Exploring Patterns of Socio-spatial Interaction in the Public Spaces of City through Big Data

[EN] Research on socio-spatial aspect of cities has never been so vibrant and exciting. The form of urban life is changing and evolving with new advancements in communication and technology. Digital communication and social media has reshaped the way people as the actors of society interact with each other and with the network of city. New social networks and widespread of mobile devises can be used to create and reinforce existing social ties. Mobile devises also change the role of citizens from consumers into producers of data; they are the new reporters, photographers, videographers of everyday life. This production creates large quantities of data known as the “Big Data”. Big data has opened up many doors for researchers to investigate new aspects of cities. This paper aims to explore how people access urban public spaces through social media by taking the parameter of distance and physical proximity into account. We tried to investigate if different levels of accessibility effects the way people interact with space through social media. Through this process the study explored different socio-spatial patterns in the city that are being affected by social media. The research data was collect in two layers of Nicosia in Northern Cyprus: first, the geo-tagged social media data was collected from the target group, and it was located on the map. Twitter as a microblogging medium was selected for data collection due to its public nature, geo-tagged abilities, and manageable short content. Second, degrees of accessibility in local and global scale were calculated using Space Syntax. The data was analyzed using regression analysis, scatter plot, and outlier detention. The result shows various patterns in correlation of interactions between society and space; it illustrates the importance of exploring the outliers when reading big data on the city. The result shows clear importance of local accessibility even when social media is the effective variable.Iranmanesh, A.; Alpar Atun, R. (2018). Exploring Patterns of Socio-spatial Interaction in the Public Spaces of City through Big Data. En 24th ISUF International Conference. Book of Papers. Editorial Universitat Politècnica de València. 1127-1135. https://doi.org/10.4995/ISUF2017.2017.5254OCS1127113

(How) Does Data-based Music Discovery Work?

This paper analyses a new type of business operations that mediate the production and consumption of music. Online environment has largely abolished constraints on the variety of music that can be economically distributed, but, at the same time, it reveals another problem. How do people learn what music items do they want to listen to? In the music industry, the product space consists of thousands of artists, songs and albums, and is expanding rapidly. More effective forms of music discovery could therefore create considerable new value by allowing people to listen to music that better matches their taste. We analyse data from Last.fm music discovery service that deploys a collaborative filtering recommender system and social media features to aid music discovery. The analysis finds evidence that the new form of music discovery is valuable to consumers, yet it is relatively less important than an opportunity to listen to music for free. The findings lead us to discuss how the nature of analytical problem and product space, consumer taste, and social media features shape the potential value of created by big data

The influence of big data on monitoring the factual quality of digital media in Southern Africa.

Masters Degree. University of KwaZulu-Natal.This research study will explore how big data can drive innovation in response to dynamic change and aid society in establishing an advantage when fact-checking/monitoring new media and dealing with false information. The study emphasises that big data might answer questions and offer insights society never had access to before. In the current news media environment, the services that enable the sharing and production of large amounts of data are not sufficient to combat increasing fake news, ongoing public mistrust, and false, partisan media content for capital gains from gaining more influence in society. There is an urgent need for intervention, which big data innovation can provide. There are, however, some myths regarding the use of big data that need to be dispelled, such as the idea that an analysis of the data will ensure transparency and reliable content distribution from the developers of big data systems to the audience consuming the data. Innovating and obtaining an advantage from data is more complex than just collecting lots of data; a look at the impact big data will have on a society is vital in leveraging big data. The study explores this notion by looking at the Digital Data Genesis Capability Model. The model guides the structure and how the case study will be conducted in the media fact-checking sector. The development of the big data initiative is built on fundamental expertise. According to the findings, highly skilled employees with knowledge of both proprietary and open-source tools are essential in the development of big data systems. Furthermore, there is a high level of compatibility with the existing web environment standard and the tools being used when deploying a big data system in the web. As a result, development of a big data initiative by a technology focused organisation is only limited by their ability to implement an effective big data workflow. However, this requires detailed planning, cloud computing for hardware; software; outsourced third party services; the work on data structure built in-house; and the use of docker containers that enable mobility in the development process and the adoption of new technology when implementing the searching and querying of large datasets and streams. There was a deviation from the existing model noted. The context of the study exposed that it is possible to implement big data initiatives among more than one company as a partnership, if the companies share some business traits or the same philosophy: thus, changing the dynamic of routines and responsibility in the existing landscape

Erich Fromm and the Critical Theory of Communication

Erich Fromm (1900-1980) was a Marxist psychoanalyst, philosopher and socialist humanist. This paper asks: How can Fromm’s critical theory of communication be used and updated to provide a critical perspective in the age of digital and communicative capitalism? In order to provide an answer, the article discusses elements from Fromm’s work that allow us to better understand the human communication process. The focus is on communication (section 2), ideology (section 3), and technology (section 4). Fromm’s approach can inform a critical theory of communication in multiple respects: His notion of the social character allows to underpin such a theory with foundations from critical psychology. Fromm’s distinction between the authoritarian and the humanistic character can be used for discerning among authoritarian and humanistic communication. Fromm’s work can also inform ideology critique: The ideology of having shapes life, thought, language and social action in capitalism. In capitalism, technology (including computing) is fetishized and the logic of quantification shapes social relations. Fromm’s quest for humanist technology and participatory computing can inform contemporary debates about digital capitalism and its alternatives

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

\u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting

Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure