Open Government Data Platform Research Articles

Enhancing local governments' environmental attention through open government data: evidence from China.

Environmental conservation has ascended to a prominent position on the global agenda, and China, recognizing the urgent need for environmental protection, has implemented nationwide measures. However, varying levels of environmental attentiveness among local governments have resulted in uneven implementation of these national directives across regions. Therefore, it is crucial to investigate the factors that drive local governments' environmental attention. Our study explores the impact of open government data (OGD) on local governments' environmental attention. Utilizing city-level data from 2010 to 2020, we employ a staggered difference-in-differences (DID) model for empirical analysis. The results reveal that OGD significantly and positively influences local governments' environmental attention. This influence is partly attributed to OGD's role in promoting government digitization, mitigating fiscal pressures, and increasing energy demand. Further analysis, including heterogeneity assessments, demonstrates that OGD has a more pronounced positive effect on environmental attention in cities with higher online political participation activity and a larger internet user base. Such empirical insights underscore the imperative for an integrative policy framework that accentuates the refinement of OGD platform in tandem with strategic enhancements in political participatory mechanisms and digital infrastructure investments, thereby fostering robust local environmental stewardship paradigms.

Building open government data platform ecosystems: A dynamic development approach that engages users from the start

Open government data (OGD) platform ecosystems hold immense potential for promoting transparency, civic engagement, economic growth, and improved governmental offerings. The prevailing strategy to building OGD platform ecosystems follows a sequential approach where the OGD platform is built first and the ecosystem is built second, resulting in low engagement. In this paper, we derive insights into an alternative approach to developing OGD platform ecosystems from TourismData, a state-owned tourism initiative in Germany. We report on the phases between 2018 and 2022 and derive four dynamic and incremental phases from which we derive three learnings: context specificity, continuous adaptation, and organic expansion. Our findings have theoretical and practical implications for developing high-engagement OGD platform ecosystems that include and engage ecosystem actors from the start and, hence, take advantage of the generative potential of OGD. This approach illustrates the importance of developing OGD platform ecosystems with high contextual relevance to ensure that data can be used to enable meaningful interactions between ecosystem actors and promote continuous adaptation and expansion.

Promoting data use through understanding user behaviors: A model for human open government data interaction

AbstractRecent dramatic increases in the ability to generate, collect, and use datasets have inspired numerous academic and policy discussions regarding the emerging field of human data interaction (HDI). Given the challenges in interacting with open government data (OGD) and the existing research gap in this field, our study intends to explore HDI in the OGD domain and investigate ways HDI can further contribute to OGD promotion. Building upon two existing behavioral models, we proposed an initial conceptual model for OGD interaction, then using this model, conducted two studies to empirically examine users' behaviors when interacting with OGD. Ultimately, we refined this model for OGD interaction and invited three experts to validate it to enhance its understandability, comprehensiveness, and reasonableness. This comprehensive model for human OGD interaction will contribute to the theoretical work of the HDI field as well as the practical design of OGD platforms and data literacy education.

Factors Influencing the Effectiveness of Open Government Data Platforms: A Data Analysis of 61 Prefecture-Level Cities in China

Information disclosure, participatory governance, and collaborative innovation are open government data’s (OGD) main goals. Understanding the preconditions that enable the efficient and effective implementation of OGD initiatives is essential for achieving their overall objectives. Therefore, determining its influencing factors helps improve platform quality, prevent misunderstanding, and realize the purpose of OGD. Based on the literature, this study established a user-oriented conceptual model of OGD into which technology acceptance theory was integrated. This model constructed a data analysis model with the number of downloads of OGD platform data as the response variable and the explanatory variables comprising OGD quality score, linked data density, time to inception, and five others. Sixty-one cases of OGD platforms in Chinese prefecture-level cities were analyzed. The results indicated that the effectiveness of OGD was positively correlated with OGD quality score, time to inception, and the number of employees in the case cities. Network density and average disposable income of citizens had non-significant impacts on effectiveness. Strategies for improving the effectiveness of OGD platforms in China were discussed.

