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Welcome to today’s What is The Future For Cities podcast and its Research episode; my name is Fanni, and today I will introduce a research paper by summarising it. The episode really is just a short summary of the original paper, and, in case it is interesting enough, I would encourage everyone to check out the whole paper.

Our summary today works with the article titled Citizen-centred big data analysis-driven governance intelligence framework for smart cities from 2018 by Jingrui Ju, Luning Liu, and Yuqiang Feng, published in the Telecommunications Policy journal. Since we are investigating the future of cities, I thought it would be interesting to see an investigation into citizen-centred big data with data-to-decision research from concept to operation. This article proposes a framework for the use citizen-centred big data analysis to drive governance intelligence in smart cities from two perspectives: urban governance issues and data-analysis algorithms.

Since the Europe 2020 Strategy, the smart city concept increased in popularity, and the smart city is viewed as a public-private ecosystem among government, industry, non-profit organisations, and citizens, centred around sustainable economic growth and quality of life through participatory governance. Research on smart cities tends to focus on two aspects: technological and managerial. Ubiquitous digital devices and infrastructures give access to real-time big data about the urban environment. Big data is forever changing the way we manage, market and govern as it enables new modes of urban governance and facilitates more efficient, sustainable, competitive, productive, open and transparent cities, thus playing an important role in managerial research on smart cities. However, citizen-centred big data provides real-time insights into citizen behaviour and public opinion, thus having significant potential value but have seen relatively little research.

The mode of urban management is also changing to include citizen participation and collaboration. Therefore, citizen-centred governing practices have emerged to support the new modes of urban governance intelligence and to foster citizen engagement and democratic participation in urban affairs. Citizen-centred big data analysis allows the city to provide citizens with proactive, precise and personalised urban services and helps city managers to formulate incentives for citizen participation. However, researchers are yet exploring the hidden value of citizen-centred big data in terms of resolving complex issues of governance intelligence in smart cities. This paper proposed a framework to interpret how to use citizen-centred big data analysis to drive governance intelligence in smart cities.

In the authors’ understanding, smart city is a multi-agent ecosystem built up of societal actors like public sectors, private companies, non-profit organisations, and citizens, and smart city is a multidisciplinary field constantly shaped by advancements in technology and urban development. Citizens can no longer be simplified as customers since they are producing, delivering and monitoring besides voting. Additionally, they work as living sensors providing data to help city managers understand their behaviours in various channels, such as social media, websites and applications, while also creating a more transparent urban management. However, many studies focus on macro-level data analysis discouraging citizens to play a positive role: these approaches benefit the urban managers but are less beneficial for the citizens.

Smart cities, on the other hand, necessitate governance that is collaborative – urban managers must engage with citizens and citizens must participate in their governance, for the smart city to truly thrive. A citizen-centred city governance has two essential functions: providing citizens with proactive, precise, and personalised urban public services, and discovering and enhancing citizens’ willingness to participate in urban affairs governance. Citizen participation strengthens responsiveness and accountability within the community and further the development of inclusive and cohesive societies. Citizen-centred big data analysis allows managers to predict the degree to which citizens are willing to participate before actions are taken.

Big data analysis created a marked shift in 2009 from structured to unstructured data analysis, from a static terminal environment to a constantly changing technical environment. Big data analysis is required because of business intelligence for economic success and rapid advancements in software applications for enabling end users to process big data within or across organisations in real time. Big data analysis models have been initially established for knowledge derivation from large amounts of data. Now researchers are integrating new information technologies for more comprehensive analysis and decision-making across multiple disciplines. However, these frameworks only provide an effective reference instead of a path towards implementation for citizen-centred big data analysis-driven urban governance.

Cities create huge amounts of data about their environments and this urban data is now storable, usable and searchable helping urban managers to conduct more effective policy- and decision-making. The increasing interaction between the citizen and public sector is a key factor supporting the enrichment of citizen-centred panoramic data sets. The boom in citizen-centred data may provide more and better insights into citizen behaviour and public opinion which may in turn improve the intelligence of urban governance through more accurate analysis and prediction.

