Open Data Portals Research Articles

Criteria for the identification of ineffective open data portals: pretender open data portals

Open data are considered an essential resource for governments, businesses, and citizens. In that context, open data portals have potential for creating enormous economic growth. Open data portals should allow the reuse of open data, ensure the efficiency of data transmission, and enable professional initiatives based on data reuse. However, there are portals that are inefficient because they do not allow reuse of their data. The objective of this work is to define and identify open data portals that do not offer the possibility for professional reuse of their data. We refer to them as “pretender open data portals”. The following research questions are considered herein: What minimum criteria must an open data portal satisfy to enable professional reuse of open data? How can portals that do not meet these criteria be identified? And, what problems might these portals present, and how they might be solved? The results of an analysis of two samples of open data portals in Spain reveal that 63.8% and 56.1% of the portals analyzed in 2019 and 2021, respectively, can be considered pretender open data portals. The existence of pretender open data portals can have negative economic and social impacts, such as wasting public resources and projecting a negative image of the government’s open data policies. To find coordination mechanisms to develop open data portals that, through the professional re-use of their data can create economic and social value, is one important challenge. The analysis of best practices of open data portals can be also a way to go in deep in the understanding of open data reuse impact not only from a professional standpoint.

A FAIR and AI-ready Higgs boson decay dataset

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

An Empirical Taxonomy of Common Curb Zoning Configurations in Seattle

This work applies an unsupervised clustering algorithm to blockface zoning data to identify typical curb configurations in a city. Data is obtained via the city of Seattle’s (Washington, USA) open data portal. To compare the distribution of blockfaces of varying length, all blockfaces are normalized where each zone type is presented as a percentage of the total blockface length in an order-preserving format. Common zone sequences are identified via k-modes clustering, where an optimal choice of k is cross-validated, quantifying the number of curb configurations to represent the majority of Seattle’s blockfaces. All documented code and data are open source and available at https://github.com/pnnl/curbclustering.

CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs

Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.

Using OpenGovB Transparency Indicator to Evaluate National Open Government Data

Transparency evaluation in open government is a process of measuring the extent of transparency against a predefined set of indicators. In this paper, we address the existing initiatives regarding data and government transparency evaluation as two separate indicators and present the analysis of their advantages and drawbacks. Based on that analysis, we extend a part of the OpenGovB benchmark related to transparency in open government. What is unique about this benchmark is that it utilizes metadata of data published on the open government data portals to calculate the majority of indicators related to data transparency indicators. For the government transparency indicator evaluation, the benchmark utilizes some of the well-known transparency indicators. The article shows concrete results obtained from the application of the defined transparency evaluation model on 22 open data portals, thus demonstrating the possibilities of its application as well as the gains regarding generated results. The proposed model bridges the gap between available methodologies for evaluating transparency based on collaboration and participation and methodologies for evaluating transparency based on open data.

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for multistep discrete charging occupancy state prediction. Unlike the existing LSTM networks, the proposed model separates different types of features and handles them differently with mixed neural network architecture. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of the city of Dundee, UK. The results show that the proposed method produces very accurate predictions (99.99% and 81.87% for 1 step (10 min) and 6 steps (1 h) ahead, respectively, and outperforms the benchmark approaches significantly (+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). A sensitivity analysis is conducted to evaluate the impact of the model parameters on prediction accuracy.

A multicriteria decision analysis framework to measure equitable healthcare access during COVID-19

The ongoing novel coronavirus (COVID-19) pandemic has highlighted the need for individuals to have easy access to healthcare facilities for treatment as well as vaccinations. The surge in COVID-19 hospitalizations during 2020 also underscored the fact that accessibility to nearby hospitals for testing, treatment and vaccination sites is crucial for patients with fever or respiratory symptoms. Although necessary, quantifying healthcare access is challenging as it depends on a complex interaction between underlying socioeconomic and physical factors. In this case study, we deployed a Multi Criteria Decision Analysis (MCDA) approach to uncover the barriers and their effect on healthcare access. Using a least cost path (LCP) analysis we quantified the costs associated with healthcare access from each census block group in the Los Angeles metropolitan area (LA Metro) to the nearest hospital. Social vulnerability reported by the Centers for Disease Control and Prevention (CDC), the daily number of COVID-19 cases from the Los Angeles open data portal and built environment characteristics (slope of the street, car ownership, population density distribution, walkability, traffic collision density, and speed limit) were used to quantify overall accessibility index for the entire study area. Our results showed that the census block groups with a social vulnerability index above 0.75 (high vulnerability) had low accessibility owing to the higher cost of access to nearby hospitals. These areas were also coincident with the hotspots for COVID-19 cases and deaths which highlighted the inequitable exposure of socially disadvantaged populations to COVID-19 infections and how the pandemic impacts were exacerbated by the synergistic effect of socioeconomic status and built environment characteristics of the locations where the disadvantaged populations resided. The framework proposed herein could be adapted to geo-target testing/vaccination sites and improve accessibility to healthcare facilities in general and more specifically among the socially vulnerable populations residing in urban areas to reduce their overall health risks during future pandemic outbreaks.

