Geo-Enriched Data Modeling & Mining

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 12213

Special Issue Editors

Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
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 Issues, Collections and Topics in MDPI journals
Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA
Interests: spatio-temporal databases; data modeling; data mining; gis; agent based modeling

Special Issue Information

Dear Colleagues,

Both of the current trends in technology such as smartphones, general mobile devices, stationary sensors and satellites as well as a new user mentality of utilizing this technology to voluntarily share information produce a huge flood of geospatial data. This data is enriched by multiple additional sources or contexts such as social information, text, multimedia data, and scientific measurements, called geo-enriched data. This data flood provides a tremendous potential of discovering new and possibly useful knowledge. The novel research challenge is to model, share, search, and mine this wealth of geo-enriched data. The focus of this Special Issue is to analyze what has been achieved so far and how to further exploit the enormous potential of this data flood. The ultimate goal of this Special Issue is to develop a general framework of methods for modeling, searching and mining enriched geospatial data in order to fuel an advanced analysis of big data applications beyond the current research frontiers. Furthermore, this Special Issue intends to compile an interdisciplinary research collection in the fields of databases, data science, and geoinformation science.

This Special Issue is dedicated to giving an overview of state-of-the-art solutions, techniques and applications on modeling, managing, searching and mining geo‐enriched data, such spatio-textual, spatio-temporal, spatio-social, geo-social network, mobile and wireless data.

We call for original papers from researchers around the world that focus on topics including, but not limited to, the following:

Dr. Andreas Züfle
Dr. Joon-Seok Kim
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 1700 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

  • Big Spatial Data
  • Crowdsourcing Computing Resources
  • Data Extraction Techniques including NLP
  • Data Mining on Geo-Enriched data
  • Geo-Multimedia DataM Geo-Social Data
  • Geo-Textual Data
  • Indexing Geo-Enriched data
  • Location-Based Social Networks
  • Spatial Data Models and Representation
  • Spatial Privacy and Confidentiality
  • Spatial Reasoning and Analysis
  • Spatial Recommendation Systems
  • Temporal Geo-Enriched Data
  • Uncertainty in Spatial and Spatio-Temporal Data
  • Visualization of Geo-Enriched Data

Published Papers (3 papers)

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20 pages, 18905 KiB  
Article
A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects
ISPRS Int. J. Geo-Inf. 2021, 10(12), 814; https://doi.org/10.3390/ijgi10120814 - 01 Dec 2021
Cited by 4 | Viewed by 2235
Abstract
The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are [...] Read more.
The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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17 pages, 7434 KiB  
Article
Urban Geological 3D Modeling Based on Papery Borehole Log
ISPRS Int. J. Geo-Inf. 2020, 9(6), 389; https://doi.org/10.3390/ijgi9060389 - 12 Jun 2020
Cited by 14 | Viewed by 5594
Abstract
Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it [...] Read more.
Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it is difficult to extract the information from the papery borehole log quickly. What is more, it is unreliable to rely entirely on automated algorithms for modeling without artificial participation, but there is no effective way to integrate geological knowledge into 3D geological modeling currently. Therefore, it is necessary to research how to use existing papery borehole logs efficiently. To overcome the above obstacles, we designed a method that combines structural analysis and layout understanding to extract information from the borehole log. Then, the knowledge-driven three-dimensional geological modeling is proposed based on dynamic profiles. With these methods, the papery borehole log can be converted into structured data which can be used for data analysis directly, and geological knowledge can be integrated into the process of 3D geological modeling. The 3D geological modeling of Xinyang City based on a papery borehole log has been taken as an example to verify the feasibility of the method. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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25 pages, 10353 KiB  
Article
A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information
ISPRS Int. J. Geo-Inf. 2020, 9(6), 385; https://doi.org/10.3390/ijgi9060385 - 10 Jun 2020
Cited by 4 | Viewed by 3666
Abstract
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network [...] Read more.
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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