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Journal articles on the topic 'Geo-tagged big data'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Peng, Xia, and Zhou Huang. "A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data." ISPRS International Journal of Geo-Information 6, no. 7 (2017): 216. http://dx.doi.org/10.3390/ijgi6070216.

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2

Zhu, Ruoxin, Diao Lin, Michael Jendryke, Chenyu Zuo, Linfang Ding, and Liqiu Meng. "Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events." ISPRS International Journal of Geo-Information 8, no. 1 (2018): 15. http://dx.doi.org/10.3390/ijgi8010015.

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Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a b

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3

Gotmare, Abhay, Gandharva Thite, and Laxmi Bewoor. "A multimodal machine learning approach to generate news articles from geo-tagged images." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3434. http://dx.doi.org/10.11591/ijece.v14i3.pp3434-3442.

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Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge development

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4

Liu, Zhang, Yunyan Du, Jiawei Yi, Fuyuan Liang, Ting Ma, and Tao Pei. "Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data." International Journal of Digital Earth 13, no. 9 (2019): 1072–92. http://dx.doi.org/10.1080/17538947.2019.1645894.

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5

Gotmare, Abhay, Gandharva Thite, and Laxmi Bewoor. "A multimodal machine learning approach to generate news articles from geo-tagged images." A multimodal machine learning approach to generate news articles from geo-tagged images 14, no. 3 (2024): 3434–42. https://doi.org/10.11591/ijece.v14i3.pp3434-3442.

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Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by

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6

Chen, Tongxin, Di Zhu, Tao Cheng, Xiaowei Gao, and Huanfa Chen. "Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic." PLOS ONE 18, no. 1 (2023): e0277913. http://dx.doi.org/10.1371/journal.pone.0277913.

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Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic deter

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7

Gong, Lunsheng, Meihan Jin, Qiang Liu, Yongxi Gong, and Yu Liu. "Identifying Urban Residents’ Activity Space at Multiple Geographic Scales Using Mobile Phone Data." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 241. http://dx.doi.org/10.3390/ijgi9040241.

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Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commu

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8

Chen, Tongxin, Kate Bowers, and Tao Cheng. "Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data." ISPRS International Journal of Geo-Information 12, no. 12 (2023): 488. http://dx.doi.org/10.3390/ijgi12120488.

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This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of ‘Least Absolute Shrinkage and Selection Operator’ (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into e

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9

Liu, Tao, Ying Zhang, Huan Zhang, and Xiping Yang. "A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China." Sustainability 13, no. 9 (2021): 4720. http://dx.doi.org/10.3390/su13094720.

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Insights into the association rules of destinations can help to understand the possibility of tourists visiting a destination after having traveled from another. These insights are crucial for tourism industries to exploit strategies and travel products and offer improved services. Recently, tourism-related, user-generated content (UGC) big data have provided a great opportunity to investigate the travel behavior of tourists on an unparalleled scale. However, existing analyses of the association of destinations or attractions mainly depend on geo-tagged UGC, and only a few have utilized unstru

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10

Ren, Jiang, and Seipel. "Capturing and Characterizing Human Activities Using Building Locations in America." ISPRS International Journal of Geo-Information 8, no. 5 (2019): 200. http://dx.doi.org/10.3390/ijgi8050200.

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Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activ

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11

Gibbons, Joseph, Atsushi Nara, and Bruce Appleyard. "Exploring the imprint of social media networks on neighborhood community through the lens of gentrification." Environment and Planning B: Urban Analytics and City Science 45, no. 3 (2017): 470–88. http://dx.doi.org/10.1177/2399808317728289.

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Gentrification, the rise of affluent socioeconomic populations in economically depressed urban neighborhoods, has been accused of disrupting community in these neighborhoods. Social media networks meanwhile have been recognized not only to create new communities in neighborhoods, but are also associated with gentrification. What relation then does gentrification and social media networks have to urban communities? To explore this question, this study uses social media networks found on Twitter to identify communities in Washington, DC. With space-time analysis of 821,095 geo-tagged tweets gene

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12

Naeem, Zafar Azeemi. "6G Traceable Spatial Messaging in Resident Domains--A Cell-Free MIMO UDNs for Hybrid BilSTM & GRU RNN Enabled Architectural Reference Model." Engineering Applications of Artificial Intelligence 118, no. 1 (2023): 548–59. https://doi.org/10.5281/zenodo.7831710.

