Special Issue "Geovisualization and Social Media"

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

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Yeran Sun
Website
Guest Editor
Swansea University | SU · College of Science, Geography Department, Swansea, Wales, UK
Interests: GIS; Urban Informatics; Urban big data; Crowdsourced geographic information; Social media analytics
Dr. Shaohua Wang
Website
Guest Editor
CyberGIS Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, US
Interests: Spatial Big Data; Location Modeling; Spatial Optimization; Spatial Data Science; Geovisual Analytics

Special Issue Information

Due to the popularity of location-based services, popular social media, like Twitter, Facebook, Instagram, Flickr, Weibo, and Wechat, offer not only a massive volume of geospatial data but also spatiotemporally fine-grained data at both individual and aggregate levels. Compared to conventional geospatial data, georeferenced social media data are unstructured and biased. Owning to the peculiar characteristics of georeferenced social media data, new geovisualization methods are needed to better map and analyze social media data in support of deriving findings related to individual-level human travel-activity patterns, human responses to events (e.g., natural hazards, flu outbreak, etc.) and aggregate-level socioeconomic phenomena (e.g., political elections, social connections, migration, urban vibrancy, etc.) in the field of cultural, economic, and political geography. Social media data mapping and analytics (SMDMA) methods and techniques have an increasing potential to supplement and enhance the existing relevant studies around transport, public health, disaster management, urban planning, and social sciences. Besides, data quality and geo-localization of non-georeferenced social media data are also discussed theoretically and empirically, although deeper discussions are needed with more empirical comparisons of social media data and other geospatial data. Topics include, but are not limited to, the following ones:

  • Application of new geovisualization methods to social media data
  • SMDMA in support of route selection, indoor navigation, or outdoor navigation.
  • SMDMA for deriving travel-activity patterns
  • SMDMA in support of travel-related health studies
  • SMDMA in support of mapping and simulating spread of disease (i.e., flu)
  • Combination of social media data and conventional geospatial data in support of disaster management
  • SMDMA in support of revealing underlying spatio-social structures of socioeconomic phenomena
  • SMDMA in support of social connection studies and social network analysis
  • SMDMA in support of urban and regional planning
  • Geo-localization of non-georeferenced social media data
Dr. Yeran Sun
Dr. Shaohua Wang
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 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

  • geovisualization
  • social media data analytics
  • flow mapping
  • data quality
  • social network analysis

Published Papers (2 papers)

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Research

Open AccessArticle
Using Local Toponyms to Reconstruct the Historical River Networks in Hubei Province, China
ISPRS Int. J. Geo-Inf. 2020, 9(5), 318; https://doi.org/10.3390/ijgi9050318 - 12 May 2020
Abstract
As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water [...] Read more.
As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water and direction is proposed. It is suitable for the study area which possesses rich water resources. To reconstruct the historical shape of the river network, (1) water-related toponyms and direction-related toponyms are extracted as two datasets based on the key words in each village toponym; (2) the feasibility of the river network reconstruction based on these toponyms is validated via a quantitative analysis, according to the spatial distributions of toponyms and rivers; (3) the reconstructed historical shape of the river network can be obtained via qualitative knowledge and geometrical analysis; and (4) the reconstructed rivers are visualized to display their general historical trends and shapes. The results of this paper demonstrate the global correlation and local differences between the toponyms and the river network. The historical river dynamics are revealed and can be proven by ancient maps and local chronicles. The proposed method provides a novel way to reconstruct historical river network shapes using toponym datasets. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Open AccessArticle
Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach
ISPRS Int. J. Geo-Inf. 2020, 9(4), 217; https://doi.org/10.3390/ijgi9040217 - 04 Apr 2020
Abstract
With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor [...] Read more.
With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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