Open Government Data Research Articles

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

Privacy Risks in the Storytelling of Open Government Data: A Study from the Perspective of User Cognitive Reasoning

ABSTRACTIn recent years, there has been an increase in the openness of government data. Data storytelling has emerged as a means to enhance communication by transforming mundane data into easily understandable narratives. However, it is crucial to pay attention to privacy risks. This paper delves into the concept of storytelling government data, integrating cognitive reasoning and the S‐O‐R model, and evaluates privacy risks. Through comparative experiments and interviews, this study examines how users may infer sensitive information. The research reveals that storytelling data has a positive impact on users' cognition. De‐storytelling, on the other hand, can mitigate privacy risks, reduce information relevance, simplify narratives, and strike a balance between user experience and privacy. Building on this research, the paper proposes a strategy to address privacy risks, recommends strengthening privacy protection awareness from the perspectives of both users and platforms, optimizing data presentation methods, reducing the risk of sensitive information leakage, and ensuring the convenience and security of government data disclosure.

Putting People First in Data Quality: Feminist Data Ethics for Open Government Datasets

ABSTRACTOpen government information systems offer great potential for advancing civic life and democracy, but they also reflect and reinforce the biases and systematic inequalities faced by members of socially marginalized groups. We present results from a critical data modeling project that uses a data quality framework to examine open datasets published by police departments in order to understand how data modeling choices shape the social impact of these datasets. Using an arrest record dataset published by the Los Angeles Police Department as a case study, we present we present results detailing the representation of racial data and the presence of children in the dataset. We argue that current data quality frameworks for open government data are insufficient for critical data studies due to an orientation around institutional and computational interests. Incorporating feminist data ethics into data quality analysis provides an approach to data quality that centers people and communities. We propose a definition for data quality of open government datasets based on an ethics of care that centers the needs of vulnerable populations and accountability of institutions toward their communities.

Unleashing the Value of Open Government Data: Examining the Influence of Platform and Task

Unleashing the Value of Open Government Data: Examining the Influence of Platform and Task

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

Institutional Dimensions in Open Government Data: A Deep Dive Into Indonesia’s Satu Data Initiative and Its Implications for Developing Countries

Institutional factors such as data standardization, data interoperability, and the ability to include other stakeholders (e.g., civil society) in ecosystems are now understood as crucial considerations in Open Government Data (OGD) implementation. However, most of the current understanding of institutional factors linked to OGD growth has evolved around highly digitalized and developed countries. This study aims to address this gap by investigating open data implementation in Indonesia, a developing country that has been an early advocate of OGD in the "Asia Pacific". In 2014, Indonesia implemented Satu Data Indonesia (One Data Indonesia) as a national initiative to promote OGD objectives. We employ institutional dimensions as the central theoretical lens to unpack the successes and impediments to Satu Data Indonesia’s development. This research dataset is built on 16 expert interviews from government (n = 14) and private (n = 2) organizations that directly contribute to the growth of Satu Data Indonesia, qualitatively triangulated with documentary analysis of key policy and regulatory documents. These new data provide important insights into the complexity and challenges of nationwide OGD implementation and data-sharing. Our findings show that despite its being an early advocate, Indonesia’s OGD initiative is still in the early stages of development, impeded by several policy and administrative bottlenecks.

Network relations among open government data stakeholders: a structural social capital and ERGM analysis

Network relations among open government data stakeholders: a structural social capital and ERGM analysis

Historical Performance Gap, Readiness of Government, and Open Data Quality: Transnational Empirical Analysis Based on Performance Feedback Theory

Open government data has become an important movement for government administrations in numerous countries. Previous studies have paid less attention to the complex relationship among the historical performance gap, the readiness of government (including policy and action dimensions), and open data quality. Therefore, on the basis of performance feedback theory, a theoretical model has been developed and tested to analyze the impact of the historical performance gap on the readiness of government and open data quality. The model is empirically tested by the partial least squares method using sample data from 115 countries. The results show that the historical performance gap could indirectly contribute to a country's open data quality by promoting the readiness of government. In addition, the impact of this gap on the readiness of government and open data quality shows a U-shaped curve. In other words, a greater change in the effect of open data utilization in a country in the previous year entails a more significant promotion of readiness of government and open data quality in the next year. These findings drive governments to pay more attention to performance evaluation in open data and improve the readiness of government to improve their respective country's open data quality.

