scholarly journals Evaluation of Plan Implementation in the Fast-Growing Chinese Mega-City: A Case of a Polycentric System in Hangzhou Core Built-Up Area

2020 ◽  
Vol 12 (5) ◽  
pp. 1723
Author(s):  
Liang Ding ◽  
Cheng Shi ◽  
Xinyi Niu
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Path Dependence ◽  
Spatial Relationship ◽  
Rapid Expansion ◽  
Plan Implementation ◽  
Full Sample ◽  
The Public ◽  
Mobile Positioning ◽  
Old City

Previous evaluations of plan implementation focused on whether the materiality construction was in accordance with the plan. Without proper data, it is difficult to confirm whether the planning goals have been achieved. In this study, two types of big data have been used—full sample built-environment data and mobile-positioning big data—to evaluate the results of the implementation of the polycentric system in master planning in the Hangzhou core built-up area. Using the full sample built-environment data, the evaluation of materiality construction will be more objective and accurate. Using the mobile-positioning big data, the evaluation of the planning goals can be realized; this was almost impossible in the past. However, two aspects are considered: whether daily public activities, such as employment and recreation, have been dispersed from the old city and subsequently re-gathered in multiple centers outside the old city, and whether the polycentric system aids in optimizing the spatial relationship between residence and public activity. The following conclusions were drawn. In terms of actual materiality construction, the results showed minimal discrepancy from the plan. Fifteen city-level public centers have been constructed at principal, secondary, and sub-secondary levels. However, the polycentric system failed to achieve the expected effects of the planning goals. First, the public centers contributed in the gathering of public activities; however, the level of gathering at the newly built-up public centers was considerably lower than that at traditional public centers. Second, the public centers failed to encourage people to visit the nearest blocks for daily public activities, mainly because of the path dependence on the traditional centers in the process of multi-centralization and over-rapid expansion of the city. Owing to this, residents did not have sufficient time to adjust to the spatial relationship between the residence and daily public activities.

Big Data and the Economics of TV Broadcasting. Where is the Public Value?

MedienJournal ◽  
2017 ◽  
Vol 41 (3) ◽  
pp. 15-28 ◽  
Author(s):  
Paul Clemens Murschetz
Keyword(s):  
Big Data ◽  
Public Value ◽  
The Public ◽  
Tv Broadcasting

Der vorliegende Beitrag untersucht Potenziale und Risiken von Big Data für das Leitmedium Fernsehen. Er nimmt dabei eine betont kritisch-normative Perspektive aus Sicht der Medienökonomie ein und analysiert diese anhand des Beispiels Konvergenzfernsehen. Eine der vielen Dimensionen von Big Data ist nämlich die Analyse des Nutzungsverhaltens einer Vielzahl von Konsumenten. Big Data-Dienste verwenden die Analyseergebnisse nicht nur dazu, individuelle Filmempfehlungen zu geben, sondern entscheiden vielmehr darüber, welche Inhalte überhaupt in das Portfolio eines Anbieters aufgenommen bzw. produziert werden. Auch wenn diese Dienste zu einer Optimierung von TV-Vermarktung führen, ist bis heute umstritten, inwiefern Big Data auch Mehrwert für Nutzer generiert. Auf der Sollseite stehen Überwachung, die Frageder Individualisierung und Rationalisierung des Konsums und generell die Kommodifizierung des Mediums.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

Closing Remarks

2019 ◽  
Vol 15 (S367) ◽  
pp. 515-517
Author(s):  
Debra Meloy Elmegreen
Keyword(s):  
Big Data ◽  
Education Research ◽  
Strategic Plan ◽  
Astronomy Education ◽  
The Public ◽  
The Many ◽  
Collaborative Efforts ◽  
Made In

AbstractThis symposium has highlighted key first steps made in addressing many goals of the IAU Strategic Plan for 2020–2030. Presentations on initiatives regarding education, with applications to development, outreach, equity, inclusion, big data, and heritage, are briefly summarized here. The many projects underway for the public, for students, for teachers, and for astronomers doing astronomy education research provide a foundation for future collaborative efforts, both regionally and globally.

scholarly journals Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization

2018 ◽  
Vol 10 (7) ◽  
pp. 2488 ◽  
Author(s):  
Hanliang Fu ◽  
Zhaoxing Li ◽  
Zhijian Liu ◽  
Zelin Wang
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Key Words ◽  
Water Use ◽  
Particle Swarm ◽  
Recycled Water ◽  
Swarm Optimization ◽  
The Public ◽  
Key Factor ◽  
Important Significance

The public’s acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public’s attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the “follow-be followed” and “forward-dialogue” behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public’s attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology.

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

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