Government Data Portals Research Articles

A semantic network analysis of categorization in open government data portals

Purpose This study aims to evaluate the semantic relationships between category terms that are used in open government data (OGD) portals and those identified in policy documents through the implementation of a semantic network analysis. Design/methodology/approach This study was conducted in three stages. Firstly, the study examined the semantic relationships between category terms in OGD portals by constructing a similarity matrix based on the terms’ co-occurrence and visualizing six-word groups. Secondly, the study investigated the semantic relationships among terms in OGD policy documents using latent semantic analysis and community detection methods, resulting in the identification and visualization of three network groups. Finally, the study used chi-squared and Z-tests to analyse differences in category terms between countries with and without redefined categories. Findings The results indicate that the three-word groups were identified by community detection, covering various aspects of government. In addition, there is a significant difference between the two country groups, with category terms being more prevalent in countries with predefined categories. This emphasizes the impact of categorization on term prevalence within OGD portals. Originality/value This study uniquely focuses on the categorization of government portals for sustainable open data management. The findings underscore the importance of effectively structuring and organizing data categories to enhance user discoverability and accessibility in OGD portals.

Dados Abertos para Sustentabilidade Ambiental

Sustainability has been a topic with increasing focus in all areas of scientific knowledge, including Information Science. Open datasets have the potential to be used for sustainability. The study´s aim was to evaluate open datasets made available by Brazilian federal environmental agencies regarding their adherence to the FAIR principles and the W3C Good Practices for Data on the Web. The method consisted of preparing a protocol in the form of a checklist, composed of questions based on these principles and good practices, which was applied to the 166 data sets located on Brazilian government data portals. The results indicate that, although the government makes a substantial amount of this type of data available under open licenses, the potential for its location, access and reuse is compromised by the low use of permanent identifiers and good quality metadata. Conclusion: The use of data can boost and assist social and environmental causes in the Brazilian context, as, for action, arguments evidenced with data are necessary to support advocacy and public policies that aim to disseminate information to raise awareness.

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.

Open Government Data for Urban Management in Indonesia’s Smart Cities

Rapid implementation of Open Government Data (OGD) across the world comes as the result of the advancement of Information, and Communication Technology in the government sectors to support decision making process including for urban planning. In one side, countries such as European countries, UK, and US has been implementing OGD while developing the framework and the maturity standard to ensure its quality. In the other side, Indonesia had just begun to develop several OGD portals which some of the practices could be found in its smart city namely Jakarta, Bandung, and Surabaya. The established OGD portals goals are to achieve transparency, government accountability, support innovation and development. This data-rich portals have potential roles for creating data-driven urban management. However, very least research observed implication of OGD practices in Indonesia for urban management. Therefore, this study aims to answer the question on how OGD portal could support urban management in Indonesia smart cities. The study identifies the implication of three open government data portals operated by smart cities in Indonesia by extracting and analysing the available data for urban management. The identification will be conducted with descriptive qualitative method based on open data principles and data-driven urban management dimensions. The study finding will explore the OGD practice level for urban management in Indonesia and recommend the improvement if needed.

Dados abertos governamentais: Modelo de diagnóstico de maturidade voltado a qualidade de dados para publicação na web

Abstract Increased interest in open government data initiatives stems from long debates about state modernization. This study investigates the challenges imposed on public administration regarding data publishing from an open data perspective to propose a maturity diagnosis model for open data portals, aiming at the provision of in-depth, consistent, efficient, and transparent government information. The research comprised three phases. Firstly, we conducted a pre-test on open data portals from the United States, the United Kingdom, Brazil and Ireland, motivated by studies on the history of the open data movement as well as the current panorama of initiatives, evolution stages and challenges, which defined the fundamental analytical dimensions for the proposed maturity model. Secondly, we developed a maturity diagnosis model for open government data portals. Finally, the model was applied and validated on the pre-tested portals. Results from the diagnosis instrument can guide public administration in realizing a more efficient and responsible data governance, benefiting the government, the open data movement and civil society.

Do open government data (OGD) portals show signs of knowledge management (KM) practices?: an empirical investigation

ABSTRACT Open Government Data (OGD) is a build-up of the data accumulated in the government organisations pertaining to the structural and functional dimensions and it is imperative for OGD to be high-value for facilitating value creation and innovation. The present study purports to provide a launchpad to the aforementioned truism by advancing the concept of Open Government Data Capital (OGDC) resting on the principles of Knowledge Management (KM) given that the high-value OGD can result only with the engagement of the concerned administrative agencies in knowledge sharing for being made accessible for wider use via dedicated web portals. To drive home the arguments, an empirical investigation is conducted with four top-notch countries, viz., Canada, Australia, New Zealand and the United States, in terms of the quantitative evaluation of their OGD portals’ quality and inferences are drawn as to how OGDC may be furthered with the provision and maintenance of high-value datasets. Thus, it is shown that the Australian OGD portal is qualitatively robust and leads in terms of OGDC which may be beefed up with more integration of the KM practices in terms of the inter-governmental agencies’ coordination and the other countries are lagging behind in terms of the quality parameters.

To what Extent Open Government Data (OGD) Portals Accessibility under the Indonesian Provincial Government?

This article aims to examine the accessibility of providing open data services under open government data (OGD) portals at the local government level, where our have found 17 OGD official websites and have been selected under the control of several provincial governments in Indonesia. For this paper, we employed the highlighted WAVE and AChecker tools. Significantly, findings indicate that the most of 17 OGD portals have failed accessibility assessments with striking viewpoints obtained in the “ARIA” tool and followed by the “Structural Elements” sections analyzed using the WAVE tool, wherein only OP11 has a range of passed website accessibility tests. It is also consistent with the findings by the AChecker tool in the “Known Problem” and also “Likely Problem” sections pointed out that passed the test. However, it should be noted that the only three OGD websites that passed the test by AChecker were OP3, OP9, and OP16. As a result, there is a constant high frequency of “potential problems” discovered by AChecker than by the WAVE tool on the OGD websites. Artikel ini bertujuan untuk mengkaji aksesibilitas penyediaan layanan data terbuka di bawah portal data pemerintah terbuka (OGD) di tingkat pemerintah daerah, di mana kami telah menemukan 17 situs web resmi OGD dan telah dipilih di bawah kendali beberapa pemerintah provinsi di Indonesia. Untuk makalah ini, kami telah menggunakan alat WAVE dan Achecker sebagai sorotan pengujian. Secara signifikan, temuan menunjukkan bahwa sebagian besar dari 17 portal OGD telah gagal dalam penilaian aksesibilitas dengan sudut pandang mencolok yang diperoleh dalam alat “ARIA” diikuti oleh bagian “Elemen Struktural” yang dianalisis menggunakan alat WAVE, di mana hanya OP11 yang memiliki rentang aksesibilitas situs web yang lulus tes. Ini juga konsisten dengan temuan alat AChecker di bagian “Masalah yang Diketahui” dan juga “Kemungkinan Masalah” yang ditunjukkan telah lulus uji. Namun, perlu dicatat bahwa hanya tiga situs web OGD yang lolos uji oleh AChecker adalah OP3, OP9, dan OP16. Akibatnya, ada frekuensi tinggi “masalah potensial” yang ditemukan oleh AChecker daripada oleh alat WAVE di situs web OGD.

K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals

More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.

Path analytic investigation of the intention to adopt open government data in Qatar (TAM revisited)

Purpose This study aims to examine the factors that influence end users’ intention to adopt open government data (OGD) portals, envisioning this relationship through a path causal model. Design/methodology/approach The study surveyed 696 Qataris and 730 white-collar expatriates using a questionnaire. The Kaiser–Meyer–Olkin measure of sample adequacy and Bartlett’s test of sphericity were used to determine the questionnaire’s construct validity. Findings The multiple regression analysis revealed that previous experience in using OGD portals, perceived usefulness and ease of use of OGD portals, marital status, age and attitude toward using OGD portals significantly predicted the intention to adopt OGD portals. Moreover, age and marital status significantly affected the intention to adopt OGD portals. The outcomes of the path causal model show that the direct effects of each explanatory variable are enhanced by the effects of the other independent variables in the causal model. Practical implications The study pioneers the application of the Technology Acceptance Model (TAM) to analyze motivating factors for Qatari residents to adopt OGD portals. Using this framework can help policymakers build strategies to boost the use of OGD portals in Qatar. Originality/value To the best of the authors’ knowledge, the TAM has never been examined in the Qatari setting to analyze the adoption of OGD portals. The present study fills knowledge gaps about and offers a clearer understanding of the elements influencing the adoption of OGD portals.