Modelado de datos gubernamentales abiertos para mapeo de redes de escuelas similares en la Microrregión de Salvador - Bahía.

Introduction: The aim of this study is to represent similar schools located in the urban locality of the Microregion of Salvador - Bahia, to identify possible paths for collaboration among them, using the Technical Note "Profile of Similar Schools" 2014 from INEP as a basis. Methodology: Social network analysis and modeling from open governmental data were employed to investigate the interconnection between schools, considering their similar characteristics and educational indicators. Results: The open data includes information on school performance and socioeconomic levels, processed and analyzed through algorithms and the development of specific software. The study resulted in the web visualization of a multidimensional network, with several possibilities for analysis, showing that, out of the 408 schools, each one establishes connections with at least 70 other schools on average. Conclusion: This interconnection suggests the trend of schools to share similar features, offering potential to promote collaboration and sharing of resources and successful educational practices, contributing to better governance in education in the region.

Open government data initiatives as agents of digital transformation in the public sector: Exploring the extent of use among early adopters

Open government data initiatives are important agents of public sector digital transformation and understanding how agencies design, implement, and evaluate strategies for these initiatives is paramount to their ongoing success. However, a challenging and little understood aspect of open government data initiatives is precisely how open data users engage with and use these vital resources. This study examines the extent of open dataset use among early adopters using a cross-sectional analysis of web analytic behavioral data. We quantify early users' extent of use of open datasets from Health Data NY, the first state-level open health data platform. Results of a PLS-SEM analysis provide new empirical evidence that prior, or initial levels of open dataset use positively influence the later extent of open dataset use. Further, prior open government data use influences not only the later variety of open data activities, but also their depth of sophistication. Implications for practice include open data strategies to facilitate deeper, more extensive use of open dataset resources that lead to increased value creation, and ultimately, more effective digital transformation. Theoretically, this study contributes to the body of research on the Unified Theory of Acceptance and Use of Technology (UTAUT) by empirically testing its Use Behavior construct in light of early open data users' extent of use of open datasets, thus providing a more refined understanding of open data use behavior.

Addressing the Era of Open Data and Policy Solutions in Indonesia

Openness of Government Data is considered important and is an innovation that influences from the community to prevent corruption by increasing transparency of information and data. This has been done by almost most of the Ministries / Government Agencies in Indonesia in all aspects that include data disclosure in the social, economic, cultural, political and security fields. One of the drivers of the emergence of Open Government Data (OGD) is the era of the industrial revolution 4.0 which has affected all aspects of life with all technological advances so that people tend to require information disclosure in supporting and overseeing the running of the government. Conversely, the lack of utilisation of OGD is something that the Government really needs to pay attention to. Here the author discusses the openness of government data in Indonesia, with several literature review studies and reviews of several journals and interviews with several experts who are expected to provide recommendations related to the importance of data disclosure using several measurement parameters so that the gap between the framework and the OGD concept itself in Indonesia can be considered. In addition, the final goal of this study aims to provide foresight for policies that can be taken by the Government aimed at data transparency that can be used optimally by the general public and does not conflict with policies that have been implemented on data disclosure information.

Stakeholders Mapping in The Implementation of Anti-Bullying Policies in the School Environment Lubuklinggau City

The research aims to find the actors who play a role, their perceptions, and how patterns of interdependence work together in implementing anti-bullying policies in the school environment. The research framework adopted the concept of network governance related to actors, perceptions, and patterns of independence and roles in policy implementation. The research was conducted for six months using qualitative methods with 19 informants from regional apparatus organizations, task forces and public and private schools, parents and related organizations. Data were collected through FGDs, interviews, documentation and observation. Triangulation of sources and actors was conducted for data validity and reliability. Data analysis was conducted interactively through data display, reduction, and conclusion drawing. The results showed that the actors involved in reducing the stunting rate in Kaur Regency were identified as 19 actors from the government, semi-government, private sector, and social community The perception of actors related to the field of anti-bullying in the school environment generally regarding the context and content of anti-bullying policies in the school environment found a gap in the form of the reluctance / attitude of victims of bullying to report cases of bullying that occurred. The research recommends strengthening the participation model, increasing campaigns, building an information technology-based complaint system and open government data.