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In the era of 6G, a game-changing approach will redefine the communications and networks depending on the required services. A large volume of geo-tagged data can be fundamental to providing applications of location based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and reports

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13

Naeem, Z. Azeemi, A. Azeemi Naveed, and Abdullah Umaira. "6G Traceable Spatial Messaging in Resident Domains—A Cell-Free MIMO UDNs for Hybrid BilSTM & GRU RNN Enabled Architectural Reference Model (ARM)." Engineering Applications of Artificial Intelligence 118, no. 1 (2023): 549–60. https://doi.org/10.5281/zenodo.7875000.

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In the era of 6G, a game-changing approach will redefine the communications and networks depending on the required services.  A large volume of geo-tagged data can be fundamental to providing applications of location-based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and re

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14

Jie, Fan, Liu Hanchu, and Sun Zhongrui. "Dataset for Fan et al.(2023), Stress of spatial orientation of floating populations into Tibet on fragile ecosystem—Using geo-tagged big data, in People and Nature." People and Nature, August 7, 2023. https://doi.org/10.5281/zenodo.8221978.

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This is the dataset for an article published in People and Nature by Fan et al. (2023), entitled Stress of spatial orientation of floating populations into Tibet on fragile ecosystem—Using geo-tagged big data.

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15

Fan, Jie, Hanchu Liu, Zhongrui Sun, et al. "Stress of spatial orientation of floating populations into Tibet on fragile ecosystem—Using geo‐tagged big data." People and Nature, September 7, 2023. http://dx.doi.org/10.1002/pan3.10533.

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Abstract Increased global floating populations is an important driver of local social‐ecological systems change, especially in ecologically fragile areas. However, the limitations of accurate data and specific methods have prevented the exploration of spatial visitation of floating populations and its risk of negative effects on fragile ecosystems. Here, we contribute to filling the knowledge gap in Tibet by adopting large‐scale geolocation data and incorporating factors such as landscape aesthetic value, ecological vulnerability and traffic accessibility. We found that 89.97% of the digital f

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16

Nagendra, Kumar V., and D. Sundarpandian. "Modified Move Sequence Recommendation on Multi-Source Big Social Media." March 29, 2019. https://doi.org/10.5281/zenodo.2616507.

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Now a day, traveling recommendation is important for user who is the plan for trave ling. There are many existing techniques which are used for travel recommendation. In this paper explain a personalized travel sequence suggestion system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. It recommends personalized users travel interest and recommend a sequence of travel interest instead of an individual point of interest. The existing system cannot complete the requirement i.e. personalized and sequential recommendation togethe

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17

Zhai, Wei. "A multi-level analytic framework for disaster situational awareness using Twitter data." Computational Urban Science 2, no. 1 (2022). http://dx.doi.org/10.1007/s43762-022-00052-z.

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AbstractDuring a natural disaster, mining messages from social media platforms can facilitate local agencies, rescue teams, humanitarian aid organizations, etc., to track the situational awareness of the public. However, for different stakeholders, the concerns about people’s situational awareness in a natural disaster event are different. Therefore, I developed a Twitter-based analytic framework to take perception-level situational awareness, humanitarian-level situational awareness, and action-level situational awareness into consideration. Specifically, perception-level situational awarenes

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18

Naeem, Zafar Azeemi. "A Hybrid BilSTM & GRU RNN Enabled Architectural Reference Model (ARM) in 6G Cell-Free MIMO UDNs for Traceable Spatial Messaging in Resident Domains." January 26, 2023. https://doi.org/10.5281/zenodo.7572065.

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In the era of 6G, a game-changing approach will redefine the communications and networks depending on the required services.  A large volume of geo-tagged data can be fundamental to providing applications of location-based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and re

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