Open Government Data Research Articles

Linked Open Government Data to Predict and Explain House Prices: The Case of Scottish Statistics Portal

Accurately estimating the prices of houses is important for various stakeholders including house owners, real estate agencies, government agencies, and policy-makers. Towards this end, traditional statistics and, only recently, advanced machine learning and artificial intelligence models are used. Open Government Data (OGD) have a huge potential especially when combined with AI technologies. OGD are often published as linked data to facilitate data integration and re-usability. EXplainable Artificial Intelligence (XAI) can be used by stakeholders to understand the decisions of a predictive model. This work creates a model that predicts house prices by applying machine learning on linked OGD. We present a case study that uses XGBoost, a powerful machine learning algorithm, and linked OGD from the official Scottish data portal to predict the probability the mean prices of houses in the various data zones of Scotland to be higher than the average price in Scotland. XAI is also used to globally and locally explain the decisions of the model. The created model has Receiver Operating Characteristic (ROC) AUC score 0.923 and Precision Recall Curve (PRC) AUC score 0.891. According to XAI, the variable that mostly affects the decisions of the model is Comparative Illness Factor, an indicator of health conditions. However, local explainability shows that the decisions made in some data zones may be mostly affected by other variables such as the percent of detached dwellings and employment deprived population.

Understanding the determinants of the intention to innovate with open government data among potential commercial innovators: a risk perspective

PurposeThis study aims to examine factors influencing potential commercial innovators' intention to innovate with open government data (OGD) via a risk perspective.Design/methodology/approachThe authors develop a theoretical model that explains how different forms of uncertainty (i.e. financial, technology, competitive, demand, and data) and their inter-relationships influence potential commercial innovators' intention to innovate with OGD. The model is tested using survey data collected from 144 potential commercial innovators from a developed Asian country.FindingsThe results suggest that all other forms of uncertainty, except competitive uncertainty, negatively influence potential commercial innovators' intention to innovate, mediated by their perceived risk of innovating with OGD. The results also show positive relationships between different forms of uncertainty, i.e. competitive and financial, demand and competitive, data and financial uncertainty.Originality/valueThis paper identifies major forms of innovation uncertainty, perceived risk, their inter-relationships, and impacts on the intention to innovate with OGD. It also finds support for a unique form of uncertainty for OGD innovation (i.e. data uncertainty).

Open Government Data and Public Transportation

Governments are increasingly making public transportation data available to the public on the Internet. The data can be used to explore and characterize current and historical service levels or to forecast operations in the immediate future. This paper considers, as an example, real-time bus location data provided by the San Francisco Municipal Transit Agency. General techniques for making use of such data to benefit both providers and users of public transportation are described. There is a brief discussion of why the advantages of making data available often outweigh the disadvantages.

Public Servants’ Perception Towards Publishing Quality and Impactful Open Data to Support Open Science Initiatives in Malaysia

Open Data Portal is the key intervention of e-Government initiative for information management. The Government of Malaysia has introduced the initiatives since 2015 to facilitate open data portal and strengthen transparency governance. This Open Data is crucial because it provides significant benefits to government in term of political, social, economic, operational, and technical. However, World Bank’s Open Data Readiness Assessment (ODRA) for Malaysia stated that despite the clear evidence of readiness and the country’s data-rich environment, it was significantly difficult to access high- quality data from Malaysia. As a result, lower scores on impact criterion in three consecutive years has landed Malaysia on 53rd of the ranking of the Global Open Data Barometer. Therefore, the objectives of this research re to determine the general perception of public servants toward open data initiatives and to identify determinants that impede the publication of open government data by the existing government entity. This research adopted qualitative method through semi-structured interview. There are six common themes of perception identified and ten common themes emerged as determinants. The result indicated that that Malaysian public servant demonstrates highly positive perception toward the initiative. It is very useful in enhancing public policy towards effective data publishing and sharing management. Subsequently may serve as input to to devise strategies to improve perceptions by addressing the perceptual biases or translating it into actionable policy to promote the success factors onto the non-providing agencies which would escalate the number of providing agencies, and consequently boost the number of data set, and eventually enhance the availability of impactful dataset to support Malaysia Government Data Sharing and Open Science initiatives.