Gulf Cooperation Council Countries and Urbanisation: Are Open Government Data Portals Helping?

Today, the concepts of the smart city and smart government are well established, and have become key in dealing with the challenges faced by rapidly increasing urbanisation across the world. One of the many ways in which smart cities and government can help meet these challenges is by empowering citizens to take more and better control of their own decisions, by giving them access to open (i.e., public and unrestricted) data via online portals. These portals can enable citizens and professionals to make more informed decisions across a wide variety of areas, from health and traffic issues to developing new businesses. They can also encourage citizens to become more involved in the making of more effective social policy. Open data is increasingly seen as a key enabler in designing and implementing knowledge-based solutions for more sustainable urban development. For these reasons, as well as others, OGD (Open Government Data) portals are already being implemented in many countries. However, if smart cities are to maximise their potential, OGD portals must be understood, accepted and—crucially—used by the public. The extent to which this is the case is not clear, as there have been few studies that have specifically examined the impact of open data in a smart city context. This is particularly true in the GCC (Gulf Cooperation Council) countries. This research uses a mixed methods (questionnaires and interviews) approach to examine this issue by carrying out a comparative study of the use of portals across the region and, therefore, whether these countries are making optimum use of open data. An existing evaluation framework was used with a group of evaluators representing professional users who are not experienced in using data portals. The findings of the research suggest that these countries are not yet realising the full potential of their portals, and more effective support for the transition to smart cities could be achieved with fuller and better cross-national cooperation.

Climate Change Impacts on Rainwater Tank’s Potential Water Savings, Efficiency and Reliability Presenting Relationship Between ‘Seasonality Index’ and Water Savings Efficiency

The paper presents potential impacts of climate change on rainwater tanks outcomes such as water savings, reliability and water savings efficiency under two projected climate change scenarios; RCP4.5 and RCP8.5 utilising a case study using rainfall data from four stations within the city of Brisbane, Australia. Historical rainfall data were collected from the Australian Bureau of Meteorology website for the selected stations. Projected daily rainfall data were collected from Australian government data portal for the same stations within Brisbane. Using an earlier developed daily water balance model, eTank potential annual water savings were calculated for several weather and climate change scenarios with varied roof areas and rainwater demandValues of ‘Seasonality Index (SI)’, a commonly used factor representing rainfall variability within a year, were explored to validate earlier developed relationships between SI and water savings efficiency through rainwater tanks. It is found that in most cases water savings in future periods are expected to decrease and such decrease is not necessarily attributed to the expected decrease in rainfall amounts in future, rather also affected by future reductions in rainwater tank reliability. Linear relationships between potential future water savings and reliabilities for all the stations are found. Relationship between SI and water savings efficiency was established, and it is found that the relationship slightly varies with an earlier developed relationship using historical data. Compared to earlier established relationship, for lower SI values water savings efficiency is expected to become better, while for higher SI values the water savings efficiency is expected to become worse. Also, an increase of roof area from 100 m2 to 200 m2 will cause an average increase of water savings efficiency by 25% and an increase of rainwater usage from 200 L/day to 300 L/day will cause an average increase of water savings efficiency by 20%.

Geospatial Hotspot Analysis and Endemicity Trends of Missing and Unrecovered Children in India.

