Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.4
Journals
IJGI
Special Issues
Mobility and Geosocial Networks
share announcement

Mobility and Geosocial Networks

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 8385

Special Issue Editors

Prof. Dr. Milan Konecny

E-Mail Website
Guest Editor
Former President of ICA, Chairman of the ICA Commission Cartography for Early Warning and Crises Management, Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic
Interests: early warning and disaster/crises management; disaster risk reduction; big data; space and geospatial solutions; GI Science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu Liu

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China
Interests: GIScience; spatiotemporal big data; human geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobility and social networks are two key ingredients of human behavior. At the collective level, the derived spatial–temporal behavioral patterns help us to understand the underlying geographical environments.

Thanks to the power of social webs, people have found new ways to behave and communicate socially. Data, information or content are shared on the web in the form of texts, videos, images, blogs, etc. These can also be connected to one’s geographical location, in what is known as geosocial information and geosocial networks. In our Special Issue, we expect papers that will respond to the continuous evolution of the internet and web 2.0 technologies which facilitate the creation of dynamic content. Social networks with georeference can be helpful to handle information from different sources and provide user-oriented services.

All the aforementioned approaches are providing new effective tools to collect information about nature and society and their dynamic natural and social changes. Everything is enriched by methods developed in connection with Big Data and Spatial Big Data, with the help of which smart solutions are sought.

There are many areas where mobility and geosocial networks improve and refine existing solutions. One of them is geodesign, which works with social media geographic information (SMGI). In fact, authoritative information provided by national mapping organizations dealing with space and themes is extended by the time aspect and especially the user and their preferences, which can be communicated through information media via texts, images, audio, and video.

Inspiring Topics for the Special Issue:

  • Human mobility patterns derived from big-geo data
  • Cross-scale (inter-urban and intra-urban) human mobility models
  • Long-term human mobility models
  • Understanding human mobility patterns for sustainable development goals
  • Human perceptions derived from social media and social networking data
  • Understanding human perception patterns for Sustainable Development Goals
  • Sustainability research supported by social networking services
  • Spatially–temporally analytical methods for mobility and social networking data
  • Interplay between human mobility and social networking
  • Data quality issues in mobility and social networking studies
  • Epidemic research with human mobility and social networking
  • Disaster management from the perspective of human mobility and social networking
  • Smart city studies of human mobility and social networking
  • GeoAI enabled applications of human mobility and social networking

Prof. Dr. Milan Konecny
Prof. Dr. Yu Liu
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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 13244 KiB  
Article
Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data
by Yusi Liu, Xiang Gao, Disheng Yi, Heping Jiang, Yuxin Zhao, Jun Xu and Jing Zhang
ISPRS Int. J. Geo-Inf. 2022, 11(2), 140; https://doi.org/10.3390/ijgi11020140 - 15 Feb 2022
Cited by 4 | Viewed by 3233
Abstract
Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activities and trip purposes, and lead to [...] Read more.
Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activities and trip purposes, and lead to a deeper understanding of human travel patterns. Most existing literature analyses of activity semantics mainly focus on the characteristics of the destination. However, the movement from the origin to the destination can be represented as the flow. The flow can completely represent the activity semantic and describe the spatial interaction between the origin and the destination. Therefore, in this paper, we proposed a two-layer framework to infer the activity semantics of each taxi trip and generalized the similar activity semantic flow to reveal human travel patterns. We introduced the activity inference in the first layer by a combination of the improved Word2vec model and Bayesian rules-based visiting probability ranking. Then, a flow clustering method is used to uncover human travel behaviors based on the similarity of activity semantics and spatial distribution. A case study within the Fifth Ring Road in Beijing is adopted and the results show that our method is effective for taxi trip activity inference. Six activity semantics and four activity semantics are identified in origins and destinations, respectively. We also found that differences exist in the activity transitions from origins to destinations at distinct periods. The research results can inform the taxi travel demand and provide a scientific decision-making basis for taxi operation and transportation management. Full article
(This article belongs to the Special Issue Mobility and Geosocial Networks)
►▼ Show Figures

Figure 1

19 pages, 22508 KiB  
Article
Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
by Wenbo Zhang and Chang Xu
ISPRS Int. J. Geo-Inf. 2021, 10(11), 751; https://doi.org/10.3390/ijgi10110751 - 08 Nov 2021
Cited by 2 | Viewed by 1550
Abstract
This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips but also their [...] Read more.
This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips but also their aggregations. Moran’s I and its variations are applied to explore spatial autocorrelations among different rides. PageRank centrality is applied for a functional network representing traffic flows to discover places of interest. Gyration radius measures the scope of passenger mobility and driver passenger searching. Moreover, cumulative distribution and data visualization techniques are adopted for trip level characteristics and features analysis. The results indicate that the app-based taxi services are serving more neighborhoods other than downtown areas by taking large proportion of relatively shorter trips and contributing to net increase in total taxi ridership although net decrease in downtown areas. The spatial autocorrelations are significant not only within each service but also among services. With the smartphone-based applications, app-based taxi services are able to search passengers in a larger area and move more efficiently during both occupied and unoccupied periods. Mining from huge empty trip trajectory by app-based taxis, we also identify the existence of stationary/stops state and circulations. Full article
(This article belongs to the Special Issue Mobility and Geosocial Networks)
►▼ Show Figures

Figure 1

13 pages, 1166 KiB  
Article
Accuracy of Regional Centrality Using Social Network Analysis: Evidence from Commuter Flow in South Korea
by Jongsang Lee and Ducksu Seo
ISPRS Int. J. Geo-Inf. 2021, 10(10), 642; https://doi.org/10.3390/ijgi10100642 - 25 Sep 2021
Cited by 5 | Viewed by 2104
Abstract
With the recent exponential growth in inter-regional movements of population and information, there is an urgent need for accurately measuring the connectivity and centrality of cities. This study aims to investigate the differences in centrality between different scales of a dataset and to [...] Read more.
With the recent exponential growth in inter-regional movements of population and information, there is an urgent need for accurately measuring the connectivity and centrality of cities. This study aims to investigate the differences in centrality between different scales of a dataset and to propose a calibration method to minimize the gap between the measures from the two scales. Although urban and regional centrality is examined by analyzing regional commuting datasets, this study proposes that it should be measured using nationwide data to validate the centrality results. To demonstrate this, the differences in regional centrality between different spatial scales of commuting trips for two data groups are shown: Seoul regional data and nationwide data. In this structure, the centrality levels of the 25 districts of Seoul were calculated for both groups. The results clearly show the differences in the centrality levels of districts in both groups: Seongbuk district ranked 10th in the local dataset but fell to 18th in the nationwide dataset; Geumcheon district ranked 22nd in the former but rose to 9th in the latter. The ratio of inner commuting in Seoul is thus relatively low, and each district has dynamic connections with other provinces. Furthermore, the results of a linear regression analysis, which was conducted on a local dataset to obtain similar results as those obtained using a national dataset, demonstrate the significance of a wide-ranging commuting dataset for regional centrality analysis of a specific region. Full article
(This article belongs to the Special Issue Mobility and Geosocial Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop