Datafied and Divided: Techno–Dimensions of Inequality in American Cities

2017 ◽  
Vol 16 (1) ◽  
pp. 20-24 ◽  
Author(s):  
Monica M. Brannon
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Data Collection ◽  
Smart City ◽  
Urban Landscape ◽  
Classification Systems ◽  
Data Driven ◽  
Predictive Policing ◽  
American Cities

This essay interrogates the effects that “big data” have on constructing space and subjects that reproduce inequality in the urban landscape. By comparing two different data–driven projects within the same city, data collection and collation is seen to contribute to existing divides along racial and class lines. As urban sociologists seek to capitalize on the vast quantity of data generated by automated devices and networked computation, they must first interrogate and deconstruct the hidden protocols and ideologies that define algorithmic classification systems. Predictive policing and “smart city” economic development operate to construct subjects tied to spatial markers encoded in databases. Therefore, technological structures must be theorized alongside racial and class structures as entrenching historical inequities.

scholarly journals Simulation and Formation Mechanisms of Urban Landscape Design Based on Discrete Dynamic Models Driven by Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Ke Cao ◽  
Jing Xiao ◽  
Yan Wu
Keyword(s):  
Big Data ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Urban Landscape ◽  
Landscape Design ◽  
Data Driven ◽  
Interactive Methods ◽  
Formation Mechanisms ◽  
Discrete Dynamic ◽  
Discrete Dynamic Model

Urban landscape design as a contemporary art embodies postmodernist philosophical thinking, aesthetic thinking, and breaking the traditional concept of art, and it is a new way of creating and presenting art. Big data technology characterized by large scale, speed, variety, value, and uncertainty of data is used to achieve urban landscape design. In this article, during the research process, we strive to raise the revelation of the design layer rather than the brand new level of cross-fertilization and interaction between big data-driven discrete dynamic model and urban landscape design; we also reveal how the benefits of promoting urban development and harmonious life are achieved in the interactive expression of the urban landscape after the application of the big data-driven discrete dynamic model, which provides designers and related professionals with more detailed and novel design ideas at the theoretical level and makes the theory of big data-driven discrete dynamic models in landscape design interactive methods more enriched. Finally, this article puts forward its thinking and outlook on the design of the big data-driven discrete dynamic model in the interactivity of urban landscape design, hoping that artists will strengthen its functional and material design elements when creating performance. Moreover, more design means of emerging technologies of modern science and technology should be integrated so that modern urban landscape can achieve ordinary and uncommon benefits and promote the rapid development of the big data-driven discrete dynamic model in urban landscape design development.

Big Data Driven Smart City: Way to Smart City Governance

2020 ◽  
Author(s):  
Md Nazirul Islam Sarker ◽  
Most Nilufa Khatun ◽  
GM Monirul Alam ◽  
Md Shahidul Islam
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Driven ◽  
City Governance

scholarly journals Construction of Smart City Street Landscape Big Data-Driven Intelligent System Based on Industry 4.0

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhe Li ◽  
YuKun He ◽  
XinYi Lu ◽  
HengYi Zhao ◽  
Zheng Zhou ◽  
...  
Keyword(s):  
Big Data ◽  
Smart City ◽  
Industry 4.0 ◽  
Engineering Management ◽  
Data Driven ◽  
Intelligent Decision Support System ◽  
Urban Street ◽  
Intelligent Decision ◽  
City Construction ◽  
Intelligent Decision Support

With the application of engineering management in smart city construction under Industry 4.0, the intelligent design of urban street landscape has attracted extensive attention. Affected by the low intelligent level of traditional landscape design, the existing urban landscape composite system has difficulty in meeting the needs of smart city construction. Therefore, this paper proposes the construction of street landscape big data-driven intelligent decision support system based on Industry 4.0. Based on the complex network theory, this paper analyzes the structure, links, nodes, driving forces, and functional requirements of urban street landscape and then puts forward the construction content and implementation method of urban street landscape intelligent decision support system. The system consists of four aspects: intelligent infrastructure, service, protection and maintenance, and management and evaluation system. Its implementation not only reflects the cooperation and effective application of intelligent technology in each stage of street landscape construction, but also provides reference for the application of engineering management in other fields under Industry 4.0.

scholarly journals The “black box” at work

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172096618
Author(s):  
Ifeoma Ajunwa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Collection ◽  
Equal Opportunity ◽  
Personal Information ◽  
Wearable Technology ◽  
Black Box ◽  
Data Driven ◽  
Mission Creep ◽  
Rawlsian Justice

An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to algorithmic processes of evaluations which will make authoritative claims as to the workers’ productivity. Furthermore, in and out of the workplace, the worker is governed by an invisible data-created leash deploying wearable technology to collect intimate worker data. At all stages, the worker is confronted with a lack of transparency, accountability, or explanation as to the inner workings or even the logic of the “black box” at work. This data revolution of the workplace is alarming for several reasons: (1) the “black box at work” not only serves to conceal disparities in hiring, but could also allow for a level of “data-laundering” that beggars any notion of equal opportunity in employment and (2) there exists, the danger of a “mission creep” attitude to data collection that allows for pervasive surveillance, contributing to the erosion of both the personhood and autonomy of workers. Thus, the “black box at work” not only enables worker domination in the workplace, it deprives the worker of Rawlsian justice.