Empirical analysis of open government data usage in Tanzania

PurposeDespite the potential benefits of open government data (OGD) and its recent adoption by many governments, little is known about monitoring its use by the public. This paper aims to address the knowledge gap by empirically examining OGD usage in Tanzania.Design/methodology/approachA field survey design was used to gather quantitative data from 288 people in seven regions of Tanzania to facilitate an analysis of the determinants of users’ behavioural intention and use behaviour of OGD. Guided by the Unified Theory of Acceptance and Use of Technology model, the study analysed the tendencies of these determinants on the use of the OGD portal.FindingsThe results show that performance expectancy, effort expectancy, social influence and facilitating conditions affect users’ acceptance and use of the OGD portal. The results suggest that OGD initiatives should focus more on their potential applications than publication. It is important to monitor and evaluate OGD use and the conditions that allow it to continue. By linking OGD with other government initiatives, governments can build strategies, capacity and social awareness. During the development of OGD platforms for extensive reuse, new data technologies like the semantic web should be considered.Originality/valueHitherto, research has underlined the significance of OGD initiatives at large and that studies in developing countries are scarce, especially that probe the uptake of the current efforts. The present study seeks to plug this research gap by examining the use of OGD in Tanzania’s context.

Air Quality Index Prediction

Falling back past few years rapid progress in Air pollution has become a life-threatening concern in many nations throughout the world due to human activity, industrialisation, and urbanisation.. As a result of these activities, sulphur oxides, carbon dioxide (CO2), nitrogen oxides, carbon monoxide (CO), chlorofluorocarbons (CFC), lead, mercury, and other pollutants be emitted into atmosphere. Simultaneously, estimating quality of air is a tough undertaking because of evolution, variability, also unreasonable unpredictability over pollution and particle region and time. In this project we compare the two Algorithms of machine learning in predicting Index of Air Quality and its predominant. Support vector machine (SVM) exists as prominent machine learning method beneficial to forecasting pollutant plus particle levels and predicting the air quality index (AQI), and Random Forest Regression is another. We'll be working with data from India's Open Government Data Platform. This website displays Air Quality Index readings from around India, including Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Particulate Matter (PM) are examples of contaminants (PM10 and PM2.5), Carbon Monoxide (CO), and others. The output of the project is the predict of Air Quality index using two different algorithms and the comparison of models using various error metrics.

Key factors and generation mechanisms of open government data performance: A mixed methods study in the case of China

Open government data (OGD) has attracted widespread attention and has been widely carried out on a global scale. With further promotion, OGD performance becomes a hot topic and meaningful enough for in-depth exploration. This research focuses on the influential factors and generation mechanisms of OGD performance. Based on the resource-based theory and institutional theory, this paper constructs a model from multiple dimensions of internal resources and external pressures. Subsequently, from the 122 cities in China that have constructed OGD platforms, this study adopts a mixed research methods approach, which combines the regression analysis method and qualitative comparative analysis (QCA). The regression analysis results show that the organization arrangement, legal and policy, and horizontal pressure have direct positive effects on OGD performance. On this basis, this paper use QCA method to explore the configuration paths for the generation of OGD performance of cities in different geographic regions and at different administrative ranks levels. The QCA results provide different configuration paths to achieve better OGD performance, which verified the conclusions drawn by the regression analysis, also provides alternative paths for governments with different characteristics. This paper enriches the studies on OGD performance and provides more targeted paths together with references for the implementation of OGD.

Characteristics of Clinics without National Health Insurance Contracts: A Nationwide Survey in Taiwan.

Based on the 1978 Alma-Ata Declaration, the key to achieving health for all is primary health care, and many countries have established various comprehensive health care systems. Because of the financial toll of a public health care system, government-sponsored public health insurance is not universally accepted. This study used Taiwan as the backdrop to understand why many health clinics have chosen not to accept the National Health Insurance (NHI), despite it covering 99.93% of the country’s population. The clinics’ operational details were garnered from the datasets of Taiwan’s open government data platforms and checked against the list of contracting clinics within the NHI. Of 10,907 Western medicine primary care clinics in 2016, as many as 9846 (90.3%) clinics had signed contracts with the NHI. The remaining 1061 noncontracting clinics were distributed in urban (94.5%, n = 1003), suburban (4.9%, n = 52), and rural/remote areas (0.6%, n = 6). The NHI did not have contracts with 183 plastic surgery, 88 internal medicine, and 85 surgery clinics. In conclusion, nearly one-tenth of clinics practiced independently of the NHI in Taiwan. Their reasons for declining the contract and practices for delivering their services deserve further studies.