Investigation into the adoption of open government data among students: the behavioural intention-based comparative analysis of three countries

PurposeOpen government data (OGD) are considered as a technology capable of promoting transparency openness, and accountability, which in turn has a positive impact on innovation activities and creates responsive government, collaboration, cooperation, co-creation and participation. The purpose of this paper is to explore the adoption of OGD and open data portals among students, in an attempt to discover how governments can improve their actions in this respect.Design/methodology/approachThis study develops a behavioural intention-based analysis using constructs from the Unified Theory of Acceptance and Use of Technology, which is supplemented with additional constructs that meet the purpose of the study. In total, ten constructs divided into 33 items constituted the input for our study. Input data for the developed model have been collected through a structured questionnaire distributed between bachelor's and master's level students in three countries – the Czech Republic, India and Latvia. A structural equation modelling technique was used to analyse the relationships between variables of the model and test the nine hypothesis defined.FindingsSix constructs have been identified to facilitate significant relationships with behavioural intention. The analysis of the results of the three countries allows us to draw more objective conclusions in respect to the aim of the study and to reveal country-specific aspects that need to be addressed in the future.Originality/valueThis study adds to the existing literature few theoretical and practical aspects. It highlights the role of open data portals as a central point of OGD infrastructures. It enables governments to understand the relationships among the related constructs, improving their actions and modifying their data infrastructures accordingly.

МЕХАНІЗМИ РЕГУЛЮВАННЯ ОБМІНУ ДАНИМИ

The article analyzes the foreign and domestic practice of regulating the exchange and use of data as an important intangible asset, which is a tool for managing economic and social processes. It is determined that the use of data becomes especially relevant in crisis situations, when in order to reduce the level of uncertainty and the impact of risks it is necessary to make urgent decisions in the interests of society as a whole, taking into account many internal and external factors. It is classified data by sources of origin, nature of use and impact on economic and social processes, conditions and mechanisms for effective data exchange between public and private sectors, as well as cross-border data exchange. The objective need to strengthen the requirements for mechanisms for regulating the exchange and use of data in order to increase management capacity, efficiency of economic activity and the functioning of society has been proved. The main tasks of regulating the circulation and use of data are identified, the directions of improving the existing organizational and legal forms of interaction between the public and private sectors in the field of data, as well as the mechanisms of the data market. The criteria for assessing the effectiveness of open data portals are determined, the directions of improvement of the state open data portal of Ukraine are substantiated. The basic scheme of organization of public-private partnership in the field of data exchange and use is developed, the necessity of legislative regulation of spheres of responsibility and establishment of clear rules of interaction of partners on maintenance of fair and safe conditions of data exchange is proved. In order to increase the social value of data, the main provisions and directions of practical implementation of the concept of «data of general interest» are revealed, the mechanisms of voluntary and mandatory exchange of them are identified. It is proved that such data exchange is of paramount importance for national security, combating epidemics and natural disasters, protecting the environment, meeting public needs and accelerating the socio-economic development of the country as a whole.

A Comprehensive Method for Improving the Quality of Open Government Data and Increasing Citizens’ Willingness to Use Data by Analyzing the Complex System of Citizens and Organizations

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government‐owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, the quality of open government data needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision‐making and management. In fact, we are dealing with decision‐making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high‐degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision‐making technique including the DEMATEL technique have been used to express the solutions and identify the cause‐and‐effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up‐to‐datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision‐making and proper management in the complex system of citizens and government organizations.

