Special Issue "Geovisualization and Social Media"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Yeran Sun
E-Mail 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
E-Mail 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 Issues and Collections in MDPI journals

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

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Keywords

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

Published Papers (15 papers)

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Research

Article
Portraying Citizens’ Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework
ISPRS Int. J. Geo-Inf. 2021, 10(6), 392; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060392 - 06 Jun 2021
Viewed by 343
Abstract
Mobile phone data is a typical type of big data with great potential to explore human mobility and individual portrait identification. Previous studies in population classifications with mobile phone data only focused on spatiotemporal mobility patterns and their clusters. In this study, a [...] Read more.
Mobile phone data is a typical type of big data with great potential to explore human mobility and individual portrait identification. Previous studies in population classifications with mobile phone data only focused on spatiotemporal mobility patterns and their clusters. In this study, a novel spatiotemporal analytical framework with an integration of spatial mobility patterns and non-spatial behavior, through smart phone APP (applications) usage preference, was proposed to portray citizens’ occupations in Guangzhou center through mobile phone data. An occupation mixture index (OMI) was proposed to assess the spatial patterns of occupation diversity. The results showed that (1) six types of typical urban occupations were identified: financial practitioners, wholesalers and sole traders, IT (information technology) practitioners, express staff, teachers, and medical staff. (2) Tianhe and Yuexiu district accounted for most employed population. Wholesalers and sole traders were found to be highly dependent on location with the most obvious industrial cluster. (3) Two centers of high OMI were identified: Zhujiang New Town CBD and Tianhe Smart City (High-Tech Development Zone). It was noted that CBD has a more profound effect on local as well as nearby OMI, while the scope of influence Tianhe Smart City has on OMI is limited and isolated. This study firstly integrated both spatial mobility and non-spatial behavior into individual portrait identification with mobile phone data, which provides new perspectives and methods for the management and development of smart city in the era of big data. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Detecting Urban Events by Considering Long Temporal Dependency of Sentiment Strength in Geotagged Social Media Data
ISPRS Int. J. Geo-Inf. 2021, 10(5), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050322 - 10 May 2021
Viewed by 300
Abstract
The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly from the pattern as urban events. [...] Read more.
The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly from the pattern as urban events. However, few studies have considered the long temporal dependency of sentiment strength in geotagged social media data, and thus it is difficult to further improve the reliability of detection results. In this paper, we combined a sentiment analysis method and long short-term memory neural network for detecting urban events with geotagged social media data. We first applied a dictionary-based method to evaluate the positive and negative sentiment strength. Based on long short-term memory neural network, the long temporal dependency of sentiment strength in geotagged social media data was constructed. By considering the long temporal dependency, daily positive and negative sentiment strength are predicted. We extracted anomalies that deviated significantly from the prediction as urban events. For each event, event-related information was obtained by analyzing social media texts. Our results indicate that the proposed approach is a cost-effective way to detect urban events, such as festivals, COVID-19-related events and traffic jams. In addition, compared to existing methods, we found that accounting for a long temporal dependency of sentiment strength can significantly improve the reliability of event detection. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan
ISPRS Int. J. Geo-Inf. 2021, 10(5), 299; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050299 - 05 May 2021
Viewed by 331
Abstract
The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become [...] Read more.
The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public’s sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people’s discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing
ISPRS Int. J. Geo-Inf. 2021, 10(5), 282; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050282 - 29 Apr 2021
Viewed by 334
Abstract
The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for [...] Read more.
The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea
ISPRS Int. J. Geo-Inf. 2021, 10(5), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050277 - 28 Apr 2021
Viewed by 361
Abstract
For the sustainable development of marine fishery resources, it is essential to comprehensively, accurately, and objectively obtain the spatial characteristics and evolution law of fishing intensity. However, previous studies have focused more on the use of single data sources, such as AIS (Automatic [...] Read more.