Government data openness and knowledge management: configurational patterns for national competitiveness

PurposeIn the data economy era, despite the tremendous effort of governments to actively provide and use open data, its effect on national performance such as competitiveness differs widely from country to country. A sufficient knowledge base and its appropriate management are important to effectively derive the potential value from open data. A country can implement multiple and equally viable means to effectively align open data with knowledge management, which lead to high national performance. However, previous studies lack consideration of the possibility of these various configurations. To fill the research gap, this study aims to investigate the configurational patterns constituted by government data openness and knowledge management for national competitiveness.Design/methodology/approachFrom the open innovation perspective, this study collected data from the global reports of 76 countries and examined them through fuzzy-set qualitative comparative analysis (fsQCA).FindingsFour configurational patterns are identified, namely, coupled (outbound-focused)-, coupled (inbound-focused)-, inbound-focused-, and outbound-focused national competitiveness.Originality/valueThis study provides a foundation that enables researchers to build a holistic and balanced perspective that can manage open government data and develop knowledge management capability.

Paradigm reinforcing: The assimilation of data journalism in the United Kingdom

A journalistic paradigm defines what journalism is, and what journalists should do, in relation to reporting news. Drawing from desk research and in-depth interviews with eighteen data journalists and experts in the United Kingdom, this article discusses the recent development and practices of data journalism in the United Kingdom and their implications for the journalistic paradigm, which involves the substantial use of interviewing. Embracing the opportunities provided by the datafication of society and the British government's open data initiative, UK news organizations have institutionalized and incorporated data journalism into their organizational structure. However, difficulties in practice have emerged, resulting from new, mostly ethically grounded issues surrounding data. Traditional journalistic practices, in particular interviewing and cross-checking, provide a useful, practical guide to solving related problems. The institutionalization of data journalism, as well as traditional journalistic methods and skills coming as a solution, assimilate data journalism into conventional journalistic practices, reinforcing – rather than undermining – the journalistic paradigm.

China sewage treatment engineering issues assessment

Urban clean water is an important component of water resources management and is essential for achieving the United Nations Sustainable Development Goal. The Chinese government has attached great importance to urban sewage treatment and clean water resources. Unfortunately, the urban water quality has not shown a corresponding improvement with the increasing investment in the construction of urban sewage treatment infrastructures. In this study, we established a quantitative assessment index framework for engineering issues of urban sewage treatment system, including the lacking rate of a sewage pipe network, external water infiltration rate of a sewage pipe network, surplus rate of a sewage treatment plant, and discharge rate of untreated sewage. Then the assessment for engineering issues of urban sewage treatment system in China was conducted from the national level, provincial level and city level for the first time, with the Chinese government's open data and statistics. The results showed that the lacking rate of sewage pipe networks in China was 53%, the external water infiltration rate of sewage pipe networks was 34%, the surplus rate of sewage treatment plants was 54%, and the discharge rate of untreated sewage was 38%. The engineering efficiency of urban sewage treatment system in China was not high and varied among provinces and cities. In many urban areas, sewage pipe networks were incomplete or defective, lack and external water infiltration of sewage pipe networks are common, as well as misconnection of stormwater pipelines; and the problem of sewage direct discharge is serious. Improving the engineering efficiency of urban sewage treatment system, completing, repairment and reconstruction of sewage pipelines were the top priority of urban clean water in China. The assessment index framework established in this study can provide scientific basis for further advancing urban clean water, and the assessment for engineering issues of urban sewage treatment system is significant for the sustainable development of urban water resources.