Construction of the Evaluation Model of Open Government Data Platform: From the Perspective of Citizens’ Sustainable Use

Under the background of big data, citizens can freely access and use open data to create value through the open government data platform (OGDP). The sustainable use of OGDP can meet the needs of citizens. The value created by citizens can also improve quality of life, which is of great significance to the sustainable development of society. From the citizens’ perspective, we constructed an evaluation model of citizens’ sustainable use of OGDP, including 12 indicators in four dimensions: Data, platform, outcome, and citizen. We have built the complete evaluation system with the DANP (Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process) method. It explores the main influencing factors and mutual influence of citizens’ sustainable use of OGDP. Empirical research is done on four provincial OGDPs in China’s Shanghai, Zhejiang, Guizhou, and Fujian provinces. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was used to rank the OGDPs in four pilot areas in empirical research. The results demonstrate that the improvement priorities of dimensions have the following order: Citizen, outcome, data, and platform, in which data and platform are cause dimensions, and outcome and citizen are result dimensions. The satisfaction indicator has the highest weight among all evaluation indicators, followed by the quality and quantity of outcomes. The one with the lowest weight is non-discrimination. The empirical results show that the OGDP in Zhejiang ranks the highest overall, followed by the OGDPs of Shanghai, Fujian, and Guizhou provinces. In the outcome and citizen dimensions, Zhejiang provincial OGDP does the best. Fujian provincial OGDP does the best in the platform dimension. The citizens’ sustainable use of OGDP can be promoted by timely opening of data that citizens need urgently, perfecting the policy of privacy protection and user guide of OGDP, holding open data innovation competition, providing data visualization function, providing various download formats of data sets, and simplifying the download procedures for citizens.

Enhancing the usability and usefulness of open government data: A comprehensive review of the state of open government data visualization research

Although open government data (OGD) are becoming widely available, they have not yet reached their full potential due to their limited use. Supplementing OGD with visualizations can make data more engaging, useful, and understandable. We synthesized the academic research on the development and evaluation of OGD visualizations in the IEEE Explore, Science Direct, and ACM Digital Library databases. Our findings suggest four streams of research: 1) description and evaluation of specific examples of OGD visualization, 2) general tools and frameworks, 3) users' needs assessments and evaluation of usability, and 4) other user-related outcomes. While there is a growing robust literature describing specific cases, further empirical research is needed on evaluating the usability of OGD visualizations and tools for different audiences. Promising strategies to enhance the usability and usefulness of OGD are to employ advanced OGD platforms with embedded visualizations and analytics tools and to collaborate with research centers on user studies to ensure that visualizations and analytic tools are useful for stakeholders.

UVLens

Urban villages refer to the residential areas lagging behind the rapid urbanization process in many developing countries. These areas are usually with overcrowded buildings, high population density, and low living standards, bringing potential risks of public safety and hindering the urban development. Therefore, it is crucial for urban authorities to identify the boundaries of urban villages and estimate their resident and floating populations so as to better renovate and manage these areas. Traditional approaches, such as field surveys and demographic census, are time consuming and labor intensive, lacking a comprehensive understanding of urban villages. Against this background, we propose a two-phase framework for urban village boundary identification and population estimation. Specifically, based on heterogeneous open government data, the proposed framework can not only accurately identify the boundaries of urban villages from large-scale satellite imagery by fusing road networks guided patches with bike-sharing drop-off patterns, but also accurately estimate the resident and floating populations of urban villages with a proposed multi-view neural network model. We evaluate our method leveraging real-world datasets collected from Xiamen Island. Results show that our framework can accurately identify the urban village boundaries with an IoU of 0.827, and estimate the resident population and floating population with R2 of 0.92 and 0.94 respectively, outperforming the baseline methods. We also deploy our system on the Xiamen Open Government Data Platform to provide services to both urban authorities and citizens.