Children constitute a nation's true asset. A country's future relies upon the proper development of its children, which necessitates a supportive environment and sufficient opportunities. Children, under the age of 18 yearsform a considerable percentage of India's population which imparts a huge responsibility for the nation. Everyday we come across news about a child going missing. The National Crime Record Bureau (NCRB) states that 73,138 children were reported missing overall in 2018. The prevalence increased by 8.9% in 2019, a worrisome situation. The cause behind children going missing is multi-dimensional like poverty, unemployment, loss of livelihood, natural calamities, social conflicts, and migration to urban areas. At present, missing children remain a neglected and non-urgent intervention area for everyone. Only the parents whose children are missing can feel the vacuum and sorrow of the situation. The sociologies of India's missing children merit dimensional and circumstantial examination. The sociological space into which a child goes missing is highly under-researched in India. This study helped in understanding the magnitude of missing cases across India based on existing literature and secondary sources. It also identified the potentially safe and worst areas with regard to missing children. The endemicity helped in identifying the changing trends in each of these areas of interest, which could serve as baseline data for policymakers and law enforcement alike. This is a cross-sectional analytical type of study. The data of missingand unrecovered children for the past five years (2021-2017) were obtained from open government data portal (https://data.gov.in) and geospatial hotspot analysis was done using the Getis-Order-G statisticson the same usingGeoPandas and PySAL libraries of python. The endemicity of missing cases was studied using hierarchical cluster analysis and self-organizing maps using Python. For boys,Uttar Pradesh, Rajasthan, and Madhya Pradesh remained consistent hotspots (high risk of missing cases) across all five years of study whereas Karnataka became a hotspot for 2020 and 2021.Among the cold spots (low risk of missing cases), Andaman and Nicobar Islands remained consistent cold spot across all five years of study and has emerged as a safe haven for boys.The missing girls' data reveals that Uttar Pradesh has consistently been the worst state for girls (along with their neighbors) as it possess the risk of girls going missing all through the study period with Chhattisgarh being included from 2019 onwards.Jharkhand, Gujarat, Sikkim, and Andaman and Nicobar Islands have consistently been cold spots across the board and have emerged as areas with low risk for missing girls. This study helps us in understanding the magnitude of missing cases across India and it also identifies which are potentially safe areas as well as worst areas with regard to missing children. The endemicity also helps us in identifying the changing trends in each of these areas of interest. This will serve as a great resource for policy makers and law enforcement alike.

Usability and Accessibility of Open Government Data Portals of Countries Worldwide

The open government data (OGD) portal is an initiative by governments worldwide to promote transparency and accountability and enhance citizen participation and public value creation. The ability of these portals to deliver the expected benefits depends on their usability and accessibility, besides their content. This study conducts an integrated assessment and comparison of the OGD portals of 75 countries based on their usability and accessibility. The usability of the portals is measured through several technical indicators, classified into performance metrics, browser timings, and optimisation using automated diagnostic tools. Accessibility is evaluated through portals' conformance to web content accessibility guidelines (WCAG). The ‘technique for order of preference by similarity to ideal solution' (TOPSIS) and entropy weight method are used to rank the portals. The findings of this study could be helpful for policymakers and internal stakeholders of OGD initiatives to identify the focus area and make decisions on resource allocation.

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Citizens’ utilization of open government data portals in China: a comparative case study of supply vs demand

PurposeThe rising volume of open government data (OGD) contrasts with the limited acceptance and utilization of OGD among citizens. This study investigates the reasons for citizens’ not using available OGD by comparing citizens’ attitudes towards OGD with the development of OGD portals. The comparison includes four OGD utilization processes derived from the literature, namely OGD awareness, needs, access and consumption.Design/methodology/approachA case study in China has been carried out. A sociological questionnaire was designed to collect data from Chinese citizens (demand), and personal visits were carried out to collect data from OGD portals (supply).FindingsResults show that Chinese citizens have low awareness of OGD and OGD portals. Significant differences were recognized between citizens’ expectations and OGD portals development in OGD categories and features, data access services and support functions. Correlations were found between citizens’ OGD awareness, needs, access and consumption.Originality/valueBy linking the supply of OGD from the governments with each process of citizens’ OGD utilization, this paper proposes a framework for citizens’ OGD utilization lifecycle and provides a new tool to investigate reasons for citizens’ not making use of OGD.

