
Abstract
Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban [...] Read more.
Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA2-ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities. Full article
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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