A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an AI-powered method to automatically assess digital transformation in public administrations. The approach combines surveys and AI models, demonstrating effective performance in a Spanish case study for enhanced public services.
Area Of Science
- Public Administration
- Artificial Intelligence
- Digital Transformation
Background
- Public administrations face increasing pressure to enhance service efficiency and citizen-centricity.
- Alignment with Environmental, Social, and Governance (ESG) criteria and UN Sustainable Development Goals (UN SDGs) is a growing priority.
- Existing methods for evaluating digital transformation (DT) in the public sector require innovative, automated solutions.
Purpose Of The Study
- To propose an innovative methodology for the automatic evaluation of digital transformation (DT) levels in public sector organizations.
- To integrate traditional assessment techniques with advanced Artificial Intelligence (AI) models for a comprehensive evaluation.
- To validate the proposed methodology in a real-world case study within local public administrations.
Main Methods
- A dual approach combining expert surveys with AI-based models, including neural networks and transformer architectures.
- Development of a domain-specific corpus using survey data and organizational websites for model training.
- Application and validation of the methodology using real-world data from local public administrations in the Valencian Community, Spain.
Main Results
- The proposed methodology demonstrated effective performance in assessing the digital transformation (DT) level of public organizations.
- AI-based models, trained on a custom corpus, successfully estimated DT levels, complementing traditional survey methods.
- The study confirmed the potential for international scalability due to the methodology's modular structure and dual-source data foundation.
Conclusions
- The integration of AI and traditional methods offers an effective solution for automatically evaluating public sector digital transformation (DT).
- The methodology's modularity and data foundation support its adaptability to different administrative and regulatory contexts.
- Technologies like IoT, sensor networks, and AI analytics are crucial for developing resilient, agile, and sustainable Smart City models.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...