Skip to main content
SpringerLink
Log in

Building Adaptable Dashboards for Smart Cities: Design and Evaluation

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

Abstract

Today there are smart cities that, through the use of information technologies, sensors, and specialized infrastructure, focus their efforts on improving the quality of life of their inhabitants. From these efforts arose the need to analyze and represent data within a system to make it useful and understandable to people, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and preferences. However, the analysis of adaptability through user interaction and its benefits is a topic still under exploration. This paper analyzes the literature on information visualization in adaptable dashboards for smart cities. Based on the elements of adaptable dashboards identified in the literature review, we propose an adaptable dashboard architecture, identify the main characteristics of the users of a smart city dashboard, and build an adaptable dashboard prototype using user-centered techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.

Similar content being viewed by others

REFERENCES

  1. Alvarado-López, R.A., Ciudades inteligentes y sostenibles: una medición a cinco ciudades de México, Estud. Soc. Rev. Aliment. Contemp. Desarrollo Reg., 2020, vol. 30, no. 55.

  2. Zapata, J.S.B. and Arce, R.M., Modelo para evaluar el cumplimiento de objetivos de ciudades inteligentes, Proc. Conf.: Sustentabilidade Urbana Jornada URBENERE CIRES, Vitoria, 2018.

  3. Tachtler, F.M., Best way to go? Intriguing citizens to investigate what is behind smart city technologies, Proc. 2017 ACM Conf. on Designing Interactive Systems, DIS 2017 Companion, Edinburgh, 2017, pp. 28–33.

  4. Few S. and Edge, P., Dashboard confusion revisited, in Perceptual Edge, 2007, pp. 1–6.

  5. Vila, R.A., Estevez, E., and Fillottrani, P.R., The design and use of dashboards for driving decision-making in the public sector, Proc. 11th Int. Conf. on Theory and Practice of Electronic Governance, ICEGOV’18, Galway, 2018, pp. 382–388.

  6. Latifah, A., Supangkat, S.H., and Ramelan, A., smart building: a literature review, Proc. 7th Int. Conf. on ICT for Smart Society: Aiot for Smart Society, ICISS 2020, Bandung, 2020, pp. 17–22.

  7. Cepero Garcia., M.T. and Montane-Jimenez, L.G., Visualization to support decision-making in cities: advances, technology, challenges, and opportunities, Proc. 8th Int. Conf. Software Engineering Research and Innovation, CONISOFT 2020, 2020, pp. 198–207.

  8. Laramee, R.S., Turkay, C., and Joshi, A., Visualization for smart city applications, IEEE Comput. Graph. Appl., 2018, vol. 38, no. 5, pp. 36–37.

    Article  Google Scholar 

  9. Ergasheva, S., Ivanov, V., Khomyakov, I., Kruglov, A., Strugar, D., and Succi, G., InnoMetrics Dashboard: the Design, and Implementation of the Adaptable Dashboard for Energy-Efficient Applications Using Open Source Tools, Springer Int. Publ., 2020, vol. 582.

    Google Scholar 

  10. Tokola, H., Groger, C., Jarvenpaa, E., and Niemi, E., Designing manufacturing dashboards on the basis of a key performance indicator survey, Procedia CIRP, 2016, vol. 57, pp. 619–624.

    Article  Google Scholar 

  11. Pettit, C., Widjaja, I., Russo, P., Sinnott, R., Stimson, R., and Tomko, M., Visualisation support for exploring urban space and place, ISPRS Ann. Photogramm., Remote Sens. Spatial Inf. Sci., 2012, vol. 1, pp. 153–158.

    Google Scholar 

  12. Zdraveski, V., Mishev, K., Trajanov, D., and Kocarev, L., Isostandardizedsmart city platform architecture and dashboard, IEEE Pervasive Comput., 2017, vol. 16, no. 2, pp. 35–43.

    Article  Google Scholar 

  13. Shafiq, S.I., Szczerbicki, E., and Sanin, C., Proposition of the methodology for data acquisition, analysis and visualization in support of Industry 4.0, Procedia Comput. Sci., 2019, vol. 159, pp. 1976–1985.

    Article  Google Scholar 

  14. Contreras-Figueroa, V., Montané-Jiménez, L.G., Cepero, T., Benítez-Guerrero, E., and Mezura-Godoy, C., Information visualization in adaptable dashboards for smart cities: A systematic review, Proc. 9th Int. Conf. in Software Engineering Research and Innovation (CONISOFT), San Diego, CA, 2021, pp. 34–43.