From Barriers to Boosters: Optimizing Open Government Data through Publishing Guidelines and Data Protection Strategies

Open Government Data (OGD) is a global endeavor, a collaborative effort between governments worldwide to share datasets that encapsulate a wide spectrum of government activities, from environmental issues like pollution and climate to social aspects like education and childcare, and urban concerns like traffic and congestion, and healthcare statistics. As governments, being among the largest producers and collectors of data, are making OGD available online in diverse formats, primarily Word, PDF, or Excel, they are contributing significantly to this global initiative. The OGD initiative holds immense potential to revolutionize the way we access and use government data. Its primary objective is to enhance the discoverability, accessibility, and availability of data in alternative and preferably machine-readable formats. This, in turn, empowers a diverse set of stakeholders to develop innovative data applications under licensing schemes that permit unrestricted reuse. Despite these promising aspects, challenges such as data heterogeneity, data protection, data quality, and data provenance issues persist. This study aims to analyze and categorize these challenges and obstacles that hinder the OGD initiative from realizing its full potential, with a particular emphasis on data protection and security concerns for data providers.

Understanding the factors associated with nurse employment in clinics: Experiences in Taiwan.

The shortage and distribution of nurses affect healthcare access in aging societies. Limited research has explored the nursing workforce in clinics, which is vital for healthcare delivery. This study aimed to investigate the shortage and distribution of nurses in clinics in Taiwan, considering geographical, institutional, and specialty variations. We conducted a cross-sectional analysis of the national nursing workforce in Western medicine clinics in Taiwan using open government data. The nursing practice rate (NPR) was calculated. The ratio of clinics employing nurses (RCN) was determined by calculating the percentage of clinics with nurses in each category. A logistic regression model was fitted to examine the factors associated with nurse employment, including urbanization level and different specialty clinics. Adjusted odds ratios (OR) and 95% CIs were calculated. The study revealed an overall NPR as low as 59.1%, and a decreasing trend with age. Among the 11 706 clinics in the study, nearly a quarter did not employ nurses, with an overall RCN of 72.3%. Urbanization level and clinic specialty were associated with nurse employment ( p < 0.05). After adjusting for urbanization level, the fitted regression model identified the top three specialties as plastic surgery (OR = 11.37, RCN = 96.8%), internal medicine (OR = 1.94, RCN = 84.1%), and orthopedics (OR = 1.89, RCN = 83.6%), while the bottom three were otolaryngology (OR = 0.59, RCN = 61.5%), psychiatry (OR = 0.49, RCN = 57.1%), and rehabilitation medicine (OR = 0.30, RCN = 45.2%). Nurses were more likely to be employed in areas at urbanization level 1 (OR = 1.17), 3 (OR = 1.37), and 6 (OR = 1.48), which represent highly urbanized areas within the urban, suburban, and rural categories, respectively, than in urbanization level 4. Clinics in Taiwan showed nursing shortages and maldistribution, with 72.3% RCN and variations based on urbanization and specialty. These factors may be considered in nursing research conducted in other countries to inform future workforce planning.

Political determinants of government transparency: Evidence from open government data initiatives