Envisioning the future through e-transparency: Using OGD platforms to monitor government capacity in achieving policy goals

In this study, a method was developed to allow a civil society organization to seek public budget information using open government data (OGD) available on platforms. Accordingly, an empirical study was conducted based on Design Science Research (DSR) to address how to monitor the future outcome of national education policy through e-transparency tools. The method designed, represented in a flowchart tool, revealed some aspects regarding the implications of e-transparency for the monitoring and projection of public policy accomplishment, identifying and addressing five constraints for the use of OGD platforms: granularity, traceability, correlation, accessibility, and usability. When monitoring is possible, civil society is interested in using the information to project the future outcomes of public policy rather than monitor the past, which creates a feature in envisioning the future public policy accomplishment.

Heat stroke-related deaths in India: An analysis of natural causes of deaths, associated with the regional heatwave.

Soaring temperatures cause deaths in large numbers in various parts of India. The number of deaths vary with region and are influenced by the demographics and socioeconomic characteristics of the region. This study tried to estimate the number of deaths associated with exposure to heat in the different states of India. Secondary data was used, which was collected from the website data.gov.in, an Open Government Data (OGD) Platform of the Indian government. Descriptive statistics were applied using Microsoft Excel-10. It was found that there 3014 men died from heat-related causes in 2001-05, which increased to 5157 in the period 2011-15. For women the number of deaths in the corresponding periods were 849 and 1254 respectively. Deaths caused by heatwaves were found to be higher than those resulting from avalanches, exposure to cold, cyclone, tornado, starvation due to natural calamity, earthquake, epidemic, flood, landslide, torrential rain and forest fire. The study revealed that there are regional variations in the number deaths due to heatstroke. From the perspective of disaster preparedness, it is important to note that deaths from heat strokes occur every year. With rising temperatures, the numbers are likely to increase. The findings of the study highlight this concern. Therefore, there is a need for targeted region-specific interventions for reducing the number of deaths due to heatwaves.

Data Visualization for Accelerated Business Intelligence in the Indian Health Care Sector using Tableau

Purpose Big data analytics capability is the new source of competitive advantage. This is very axiomatic and it is also known that this capability covers the domain of descriptive, diagnostic, predictive and prescriptive analytics. Apart from the capability of managing data coming with high velocity, huge volume, and high variety the capability to analyze data with the same or more speed is a not only a hygiene factor but a compulsion for any organization. Ease of use of data visualizing tools which help in describing the data is driving the consumption of visualization tools and Tableau has taken a clear lead, and is witnessing mass adoption. The paper is a detailed account of creating a interactive visualization of heath care infrastructure data using Tableau Publix as a business intelligence tool. Methods: The Visualization is done on data taken from Open Government Data (OGD) Platform India. The paper is a demonstration of the use of visualization for exploration of data, generation of relevant questions and thereafter finding the right answers. Result: The papers explores the data and does the following through visualization 1. Maps the geography (Indian states) with total number of patients) 2. Maps the state name with segregated number of patients suffering from acute and chronic disease. 3. Maps the state name with segregated number of patients suffering from chronic disease. 4. Maps the state name with segregated number of patients suffering from acute disease. 5. Builds all the above in an interactive dashboard.

Open government data platforms – A complex adaptive sociomaterial systems perspective

In this paper we focus on the emerging phenomenon of Open Government Data Platforms (OGDPs), in particular those that provide open performance data to general public. Governments world-wide continue to implement these platforms, aiming to increase transparency and accountability. However, in spite of their positive intentions, ODGPs that provide performance data (e.g. about schools or hospitals) are reported to create serious harmful social effects. While the related literature has reported numerous cases of these unintended effects, the questions regarding why and how they emerge remain open. This is not surprising, given the complexity and dynamics of processes instigated through the use of ODGPs by a very large number of known and unknowable actors. Through a diffractive reading of complexity theories, in particular Complex Adaptive Systems (CAS) and sociomateriality, in this paper we propose a Complex Adaptive Sociomaterial Systems (CASS) theorization of OGDPs. Drawing from a case of OGDP in Australia called My School, which provides open performance data for more than 10.000 schools, we demonstrate how the proposed theoretical lens of CASS enables us to reveal and explain why and how these platforms perform unintended, yet serious social harm. Given that OGDPs are rapidly emerging around the world, our research opens a pathway for a research-informed public discourse about their harmful effects and responsibilities of different stakeholders.