  15. Kitchenham, B., Procedures for performing systematic reviews, Tech. Rep., Keele Univ., 2004, no. TR/SE-0401.

  16. Few, S., Information dashboard design: displaying data for at-a-glance monitoring, in Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2013, p. 246.

    Google Scholar 

  17. Zhang, J., Johnson, K.A., Malin, J.T., and Smith, J.W., Human-Centered Information Visualization, 1995.

  18. Matheus, R., Janssen, M., and Maheshwari, D., Data science empowering the public: data-driven dashboards for transparent and accountable decision-making in smart cities, Gov. Inf. Q., 2020, vol. 37, no. 3, pp. 101284.

  19. Protopsaltis, A., Sarigiannidis, P., Margounakis, D., and Lytos, A., Data visualization in internet of things: tools, methodologies, and challenges, Proc. 15th Int. Conf. on Availability, Reliability and Security ARES 2020, 2020.

  20. Tong, X. and Wu, Z., Study of chinese city “portrait” based on data visualization: take city dashboard for example, in Design, User Experience, and Usability: Designing Interactions, Marcus, A. and Wang, W., Eds., Cham: Springer Int. Publ., 2018, pp. 353–364.

    Google Scholar 

  21. Batty, M., A perspective on city dashboards, Reg. Stud., Reg. Sci., 2015, vol. 2, no. 1, pp. 29–32.

    Google Scholar 

  22. Han, Q., Nesi, P., Pantaleo, G., and Paoli, I., Smart city dashboards: design, development, and evaluation, Proc. 2020 IEEE Int. Conf. on Human-Machine Systems, ICHMS 2020, 2020.

  23. Dobraja, I., Kraak, M.-J., and Engelhardt, Y., Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data, Proc. Int. Cartographic Conf. ICA2018, Washington, 2018, vol. 1, pp. 1–6.

  24. Kourtit, K. and Nijkamp, P., Big data dashboards as smart decision support tools for i-cities. an experiment on stockholm, Land Use Policy, 2018, vol. 71, pp. 24–35.

    Article  Google Scholar 

  25. Sharifi, A., A typology of smart city assessment tools and indicator sets, Sust. Cities Soc., 2019, vol. 53, p. 101936.

  26. O’Neill, D. and Peoples, C., Using IT to monitor well-being and city experiences, IEEE Potentials, 2016, vol. 35, no. 6, pp. 29–34.

    Article  Google Scholar 

  27. Peddoju, S.K. and Upadhyay, H., Evaluation of iot data visualization tools and techniques, in Data Visualization: Trends and Challenges Toward Multidisciplinary Perception, Springer, 2020, pp. 115–139.

    Google Scholar 

  28. Lv, Z., Li, X., Wang, W., Zhang, B., Hu, J., and Feng, S., Government affairs service platform for smart city, Future Gener. Comput. Syst., 2018, vol. 81, pp. 443–451.

    Article  Google Scholar 

  29. Vicuna, P., Mudigonda, S., Kamga, C., Mouskos, K., and Ukegbu, C., A generic and flexible geospatial data warehousing and analysis framework for transportation performance measurement in smart connected cities, Procedia Comput. Sci., 2019, vol. 155, no. 2018, pp. 226–233.

  30. Purahoo, Z. and Cheerkoot-Jalim, S., SenseAPP: an IoTBased mobile crowdsensing application for smart cities, Proc. 3rd Int. Conf. on Emerging Trends in Electrical, Electronic and Communications Engineering, ELECOM 2020, 2020, pp. 47–52.

  31. Moustaka, V., Maitis, A., Vakali, A., and Anthopou-los, L.G., CityDNA dynamics: a model for smart city maturity and performance benchmarking, Proc. Web Conf. 2020, Companion of the World Wide Web Conference, WWW 2020, 2020, pp. 829–833.

  32. Chen, S. and Dong, H., Visualizing Toronto City data with HoloLens: using augmented reality for a city model, IEEE Consum. Electron. Mag., 2018, pp. 73–80.

  33. Brunetto, O., City dashboards: the case of Trieste: Trieste overview, Proc. Int. Conf. on Computational Science and Its Applications ICCSA 2017, Trieste, 2017.

  34. Habibzadeh, H., Kaptan, C., Soyata, T., Kantarci, B., and Boukerche, A., Smart city system design, ACM Comput. Surv., 2019, vol. 52, no. 2, pp. 1–38.