The BITS Project – Making cycling data available and comparable on a European scale

Intelligent Transport Systems (ITS) could be the basis for new data driven approaches and mobility applications, as many examples from motorized means of traffic have shown. However, ITS can also contribute to an improvement of the bicycle infrastructure in European cities and to the creation of new data driven innovations that make cycling more comfortable, safe and smart. The EU funded project BITS (Bicycles and Intelligent Transport Systems) was initiated to identify the ITS cycling solutions with the highest potential and to make cycling data available for all interested stakeholders. Until this development stage of the BITS project, ITS for cyclists as bicycle counting, 3D camera systems to detect near accidents, bike sensors to measure air quality and smartphone applications have been implemented and evaluated in the whole North Sea Region (Netherlands, Belgium, Denmark, Germany, UK). The newly gathered cycling data will not stay inside the project team: To support the further expansion of European cycling networks, one of the main deliverables of the BITS project is an open bicycle data portal, the so called CyclingDataHub (CDH). All interested stakeholders get access to different categories of cycling datasets with an open license and can share their personal cycling data with the general public. The data team of the University of Oldenburg, Department of Business Informatics VLBA, collected and preprocessed all the data sets and published these on a Bicycle Data website. The related Key Performance Indicators (KPIs) should contribute to the improvement of the comparability of the different applications and regions. The harmonization of the different data sets is a big issue as a common understanding of how to collect and to publish cycling data is not established so far on the European scale. Dynamic and static KPIs as well as suitable graphs deliver new insights into bicycle use in Europe. The preprocessed and enriched cycling data can make an important contribution to the further uptake of cycling in the North Sea Region.

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities.

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.

Back to Basics: City Services and 311 Service Requests

With data from service delivery requests in twenty-nine cities, this article reports on a comparative analysis of city service request systems (311 systems) and their operations. The study uses actual 311 service request data available through cities’ open data portals. A typology of thirty service type categories was created. Various hypotheses were tested. As city population increases, the number of service requests also increases. Garbage/recycling is the most commonly requested service, followed by code enforcement requests, parking, pickup of bulk items, and abandoned vehicles. Cities are no longer just using telephones for their users to submit requests but have also now incorporated other service channels. Service resolution times vary across cities and service category but there is some evidence that safety and health types of services receive priority and are resolved more quickly. The article ends with managerial and policy implications.

Towards defining data interpretability in open data portals: Challenges and research opportunities

Open data portals are growing in scope, and the development of this initiative remains one of the main ways to help create new value for society and the economy. Citizens can use the open data made available on these portals to participate more effectively in democratic processes. For that, they have to be able to access, manipulate and interpret such data. Different authors present different definitions and perceptions about the meaning of data interpretability. Today there is no formal consensus on the concept of data interpretability. The goal of this work is to conceptualize what data interpretability is formal. For this, we carried out literature research to identify the definitions of data interpretability. In addition, we studied the information quality literature to identify the Non-Function Requirements that shape the concept of information quality. So, we aligned the interpretability characteristics with the NFR Framework characteristics to find a unique definition. We also conduct a qualitative analysis with experts in data analysis, e-government, and transparency to identify what these experts understand by interpretability. Based on these two studies, we defined interpretability through a model composed of 8 dimensions, each consisting of different characteristics, which must be guaranteed in the data interpretability process to interpret the data correctly. We understand that, for such characteristics to being guaranteed in the interpretability of open government data, it is necessary to have computational tools to support the user. Thus, we also surveyed which technologies and methods ensure each of the interpretability characteristics and pointed out which computational tools implement such technologies and methods. Finally, we analyzed three large open data portals to identify which characteristics are present in these portals, and we note that there are still several challenges to be handled in open government data portals.

Physical Distancing, Violence, and Crime in US Cities during the Coronavirus Pandemic.

Violent crime increased and most property crime decreased in many United States (US) cities during the coronavirus pandemic. Using negative binomial regressions, we examined the association between physical distancing (a central coronavirus containment strategy) and crime within 16 large cities (in 12 US states and the District of Columbia) through July 2020. Physical distancing was measured with aggregated smartphone data and defined as the average change in the percentage of the population staying completely at home. Outcome data were obtained from the Gun Violence Archive and city open data portals. In multivariable models, increases in the percentage of the population staying home were associated with decreases in reported incidents of aggravated assault, interpersonal firearm violence, theft, rape, and robbery, and increases in arson, burglary, and motor vehicle theft. Results suggest that changes in the frequency of interpersonal interactions affected crime during the coronavirus pandemic. More research is needed on the specificity of these assocations and their underlying mechanisms.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-021-00593-4.