Open government data: A systematic literature review of empirical research

Open government data (OGD) holds great potential for firms and the digital economy as a whole and has attracted increasing interest in research and practice in recent years. Governments and organizations worldwide are struggling in exploiting the full potential of OGD and require a comprehensive understanding of this phenomenon. Although scientific debates in OGD research are intense and heterogeneous, the field lacks theoretical integration of OGD topics and their systematic consideration in the context of the digital economy. In addition, OGD has been widely neglected by information systems (IS) research, which promises great potential for advancing our knowledge of the OGD concept and its role in the digital economy. To fill in this gap, this study conducts a systematic literature review of 169 empirical OGD studies. In doing so, we develop a theoretical review framework of Antecedents, Decisions, Outcomes (ADO) to unify and grasp the accumulating isolated evidence on OGD in context of the digital economy and provide a theory-informed research agenda to tap the potential of IS research for OGD. Our findings reveal six related key topic clusters of OGD research and substantial gaps, opening up prospective research avenues and particularly outlining how IS research can inform and advance OGD research.

Semi-Automatic Spatial Classification of Heterogeneous Spatial Open Government Data – Use Case of Germany

Semi-Automatic Spatial Classification of Heterogeneous Spatial Open Government Data – Use Case of Germany

Configurational path of successful entrepreneurship based on open government data: a QCA analysis

PurposeIn the era of big data, data have become an essential factor of production. In the context of open government data (OGD), encouraging the commercial development of OGD is significant for promoting mass innovation and entrepreneurship. This study aims to explore the configurational impact of data supply, external environment and entrepreneurial foundation on data-driven entrepreneurship.Design/methodology/approachThis research used a fuzzy set qualitative comparative analysis (fsQCA). Fourteen start-ups using OGD were taken as a case sample.FindingsThis study produces two paths to achieving high entrepreneurial performance, one is a financing-enhanced entrepreneurial path and the other is a data-driven entrepreneurial path. Besides, four conditions are necessary for high performance of OGD-based entrepreneurship: good data quality, mature legal environment, favorable market environment and abundant big data entrepreneurial talents.Practical implicationsThe findings have important practical implications for formulating policies related to promoting the application of government open data and innovation and entrepreneurship in terms of strengthening top-level design, improving the legal environment, developing the data market and cultivating entrepreneurial talents.Originality/valueAlthough many studies have been conducted on OGD, studies on the paths to successful entrepreneurship based on OGD are limited. In this study, this issue is investigated from a configurational perspective by using the fsQCA technique.

The wealth of (Open Data) nations? Open government data, country-level institutions and entrepreneurial activity

ABSTRACT Lately, Open Data (OD) has been promoted by governments around the world as a resource to accelerate innovation within entrepreneurial ventures . However,it remains unclear to what extent OD drives innovative entrepreneurship. This paper sheds light on this open question by providing novel empirical evidence on the relationship between OD publishing and (digital) entrepreneurship at the country-level. We draw upon a longitudinal dataset comprising 90 countries observed over the period 2013–2016. We find a significant and positive association between OD publishing and entrepreneurship at the country level. The results also show that OD publishing and entrepreneurship is strong in countries with high institutional quality. We argue that publishing OD is not sufficient to improve innovative entrepreneurship alone, so states need to move beyond a focus on OD initiatives and promotion, to focus on a broader set of policy initiatives that promote good governance.

A knowledge elicitation approach to traffic accident analysis in open data: comparing periods before and after the Covid-19 outbreak

Extracting knowledge from open data of traffic accidents has been attracting increasing attention to policymakers responsible for road safety. This article presents a knowledge elicitation approach to exploring the determinants of traffic accidents from open government data of an urban area in Taiwan. The collected open dataset contains 34 decisional attributes and one predictive attribute (i.e., type of injury, including head, breast, leg), and 47,974 cases. Prediction models using a classification-oriented mechanism and generated rules that considered datasets from before (B-dataset; 30,116 cases) and after (A-dataset; 17,868 cases) beginning to combat the Covid-19 pandemic in an urban area of Taiwan were compared. The findings showed that prediction accuracy was acceptable but not high, at 70.73% for B-dataset and 74.77% for A-dataset. Determinants in the human and vehicle categories revealed higher classification ranks than those in the temporal and environment categories. Traffic accidents involving motorcycles were 5.13% higher in A-dataset, whereas those involving cars were 4.11% lower. Injury on leg or foot was 3.46% higher in A-dataset, whereas other types of injury were up to 1.00% lower. The average support for rules in the A-dataset rule base and the simplicity of the A-dataset decision tree were higher than those of B-dataset. The research demonstrates the value of open government data in prediction model development and knowledge elicitation to support policymaking in the traffic safety domain.