This paper proposes a framework to facilitate governance intelligence for smart cities via citizen-centred big data analysis. The authors hoped that this framework could provide a specific data-to-decision route and a better understanding of the mutual collaboration between data analysis algorithms and urban governance intelligence issues. There are three main layers in the proposed framework: a data-merging layer, knowledge-discovery layer, and decision-making layer.

Citizen-centred big data originates from different sources in a variety of formats, so data-merging is the first step to establish a citizen-centred panoramic data set. Multi-source big data in urban governance includes discrete and objective facts, activities and transactions that represent the characteristics of citizens as they interact with the public sector. These data are unorganised and unprocessed and lack any specific meaning or value without interpretation, not to mention the difficulties of acquiring the data sets. Therefore, data transformation is essential to build comprehensive and contextualised citizen profiles through cleaning, splitting, translating, merging, sorting and validating the information.

The relationship between citizen attributes and the main tasks of urban governance is established in the knowledge-discovery layer. Knowledge in this context refers to data that has been organised and processed to convey understanding, experience, accumulated learning and expertise to assist researchers and public managers in dealing with the urban service delivery and citizen participation. Citizen-centred big data can provide a more complete profile for an individual or a homogenous citizen group per the actual actions taken by citizens. This data can also be used to predict potential service demands and participation preferences. To some extent, citizen-centred big data analysis can transform the decision-making model of urban governance from ex-post to ex-ante prediction and from homogeneous to personalised.

In the third layer, models are built to support governance intelligence decision-making. One of the most significant purposes of citizen-centred big data analysis is enabling managers to conduct citizen-centred policy and decision-making. In their research, the authors built an ontology model applicable to urban governance intelligence at the decision-making layer. This model is useful for mining and standardising knowledge of governance relevant to citizen profiles and personas. There are three steps for this model: extracting attributes from original data, coding for the attributes, and establishing the reasoning and connection rules among them, while creating generalisation and personalisation based on the attributes at the same time.

The researchers wanted to validate this framework, and the selection was based on four criteria: focused on urban governance, multiple actors participating, modern technologies are used, and there are large-scale citizen data sets from multiple sources. There was no ideal case fully meeting these criteria and the proposed framework is aspirational rather than immediately practical. Therefore, one case was compliant with the chosen criteria: blood donation governance, a functional module of a Chinese urban governance app, called MyNanjing, as an example to validate the proposed framework. After data merging, the citizen profiles were created in the knowledge-layer. Based on this, governance intelligence was drawn from the data.

The framework described in this study provides a workable reference for urban governance intelligence through a specific implementation path from citizen-centred big data analysis to governance decision-making with analytical algorithms that are suitable for each step in the process. It includes three layers: data-merging, knowledge-discovery, and decision-making. Using this model on the blood donation dataset, the information presented that merged, multi-source big data for citizens and citizen groups indeed appears to enhance the efficiency and effectiveness of emergency blood supply governance after standardisation by ontology modelling.

The proposed framework is significant for the potential value of citizen-centred big data to support citizen-centred decision-making in smart cities and the operational data-knowledge-decision pathway with explicit steps and applicable analysis algorithms in each layer instead the usual looser concepts in the literature.

As the most important things, I would like to highlight 3 aspects:

1. Smart city is a multi-agent ecosystem built up of societal actors like public sectors, private companies, non-profit organisations and citizens and it also represents a multidisciplinary field constantly shaped by advancements in technology and urban development.
2. Smart cities necessitate governance that is collaborative – urban managers must engage with citizens, and citizens must participate in their governance for the smart city to truly thrive.
3. The proposed framework is to facilitate governance intelligence for smart cities via citizen-centred big data analysis with three main layers: data-merging, knowledge-discovery, and decision-making.

Additionally, it would be great to talk about the following questions:

1. Why has smart city emerged as a macro idea instead of the more personalised urban solution? Why has it been focused on urban technology and management?
2. Within these data sets, how is the privacy ensured? How can the data providers make sure that the data do not make the people identifiable?
3. How is the generalisation and personalisation balanced?
4. Are you conscious about being an urban living sensor? Do you know what kind of data you provide to your urban managers? How can you be more focused on what kind of information you want to provide them with?

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