Digital Dashboards for Smart City Governance: A Case Project to Develop an Urban Safety Indicator Model

Alberto De Marco, Giulio Mangano, Giovanni Zenezini

Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.

DOI: 10.4236/jcc.2015.35018                

Abstract

This paper illustrates a case project to design a digital dashboard for governing the urban safety of an Italian city and proposes a methodology for the definition of a set of safety measurement indicators. Results show that the method is easy to be used to identify the most crucial areas of the city, in several domains of application that have been identified. The study can substantially support policy makers in the development of their strategies and in the measurement of the effectiveness of their decisions.

Keywords

Public Safety, Urban Performance Indicators, Torino, Digital Dashboards

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	UNDP (2010) Frequently Asked Questions: The 2010 Human Development Index (HDI), New Horizons, UNDP, International Human Development Indicators. http://hdrstats.undp.org/en/tables/
[2]	Miller, H.J., Witlox, F. and Tribby, C.P. (2013) Developing Context-Sensitive Livability Indicators for Transportation Planning: A Measurement Framework. Journal of Transport Geography, 26, 51-64. http://dx.doi.org/10.1016/j.jtrangeo.2012.08.007
[3]	Botterman, S., Hooghe, M. and Reeskens, T. (2012) One Size Fits All? An Empirical Study into the Multidimensionality of Social Cohesion Indicators in Belgian Local Communities. Urban Studies, 49, 185-202. http://dx.doi.org/10.1177/0042098010397397
[4]	Carli, R., Dotoli, M., Pellegrino, R. and Ranieri, L. (2013) Mea-suring and Managing the Smartness of Cities: A Framework for Classifying Performance Indicators. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, 13-16 October 2013, 1288-1293.
[5]	Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G. and Scorrano, F. (2014) Current Trends in Smart City Initiatives: Some Stylised Facts. Cities, 38, 25-36. http://dx.doi.org/10.1016/j.cities.2013.12.010
[6]	Pan, J.G., Lin, Y.F., Chuang, S.Y. and Kao, Y.C. (2011) From Governance to Service-Smart City Evaluations in Taiwan. IEEE International Joint Conference on Service Sciences (IJCSS), Taipei, 25-27 May 2011, 334-337. http://dx.doi.org/10.1109/IJCSS.2011.74
[7]	Lombardi, P., Giordano, S., Farouh, H. and Yousef, W. (2012) Model-ling the Smart City Performance. Innovation— The European Journal of Social Science Research, 25, 133-145.
[8]	Jans, G. and Verdegem, P. (2006) DashGov: Developing an e-Government Dashboard. In IADIS international Conference e-Society Location, Dublin.
[9]	Harrison, C. and Donnelly, I.A. (2011) A Theory of Smart Cities. Proceedings of the 55th Annual Meeting of the ISSS, 17-22 July 2011, Hull, UK. Vol. 55, No. 1.
[10]	Hajduova, Z., Andrejovky, P. and Beslerova, S. (2014) Development of Quality of Life Economic Indicators with Regard to the Environment. Procedia—Social and Behavioral Sciences, 110, 747-754. http://dx.doi.org/10.1016/j.sbspro.2013.12.919
[11]	Petrosillo, I., Costanza, R., Aretano, R., Zaccarelli, N. and Zurlini, G. (2013) The Use of Subjective Indicators to Assess How Natural and Social Capital Support Residents’ Quality of Life in a Small Volcanic Island. Ecological Indicators, 24, 609-620. http://dx.doi.org/10.1016/j.ecolind.2012.08.021
