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 Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

scholarly journals Correction: Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0209722
Author(s):  
Rene Westerholt ◽  
Enrico Steiger ◽  
Bernd Resch ◽  
Alexander Zipf
Keyword(s):  
Social Media ◽  
Spatial Analysis ◽  
Social Media Data ◽  
Media Data

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 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 Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162360 ◽  
Author(s):  
Rene Westerholt ◽  
Enrico Steiger ◽  
Bernd Resch ◽  
Alexander Zipf
Keyword(s):  
Social Media ◽  
Spatial Analysis ◽  
Social Media Data ◽  
Media Data

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 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.

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emilio Pindado ◽  
Ramo Barrena
Keyword(s):  
Social Media ◽  
Clustering Algorithm ◽  
Social Representations ◽  
Local Level ◽  
Geographical Information ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Food Trends ◽  
Media Data

PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

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