scholarly journals Framing Policy Visions of Big Data in Emerging States

2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Laura Mahrenbach ◽  
Katja Mayer
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Data Driven ◽  
Technological Advances ◽  
Nous Montrons ◽  
Governance Analysis ◽  
Societal Changes ◽  
Développement Économique ◽  
The Government ◽  
India And China

Background  Emerging states, such as Brazil, India, and China (the BICs), have big plans for big data and digitalization. Research has identified distinct policy visions regarding how technological advances can facilitate economic development and improve governance. Analysis  This article examines how BIC governments frame data-driven ambitions across the diverse issue areas in which governments plan to use big data, as well as how they frame the role(s) of the government and citizens in the era of big data. Conclusion and implications  We find clear differences in discussions of big data across the BICs and across issue areas. Moreover, we show the societal changes that governments seek to effect using big data vary greatly in scope, with Brazil and India seeking more fundamental changes than China.Contexte  Les États émergents, tels que le Brésil, l’Inde et la Chine (les BIC), ont de grands projets pour Big Data et la numérisation. La recherche a identifié des visions politiques distinctes concernant la façon dont les progrès technologiques peuvent faciliter le développement économique et améliorer la gouvernance.Analyse  Cet article examine la manière dont les gouvernements BIC définissent les ambitions basées sur les données dans les divers domaines problématiques dans lesquels les gouvernements envisagent d’utiliser big data, ainsi que la façon dont ils définissent le ou les rôles du gouvernement et des citoyens à l’ère des big data.Conclusion et implications  Nous constatons des différences claires dans les discussions sur big data entre les BIC et entre les domaines problématiques. De plus, nous montrons que les changements sociétaux que les gouvernements cherchent à effectuer en utilisant big data varient considérablement, le Brésil et l’Inde recherchant des changements plus fondamentaux que la Chine.

scholarly journals Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs

2021 ◽  
Vol 10 (8) ◽  
pp. 561
Author(s):  
Fan Xue ◽  
Xiao Li ◽  
Weisheng Lu ◽  
Christopher J. Webster ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Big Data ◽  
Real Estate ◽  
Smart City ◽  
Urban Geography ◽  
Mobile Sensing ◽  
Data Driven ◽  
Traffic Lights ◽  
Street View ◽  
Computational Semantic ◽  
Road Crossing

Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named vectors of uncountable and countable objects for clustering and analysis (VUCCA) for processing 530,000 TSV photographs of Hong Kong Island. First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable objects such as vegetation, sky, paved pedestrian path, and guardrail and countable objects such as cars, trucks, pedestrians, city animals, and traffic lights. Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand. Third, pedestrians are semantically indexed using relations and activities (e.g., walking behind a guardrail, road-crossing, carrying a backpack, or walking a pet) for queries of unstructured photographic instances or natural language clauses. The experiment results showed that the pedestrians detected in the TSV photographs were successfully clustered into meaningful groups and indexed by the semantic vectors. The presented VUCCA can enrich eye-level urban features into computational semantic vectors for pedestrians to enable smart city research in urban geography, urban planning, real estate, transportation, conservation, and other disciplines.

scholarly journals Perils of data-driven equity: Safety-net care and big data’s elusive grasp on health inequality

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172092809
Author(s):  
Taylor M Cruz
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Education Reform ◽  
Large Scale ◽  
Time Window ◽  
Empirical Work ◽  
Safety Net ◽  
Data Driven ◽  
Data Systems ◽  
Social Significance

Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites of care expected to transform. In this article, I present a case study on one such initiative, focusing on a large public safety-net health system’s initiation of sexual orientation and gender identity (SOGI) data collection within the clinical setting. Drawing from ethnographic fieldwork and in-depth interviews with providers, staff, and administrators, I highlight three main challenges that elude big data’s grasp on inequality: (1) provider and staff’s limited understanding of the social significance of data collection; (2) patient perception of the cultural insensitivity of data items; and (3) clinic need to balance data requests with competing priorities within a constrained time window. These issues reflect structural challenges within safety-net care that big data alone are unable to address in advancing social justice. I discuss these findings by considering the present data-driven strategy alongside two complementary courses of action: diversifying the health professions workforce and clinical education reform. To truly advance justice, we need more than “just data”: we need to confront the fundamental conditions of social inequality.

A Collaborative Approach of IoT, Big Data, and Smart City

2019 ◽  
pp. 25-37 ◽  
Author(s):  
K. Jayashree ◽  
R. Abirami ◽  
R. Babu
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Internet Of Things ◽  
Smart City ◽  
Life Quality ◽  
Research Direction ◽  
Future Research ◽  
Collaborative Approach ◽  
Future Research Direction ◽  
Sensor Devices

During the last two decades, a number of new nations emerged and played their intense role in changing human lifestyle. The growing demand for smart city and big data stimulates innovation, and the development of new smart applications is becoming important. Internet of things comprises billions of devices, people, and services, and entitles each to connect through sensor devices. The economic development of a city leads to better life quality and improved citizen services. Thus, this chapter discusses the background of big data, IoT, and smart city. It also discusses the collaborative approach of all the above. The various related work and future research direction for implementing smart city with the concept of big data and IoT would be addressed in this chapter.

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