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Monday, 12 December 2022 12:52

SmartCitizen Voice. IBV Methodology in Open Data Environments

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Amparo López-Vicente, Carolina Soriano García, Juan F. Giménez Pla, José Solaz Sanahuja, Raquel Marzo Rosselló, Elisa Signes i PérezInstituto de Biomecánica de Valencia
Universitat Politècnica de València
Edificio 9C. Camino de Vera s/n
(46022) Valencia. Spain

 

Given the increasing availability of open data - thanks to transparency models - we have the opportunity to combine this information with social innovation tools and the exploitation made possible by Big Data. In this context, the IBV has developed and validated SmartCitizen Voice, a three-phase methodology      that integrates companies in Smart City environments and involves citizens in all the processes that have an impact on their quality of life.

INTRODUCTION

A smart city invests in human and social capital, as well as in traditional (transport) and modern (ICT) infrastructures, to enhance sustainable economic growth and quality of life through the intelligent management of natural resources and participatory governance[1].

The innovative methodologies applied in a Smart City[2], which consider the citizen as an active and key agent for development, with defined roles as an explorer, generator of ideas and opportunities, co-designer, validator of proposals and disseminator of innovations, are based on three key concepts: Co-creation[3], Social Innovation[4] in the Smart City and Inclusive Methodology[5]-[6]-[7] Although co-creation and social and inclusive innovation methodologies are mature in the relevant literature, their actual application in Smart City processes is as yet limited and primarily focused on involving citizens through surveys or polls.

Figure 1. Interconnectivity in the Smart City

The new methodological proposal put forward by the IBV is based on evolving its activity in people-oriented innovation and user experience[8] towards a data ecosystem that makes it possible to intervene in the different phases of citizen participation[9].

To achieve this, it is proposing a participatory model that provides the necessary information for decision-making in the Smart City environment, based on a profound and comprehensive knowledge of citizens and their context, with a hybrid solution  that combines technology (platforms) and meeting places (laboratories and public spaces), to imagine a people-oriented city. The methodology proposes services for the diagnosis and mapping of urban environments, by districts and neighborhoods, based on Big Data, Open Data and Netnography approaches[10]; interventions and a definition of strategies based on Thick Data[11], User Experience and Social Innovation approaches which are in turn based on qualitative and subjective information that help to understand the causes, and the subsequent definition of solutions and intervention strategies.

The SmartCitizen Voice methodology will enhance environments that are safer, healthier and more habitable, offering citizens more leisure and cultural opportunities. It will incorporate qualitative approaches to generate contextual information and will enable businesses to exploit the data generated in the Smart City to create new opportunities.

The expected impacts of applying the methodology include:

- Optimizing the exploitation of Big Data by companies, public administration and citizens.

- Benefiting companies that provide smart solutions and services for urban management.

- Strengthening the development of smarter and more sustainable cities, which will have an impact on the health and well-being of their citizens.

- Encouraging the creation and growth of different business sub-sectors.

- Collaborating with companies in the development and validation of their proposals. 

DEVELOPMENT 

The SmartCitizen Voice methodology has been developed in response to how innovation processes are being adapted to the new political and social demands and smart city models, where organizations are providing society with an enormous amount of information, which favors appropriate decision making if we are to guarantee increased well-being for citizens.

This information requires an understanding of the contexts in which the data is produced, an issue that is resolved through Netnography and Thick Data, on the basis of which co-creation processes are initiated, in which citizens participate in the definition of the interventions.

The proposed methodology responds to a sequential process that is based on an analysis of open data with Big Data models, expanded, refined and validated by methodological approaches of a subjective nature (Netnography) and ending with interventions aimed at the detailed understanding of the study situations (Thick Data), and the identification of solutions that meet sustainability requirements (i.e., they are socially desirable and environmentally and financially sustainable).

Figure 2: Diagram of the SmartCitizen Voice methodology.

 

First of all, the analysis of the open data that is available in the Smart City environment and its analysis by Big Data, makes it possible to know what the situation of different geographical areas is through the fulfilment of indicators that can be associated with quality of life.

Figure 3:  Example of a visualization of the results of the SmartCitizen Voice methodology. Big Data

 

Secondly, the information provided by social networks (Netnography) and the analysis of natural language allows us to understand the study context and to delve deeper into the perception of and data related to citizens’ attitudes, behavior and interests, as a starting point for any research or development.


Figure 4: Example of a visualization of the results of the SmartCitizen Voice methodology. Netnography

 

Information from companies is subsequently contrasted with the analyzed data to generate accurate diagnoses that provide companies and organizations with valuable data.

Finally, we create a joint IBV-company team to define the future challenges and the new solutions, we identify the most appropriate Thick Data strategy (user experience and people-oriented innovation), and we propose an intervention that incorporates the key elements of the strategy to be followed.

Figure 5: Example of a visualization of the results of the SmartCitizen Voice methodology. Thick Data

The different analyses that the methodology allows us to perform offer a wealth of information, structured with different formats depending on the content and the levels of depth and on the specific interests of each company or organization.

The work carried out during the fine-tuning of the methodology[12]is based on an in-depth analysis of the quality-of-life conditions in the city of Valencia, which in turn is based on an initiative by the OECD known as the “Better Life Index” which can in fact be applied to any city that has open data on the proposed criteria. This methodology has been validated through its application in case studies with 3 different organizations, which involved the identification of a question that had to be answered, based on which the 3 stages of the methodology have been applied, providing an answer to it. The methodology has successfully identified strategies to address the challenge posed by the triangulation of different techniques, validating the value provided and the consistency of results offered by the combination of the different techniques of the developed methodology.

