scholarly journals Data Visualization of European Regional Operational Programmes: Unleashing the Informative Potential of Open Data for Performance Assessment

2018 ◽  
Author(s):  
Emanuele Frontoni ◽  
Roberto Palloni
Keyword(s):  
Economic Growth ◽  
Data Mining ◽  
Performance Assessment ◽  
Data Visualization ◽  
Open Data ◽  
Cohesion Policy ◽  
Investment Funds ◽  
Data Portal ◽  
European Regional ◽  
New Jobs

The implementation of the European Cohesion Policy aiming at fostering regions competitiveness, economic growth and creation of new jobs is documented over the period 2014–2020 in the publicly available Open Data Portal for the European Structural and Investment funds. On the base of this source, this paper aims at describing the process of data mining and visualization for information production on regional programmes performace in achieving effective expenditure of resouces.

Promote Good Governance in Public Financial: The Practice of Local Budget (APBD) Transparency Through Open Data Jakarta in Jakarta Provincial Government

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Dodi Faedlulloh ◽  
Fetty Wiyani
Keyword(s):  
Public Finance ◽  
Good Governance ◽  
Open Data ◽  
Public Trust ◽  
Provincial Government ◽  
Financial Governance ◽  
One Stop ◽  
Data Portal ◽  
Data Transparency ◽  
Local Budget

This paper aimed to explain public financial governance based on good governance implementation in Jakarta Provincial Government. This paper specifically discussed towards transparancy implementation of local budget (APBD) through open data portal that publishes budget data to public. In general, financial transparency through open data has met Transparency 2.0 standards, namely the existence of encompassing, one-stop, one-click budget accountability and accessibility. But there are indeed some shortcomings that are still a concern in order to continue to maintain commitment to the principle of transparency, namely by updating data through consistent data visualization.Transparency of public finance needs to continue to be developed and improved through various innovations to maintain public trust in the government.Keywords: Public Finance, Open Data, Transparency

Data Visualization Indicator Disease (Malaria, Dengue Fever, and Measles) in The Year 2012-2015

2017 ◽  
Vol 4 (2) ◽  
pp. 87-93
Author(s):  
Immanuel Luigi Da Gusta ◽  
Johan Setiawan
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Data Visualization ◽  
Health Sector ◽  
Medical Personnel ◽  
Database Systems ◽  
Process Time ◽  
Acceptance Test ◽  
Visual Data Mining ◽  
The Government

The aim of this paper are: to create a data visualization that can assist the Government in evaluating the return on the development of health facilities in the region and province area in term of human resources for medical personnel, to help community knowing the amount of distribution of hospitals with medical personnel in the regional area and to map disease indicator in Indonesia. The issue of tackling health is still a major problem that is not resolved by the Government of Indonesia. There are three big things that become problems in the health sector in Indonesia: infrastructure has not been evenly distributed and less adequate, the lack of human resources professional health workforce, there is still a high number of deaths in the outbreak of infectious diseases. Data for the research are taken from BPS, in total 10,600 records after the Extract, Transform and Loading process. Time needed to convert several publications from PDF, to convert to CSV and then to MS Excel 3 weeks. The method used is Eight-step Data Visualization and Data Mining methodology. Tableau is chosen as a tool to create the data visualization because it can combine each dasboard inside a story interactive, easier for the user to analyze the data. The result is a story with 3 dashboards that can fulfill the requirement from BPS staff and has been tested with a satisfied result in the UAT (User Acceptance Test). Index Terms—Dashboard, data visualization, disease, malaria, Tableau REFERENCES [1] S. Arianto, Understanding of learning and others, 2008. [2] Rainer; Turban, Introduction to Information Systems, Danvers: John Wiley & Sons, Inc, 2007. [3] V. Friedman, Data Visualization Infographics, Monday Inspirition, 2008. [4] D. A. Keim, "Information Visualization and Visual Data Mining," IEEE Transactions on Visualization and Computer Graphics 8.1, pp. 1-8, 2002. [5] Connolly and Begg, Database Systems, Boston: Pearson Education, Inc, 2010. [6] E. Hariyanti, "Pengembangan Metodologi Pembangunan Information Dashboard Untuk Monitoring kinerja Organisasi," Konferensi dan Temu Nasional Teknologi Informasi dan Komunikasi untuk Indonesia, p. 1, 2008. [7] S. Darudiato, "Perancangan Data Warehouse Penjualan Untuk Mendukung Kebutuhan Informasi Eksekutif Cemerlang Skin Care," Seminar Nasional Informatika 2010, pp. E-353, 2010.