TOOL FOR INTERACTIVE VISUAL ANALYSIS OF LARGE HIERARCHICAL DATA STRUCTURES

In the Big Data era data visualization and exploration systems, as means for data perception and manipulation are facing major challenges. One of the challenges for modern visualization systems is to ensure adequate visual presentation and interaction. Therefore, within this paper, we present a tool for interactive visualization of data with a hierarchical structure. It is a general-purpose tool that uses a graph-based approach. However, its main focus is on the visual analysis of concept lattices generated as the output of the Formal Concept Analysis algorithm. As the data grow, concept lattice can become complex and hard for visualization and analysis. In order to address this issue, functionalities important for the exploration of the large concept lattices are applied within this tool. The usage of the tool is presented in the example of visualization of concept lattices generated based on the available data on the Canadas open data portal and can be used for exploring the usage of tags within datasets.

Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea

In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the Korean War in 1950, and to overcome them, the six most serious cases in National Notifiable Infectious Diseases (NNIDs) category I were defined. Although most infectious diseases have been overcome, viral hepatitis A has been on the rise in Korean society since 2010. Therefore, in this paper, the prediction of viral hepatitis A, which is rapidly spreading in Korean society, was predicted by region using the deep learning technique and a publicly available dataset. For this study, we gathered information from five organizations based on the open data policy: Korea Centers for Disease Control and Prevention (KCDC), National Institute of Environmental Research (NIER), Korea Meteorological Agency (KMA), Public Open Data Portal, and Korea Environment Corporation (KECO). Patient information, water environment information, weather information, population information, and air pollution information were acquired and correlations were identified. Next, an epidemic outbreak prediction was performed using data preprocessing and 3D LSTM. The experimental results were compared with various machine learning methods through RMSE. In this paper, we attempted to predict regional epidemic outbreaks of hepatitis A by linking the open data environment with deep learning. It is expected that the experimental process and results will be used to present the importance and usefulness of establishing an open data environment.

Examination of Social Determinants of Health, District Health Needs, School Nurse Caseload, and Academic Outcomes in Washington State.

The purpose of this study was to examine associations between caseload, social determinants, health needs, students meeting grade-level English and Math standards, and attendance. Data from the Washington State Open Data Portal and Report Card were combined with District Health Assessment data from 264 school districts. Analyses of variance and linear stepwise regression analyses were conducted. Key findings indicate significant differences in English and Math outcomes by caseloads, with higher caseload districts have lower percentages of students meeting English and Math standards, but not attendance. Caseload is a significant predictor of students meeting English and Math standards, after controlling for social determinants and district health needs. Findings point to the complexity of school nursing work and builds a foundation to guide future nursing research. Large prospective studies that allow for comprehensive measurement of structure, process and outcomes variables are needed to advance school nursing research.

An Automatic Data Completeness Check Framework for Open Government Data

In recent years, the governments in many countries have recognized the importance of data in boosting their economies. As a result, they are implementing the philosophy of open government data (OGD) to make public data easily and freely available to everyone in standardized formats. Because good quality OGD can boost a country’s economy, whereas poor quality can jeopardize its efficient use and reuse, it is very important to maintain the quality of data stored in open government data portals (OGDP). However, most OGDPs do not have a feature that indicates the quality of the data stored there, and even if they do, they do not provide real-time service. Moreover, most recent studies focused on developing approaches to quantify the quality of OGD, either qualitatively or quantitatively, but did not offer an approach to automatically calculate and visualize it in real-time. To address this problem to some extent, this paper proposes a framework that can automatically assess the quality of data in the form of a data completeness ratio (DCR) and visualize it in real-time. The framework is validated using the OGD of South Korea, whose DCR is displayed in real-time using the Django-based dashboard.

The Challenges of Open Data Movement for Education: What is the Say of Young Employees and Students in Croatia?

This paper aims to shed light on how students and young employees in Croatia assess their education for open data and what is their opinion on the compliance of the central Open Data Portal with the needs of young people as well as how they evaluate open data policy related to the young people in Croatia. This research highlights the lack of technical knowledge as a serious obstacle to the productive use of open data. As many as 56% of respondents from companies that have undergone digital transformation believe that they do not have enough knowledge to participate in open data projects, and the same scepticism is expressed by 59.6% of non-technical respondents and 45.7% of students. The data presented in this paper is part of a broader empirical research on the impact of digitalization on the transformation of the Croatian economy, carried out by the author in late 2018 on a sample of 51 young employees from 10 companies in the city of Zagreb and 70 students from 16 technical and non-technical Faculties of Zagreb University.