scholarly journals Direct Passive Participation: Aiming for Accuracy and Citizen Safety in the Era of Big Data and the Smart City

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 336-348
Author(s):  
Ken Dooley
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Smart Cities ◽  
Data Driven ◽  
Healthy Lifestyles ◽  
Accurate Representation ◽  
The Public ◽  
Strong Indicator ◽  
Clear Link

The public services in our smart cities should enable our citizens to live sustainable, safe and healthy lifestyles and they should be designed inclusively. This article examines emerging data-driven methods of citizen engagement that promise to deliver effortless engagement and discusses their suitability for the task at hand. Passive participation views citizens as sensors and data mining is used to elicit meaning from the vast amounts of data generated in a city. Direct passive participation has a clear link between the creation and the use of the data whereas indirect passive participation does not require a link between creation and use. The Helsinki city bike share scheme has been selected as a case study to further explore the concept of direct passive participation. The case study shows that passive user generated data is a strong indicator of optimum city bike station sizing relative to the existing methods that are already in use. Indirect passive participation is an important area of development; however, it still needs to be developed further. In the meantime, direct passive participation can be one of the tools used to design inclusive services in a way that is safe and an accurate representation of the citizens’ needs.

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.

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

Implementation of Social Media Concepts for E-Government

2019 ◽  
pp. 1071-1091
Author(s):  
Raimundo Díaz-Díaz ◽  
Daniel Pérez-González
Keyword(s):  
Social Media ◽  
Citizen Participation ◽  
Smart Cities ◽  
City Council ◽  
Political Agenda ◽  
The Public ◽  
Partnership Model ◽  
The Government ◽  
Technological Platform

Some governments have proven social media's potential to generate value through co-creation and citizen participation, and municipalities are increasingly using these tools in order to become smart cities. Nevertheless, few public administrations have taken full advantage of all the possibilities offered by social media and, as a consequence, there is a shortage of case studies published on this topic. By analyzing the case study of the platform Santander City Brain, managed by the City Council of Santander (Spain), the current work contributes to broaden the knowledge on ambitious social media projects implemented by local public administrations for e-Government; therefore, this case can be useful for other public sector's initiatives. The case studied herein proves that virtual social media are effective tools for civil society, as it is able to set the political agenda and influence the framing of political discourse; however, they should not be considered as the main channel for citizen participation. Among the results obtained, the authors have found that several elements are required: the determination and involvement of the government, a designated community manager to follow up with the community of users, the secured privacy of its users, and a technological platform that is easy to use. Additionally, the Public Private Partnership model provides several advantages to the project, such as opening new sources of funding.

Smart and Connected Cities Through Technologies

2019 ◽  
pp. 1-24 ◽  
Author(s):  
Vrushali Gajanan Kadam ◽  
Sharvari Chandrashekhar Tamane ◽  
Vijender Kumar Solanki
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Smart City ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Crowd Sensing ◽  
The World ◽  
Important Challenge ◽  
Mobile Crowd

The world is growing and energy conservation is a very important challenge for the engineering domain. The emergence of smart cities is one possible solution for the same, as it claims that energy and resources are saved in the smart city infrastructure. This chapter is divided into five sections. Section 1 gives the past, present, and future of the living style. It gives the representation from rural, urban, to smart city. Section 2 gives the explanations of four pillars of big data, and through grid, a big data analysis is presented in the chapter. Section 3 started with the case study on smart grid. It comprises traffic congestion and their prospective solution through big data analytics. Section 4 starts from the mobile crowd sensing. It discusses a good elaboration on crowd sensing whereas Section 5 discusses the smart city approach. Important issues like lighting, parking, and traffic were taken into consideration.

