Society: Build digital democracy

Helbing, Dirk; Pournaras, Evangelos

doi:10.1038/527033a

Cited by 76 publications

(50 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, urban neighborhoods that are deteriorating and face issues of blight, may lack quality data and participatory data can fill a large measurement gap. To date, no such assessment techniques or protocols that support the generation and incorporation of resident-driven data collection around infrastructure and other built features are widely available to planners and managers despite calls in the literature for their development (Helbing & Pournaras, 2015; Elwood, Goodchild, & Sui, 2012). …”

Section: Participatory Action Geographic Information and Infrastrucmentioning

confidence: 99%

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

Hendricks

Meyer

Gharaibeh

et al. 2018

Sustainable Cities and Society

View full text Add to dashboard Cite

Climate change and increasing natural disasters coupled with years of deferred maintenance have added pressure to infrastructure in urban areas. Thus, monitoring for failure of these systems is crucial to prevent future impacts to life and property. Participatory assessment technique for infrastructure provides a community-based approach to assess the capacity and physical condition of infrastructure. Furthermore, a participatory assessment technique for infrastructure can encourage grassroots activism that engages residents, researchers, and planners in the identification of sustainable development concerns and solutions. As climate change impacts disproportionately affect historically disenfranchised communities, assessment data can further inform planning, aiming to balance the distribution of public resources towards sustainability and justice. This paper explains the development of the participatory assessment technique for infrastructure that can provide empirical data about the condition of infrastructure at the neighborhood-level, using stormwater systems in a vulnerable neighborhood in Houston, Texas as a case study. This paper argues for the opportunity of participatory methods to address needs in infrastructure assessment and describes the ongoing project testing the best use of these methods.

show abstract

Section: Participatory Action Geographic Information and Infrastrucmentioning

confidence: 99%

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

Hendricks

Meyer

Gharaibeh

et al. 2018

Sustainable Cities and Society

View full text Add to dashboard Cite

show abstract

“…This paper contributes lessons learnt such as how to make choices in regards to the content size and difficulty level, the diversity of students, students' projects and project teams, the choice of software tools for different data science tasks, the use of research projects as a pedagogical artifact and how data requirements influence what a student can learn from data. Cross-disciplinary data science education qualifies more versatile data scientists in the job market, can reduce business costs for training and ultimately cultivate a more democratic and participatory citizen prepared to respond to the upcoming challenges of the digital society [25].…”

Section: Resultsmentioning

confidence: 99%

“…In other words, it cultivates these mental capacities to withstand the challenges of our nowadays digital societies [25] concerning the interpretation and wise use of information from (social) media [17], populism leading to ineffective voting [8,10], privacy and autonomy violations from big data profiling technologies or profit-oriented recommender systems [24,41], manipulative actions and means of propaganda in social networks and beyond [46].…”

Section: Lessons Learnt and Societal Implicationsmentioning

confidence: 99%

Cross-disciplinary higher education of data science – beyond the computer science student

Pournaras

2017

Self Cite

View full text Add to dashboard Cite

“…Such networks can encode at a fine-grained granularity personal information sensitive to citizens. Mining this sensitive information raises serious privacy threats and opportunities for discriminatory and surveillance actions [6] that have significant implications on the autonomy of citizens [7]. However, if the sparsity of the data increases as a result of self-determined decisions that improve the privacy of citizens, the effectiveness of mining social interactions over temporal networks come in questions.…”

Section: Introductionmentioning

confidence: 99%

Mining social interactions in privacy-preserving temporal networks

Musciotto¹,

Delpriori²,

Castagno³

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Self Cite

View full text Add to dashboard Cite

Abstract-The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time finegrained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts etc., using systematic methologies from complex networks and graph spectra analysis. However, a misuse of these methods may result in privacyintrusive and discriminatory actions that may threaten citizens' autonomy and put their life under surveillance. This paper studies highly sparse temporal networks that model social interactions such as the physical proximity of participants in conferences. When citizens can self-determine the anonymized proximity data they wish to share via privacy-preserving platforms, temporal networks may turn out to be highly sparse and have low quality. This paper shows that even in this challenging scenario of privacy-by-design, significant information can be mined from temporal networks such as the correlation of events happening during a conference or stable groups interacting over time. The findings of this paper contribute to the introduction of privacy-preserving data analytics in temporal networks and their applications.

show abstract

Society: Build digital democracy

Cited by 76 publications

References 7 publications

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

Cross-disciplinary higher education of data science – beyond the computer science student

Mining social interactions in privacy-preserving temporal networks

Contact Info

Product

Resources

About