A Goal-Oriented Framework for Analyzing and Modeling City Dashboards in Smart Cities
For several years, many cities around the world are moving through a number of initiatives to implement the so-called “city dashboards”, as an opportunity for a new quality of urban life in terms of knowing and governing cities. The main contribution of this paper is to examine how city dashboards are performing on various metrics and comparing them in order to understand what they do. Starting from this perspective, to the best of our knowledge and by examining dashboard examples, there are many differences in the products that go by the name “city dashboards”. Moreover there are several methodological and technical issues that are not dealt with and yet solved in terms of data, indicators and benchmarking. The design of a city dashboard needs a clear vision of the direction that public administrations intend to undertake, alongside an ability to build scenarios and analyze the results of experiments in the context of the changing urban variables. Given the gap in academic literature concerning this subject, we developed a goal-oriented framework for examining the characteristics of various city dashboards and developing a taxonomy. Our framework enables a more systematic process for developing an effective city dashboard and provides useful insights to decision makers. The results suggest that some features emerge and our findings highlight specific clusters.
KeywordsCity dashboard Urban governance Taxonomy Smart cities, goal/question/metric
This study was supported by the MIUR (Ministry of Education, Universities and Research [Italy]) through a project entitled Governing the smart city: a governance-centred approach to Smart urbanism—GHOST (Project code: RBSI14FDPF; CUP Code: F22I15000070008), financed with the SIR (Scientific Independence of Young Researchers) programme.
- Akbar, M., Sukmana, H. T., & Khairani, D. (2014). Models and software measurement using Goal/Question/Metric method and CMS Matrix parameter (Case study discussion forum). In 2014 International Conference on Cyber and IT Service Management (CITSM) (pp. 34–38). South Tangerang.Google Scholar
- Basili, V. R. (1993). Applying the Goal/Question/Metric paradigm in the experience factory (pp. 21–44). Software Quality Assurance and Measurement: A Worldwide Perspective.Google Scholar
- Basili, V., Heidrich, J., Lindvall, M., Münch, J., Regardie, M., Rombach, D., & Trendowicz, A. (2014). GQM + Strategies: A comprehensive methodology for aligning business strategies with software measurement.Google Scholar
- Bui, L. (2015, October). Breathing smarter: A critical look at representations of air quality sensing data across platforms and publics. In 2015 IEEE First International Smart Cities conference (ISC2) (pp. 1–5).Google Scholar
- Cagliero, L., Cerquitelli, T., Chiusano, S., Garino, P., Nardone, M., Pralio, B., et al. (2015, April). Monitoring the citizens’ perception on urban security in smart city environments. In 2015 31st IEEE International Conference on Data Engineering Workshops (ICDEW) (pp. 112–116).Google Scholar
- Caldiera, V. R. B. G., & Rombach, H. D. (1994). The goal question metric approach. Encyclopedia of Software Engineering, 2(1994), 528–532.Google Scholar
- Carta, M. (2017). Augmented city. A Paradigm Shift. Trento-Barcelona: Listlab (in press).Google Scholar
- Dameri, R. P. (2016). Smart city implementation: Creating economic and public value in innovative urban systems. Cham, Switzerland: Springer.Google Scholar
- Fegraus, E. H., Zaslavsky, I., Whitenack, T., Dempewolf, J., Ahumada, J. A., Lin, K., et al. (2012). Interdisciplinary decision support dashboard: A new framework for a Tanzanian agricultural and ecosystem service monitoring system pilot. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), 1700–1708.CrossRefGoogle Scholar
- Facility, Global City Indicators. (2013). Data, boundaries, and competitiveness: The Toronto urban region in global context. Global City Indicators Facility: University of Toronto.Google Scholar
- ISO 37101. (2016). Sustainable development and resilience of communities—management systems—general principles and requirements. Technical Committee: ISO/TC 268 Sustainable cities and communities. ICS: 13.020.20 Environmental economics. Sustainability. Publication date: 2016-07. Available at https://www.iso.org/standard/61885.html.
- ISO 37120. (2014). Sustainable development in communities: Indicators for city services and quality of life. Technical Committee: ISO/TC 268. Sustainable cities and communities. ICS: 13.020.20 Environmental economics. Sustainability. Publication date: 2014-05. Available at https://www.iso.org/standard/62436.html.
- Kitchin, R., Coletta, C., & McArdle, G. (2017). Urban informatics, governmentality and the logics of urban control. Available at osf.io/preprints/socarxiv/27hz8.
- Kitchin, R., & McArdle, G. (2016). Urban data and city dashboards: Six key issues. Working paper. Programmable city working paper 21, Maynooth.Google Scholar
- Mannaro, K., Melis, M., & Marchesi, M. (2004). Empirical analysis on the satisfaction of IT employees comparing XP practices with other software development methodologies. In International Conference on Extreme Programming and Agile Processes in Software Engineering (pp. 166–174). Springer Berlin Heidelberg.CrossRefGoogle Scholar
- Mendonça, M., Moreira, B., Coelho, J., Cacho, N., Lopes, F., Cavalcante, E., et al. (2016). Improving public safety at fingertips: A smart city experience. In 2016 IEEE International Smart Cities Conference (ISC2) (pp. 1–6).Google Scholar
- Nesi, P., Badii, C., Bellini, P., Cenni, D., Martelli, G., & Paolucci, M. (2016). Km4City smart city API: An integrated support for mobility services. In 2016 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 1–8).Google Scholar
- Osella, M., Ferro, E., & Pautasso, M. E. (2016). Toward a methodological approach to assess public value in smart cities. In Smarter as the New Urban Agenda (pp. 129–148). Springer International Publishing.Google Scholar
- Pani, F. E., Lunesu, M. I., Concas, G., & Baralla, G. (2015). The web knowledge management: A taxonomy-based approach. In Knowledge Discovery, Knowledge Engineering and Knowledge Management: 5th International Joint Conference, IC3 K 2013. Vilamoura, Portugal, September 19–22, 2013.Google Scholar
- Southekal, P. (2017). Data for business performance: The goal-question-metric (GQM) model to transform business data into an enterprise asset. Technics Publications; First edition.Google Scholar
- Suakanto, S., Supangkat, S. H., & Saragih, R. (2013). Smart city dashboard for integrating various data of sensor networks. In 2013 International Conference on ICT for Smart Society (ICISS) (pp. 1–5).Google Scholar
- Usurelu, C. C., & Pop, F. (2017). My city dashboard: Real-time data processing platform for smart cities. Journal of Telecommunications and Information Technology, 1, 89.Google Scholar