Figure 6. Companies and challenges addressed in the validation of the methodology.

 

 

 Figure 7. Open data results: Analysis of the Quality of Life in Valencia (by districts)

CONCLUSIONS

The companies that have participated in the case studies have rated the methodology in a very positive way. Some of the aspects they have highlighted are the fact that:

- It offers quality insights that help to make the right decisions to increase the impact of the business.

- It allows us to know the whys and wherefores of users’ feelings and it gives us the keys we need to improve them.

- It can be scaled to different locations, and it could even provide more value given that the company has less information on user behavior.

This favorable assessment of the methodology by the companies and organizations that took part in the case studies, and by different agents in the quadruple helix of the Valencian Community, encourages us to continue with this exciting line of work and to tackle the following challenges:

- DATA UTILITY: The need to define, for each project, the objectives and use of the information, given the large amount of existing data.

- PROMINENCE OF OPEN DATA: There is currently a large amount of heterogeneous data that is not being tapped by organizations and companies, which requires major selection and analysis efforts.

- THICK DATA: Open data provides static information but does not explain the whys and wherefores, nor does it allow us to anticipate desirable solutions for citizens. Our methodological proposal covers the need to understand and intervene in new developments.

- SIMPLICITY AND CLARITY OF INFORMATION: It is important to define graphs and keys for communicating and disseminating results with different levels of depth, which allow us to understand the information displayed by the control panels.

- STANDARDIZATION OF OPEN DATA KPIs: Critical variables should be identified and their use standardized across cities so that situations can be compared.

- SPECIFIC KPIs: Taking on the challenges posed by companies implies the need to identify specific variables (Netnography and Thick Data) for each project.

ACKNOWLEDGMENTS

This methodology has been developed thanks to the support and confidence of IVACE (the Valencian Institute of Business Competitiveness). The CiuDATÀ project was financed by the 2021 IVACE Aid Program which was aimed at technology centers in the Valencian Community for the development of non-economic R&D projects carried out in cooperation with companies. It was also co-financed by ERDF funds within the Operational Program of the Valencian Community 2021, IMDEEA/2021/69) and by the companies that have participated in this initiative together with the Instituto de Biomecánica (IBV): MOVILIDAD URBANA SOSTENIBLE, S.L. (MOVUS), CUMULUS CITY, S.L., ENTIDAD METROPOLITANA PARA EL TRATAMIENTO DE RESIDUOS (EMTRE), ZUBICITIES S.L., EMPRESA MUNICIPAL DE TRANSPORTES DE VALENCIA, S.A.U. (EMT), 5G COMMUNICATIONS FOR FUTURE INDUSTRY VERTICALS, S.L. (FIVECOMM), LABERIT, ETRA INVESTIGACIÓN Y DESARROLLO S.A. (ETRA).

 

[1] Caragliu, A., Del Bo, C., Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82.

[2] Margherita, E. G., Esposito, G., Escobar, S. D., & Crutzen, N. (2021). Exploring the smart city adoption process: evidence from the Belgian urban context. arXiv preprint arXiv:2101.05670

[3] Sanders, E. B.-N. and Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-design, 4(1):518

[4] H. K. Amheier and Krlev. G., (2014) ‘Social Innovation as Impact of the Third Sector. Deliverable 1.1 of the project: “Impact of the Third Sector as Social Innovation” (ITSSOIN)’. European Commission,

[5] Lucas, K. y Markovich, J. (2011) International Perspectives on Social Exclusion Research in Transport. New Perspectives and Methods in Transport and Social Exclusion Research.

[6] Mobility, social exclusion and well-being: exploring the links. Stanley, John, y otros, y otros. (2011), Transportation Research Part A Policy and Practice, Vol. 45(8), págs. 789-801

[7] Currie, G. (2011) New Perspectives and Methods in Transport and Social Exclusion. s.l. : Emerald Group Publishing Limited.

[8] https://ux.ibv.org/

[9] Arnstein, Sherry R., (1969), A Ladder Of Citizen Participation, Journal of the American Planning Association, 35: 4, 216 -224

[10] Kozinets, R. V. (2002). The Field Behind the Screen: Using Netnography for Marketing Research in Online Communities. Journal of Marketing Research, 39(1), 61-72.

El Hilali, S., & Azougagh, A. (2021). A Netnographic Research on Citizen's Perception of a Future Smart City. Cities, 115, 103233.

[11] Bornakke, T., Due, B.L., Big–Thick Blending: A Method for Mixing Analytical Insights from Big and Thick Data Sources, Big Data & Society January–June 2018: 1–16, DOI: 10.1177/2053951718765026

Fiaidhi, J., Mohammed, S., Thick Data: A New Qualitative Analytics for Identifying Customer Insights, IT Professional, IEEE Computer Society, 10.1109/MITP.2019.2910982

[12] Soriano García C.; Gayo Corbella G.; Cascant i Sempere C. (2022) Data Speak of Happiness, Quality and Life Expectancy in Each Neighborhood of Valencia. 0D4D AGENTS: Generating Capacities and Networks for the Development of the Open Data Ecosystem for Sustainable Development. https://www.comunica360.org/od4d/publicacion 

 

 

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