scholarly journals Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

2019 ◽  
Vol 11 (21) ◽  
pp. 6140
Author(s):  
Eunmi Lee ◽  
Sanghyuk Lee ◽  
Kyeong Soo Kim ◽  
Van Huy Pham ◽  
Jinbae Sul
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Disaster Management ◽  
Local Governments ◽  
Economic Status ◽  
Big Data Analytics ◽  
Urban Residents ◽  
City Governments ◽  
Use Of Data ◽  
The Government

Conventional studies on policy demand identification that are anchored in big data on urban residents are limited in that they mostly involve the top-down and government-oriented use of such data. It restricts treatment to specific issues (e.g., public safety and disaster management), even from the beginning of data collection. Scant research has emphasized the general use of data on civil complaints—which are independent of areas of application—in the examination of sustainable cities. In this work, we hypothesized that the analyses of civil complaint data and big data effectively identify what urban residents want from local governments with respect to a broad range of issues. We investigated policy demand using big data analytics in examining unstructured civil complaint data on safety and disaster management. We extracted major keywords associated with safety and disaster management via text mining to inquire into the relevant matters raised in the civil complaints. We also conducted a panel analysis to explore the effects exerted by the characteristics of 16 locally governed towns on residents’ policy demands regarding safety and disaster management-related complaints. The results suggest that policy needs vary according to local sociocultural characteristics such as the age, gender, and economic status of residents as well as the proportion of migrants in these localities, so that, city governments need to provide customized services. This research contributes to extend with more advanced big data analysis techniques such as text mining, and data fusion and integration. The technique allows the government to identify more specifically citizens’ policy needs.

scholarly journals The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 54595-54614 ◽  
Author(s):  
Syed Attique Shah ◽  
Dursun Zafer Seker ◽  
Sufian Hameed ◽  
Dirk Draheim
Keyword(s):  
Big Data ◽  
Disaster Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Recent Advances

Big Data Analytics and Social Media in Disaster Management

2018 ◽  
pp. 287-294 ◽  
Author(s):  
Joice K. Joseph ◽  
Karunakaran Akhil Dev ◽  
A.P. Pradeepkumar ◽  
Mahesh Mohan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Disaster Management ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Use of Big Data to Improve Environmental Sustainability in Developing Countries

2017 ◽  
Vol 12 (11) ◽  
pp. 249 ◽  
Author(s):  
Maged Adel Abdo Mukred ◽  
Zheng Jianguo
Keyword(s):  
Big Data ◽  
Multinational Corporations ◽  
Environmental Sustainability ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Government Sector ◽  
Wide Range ◽  
Private And Public ◽  
The Government ◽  
Set Up

Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

scholarly journals Text Mining and Big Data Analytics for Retrospective Analysis of Clinical Texts from Outpatient Care

2015 ◽  
Vol 15 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Svetla Boytcheva ◽  
Galia Angelova ◽  
Zhivko Angelov ◽  
Dimitar Tcharaktchiev
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Text Mining ◽  
Retrospective Analysis ◽  
Outpatient Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Free Text ◽  
Arterial Blood ◽  
And Control

Abstract This paper presents the results of an on-going research project for knowledge extraction from large corpora of clinical narratives in Bulgarian language, approximately 100 million of outpatient care notes. Entities with numerical values are mined in the free text and the extracted information is stored in a structured format. The Algorithms for retrospective analyses and big data analytics are applied for studying the treatment and evaluating the diabetes compensation and control of arterial blood pressure.

The Strategic Value of Big Data Analytics in Health Care Policy-Making

2018 ◽  
Vol 14 (3) ◽  
pp. 20-33 ◽  
Author(s):  
Hamed M. Zolbanin ◽  
Dursun Delen ◽  
Sushil K Sharma
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Health Care Policy ◽  
Big Data Analytics ◽  
Advantages And Disadvantages ◽  
Strategic Value ◽  
Before And After ◽  
The Government ◽  
New Policies

This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymakers to obtain a better understanding of loopholes and to propose more effective policies in prospective endeavors.

scholarly journals The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning

2019 ◽  
Vol 15 (2) ◽  
pp. 1086
Author(s):  
M. Ali ◽  
T. K. Sheng ◽  
K. M. Yusof ◽  
M. R. Suhaili ◽  
N. E. Ghazali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Traffic Congestion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Active Role ◽  
Machine Learning Algorithms ◽  
Asia Pacific ◽  
The Government

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.

Social media in aid of post disaster management

2020 ◽  
Vol 14 (2) ◽  
pp. 237-260 ◽  
Author(s):  
Ajree Ducol Malawani ◽  
Achmad Nurmandi ◽  
Eko Priyo Purnomo ◽  
Taufiqur Rahman
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Disaster Management ◽  
Practical Implication ◽  
Legal Framework ◽  
Topic Modelling ◽  
Content Type ◽  
The Government ◽  
Post Disaster

Purpose This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year. Design/methodology/approach Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed. Finding Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking. Research limitation This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck. Practical implication For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of. Originality/value Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.

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