Research to Results: An Analysis of Citizen-Generated Open Data

Data-Smart City Solutions

By Stefanie Le, Ash Center for Democratic Governance and Innovation

Our From Research to Results series highlights articles from contemporary academic research that have important practical implications for policymakers.  

There’s an old adage, “the more, the merrier,” which maintains that the more people or things there are, the more enjoyable something will be. As in—the more people there are, the more fun this party will be; or—the more information we have, the more accurate our data-based conclusions will be. 

And in today’s technology and social media landscape, there is more accessible information than ever before. People, countries, industries, and classes, among others are virtually at our fingertips with a single Tweet or Facebook message. This also means that a city’s residents have more access to communicate with city governments and officials than ever before through a city’s social media presence. But is that ultra-connectivity for the better?

Albert Meijer, Professor of Public Innovation at the School of Governance of Utrecht University, and Suzanne Potjer, from the Urban Futures Studio at the same university,  published a study in 2018 that took a look at citizen-generated open data in an analysis of 25 cases relating to public governance. 

In their study, Meijer and Potjer compared cases in different countries in order to better understand the “multi-actor collaboration” between social media users and government officials. They then examined the results in three areas: 1) the motivations of citizens who generated the data, 2) the organization of the data intermediaries, and 3) the following influence, if any, on public governance. 

Previous publications on citizen-generated open data had only ever evaluated public governance use cases singularly (i.e., one use case per publication), but this study aimed to fill the gap in the academic literature by developing an academic comprehension of citizen-generated data based on a case comparison. At the time of the study’s publication, there was not a generally accepted definition of citizen-generated open data, therefore for the purpose of the study Meijer and Potjer defined the term as “data that individuals consciously generate and that are openly available for use in the public domain.”

Data can be generated consciously (i.e., Facebook posts or Google searches) or unconsciously (i.e., geodata from smartphones or browsing cookies), but that type of data only takes on a “civic” trait and can be determined to be citizen-generated data when the user-generated data is used solely and explicitly with a public purpose as in a democratic debate or coming up with solutions for public problems.

In order to answer their research question of how citizen-generated open data contributes to public governance, Meijer and Potjer first took a public governance view to evaluate the relationship between the data and governance, and then analyzed the comparative case studies with in-depth examinations of the citizen-generated open data. 

In the multi-actor collaboration, both the citizen and the government are actors in the public domain who generate data. When looking into these collaborations, the researchers asked the questions: 1) under which conditions are citizens willing to provide data? And 2) which actor organizes this data and how? From previous literature, the researchers hypothesized that citizens would provide data if they felt that sharing would help themselves, the group they identify with, or the public cause at large. Furthermore, Meijer and Potjer projected that while not much is known about the data intermediaries, the goal of the intermediary was to focus and emphasize the quality of the data to bolster the idea that the data provided a neutral intervention in public governance.

The final question the researchers asked was: how does the initiative influence public governance? Again, based on previous literature, Meijer and Potjer deduced that citizen-generated open data can contribute to public value but that the influence of the impact of this data is still understudied. 

The next step involved a deep dive into 25 use cases around the world. In selecting the cases, Meijer and Potjer had five criteria for the cases: they 1) had to be practices of citizen-generated open data, 2) had to be in the implementation stage, 3) had to use data generated by individuals as opposed to organizations, 4) had to be concerned with public issues, and 5) there had to be enough information for analysis. In order to diversify the cases, the researchers looked at the following factors: 1) the sector (i.e., environmental, mobility, etc); 2) actors running the platform, whether they be public, private, or combinations; 3) type of data; 4) roles of citizens; 5) size; and 6) country where the case is located. Each case was analyzed using multiple data sources—the initiative’s website first, and then supplemented by secondary sources (media, previous studies) and interviews when possible. 

Once Meijer and Potjer finished their empirical analysis of the 25 cases, Meijer and Potjer concluded that citizen-generated open data can certainly provide improved information for public governance, but concurrently can also be used to challenge current power structures and city decisions. As a result, Meijer and Potjer posited that the addition of citizen-generated open data to public governance should be viewed as both “collaboration and contestation.”

For example, while citizen-generated open data produces data as a foundation for collaborative governance, it can also strengthen and work with governance by providing new checks and balances based on the data collected. Meijer and Potjer explained that to understand the role of citizens in this new environment of government with social media, is to know that the public governance will include both collaboration and conflict.

Meijer and Potjer’s second conclusion states that citizens engage in the generation of data to both collaborate with their governments and challenge current government positions and policies. There are distinctions between friendly, adversarial and neutral interactions—yet all of these interactions better inform governments on what their citizens are looking for. However, the researchers both concede that the impact of the data is too narrow and still in the exploratory phase.

In the end, despite realizing that citizen-generated open data can also challenge the positions and structure of city government, the greater amount of information and “multi-actor collaboration” utilizing that data does indeed help governments make more accurate data-based decisions for their cities by taking in both suggestions and criticism from the new form of data.

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