A surveillance of food borne disease outbreaks in India: 2009–2018

Knowledge about distribution of food-borne outbreaks and implicated food-vehicles helps in mitigating the risk of food-borne diseases and is critical for designing strategies to control them. In this study data from Integrated Disease Surveillance Program (IDSP) and Open Government Data Platform India (OGDPI) on food-borne outbreaks for the period 2008–2018 was consolidated and analysed. The modelling methods of Gaussian distribution model (GAM) and Autoregressive Integrated Moving Average (ARIMA) were used to probe influence of climatic factors (temperature and rainfall) on food-borne outbreaks. Data analysis showed that states of West Bengal (31.22), Karnataka (29.11) and Gujarat (22.67) reported maximum average outbreaks and contributed to 31.5% illnesses and 8.7% deaths. Amongst 19.6% of outbreaks, grains and beans were found to be food-vehicle causing maximum outbreaks (32.7%), while chemically contaminated food caused maximal deaths (70%). Weak correlations of climatic factors with outbreaks resulted in poor performance of ARIMA models. GAM model was validated and predicted 356 outbreaks for the year 2020, late-April to mid-July being most prevalent months. The analysis also revealed inclination of current surveillance program towards chemically contaminated food that resulted in maximal deaths (70%), while biological agents were observed to be under-reported. Despite the limitations, available data shows that food-borne disease outbreaks remain a public health concern in India. Therefore, it is imperative for India to strengthen its disease surveillance program by undertaking capacity-building initiatives at state/local health-care levels and connecting causative agents of outbreaks. This would help in efficient implementation of risk assessment and risk management strategies.

Real Time Air Quality Index from Various Locations

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

Cultivating open government data platform ecosystems through governance: Lessons from Buenos Aires, Mexico City and Montevideo

Open government data (OGD) initiatives are an emergent platform research topic. There is little understanding how these platforms are governed for the innovation of services using open data, where the cultivation of an installed base of heterogeneous service innovators can lead to increased usage of OGD. In this paper, we draw on established literature from digital platforms research to investigate how service innovation is cultivated in open government data contexts. We employ a comparative case study of open government data platforms in three leading Latin American cities and draw upon the concept of boundary resources taken from platform theory. Our research generates two key contributions. First, we propose a theoretical model, which explains how an OGD platform owner is able to govern the demand and the supply side of its platform to facilitate the cultivation of a platform ecosystem. Second, we develop contributions to practice in terms of a set of recommendations for governments in emerging countries concerning how to establish and manage a vibrant OGD platform ecosystem.

Production of linked government datasets using enhanced LIRE architecture

This paper describes the enhanced LIRE (LInked RElations) architecture for creating relations between datasets available on open government portals. The architecture is improved to be applicable on different open government data platforms using minimal configuration at the Data processing layer. We evaluated the applicability of enhanced LIRE, its advantages and disadvantages, which resulted in necessary recommendations for the publication of dataset?s metadata to obtain better associations between datasets. Moreover, we introduced a LINDAT indicator that reflects the percentage of linked data in the total possible number of linked data on open government data portals.

KCC QA Latent Semantic Representation using Deep Learning & Hierarchical Semantic cluster Inferential Framework

Abstract Rapid digitization is in progress in even the most traditional domains like Agriculture. In line with this, the Government of India released call centre question-answer data called the Kisan Call Centre data ( KCC ). Using the KCC data available at Open Government Data platform this paper suggests a novel double-headed autoencoder architecture that outperforms ubiquitous deep learning architectures used as baselines. Experiments are carried out for the data from Tamil Nadu call centres. The various sentence embeddings generated are clustered and externally analyzed against carefully hand-annotated data using various measures like cluster entropy for various levels of semantic resolution. A framework is created to model and evaluate semantic embeddings to create scope for various downstream tasks such as predicting rare events like drought and low yield etc