Open government data awareness: eliciting citizens’ requirements for application design

PurposeOver the past decade, governments around the world have implemented open government data (OGD) policies to make their data publicly available, with collaboration and citizen engagement being one of the main goals. However, even though a lot of data is published, only a few citizens are aware of its existence and usefulness. The purpose of this paper is to identify the requirements for an application that raises awareness of Open Government Data (OGD) to citizens.Design/methodology/approachThis study followed a design research science approach to collect citizens’ requirements for the design of such an application through interviews with 10 citizens and evaluated through user testing with 25 citizens.FindingsThis study identified and validated 11 requirements that can be implemented to raise citizens’ awareness of OGD. The most useful are listing OGD reuses with information about data used and receiving notification when a new OGD reuse is released. Furthermore, the evaluation results provided evidence of the effectiveness of using an application to improve OGD awareness to citizens.Originality/valueThis research provides requirements that can be used by developers to implement a usable tool to raise citizen awareness or by researchers to evaluate applications whose objective is to raise citizen awareness. Finally, it provides a mobile application that can used by developers to showcase their OGD reuses or by researchers to aware citizens of OGD through real-world examples.

An Integrated Model for Predicting the User Continuance Intention towards Utilizing Open Government Data

An Integrated Model for Predicting the User Continuance Intention towards Utilizing Open Government Data

The Construction of Probabilistic Wildfire Risk Estimates for Individual Real Estate Parcels for the Contiguous United States

The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science community and implements computationally efficient and scalable modeling techniques to allow for new high-resolution, CONUS-wide hazard generation. Burn probability, flame length, and ember spread for the years 2022 and 2052 are computed from two ten-year representative Monte Carlo simulations of wildfire behavior, utilizing augmented LANDFIRE fuel estimates updated with all the available disturbance information. FSF-WFM utilizes ELMFIRE, an open-source, Rothermel-based wildfire behavior model, and multiple US Federal Government open data sources to drive the simulations. LANDFIRE non-burnable fuel classes within the wildland–urban interface (WUI) are replaced with fuel estimates from machine-learning models, trained on data from historical fires, to allow the propagation of wildfire through the WUI in the model. Historical wildfire ignition locations and NOAA’s hourly time series of surface weather at 2.5 km resolution are used to drive ELMFIRE to produce wildfire hazards representative of the 2022 and 2052 conditions at 30 m resolution, with the future weather conditions scaled to the IPCC CMIP5 RCP4.5 model ensemble predictions. Winds and vegetation were held constant between the 2022 and 2052 simulations, and climate change’s impacts on the future fuel conditions are the main contributors to the changes observed in the 2052 results. Non-zero wildfire exposure is estimated for 71.8 million out of 140 million properties across CONUS. Climate change impacts add another 11% properties to this non-zero exposure class over the next 30 years, with much of this change observed in the forested areas east of the Mississippi River. “Major” aggregate wildfire exposure of greater than 6% over the 30-year analysis period from 2022 to 2052 is estimated for 10.2 million properties. The FSF-WFM represents a notable contribution to the ability to produce property-specific, climate-adjusted wildfire risk assessments in the US.

Feasibility of a national open data policy in Zimbabwe.

A study on the feasibility of a national open data policy in Zimbabwe was done to document open government data globally and in Zimbabwe. The study showcases the benefits of open government data and the opportunities and challenges toward the development of a national open data policy. Web content analysis and document analysis were used to collect data concerning the readiness of the country in implementing open data activities. The open data barometer was used to gather qualitative data which is essential in assessing the preparedness of the country in opening up government and research data. Content analysis was used to analyse the data which was presented thematically based on the objectives of the study. The findings indicated that the Government of Zimbabwe has endorsed a couple of open data frameworks though some projects are done by non-governmental organizations. The major challenge is implementation of these conventions and commitment to make the data accessible. The results indicated that open data must be made available and accessible within Zimbabwe as a matter of national policy. The author recommends the need for advocacy and continuous awareness creation among the stakeholders so that a national open data policy can be crafted and enacted. The enactment of a national open data policy would guide the use of and access to government data and research data which is valuable in research.