    Article  Google Scholar 

  35. Rolim, D., Silva, J., Batista, T., and Cavalcante, E., Web-based development and visualization dashboards for smart city applications, Proc. Int. Conf. on Web Engineering ICWE 2020, 2020.

  36. Chrysantina A. and Sæbø, Assessing user-designed dashboards: a case for developing data visualization competency, IFIP Adv. Inf. Commun. Technol., 2019, vol. 551, pp. 448–459.

    Article  Google Scholar 

  37. Elshehaly, M., Randell, R., Brehmer, M., McVey, L., Alvarado, N., Gale, C.P., and Ruddle, R.A., QualDash: adaptable generation of visualisation dashboards for healthcare qualityimprovement, IEEE Trans. Vis. Comput. Graph., 2021, vol. 27, no. 2, pp. 689–699.

    Article  Google Scholar 

  38. Dostal, R., Pribyl, O., and Svitek, M., City infrastructure evaluation using urban simulation tools, in Proc. 2020 Smart Cities Symp., Prague: SCSP, 2020, pp. 1–6.

  39. Lu, M., Liu, S., Liu, P., and Li, Q., Design and implementation of power information visualization platform based on smart meter, Proc. 3rd Int. Conf. on Information Science and Control Engineering, ICISCE 2016, Beijing, 2016, pp. 297–301.

  40. Curry, E., Fabritius, W., Hasan, S., Kouroupetroglou, C., and Derguech, U., A model for Internet of Things enhanced user experience in smart environments, in Real-Time Linked Dataspaces, Springer, 2020, pp. 271–294.

    Book  Google Scholar 

  41. Molich, R. and Nielsen, J., Improving a human-computer dialogue, Commun. ACM, 1990, vol. 33, no. 3, pp 338–348.

    Article  Google Scholar 

  42. Limon-Ruiz, M., Larios-Rosillo, V.M., Maciel, R., Beltran, R., Orizaga-Trejo, J.A., and Ceballos, G.R., User-oriented representation of Smart Cities indicators to support citizens governments decision-making processes, Proc. 5th IEEE Int. Smart Cities Conf., ISC2019, Casablanca, 2019, pp. 396–401.

  43. Chan, A.L., Chua, G.G., Chua, D.Z.L., Guo., S., Lim, C., Mak, M.T., and Ng, W.S., Practical experience with smart cities platform design, Proc. IEEE World Forum on Internet of Things, WF-IoT 2018, Singapore, 2018, pp. 470–475.

  44. Few, S., Why most dashboards fail, in Perceptual Edge, 2007.

  45. Alves, A.P., Milani, A.M., and Manssour, I.H., Visual analytics system for energy data in smart cities and buildings, Proc. IEEE Int. Smart Cities Conf., ISC2020, 2020.

  46. Rojas, E., Bastidas,V., and Cabrera, C., Cities-board: a framework to automate the development of smart cities dashboards, IEEE Internet Things J., 2020, vol. 7, no. 10, pp. 10128–10136.

    Article  Google Scholar 

  47. Silva, J., Mojica, J., Pineres, A., Rojas, R., Acosta, S., Guliany, J.G., and Sanabria, E.S., Algorithms for the control of key performance indicators for smart cities, Procedia Comput. Sci., 2020, vol. 170, pp. 971–976.

    Article  Google Scholar 

  48. Tobergte, D.R. and Curtis, S., Ingeniería de Software un enfoque practico, McGrawhill, 2013.

    Google Scholar 

  49. Ivanov, V., Larionova, D., Strugar, D., and Succi, G., Design of a dashboard of software metrics for adaptable, energy efficient applications, Proc. 25th Int. DMS Conf. on Visualization and Visual Languages, DMSVIVA 2019, Lisbon, 2019, pp. 75–82.

  50. Young, G.W., Kitchin, R., and Naji, J., Building city dashboards for different types of users, J. Urban Technol., 2021, vol. 28, pp. 289–309.