Big Data IoT Analytics for Smart Cities With Cloud Computing Technique

2021 ◽  
pp. 104-126
Author(s):  
Chellaswamy C. ◽  
Sathiyamoorthi V.
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
Computing Technique ◽  
Strong Connectivity ◽  
The Public ◽  
Environmental Footprints ◽  
5G Network ◽  
Information And Communication ◽  
Data Analytic

Currently, cities are being reconstructed to smart cities that use an information and communication technology (ICT) framework alongside the internet of things (IoT) technology to increase efficiency and also share information with the public, helping to improve the quality of government services citizens' welfare. This large, diverse set of information called big data is obtained by ICT and IoT technologies from smart cities. This information does not have any meaning of its own but a high potential to make use of smart city services. Therefore, the information collected is mined and processed through use of big data analytic techniques. The environmental footprints in smart cities can be monitored and controlled with the help of ICT. Big data analytic techniques help enhance the functionalities of smart cities and the 4G and 5G network provides strong connectivity for professional devices.

scholarly journals Data-Driven Approach for Incident Management in a Smart City

2020 ◽  
Vol 10 (22) ◽  
pp. 8281
Author(s):  
Luís B. Elvas ◽  
Carolina F. Marreiros ◽  
João M. Dinis ◽  
Maria C. Pereira ◽  
Ana L. Martins ◽  
...  
Keyword(s):  
Data Mining ◽  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Data Driven ◽  
Incident Management ◽  
Cascading Effects ◽  
Data Mining Approach ◽  
Industry Standard ◽  
Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

scholarly journals Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Simon Elias Bibri ◽  
John Krogstie
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Real Time ◽  
Environmental Sustainability ◽  
Smart Cities ◽  
Data Driven ◽  
Urban Metabolism ◽  
Pollution Reduction ◽  
Illustrative Case ◽  
Sustainable Cities

AbstractThe IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

A Social Media Big Data-Based Disaster Assessment Framework for Typhoon-induced Flood: Case Study of Typhoon Lekima

2020 ◽  
Author(s):  
Jiting Tang ◽  
Saini Yang ◽  
Weiping Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Emergency Management ◽  
Text Classification ◽  
Text Clustering ◽  
Assessment Framework ◽  
The Public ◽  
Source Data ◽  
Disaster Assessment

<p>In 2019, the typhoon Lekima hit China, bringing strong winds and heavy rainfall to the nine provinces and municipalities on the northeastern coast of China. According to the Ministry of Emergency Management of the People’s Republic of China, Lekima caused 66 direct fatalities, 14 million affected people and is responsible for a direct economic loss in excess of 50 billion yuan. The current observation technologies include remote sensing and meteorological observation. But they have a long time cycle of data collection and a low interaction with disaster victims. Social media big data is a new data source for natural disaster research, which can provide technical reference for natural hazard analysis, risk assessment and emergency rescue information management.</p><p>We propose an assessment framework of social media data-based typhoon-induced flood assessment, which includes five parts: (1) <strong>Data acquisition.</strong> Obtain Sina Weibo text and some tag attributes based on keywords, time and location. (2) <strong>Spatiotemporal quantitative analysis.</strong> Collect the public concerns and trends from the perspective of words, time and space of different scales to judge the impact range of typhoon-induced flood. (3) <strong>Text classification and multi-source heterogeneous data fusion analysis.</strong> Build a hazard intensity and disaster text classification model by CNN (Convolutional Neural Networks), then integrate multi-source data including meteorological monitoring, population economy and disaster report for secondary evaluation and correction. (4) <strong>Text clustering and sub event mining.</strong> Extract subevents by BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) text clustering algorithms for automatic recognition of emergencies. (5) <strong>Emotional analysis and crisis management.</strong> Use time-space sequence model and four-quadrant analysis method to track the public negative emotions and find the potential crisis for emergency management.</p><p>This framework is validated with the case study of typhoon Lekima. The results show that social media big data makes up for the gap of data efficiency and spatial coverage. Our framework can assess the influence coverage, hazard intensity, disaster information and emergency needs, and it can reverse the disaster propagation process based on the spatiotemporal sequence. The assessment results after the secondary correction of multi-source data can be used in the actual system.</p><p>The proposed framework can be applied on a wide spatial scope and even full coverage; it is spatially efficient and can obtain feedback from affected areas and people almost immediately at the same time as a disaster occurs. Hence, it has a promising potential in large-scale and real-time disaster assessment.</p>

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