Data-driven Decision Support for Urban Management: Trends and Challenges
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".
Deadline for manuscript submissions: closed (1 March 2021) | Viewed by 7687
Special Issue Editors
Interests: intelligent systems; smart cities; industry 4.0; data quality; decision support
Special Issues, Collections and Topics in MDPI journals
Interests: big data; smart cities; data quality; GPS; city logistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With almost 70% of the world’s population expected to live in urban areas by 2050, cities increasingly face complex challenges to keep the urban environment accessible, attractive, healthy and sustainable. Administrations need to manage urban space, resources and activity in line with the needs of citizens and local economy. At the same time, modern urban services are rapidly changing with new models like autonomous vehicles, co-housing, same day delivery entering the market and fundamentally changing traditional patterns of behaviour.
Modern data sources can help to give detailed and current insight in different aspects of a city like transport and mobility, energy, environment, tourism. Emerging sources like massive location traces, IoT sensors, smart cards and social media offer the possibility of capturing the pulse of a city in realtime and complement traditional sources that are often estimated and delayed in time. They give insight with a detailed spatial, time and user granularity. Many cities have started to collect and explore data like live traffic data, citizen science mobile data campaigns and cell phone-based pedestrian monitoring. Data collection is however only a first step. There are big challenges in using this data for policy support in an efficient and reliable way. The support can range from city dashboards that monitor urban indicators for operational support (e.g. traffic management, environment) up to data-driven prediction models that predict short-term events (e.g. traffic jams) or long-term trends (e.g. energy consumption). Depending its use, challenges lie in the quality and reliability of the data, inter-operability and standardization for data integration, the transformation and mining of raw data into policy-relevant information, the integration of data into urban decision support systems, privacy and governance of urban data. In this special issue, we invite papers that explore the use of modern data sources in urban decision making. Suggested topics include, but are not limited to:
- Reviews on the state of art in urban data sources and policy support;
- Applications in data-driven urban decision support;
- Methods for quality assessment of urban big data;
- Interoperability and standardization of governmental and commercial (open) data and services;
- Data mining for urban policy indicators and information;
- Data-driven predictive modelling (e.g. neural networks, agent-based simulation) for urban management.
Prof. Dr. Sidharta Gautama
Prof. Dr. Ivana Semanjski
Guest Editors
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Keywords
- Data-driven decision support
- Predictive modelling
- Data mining
- Data governance
- Urban management
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