[12]	Hezri, A. and Dovers, S.R. (2006) Sustainability Indicators, Policy and Governance Issues for Ecological Economics. Ecological Economics, 60, 86-99. http://dx.doi.org/10.1016/j.ecolecon.2005.11.019
[13]	Ronchi, E., Federico, A. and Musumeci, F. (2002) A System Oriented Integrated Indicator for Sustainable Development in Italy. Ecological Indicators, 2, 197-210. http://dx.doi.org/10.1016/S1470-160X(02)00045-6
[14]	Hebenton, B., Jou, S. and Chang, Y.C. (2010) Developing Public Safety and Crime Indicators in Taiwan. Asian Criminology, 5, 45-67. http://dx.doi.org/10.1007/s11417-009-9081-8
[15]	Christin, D., Ro?kopf, C. and Hollick, M. (2013) uSafe: A Priva-cy-Aware and Participative Mobile Application for Citizen Safety in Urban Environments. Pervasive and Mobile Computing, 9, 695-707. http://dx.doi.org/10.1016/j.pmcj.2012.08.005
[16]	Pissourios, I.A. (2013) An Interdisciplinary Study on Indicators: A Comparative Review of Quality of Life, Macroeconomic, Environmental, Welfare and Sustainability Indicators. Eco-logical Indicators, 34, 420-427. http://dx.doi.org/10.1016/j.ecolind.2013.06.008
[17]	Li, G. and Weng, Q. (2007) Measuring the Quality of Life in City of Indianapolis by Integration of Remote Sensing and Census Data. International Journal of Remote Sensing, 28, 249-267. http://dx.doi.org/10.1080/01431160600735624
[18]	Wickramasuriya, R., Ma, J., Berryman, M. and Perez, P. (2013) Using Geospatial Business Intelligence to Support Regional Infrastructure Governance. Knowledge-Based Systems, 53, 80-89. http://dx.doi.org/10.1016/j.knosys.2013.08.024
[19]	Mori, K. and Christodoulou, A. (2012) Review of Sustainability Indices and Indicators: Towards an New City Sustainability Index (CSI). Environmental Impact Assessment Review, 32, 94-106. http://dx.doi.org/10.1016/j.eiar.2011.06.001
[20]	Voskamp, I.M. and Van de Ven, F.H.M. (2015) Planning Support System for Climate Adaptation: Composing Effective Sets of Blue-Green Measures to Reduce Urban Vulnerability to Extreme Weather Events. Building and Environment, 83, 159-167. http://dx.doi.org/10.1016/j.buildenv.2014.07.018
[21]	Batty, M. (2012) Smart Cities, Big Data. Environment and Planning B: Planning and Design, 39, 191-193. http://dx.doi.org/10.1068/b3902ed
[22]	Miller, A. and Cioffi, J. (2004) Measuring Marketing Effectiveness and Value: The Unisys Marketing Dashboard. Journal of Advertising Research, 44, 237-243. http://dx.doi.org/10.1017/S0021849904040334
[23]	Iandoli, L., Quinto, Q., De Liddo, A. and Shum, S.B. (2012) A Debate Dashboard to Enhance Online Knowledge Sharing. VINE: The Journal of Information and Knowledge Man-agement Systems, 42, 67-93.
[24]	Arribas-Bel, D. (2014) Accidental, Open and Everywhere: Emerging Data Sources for the Understating of the Cities. Applied Geography, 49, 45-63. http://dx.doi.org/10.1016/j.apgeog.2013.09.012
[25]	Barton, D. and Court, D. (2012) Making Advanced Analytics Work for You. Harvard Business Review, 90, 79-83.
[26]	Hazen, B.T., Boone, C.A., Ezell, J.D. and Jones-Farmer, L.A. (2014) Data Quality for Data Science, Predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications. International Journal of Production Economics, 154, 72-80.
[27]	Lee Rodgers, J. and Nicewander, W.A. (1988) Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42, 59-66. http://dx.doi.org/10.1080/00031305.1988.10475524
[28]	Rice, W.R. (1988) A New Probability Model for Determining Exact P-Values for 2 × 2 Contingency Tables When Comparing Binomial Proportions. Biometrics, 44, 1-22. http://dx.doi.org/10.2307/2531892