Emerging Industrial Revolution: Symbiosis of Industry 4.0 and Circular Economy: The Role of Universities

2020 ◽  
Vol 25 (3) ◽  
pp. 505-525 ◽  
Author(s):  
Seeram Ramakrishna ◽  
Alfred Ngowi ◽  
Henk De Jager ◽  
Bankole O. Awuzie
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Technologies ◽  
Production Systems ◽  
Fourth Industrial Revolution ◽  
New Jobs

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

scholarly journals Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

2021 ◽  
Vol 13 (4) ◽  
pp. 2178
Author(s):  
Songkorn Siangsuebchart ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Surachet Pravinvongvuth
Keyword(s):  
Performance Assessment ◽  
Public Transport ◽  
Human Mobility ◽  
Open Data ◽  
Capital City ◽  
Metropolitan Region ◽  
Traffic Information ◽  
Mobility Patterns ◽  
Transport Planning ◽  
Java Program

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

scholarly journals Inequalities in the Exercise and Continuity in Building Engineering in Spain. Factor Analysis Including Gender Perspective

2021 ◽  
Vol 13 (10) ◽  
pp. 5514
Author(s):  
Irantzu Recalde-Esnoz ◽  
Daniel Ferrández ◽  
Carlos Morón ◽  
Guadalupe Dorado
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Gender Inequality ◽  
Sustainable Development Goals ◽  
The Sustainable Development ◽  
Agenda 2030 ◽  
Technical Architecture ◽  
Development Goals ◽  
Building Engineering ◽  
New Jobs

The building sector is one of the most relevant at world level in view of the percentage of gross domestic product (GDP) concerned, as well as the number of new jobs created. Nevertheless, it is a completely male-dominated industry. Different institutions and organisms, such as the Agenda 2030 and the Sustainable Development Goals, struggle to reduce gender inequality in different environments, including the working one. Aligned with these goals, this study provides the data exploited from the first survey regarding gender inequality within the professionals of the building engineering field in the Spanish population as a whole. This survey was developed in 2018 by the Spanish General Council of Technical Architecture and it was sent to its members. The sample involved 1353 cases. For this data mining, bivariate analyses were conducted in order to subsequently carry out a factor analysis and the socio–demographic composition of the dimensions found. Results exposed statistically meaningful differences in the eyes of women and men about those factors which facilitate practice and continuity in the profession. The most relevant conclusions drawn from the factor analysis reflect the existence of three factors: (1) work competences, (2) social capital and (3) physical appearance and being a man, dimensions in which women and men’s opinion was unevenly distributed.

scholarly journals VISUALIZATION OF E-COMMERCE DATA USING AUGMENTED REALITY

2020 ◽  
Vol 5 (19) ◽  
pp. 104-122
Author(s):  
Azzan Amin ◽  
Haslina Arshad ◽  
Ummul Hanan Mohamad
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Data Visualization ◽  
Data Science ◽  
Moving Average ◽  
Open Data ◽  
Visual Presentation ◽  
Depth Dimension ◽  
2D Data ◽  
Visualization Application

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).

scholarly journals Analyzing University of Virginia Health publications using open data, Python, and Streamlit

2021 ◽  
Vol 109 (4) ◽  
Author(s):  
Anson Parker ◽  
Abbey Heflin ◽  
Lucy Carr Jones
Keyword(s):  
Open Access ◽  
Data Visualization ◽  
Web Of Science ◽  
Open Data ◽  
Health Sciences ◽  
Open Access Publishing ◽  
University Of Virginia ◽  
Data Set ◽  
Novel Approach ◽  
Rest Api

As part of a larger project to understand the publishing choices of UVA Health authors and support open access publishing, a team from the Claude Moore Health Sciences Library analyzed an open data set from Europe PMC, which includes metadata from PubMed records. We used the Europe PMC REST API to search for articles published in 2017–2020 with “University of Virginia” in the author affiliation field. Subsequently, we parsed the JSON metadata in Python and used Streamlit to create a data visualization from our public GitHub repository. At present, this shows the relative proportions of open access versus subscription-only articles published by UVA Health authors. Although subscription services like Web of Science, Scopus, and Dimensions allow users to do similar analyses, we believe this is a novel approach to doing this type of bibliometric research with open data and open source tools.  

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