AbstractThe increasing availability of extensive governmental data, technological advancements, and a rising standard for government openness are encouraging global governments to implement open data initiatives. While some governments are actively adapting to these trends, others remain behind, despite the pressure. This research explores the political and administrative reasons behind these differences. By analyzing how local governments in South Korea handled requests for open data from citizens between 2007 and 2016, the study highlights the importance of political competition and administrative strength in fostering government transparency and effectively addressing citizen data requests. The study assesses open government data based on its scope, time, and quality, finding that higher levels of electoral competition and better administrative capabilities contribute to increased transparency and responsiveness. However, the study's use of a regression discontinuity design reveals that the political party controlling local governments had minimal influence on these factors.Related ArticlesHeo, Inhye. 2013. “The Political Economy of Policy Gridlock in South Korea: The Case of the Lee Myung‐bak Government's Green Growth Policy.” Politics &amp; Policy 41(4): 509–35. https://doi.org/10.1111/polp.12029.Heo, Inhye. 2022. “Energy Democratization Policy without Democratization of Policy Governance in South Korea: A Participatory Democracy Perspective.” Politics &amp; Policy 50(4): 834–50. https://doi.org/10.1111/polp.12480.Nam, Aerang, and Christopher M. Weible. 2023. “Examining Experts' Discourse in South Korea's Nuclear Power Policy Making: An Advocacy Coalition Framework Approach to Policy Knowledge.” Politics &amp; Policy 51(2): 201–21. https://doi.org/10.1111/polp.12522.

Water Body Detection Using Sen2Cube.at and Comparison to Open Government Data - Assessing for Floating Photovoltaics

Water Body Detection Using Sen2Cube.at and Comparison to Open Government Data - Assessing for Floating Photovoltaics

Academic Perspectives on Open Government Data: A Study of Quality, Trust and Intention to Use

Academic Perspectives on Open Government Data: A Study of Quality, Trust and Intention to Use

Synchronization and Standardization of Open Data Platforms: A Systematic Literature Review

This study is a systematic review that intends to improve our knowledge of the ideas of open data synchronization and standardization by reviewing the methodology and instruments utilized in previous studies through a careful examination of accessible literature. The methodology of this study was developed in several key phases. Firstly, a search was conducted in electronic databases to identify relevant studies in the field for Open Government Data (OGD). After identifying the studies, they were carefully reviewed through an initial abstract-based screening process. In the next phase, the research design and sample used in each identified study were analyzed to assess their quality and reliability. Subsequently, data from the studies were analyzed and categorized into various categories, including the usability of open data, synchronization, scalability, and reliability. The gained results highlight the importance of open data in our information-driven society. Open data positively impact transparency, anti-corruption efforts, and free access to information. To achieve these benefits, it is crucial to standardize and license open data, giving priority to their usability. The study underscores the importance of open data for transparency, innovation, and citizen engagement. Despite the disadvantages, this technology has the potential to change how governments and society use data.

How do institutional dimensions of open government data affect innovation? Evidence from research institutes in China

PurposeThis study investigates the individual and binary (i.e. combined) effects of institutional dimensions of open government data (which include instructional, structural and accessible rules) on scientific research innovation, as well as the mediating roles that researchers' perceived data usefulness and data capability play in between.Design/methodology/approachBased on a sample of 1,092 respondents, this study uses partial least squares structural equation modeling (PLS-SEM) and polynomial regression with response surface analysis to evaluate the direct and indirect effects of individual and binary institutional dimensions on scientific research innovation.FindingsThe findings demonstrate that instructional, structural and restricted access data have a positive effect on scientific research innovation in the individual effect. While the binary effect of institutional dimensions produces varying degrees of scientific research innovation. Furthermore, this study discovers that the perceived usefulness and data capability of researchers differ in the mediating effect of institutional dimensions on scientific research innovation.Originality/valueTheoretically, this study contributes new knowledge on the causal links between data publication institutions and innovation. Practically, the research findings offer government data managers timely suggestions on how to build up institutions to foster greater data usage.

Open government data and the cost of debt

Open government data refers to a government's policy of disclosing available data to society. Using China's policy of disclosing open government data, which has been progressively implemented, this paper investigates whether open government data affect the cost of debt for firms. We find that open government data can alleviate information asymmetry between creditors and firms, in turn significantly reducing the cost of debt for firms. This effect is pronounced in high-risk firms that creditors perceive to be engaging in opportunistic behavior, small firms, and regions with underdeveloped fintech or where the legal environment is not sufficiently protective of creditors. Furthermore, we find that the maturity of open government data contributes to its reducing effect on the cost of debt for firms. Finally, our results reveal that open government data facilitate firms' borrowing of both long-term loans and loans without third-party guarantees. Our study thus demonstrates the value of open government data for creditors.