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IJGI
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Convergence of GIS and Social Media
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Convergence of GIS and Social Media

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

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 39176

Special Issue Editor

Prof. Dr. James B. Pick

E-Mail Website
Guest Editor
Center for Spatial Business (CSB), School of Business, University of Redlands, Redlands, CA, USA
Interests: management information systems (MIS); geographic information systems (GIS); urban geography; population

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to gain knowledge and provide novel research insights on how GIS and social media converge and relate to each other. Social media platforms trace the proximity of persons with each other and with organizational assets for the purposes of meeting, socializing, collaborating, locating, and making decisions. This Special Issue seeks papers on varied aspects of this convergence, some of which are mentioned here. One aspect involves GIS as a way to communicate social media knowledge. Study is needed on the shift from the traditional geo-referencing of exact location to place, which is common for social media, and on analytic tools to handle social media locational big data that have multimedia attributes. Another perspective is how GIS can be utilized as a tool to map and understand the prevalence and content of social media in varied geographies. At the organizational level, the management of location-based social media for decision-making by organizations needs to be studied, as do issues on data quality, preserving personal privacy of locational social media information, and managerial ethics.  Contributions need to involve relationships of social media and GIS, and make substantive contributions to relevant topics in theory, methodology, and/or empirical studies, and on applications in geography, design, analytics, behavior, management, and/or ethics.

Prof. Dr. James B. Pick
Guest Editor

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Keywords

  • social media
  • GIS
  • convergence
  • big data
  • location analytics
  • management
  • ethics

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Published Papers (7 papers)

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Research

19 pages, 3101 KiB  
Article
A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data
by Xucai Zhang, Yeran Sun, Anyao Zheng and Yu Wang
ISPRS Int. J. Geo-Inf. 2020, 9(2), 124; https://doi.org/10.3390/ijgi9020124 - 21 Feb 2020
Cited by 35 | Viewed by 4336
Abstract
The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The [...] Read more.
The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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20 pages, 11267 KiB  
Article
Retrieving Landmark Salience Based on Wikipedia: An Integrated Ranking Model
by Noa Binski, Asya Natapov and Sagi Dalyot
ISPRS Int. J. Geo-Inf. 2019, 8(12), 529; https://doi.org/10.3390/ijgi8120529 - 26 Nov 2019
Cited by 6 | Viewed by 4039
Abstract
Landmarks are important for assisting in wayfinding and navigation and for enriching user experience. Although many user-generated geotagged sources exist, landmark entities are still mostly retrieved from authoritative geographic sources. Wikipedia, the world’s largest free encyclopedia, stores geotagged information on many geospatial entities, [...] Read more.
Landmarks are important for assisting in wayfinding and navigation and for enriching user experience. Although many user-generated geotagged sources exist, landmark entities are still mostly retrieved from authoritative geographic sources. Wikipedia, the world’s largest free encyclopedia, stores geotagged information on many geospatial entities, including a very large and well-founded volume of landmark information. However, not all Wikipedia geotagged landmark entities can be considered valuable and instructive. This research introduces an integrated ranking model for mining landmarks from Wikipedia predicated on estimating and weighting their salience. Other than location, the model is based on the entries’ category and attributed data. Preliminary ranking is formulated on the basis of three spatial descriptors associated with landmark salience, namely permanence, visibility, and uniqueness. This ranking is integrated with a score derived from a set of numerical attributes that are associated with public interest in the Wikipedia page―including the number of redirects and the date of the latest edit. The methodology is comparatively evaluated for various areas in different cities. Results show that the developed integrated ranking model is robust in identifying landmark salience, paving the way for incorporation of Wikipedia’s content into navigation systems. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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22 pages, 3954 KiB  
Article
Analysis of Tourism Hotspot Behaviour Based on Geolocated Travel Blog Data: The Case of Qyer
by Michael Kaufmann, Patrick Siegfried, Lukas Huck and Jürg Stettler
ISPRS Int. J. Geo-Inf. 2019, 8(11), 493; https://doi.org/10.3390/ijgi8110493 - 1 Nov 2019
Cited by 15 | Viewed by 6444
Abstract
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations [...] Read more.
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations to relieve overtourism. A pilot application in a case study of Chinese travel in Switzerland by analysing Qyer travel blog data demonstrates the potential of our method. Accordingly, we contribute four conclusions supported by empirical data. First, our method can enable discovery of plausible geographical distributions of tourist hotspots, which validates the plausibility of the data and its collection. Second, our method discovered statistically significant stochastic dependencies that meaningfully differentiate the observed user base, which demonstrates its value for segmentation. Furthermore, the case study contributes two practical insights for tourism management. Third, Chinese independent travellers, which are the main target group of Qyer, are mainly interested in the discovered travel hotspots, similar to tourists on packaged tours, but also show interest in alternative places. Fourth, the proposed user segmentation revealed two clusters based on users’ social media activity level. For tourism research, users within the second cluster are of interest, which are defined by two segmentation attributes: they blogged about more than just one location, and they have followers. These tourists are significantly more likely to be interested in alternative destinations out of the hotspot axis. Knowing this can help define a target group for marketing activities to promote alternative destinations. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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25 pages, 3103 KiB  
Article
Social Media Use in American Counties: Geography and Determinants
by James Pick, Avijit Sarkar and Jessica Rosales
ISPRS Int. J. Geo-Inf. 2019, 8(9), 424; https://doi.org/10.3390/ijgi8090424 - 19 Sep 2019
Cited by 14 | Viewed by 7878
Abstract
This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social [...] Read more.
This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social capital were posited to influence social media utilization dependent variables. Spatial analysis was conducted including exploratory analysis of geographic distribution and confirmatory screening for spatial randomness. The determinants were identified through ordinary least squares (OLS) regression analysis. Findings for the nation indicate that the major determinants are demographic factors, service occupations, ethnicities, and urban location. Furthermore, analysis was conducted for the U.S. metropolitan, micropolitan, and rural subsamples. We found that Twitter users were more heavily concentrated in southern California and had a strong presence in the Mississippi region, while Facebook users were highly concentrated in Colorado, Utah, and adjacent Rocky Mountain States. Social media usage was lowest in the Great Plains, lower Midwest, and South with the exceptions of Florida and major southern cities such as Atlanta. Measurements of the overall extent of spatial agglomeration were very high. The paper concludes by discussing the policy implications of the study at the county as well as national levels. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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23 pages, 5368 KiB  
Article
Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks
by Elahe Khazaei and Abbas Alimohammadi
ISPRS Int. J. Geo-Inf. 2019, 8(9), 406; https://doi.org/10.3390/ijgi8090406 - 12 Sep 2019
Cited by 21 | Viewed by 3792
Abstract
Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important [...] Read more.
Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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15 pages, 3003 KiB  
Article
Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities
by Wei Jiang, Yandong Wang, Mingxuan Dou, Senbao Liu, Shiwei Shao and Hui Liu
ISPRS Int. J. Geo-Inf. 2019, 8(5), 202; https://doi.org/10.3390/ijgi8050202 - 4 May 2019
Cited by 9 | Viewed by 3901
Abstract
Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity [...] Read more.
Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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21 pages, 4646 KiB  
Article
A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case
by Jingchao Yang, Manzhu Yu, Han Qin, Mingyue Lu and Chaowei Yang
ISPRS Int. J. Geo-Inf. 2019, 8(3), 111; https://doi.org/10.3390/ijgi8030111 - 28 Feb 2019
Cited by 43 | Viewed by 7144
Abstract
Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is [...] Read more.
Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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