For the sustainable development of marine fishery resources, it is essential to comprehensively, accurately, and objectively obtain the spatial characteristics and evolution law of fishing intensity. However, previous studies have focused more on the use of single data sources, such as AIS (Automatic Information System) and VBD (VIIRS boat detection), to obtain fishing intensity information and, as such, have encountered some problems, such as insufficient comprehensive data coverage for ships, non-uniform spatial distribution of data signal acquisition, and insufficient accuracy in obtaining fishing intensity information. The development of big data and remote sensing Earth observation technology has provided abundant data sources and technical support for the acquisition of fishing intensity data for marine fisheries. Based on this situation, this paper proposes a framework that integrates the data of fishing vessels from two sources (AIS, with high space-time granularity, and VBD, with short revisit cycle and high sensitivity), in order to obtain such information based on closely matching and fusing the vector point data of ship positions. With the help of this framework and the strategy of indirectly representing fishing intensity by data point density after fusion, the spatial characteristics and rules of fishing intensity in typical seasons (February, April, September, and November) in the northern South China Sea in 2018 were systematically analyzed and investigated. The results revealed the following: (1) Matching and fusing AIS and VBD data can provide a better perspective to produce robust and accurate marine fishery intensity data. The two types of data have a low proximity match rate (approximately 1.89% and 6.73% of their respective inputs) and the matching success for fishing vessels in the data was 49.42%. (2) Single AIS data can be used for nearshore (50 to 70 km) marine fishery analysis research, while VBD data reflect the objective marine fishing in space, showing obvious complementarity with AIS. (3) The fishing intensity grid data obtained from the integrated data show that high-intensity fishing in the study area was concentrated in the coastal area of Maoming City, Guangdong (0–50 km); the coastal area of Guangxi Beihai (10–70 km); around Hainan Island in Zhangzhou (10–30 km); and the Sanya nearshore area (0–50 km). However, it did not decay with increasing offshore distance, such as at the Trans-Vietnamese boundary in the Beibu Gulf, near the China–Vietnam Common Fisheries Area (50 km) and high-intensity fishing areas. (4) The obtained fishing intensity data (AIS, VBD, and AIS + VBD) were quantitatively analyzed, showing that the CV (Coefficient of Variation) of the average for each month (after fusing the two types of data) was 0.995, indicating that the distribution of the combined data was better than that before fusion (before fusion: AIS = 0.879, VBD = 1.642). Therefore, the integration of AIS and VBD can meet the need for a more effective, comprehensive, and accurate fishing intensity analysis in marine fishery resources. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos
ISPRS Int. J. Geo-Inf. 2021, 10(5), 275; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050275 - 23 Apr 2021
Viewed by 1288
Abstract
Social media is used nowadays for various location-based applications and services, aspiring to use the vast and timely potential of user-generated content. To evaluate the correctness, reliability and potential of these applications and services, they are mostly evaluated in terms of optimization or [...] Read more.
Social media is used nowadays for various location-based applications and services, aspiring to use the vast and timely potential of user-generated content. To evaluate the correctness, reliability and potential of these applications and services, they are mostly evaluated in terms of optimization or compared to existing authoritative data sources and services. With respect to route planning, criterion optimization is mostly implemented to evaluate the service effectiveness, in terms of, e.g., length, time or visited places. These evaluations are mostly limited in their effectiveness at presenting the complete experience of the route, since they are limited to a predefined criterion and are mostly implemented in two-dimensional space. In this research, we propose a comprehensive evaluation process, in which a tourism walking route is analyzed with respect to three-dimensional visibility that measures the attractiveness of the route relating to the user perception. To present our development, we showcase the use of Flickr, a social media photo-sharing online website that is popular among travelers that share their tourism experiences. We use Flickr photos to generate tourism walking routes and evaluate them in terms of the visible space. We show that the 3D visibility analysis identifies the various visible urban elements in the vicinity of the tourism routes, which are more attractive, scenery and include many tourism attractions. Since urban attractivity is often reflected in the photo-trails of Flickr photographers, we argue that using 3D visibility analysis that measures urban attractiveness and scenery should be considered for the purpose of analysis and evaluation of location-based services. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Using Content Analysis to Probe the Cognitive Image of Intangible Cultural Heritage Tourism: An Exploration of Chinese Social Media
ISPRS Int. J. Geo-Inf. 2021, 10(4), 240; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040240 - 07 Apr 2021
Viewed by 465
Abstract
The industry of intangible cultural heritage (ICH) tourism continues to grow, and social media can serve as an essential tool to promote this trend. Although ICH tourism development is outstanding in China, the language structure and restricted use of social media render ICH [...] Read more.
The industry of intangible cultural heritage (ICH) tourism continues to grow, and social media can serve as an essential tool to promote this trend. Although ICH tourism development is outstanding in China, the language structure and restricted use of social media render ICH difficult for non-Chinese speakers to understand. Using content analysis, this study investigates the structure and relationships among cognitive elements of ICH tourism based on 9074 blogs posted between 2011 and 2020 on Weibo.com, one of the most popular social media platforms in China. The main analysis process consisted of matrix construction, dimension classification, and semantic network analysis. Findings indicated that the cognitive image of ICH tourism on social media can be divided into seven dimensions: institutions, ICH and inheritors, tourism products, traditional festivals and seasons, tourism facilities and services, visitors, and regions. This network vividly illustrates ICH tourism and depicts the roles of organizers, residents, inheritors, and tourists. Among these elements, institutions hold the greatest power to regulate and control ICH tourism activities, and folklore appears to be the most common type of ICH resource that can be developed into tourism activities. Practically, the results offer insight for policymakers regarding ways to better balance the relationships among heritage protection, the business economy, and people’s well-being. Such strategies can promote the industrialization of ICH tourism. In addition, through content analysis, this paper confirms the effectiveness of social media in providing a richer understanding of ICH tourism. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic
ISPRS Int. J. Geo-Inf. 2021, 10(3), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030145 - 08 Mar 2021
Cited by 1 | Viewed by 373
Abstract
The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) [...] Read more.
The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) for China obtained from the Baidu Migration platform to analyze the changes of the spatiotemporal patterns and network characteristics in population flow during the pre-outbreak period, outbreak period, and post-peak period. The results show that (1) for temporal characteristics of population flow, the total population flow varies greatly between the three periods, with an overall trend of the pre-outbreak period flow > the post-peak period flow > the outbreak period flow. Impacted by the lockdown measures, the population flow in various provinces plunged drastically and remained low until the post-peak period, at which time it gradually increased. (2) For the spatial pattern, the pattern of population flow is divided by the geographic demarcation line known as the Hu (Heihe-Tengchong) Line, with a high-density interconnected network in the southeast half and a low-density serial-connection network in the northwest half. During the outbreak period, Wuhan city appeared as a hollow region in the population flow network; during the post-peak period, the population flow increased gradually, but it was mainly focused on intra-provincial flow. (3) For the network characteristic changes, during the outbreak period, the gap in the network status between cities at different administrative levels narrowed significantly. Thus, the feasibility of Baidu migration data, comparison with non-epidemic periods, and optimal implications are discussed. This paper mainly described the difference and specific information under non-normal situation compared with existing results under a normal situation, and analyzed the impact mechanism, which can provide a reference for local governments to make policy recommendations for economic recovery in the future under the epidemic period. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data
ISPRS Int. J. Geo-Inf. 2021, 10(3), 126; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030126 - 01 Mar 2021
Viewed by 338
Abstract
As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for [...] Read more.
As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for public response to air pollution based on online complaint data and sentiment analysis. In the proposed framework, the emotional dictionary of air pollution was firstly constructed using microblog data and complaint data. Secondly, the emotional dictionary of air pollution and the sentiment analysis method were used to calculate public complaints’ emotional intensity. Besides, the spatial and temporal characteristics of air pollution complaint data and public emotional intensity, the complaints content, and their correlation with PM2.5 (particulate matters smaller than 2.5 micrometers) and PM10 were analyzed using address matching, spatial analysis, and word cloud analysis. Finally, the proposed framework was applied to 13,469 air pollution complaint data in Shandong Province from 2012 to 2018. The obtained results indicated that: the public was mainly complaining about the exhaust gas emissions from enterprises and factories. Spatially, the geographical center of complaint data was located in the inland industrial urban agglomeration of Shandong Province. Correlatively, air pollution complaints’ negative emotional intensity was significantly negatively correlated with PM2.5 (−0.73). Moreover, the number of public complaints about air pollution and the intensity of negative emotions also decreased with improved air quality in Shandong Province in recent years. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks
ISPRS Int. J. Geo-Inf. 2021, 10(1), 36; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010036 - 15 Jan 2021
Viewed by 620
Abstract
In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI [...] Read more.
In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos
ISPRS Int. J. Geo-Inf. 2021, 10(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010020 - 08 Jan 2021
Viewed by 665
Abstract
The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content [...] Read more.
The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data
ISPRS Int. J. Geo-Inf. 2020, 9(11), 666; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110666 - 06 Nov 2020
Cited by 1 | Viewed by 684
Abstract
With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, [...] Read more.
With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers’ behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace
ISPRS Int. J. Geo-Inf. 2020, 9(11), 632; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110632 - 26 Oct 2020
Cited by 2 | Viewed by 760
Abstract
Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took [...] Read more.
Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field. Full article
(This article belongs to the Special Issue Geovisualization and Social Media)
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Article
Using Local Toponyms to Reconstruct the Historical River Networks in Hubei Province, China
ISPRS Int. J. Geo-Inf. 2020, 9(5), 318; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9050318 - 12 May 2020
Cited by 3 | Viewed by 776
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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Article
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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040217 - 04 Apr 2020
Cited by 1 | Viewed by 915
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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