scholarly journals Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

2020 ◽  
Vol 9 (4) ◽  
pp. 222 ◽  
Author(s):  
Ayse Giz Gulnerman ◽  
Himmet Karaman ◽  
Direnc Pekaslan ◽  
Serdar Bilgi
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Central Area ◽  
Research Area ◽  
Time Of Day ◽  
Activity Levels ◽  
Social Media Data ◽  
Time Space ◽  
The City ◽  
Media Data

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

scholarly journals From the board to the streets: a case study of Local Property Trader

2014 ◽  
Vol 1 (3) ◽  
Author(s):  
Alison Gazzard ◽  
Mark Lochrie ◽  
Adrian Gradinar ◽  
Paul Coulton ◽  
Daniel Burnett ◽  
...  
Keyword(s):  
Social Media ◽  
Local Property ◽  
Qr Code ◽  
Mobile Media ◽  
Social Media Data ◽  
Key Points ◽  
The City ◽  
Traditional Game ◽  
Media Data

The boardgame of Monopoly has undergone various iterations since it was first published in 1934. Versions have included location-based varieties of the game, involving mobile media devices that have taken the boardgame to the city streets as a way of engaging players with location in new ways. This article examines a new version of Monopoly, titled Local Property Trader that works with NFC/QR code technologies in order to encourage players to move around the city and interact with local businesses. In doing so, the project hopes to highlight how location-based games can use social media data to update a traditional game into more contemporary contexts. Correspondingly, the differences and similarities of taking a boardgame and reworking it for the city streets are explored through ideas surrounding location, player and map as key points of intersection between the two media forms.

Harnessing social media to understand tourist mobility: the role of information technology and big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Current Trend ◽  
Social Media Data ◽  
Content Type ◽  
Use Of Data ◽  
Social Media Platforms ◽  
The City ◽  
Media Data

Purpose This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions. Design/methodology/approach This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions. Findings This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them. Originality/value The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

scholarly journals Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

2020 ◽  
Vol 13 (4) ◽  
pp. 985-1017
Author(s):  
Marco Adelfio ◽  
Leticia Serrano-Estrada ◽  
Pablo Martí-Ciriquián ◽  
Jaan-Henrik Kain ◽  
Jenny Stenberg
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
Social Life ◽  
Social Activity ◽  
Third Places ◽  
Social Media Data ◽  
The City ◽  
Media Data ◽  
Urban Activity ◽  
Activity Areas

Abstract This research focuses on the intermediate city, composed of urban areas located right outside the city center typically maintaining an in-between urban/suburban character. It aims to explore the degree to which this segment of the city exhibits urban activity and social life through the identification of activity areas in the so-called Third Places. Four intermediate city neighborhoods in Gothenburg, Sweden are adopted as case areas and are analyzed using a twofold approach. First, socio-economic statistics provide a quantitative understanding of the case areas and, second, geolocated Social Media Data (SMD) from Foursquare, Google Places and Twitter makes it possible to identify the intermediate city’s urban activity areas and socially preferred urban spaces. The findings suggest that a) the four analyzed intermediate city areas of Gothenburg all have a degree of social activity, especially where economic activities are clustered together; b) Third Places in more affluent areas tend to be linked to commodified consumption of urban space while neighborhoods with lower income levels and higher ethnic diversity seem to emphasize open public space as Third Places; and c) nowadays the typology of Third Places has evolved from the types identified in previous decades to include additional types of places, such as those you pass on the way to something else (e.g. gas and bus stations). The study has verified the value of SMD for studies of urban social life but also identified a number of topics for further research. Additional sources of SMD should be identified to secure a just representation of Third Places across diverse social groups. Furthermore, new methods for effective cross validation of SMD with other types of data are crucial, including e.g. statistics, on-site observations and surveys/interviews, not least to identify Third Places that are not frequently present (or are misrepresented) in SMD.

scholarly journals Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

2018 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ruoxin Zhu ◽  
Diao Lin ◽  
Michael Jendryke ◽  
Chenyu Zuo ◽  
Linfang Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
City Management ◽  
Social Media Data ◽  
Social Events ◽  
Time Space ◽  
The Impact ◽  
Media Data

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 better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

scholarly journals Social Media Data Analysis in Urban E-Planning

2019 ◽  
pp. 636-651
Author(s):  
Pilvi Nummi
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Urban Planning ◽  
Social Media Data ◽  
Recent Case Study ◽  
City Structure ◽  
The City ◽  
Media Data ◽  
Opening Up

Computational social media data analysis (SMDA) is opening up new possibilities for participatory urban planning. The aim of this study is to analyse what kind of computational methods can be used to analyse social media data to inform urban planning. A descriptive literature review of recent case study articles reveal that in this context SMDA has been applied mainly to location based social media data, such as geo-tagged Tweets, photographs and check-in data. There were only a few studies concerning the use of non-place-based data. Based on this review SMDA can provide planners with local knowledge about people's opinions, experiences, feelings, behaviour, and about the city structure. However, integration of this knowledge in planning and decision-making has not been completely successful in any of the cases. By way of a conclusion, a planning-led categorization of the SMDA method's tools and analysis results is suggested.