Implementation of a conversational virtual assistant for open government data portal: Effects on citizens

Insufficient support services are one of the barriers for citizen’s open government data (OGD) utilisation. Considering the strengths and effectiveness of virtual assistants (VAs) in providing instant and user-friendly support services, this study focuses primarily on its implementation for OGD portals. A conversational VA prototype is specifically developed. A between-subjects experiment with one factor (VA/text-based support services) was carried out. Results show citizens’ positive attitudes towards VAs providing support services on OGD portals. No significant difference was found for citizens’ acceptance, trustworthiness and rapport for OGD portals with and without a VA. Accuracy rate of completing tasks with VAs is slightly higher. A correlation lies between trustworthiness and rapport with OGD portals. This study benefits the improvement of OGD portals’ online services to enhance citizens’ experiences and provides a research model both for investigating VAs’ effects and for comparing different designs of a same kind of service for an interface.

QODA – Methodology and Legislative Background for Assessment of Open Government Datasets Quality

In last few years, many open government data portals have been emerging in the world. These portals publish open government datasets which can be accessed and used by everyone for their own needs. In this paper, we propose methodology named QODA (Quality of Open government DAtasets) for assessment of quality of published datasets via two aspects. First one is assessment of quality of pure open government datasets, and second is assessment of quality features on the platforms which contributes to the publication of quality datasets. It provides a step-by-step dataset analysis guidance and summarization of results. Research presented in this paper shows that open government dataset quality depends on data provider as well as proper definition of metadata behind datasets. Our findings result in recommendations to open government data (OGD) publishers, to constantly supervise the use of published datasets, with aim to have timely and punctual information on OGD portals, with special attention on quality features.

Impact of Covid-19 Prevalence in Seoul on Subway Usage

This article shows that there is a negative impact of government’s social distancing policy, daily COVID-19 confirmed cases, daily volume of new articles related to COVID-19, and a weekend dummy on subway ridership in Seoul, Korea. Multiple regression with OLS is applied to 289 daily data mostly collected from the Open Government Data Portal during the early stages of COVID-19 pandemic, from January 1 to October 30, 2020. The model is run separately for four subway station categories: commercial, office, residential, and other area. Different impacts of explanatory variables across different area have some policy and managerial implications. This study concludes with future research.

Metadata quality matters in open government data (OGD) evaluation! An empirical investigation of OGD portals of the GCC constituents

PurposeThe study seeks to investigate the quality of metadata associated with the open government data (OGD) portals of the six Gulf Cooperation Council (GCC) constituents – Bahrain (BH), Kuwait (KW), Oman (OM), Qatar (QA), Kingdom of Saudi Arabia (KSA) and United Arab Emirates (UAE).Design/methodology/approachA quantitative framework, supported by extant literature, is adopted to assess the metadata quality of the six OGD portals of the GCC constituents.FindingsAmong the six GCC countries, QA has the most advanced OGD metadata quality followed by KSA, UAE, OM, BH and KW. Furthermore, the OGD metadata quality (MQ) of UAE and OM stand at the same pedestal whereas BH and KW OGD portals are lagging behind.Originality/valueWhile the OGD quality has been investigated in extant literature, the MQ of the OGD portals for the GCC countries has not been investigated so far – the present study seeks to plug this gap.

Climate Change Impacts on ‘Seasonality Index’ and its Potential Implications on Rainwater Savings

Seasonality Index (SI) is commonly used to define rainfall variability within a year for a locality, which has got significant impact on the water resources planning and management. Both the longer periods of dry and wet conditions are reflected by SI value. As SI is calculated using historical rainfall data, a historical average value is represented for a locality. However, with the emerging impacts of climate change SI values based on historical data are likely to change. This paper presents expected changes in future SI values for the largest city of Australia (Sydney) based on projected future rainfall scenarios for the city. The paper further illustrates potential water savings through rainwater tanks under projected climate change scenarios using an earlier developed daily water balance model, eTank. For the simulations of potential water savings through rainwater tanks, projected daily rainfall data was collected from Australian government data portal for four stations within Sydney. Future potential rainwater savings efficiencies were correlated with the corresponding SI values. It is found that such correlation closely matches with an earlier developed correlation using past historical data, however the future water savings efficiency will be slightly lower than the past water savings efficiency.