    Article  Google Scholar 

  51. Open data, Paris. https://opendata.paris.fr/pages/home/. Cited Jan. 19, 2022.

  52. Vancouver City. https://vancouver.opendatasoft.com/pages/help/. Cited Jan. 19, 2022.

  53. Gobierno de Mexico. https://datos.cdmx.gob.mx/group/educacion-ciencia-y-tecnologia. Cited Jan. 19, 2022.

  54. Thingsboard. https://thingsboard.io/. Cited Jan. 19, 2022.

  55. Oppermann, R., Adaptively supported adaptability, Int. J. Human-Comput. Stud., 1994, vol. 40, pp. 544–472.

    Article  Google Scholar 

  56. Strugar, D., Complex systems: on design and architecture of adaptable dashboards, in Software Technology: Methods and Tools, Springer, 2019, pp. 176–186.

    Google Scholar 

  57. Itu, Collection Methodology for Key Performance Indicators for Smart Sustainable Cities United Smart Sustainable Cities 4 Montevideo Office Collection Methodology for Key Performance Indicators for Smart Sustainable Cities ii Foreword, 2017.

  58. Alvarado Lopez, R., Ciudad inteligente y sostenible: uná estrategia de innovacion inclusiva, PAAKAT: Rev. Tecnol. Soc., 2017, vol. 7, no. 13, p. 4.

    Google Scholar 

  59. Estrada, E., Pena, A., Negrõn, P., López, G.L., and Casilllas, L., Algoritmos para el control de indicadores clave de desempeno para Smart cities key performance indicators algorithms for smart cities, Res. Comput. Sci., 2018, vol. 147, no. 8, pp. 121–133.

    Article  Google Scholar 

  60. Bosch, P. and Jongeneel, S., CITYkeys CITYkeys indicators for smart city projects and smart cities, in European Commission within the H2020 Programme, 2017.

  61. Cohen, B., What Exactly Is a Smart City?, Elsevier, 2012.

    Google Scholar 

  62. Raymond, E., The Art of Unix Usability, Web, 2009.

  63. Tedesco, D. and Tullis, T., A comparison of methods for eliciting post-task subjective ratings in usability testing, in Usability Professionals Association (UPA), 2006, vol. 2006, pp. 1–9.

    Google Scholar 

  64. Brooke, J., Sus: a “quick and dirty’usability”, Usability Eval. Ind., 1996, vol. 189, no. 3.

  65. Human Interface and the Management of Information. Visual Information and Knowledge Management: Thematic Area, HIMI 2019, Held as Part of the 21st HCI Int. Conf., HCII 2019, Orlando, FL, July 26–31, 2019, Berlin, Heidelberg: Springer-Verlag, 2019, Part I.

  66. Celi, E., Application of dashboards and scorecards for learning models it risk management: a user experience, Lect. Notes Comput. Sci., 2015, vol. 9188, pp 153–165.

    Article  Google Scholar 

  67. Wu, D., Vennemeyer, S., Brown, K., Revalee, J., Murdock, P., Salomone, S., France, A., Clarke-Myers, K., and Hanke, S., Usability testing of an interactive dashboard for surgical quality improvement in a large congenital heart center, Appl. Clin. Inf., 2019, vol. 10, pp. 859–869.

    Article  Google Scholar 

  68. Ullmann, T., De Liddo, A., and Bachler, M., A visualisation dashboard for contested collective intelligence learning analytics to improve sensemaking of group discussion, RIED. Rev. Iberoam. Educ. Distancia, 2019, vol. 22, p. 41.

    Google Scholar 

  69. Sauro, J., Standardized usability questionnaires, in Quantifying the User Experience, Morgan Kaufmann, 2012, pp. 185–240.

    Google Scholar 

  70. Bangor, A., Kortum, P., and Miller, J., Determining what individual sus scores mean: adding an adjective rating scale, J. Usability Stud., vol. 4, no. 3.

Download references

ACKNOWLEDGMENTS

The first author gratefully acknowledges CONACYT for scholarship no. 1064132 for graduate studies.

Author information

Authors and Affiliations

  1. Facultad de estadística e informática, Universidad Veracruzana, Col. Obrero Campesina, CP. 91020, Xalapa-Veracruz, México

    V. Contreras, L. Montané, T. Cepero, E. Benitez & C. Mezura

Authors
  1. V. Contreras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Montané
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Cepero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Benitez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. Mezura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to V. Contreras, L. Montané, T. Cepero, E. Benitez or C. Mezura.

Ethics declarations

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Contreras, V., Montané, L., Cepero, T. et al. Building Adaptable Dashboards for Smart Cities: Design and Evaluation. Program Comput Soft 48, 534–551 (2022). https://doi.org/10.1134/S0361768822080072

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768822080072

Search

Navigation