We Know Where You Are

2016 ◽  
Vol 7 (2) ◽  
pp. 61-75 ◽  
Author(s):  
Xining Yang ◽  
Xinyue Ye ◽  
Daniel Z. Sui
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Space And Place ◽  
Spatial Data Analysis ◽  
Food Services ◽  
Point Of Interest ◽  
Geographic Context ◽  
Retail Shopping ◽  
The City ◽  
Media Data

The convergence of social media and GIS provides an opportunity to reconcile space-based GIS and place-based social media. For this purpose, the authors conduct an empirical study in Columbus, Ohio, aiming to enrich both the spatial and platial context of geo-tagged data, using location-based social media Foursquare checkins as an example. An exploratory analytical approached is used to enrich the geographic context of social media data in both space and place. Specifically, exploratory spatial data analysis and point of interest matching are applied to analyze about 50,000 checkins crawled from social media feeds. It is found that checkins tend to be spatially clustered near the center of the city. Popular places related to food, services, and retail shopping venues are more likely to be reported by social media users. The authors also conducted platial analysis of the top 25 popular place venues in the study area.

scholarly journals The Urban Nexus Approach for Analyzing Mobility in the Smart City: Towards the Identification of City Users Networking

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Federica Burini ◽  
Nicola Cortesi ◽  
Kevin Gotti ◽  
Giuseppe Psaila
Keyword(s):  
Social Media ◽  
Spatial Analysis ◽  
Mobile Apps ◽  
Interdisciplinary Approach ◽  
Geographical Information ◽  
Digital Environment ◽  
Social Media Data ◽  
Analysis Task ◽  
The City ◽  
Media Data

We present an interdisciplinary approach that makes possible to learn how citizens live in the city by the means of mobile social media data, that is, volunteered geographical information provided by the inhabitants through social media and mobile apps, by adopting a new reticular approach to spatial analysis. In particular, we present the general notions as background of our work, an investigation methodology to apply whenever such an analysis task must be performed, and a digital environment of tools and frameworks to support the methodology.

scholarly journals Social Media Data Analysis in Urban e-Planning

2017 ◽  
Vol 6 (4) ◽  
pp. 18-31 ◽  
Author(s):  
Pilvi Nummi
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Urban Planning ◽  
Social Media Data ◽  
Recent Case Study ◽  
City Structure ◽  
The City ◽  
Media Data ◽  
Opening Up

Computational social media data analysis (SMDA) is opening up new possibilities for participatory urban planning. The aim of this study is to analyse what kind of computational methods can be used to analyse social media data to inform urban planning. A descriptive literature review of recent case study articles reveal that in this context SMDA has been applied mainly to location based social media data, such as geo-tagged Tweets, photographs and check-in data. There were only a few studies concerning the use of non-place-based data. Based on this review SMDA can provide planners with local knowledge about people's opinions, experiences, feelings, behaviour, and about the city structure. However, integration of this knowledge in planning and decision-making has not been completely successful in any of the cases. By way of a conclusion, a planning-led categorization of the SMDA method's tools and analysis results is suggested.

scholarly journals The role of crowdsourcing and social media in crisis mapping: a case study of a wildfire reaching Croatian City of Split

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Marina Tavra ◽  
Ivan Racetin ◽  
Josip Peroš
Keyword(s):  
Social Media ◽  
Disaster Management ◽  
Forest Fire ◽  
Situational Awareness ◽  
Fire Spread ◽  
Multiple Sources ◽  
Social Media Data ◽  
External Data ◽  
The City ◽  
Media Data

AbstractAs climate change continues, wildfire outbreaks are becoming more frequent and more difficult to control. In mid-July 2017, a forest fire spread from the forests to the city of Split in Croatia. This unpredictable spread nearly caused emergency systems to collapse. Fortunately, a major tragedy was avoided due to the composure of the responsible services and the help of citizens. Citizens helped to extinguish the fire and provided a large amount of disaster-related information on various social media platforms in a timely manner. In this paper, we addressed the problem of identifying useful Volunteered Geographic Information (VGI) and georeferenced social media crowdsourcing data to improve situational awareness during the forest fire in the city of Split. In addition, social media data were combined with other external data sources (e.g., Sentinel-2 satellite imagery) and authoritative data to establish geographic relationships between wildfire phenomena and social media messages. This article highlights the importance of using georeferenced social media data and provides a different perspective for disaster management by filling gaps in authoritative data. Analyses from the presented reconstruction of events from multiple sources impact a better understanding of these types of events, knowledge sharing, and insights into crowdsourcing processes that can be incorporated into disaster management.

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