Factors Influencing Public Trust in Open Government Data

Open government data (OGD) involves exposing government data to the public, guided by the values of clarity, accountability, honesty, and integrity. This study investigates the impact of the perceived quality of data, systems, and services on citizens’ trust in OGD, with the information systems success model as the theoretical framework. A questionnaire was delivered electronically to reach OGD users around the world. A total of 358 complete responses were obtained, representing 63.58% of all responses. Structural equation modeling was used to analyze the hypothesized relationships between constructs based on users’ responses. The findings confirm the impact of data, system, and service quality on citizens’ perceived trust in OGD. Moreover, perceived system and service quality had a positive impact on perceived data quality, and perceived service quality had a positive effect on perceived system quality. These findings indicate that OGD service quality affects data and system quality, making it the most fundamental motivator of citizens’ trust in OGD. This highlights the role of open government platforms in developing public services and providing users with complete and correct data, feedback tools, and data visualization.

Engaging Citizens with Open Government Data: The Value of Dashboards Compared to Individual Visualizations

The use of individual visualizations for Open Government Data (OGD) has been shown not to be entirely efficient in engaging citizens. Dashboards constitute a promising solution, but how they should be designed and applied in an OGD context remains under-investigated. This article examines whether the use of well-designed dashboards can increase citizen engagement with OGD. To achieve this objective, a literature review on dashboard design principles is conducted. Then, the outputs of this literature review are used to compile a list of 16 dashboard design principles in the context of OGD. Next, we apply these design principles to build the Namur (Belgium) Budget Dashboard (NBDash) in order to provide a practical application of our research and use it for evaluation. Finally, we use NBDash as a use case to evaluate the usefulness of well-designed dashboards compared to individual visualizations through an experimental study. The results of the experimental design study with 108 participants suggest that the implementation of well-designed dashboards can be beneficial in encouraging the use of data on portals. In addition, the selection of meaningful metrics and the use of appropriate visualizations, all organized in a clear presentation, have proven to be the primary factors of successful dashboards.

Publishers Working with Open Government Data: A Work Framework

This paper presents an Open Government Data (OGD) publisher framework, with work roles, field of work, and environmental descriptions. Previous knowledge about publishers' work is fragmented, with gaps and variations, indicating a high level of complexity with variations in approaches and processes. A two-stage research approach, based on Design Science Research, was used to synthesize the publisher framework. First, a tentative framework was synthesized from previous research, empirical material, and public documents. Second, it was reviewed by informed OGD experts, as well as researchers attending a work conference, and evaluated in two international contexts. As a result, the publisher framework includes environments, social units, and fields of work. The publisher framework is ready to be evaluated in other international contexts, where as, practitioners can use it to inform their work.

Application of Open Government Data to Sustainable City Indicators: A Megacity Case Study

The access to open government data has been a relevant topic for societies around the world, especially over the last ten years. This paper aims to analyze the indicators of the São Paulo City Observatory (ObservaSampa), confronting them with the ISO 3712x series (sustainable, smart, and resilient cities) standards, to assess if the former meet both open data principles and the ISO prescriptions. Bibliometric analysis, comparative analysis, cluster analysis, and principal component analysis (PCA) were the methods used in this research. From the comparative analysis, 18 indicators were identified as conforming and 41 as partially conforming. Thus, 20% of the ObservaSampa indicators adhere to the ISO standards. The PCA applied to the conforming indicators shows component 1 is related to socioeconomic dimensions, while component 2 refers to social policy, with both appraisals confirmed by cluster analysis. Measuring and presenting city data in compliance with indicator standards is relevant because they open the possibility of comparing different cities. However, there is still a lack of consensus on a common set of indicators to be accommodated within the current ISO standards system.