Identifying public values and spatial conflicts in urban planning
- URL: http://arxiv.org/abs/2207.04719v2
- Date: Mon, 18 Jul 2022 08:01:59 GMT
- Title: Identifying public values and spatial conflicts in urban planning
- Authors: Rico H. Herzog, Juliana E. Gon\c{c}alves, Geertje Slingerland, Reinout
Kleinhans, Holger Prang, Frances Brazier, Trivik Verma
- Abstract summary: This paper proposes a new approach to empirically investigate public value conflicts in urban space.
We use unstructured participatory data of 4,528 citizen contributions from a Public Participation Geographic Information Systems in Hamburg, Germany.
Integrating both quantitative and qualitative results, 19 general public values and a total of 9 archetypical conflicts are identified.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Identifying the diverse and often competing values of citizens, and resolving
the consequent public value conflicts, are of significant importance for
inclusive and integrated urban development. Scholars have highlighted that
relational, value-laden urban space gives rise to many diverse conflicts that
vary both spatially and temporally. Although notions of public value conflicts
have been conceived in theory, there are very few empirical studies that
identify such values and their conflicts in urban space. Building on public
value theory and using a case-study mixed-methods approach, this paper proposes
a new approach to empirically investigate public value conflicts in urban
space. Using unstructured participatory data of 4,528 citizen contributions
from a Public Participation Geographic Information Systems in Hamburg, Germany,
natural language processing and spatial clustering techniques are used to
identify areas of potential value conflicts. Four expert workshops assess and
interpret these quantitative findings. Integrating both quantitative and
qualitative results, 19 general public values and a total of 9 archetypical
conflicts are identified. On the basis of these results, this paper proposes a
new conceptual tool of Public Value Spheres that extends the theoretical notion
of public-value conflicts and helps to further account for the value-laden
nature of urban space.
Related papers
- From Street Form to Spatial Justice: Explaining Urban Exercise Inequality via a Triadic SHAP-Informed Framework [0.14999444543328289]
Urban streets are essential public spaces that facilitate everyday physical activity and promote health equity.<n>This study proposes a conceptual and methodological framework to quantify street-level exercise deprivation through the dimensions of conceived (planning and structure), perceived (visual and sensory), and lived (practice and experiential) urban spaces.
arXiv Detail & Related papers (2025-07-04T13:28:30Z) - OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models [55.21589313404023]
OpenReview is a continually evolving repository of research papers, peer reviews, author rebuttals, meta-reviews, and decision outcomes.<n>We highlight three promising areas in which OpenReview can uniquely contribute: enhancing the quality, scalability, and accountability of peer review processes; enabling meaningful, open-ended benchmarks rooted in genuine expert deliberation; and supporting alignment research through real-world interactions reflecting expert assessment, intentions, and scientific values.<n>We suggest the community collaboratively explore standardized benchmarks and usage guidelines around OpenReview, inviting broader dialogue on responsible data use, ethical considerations, and collective stewardship.
arXiv Detail & Related papers (2025-05-24T09:07:13Z) - You Don't Have to Live Next to Me: Towards Demobilizing Individualistic Bias in Computational Approaches to Urban Segregation [0.0]
The global surge in social inequalities is one of the most pressing issues of our times.<n>The expression of social inequalities at city scale gives rise to urban segregation.<n>The increasing popularity of Big Data and computational models has inspired a growing number of computational studies.
arXiv Detail & Related papers (2025-05-03T14:15:27Z) - Conflicts in Texts: Data, Implications and Challenges [58.03478157713084]
Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs.
This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment.
We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively
arXiv Detail & Related papers (2025-04-28T04:24:01Z) - Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies [3.510270856154939]
We present findings from a community-centered study in Montreal involving 35 residents with diverse demographic and social identities.
We propose negotiative alignment, an AI framework that treats disagreement as an essential input to be preserved, analyzed, and addressed.
arXiv Detail & Related papers (2025-03-16T18:55:54Z) - A Theoretical Model for Grit in Pursuing Ambitious Ends [48.43624563381919]
We provide a model of decision-making between stable and risky choices in the improving multi-armed bandits framework.
We study the impact of various interventions, such as increasing grit or providing a financial safety net.
arXiv Detail & Related papers (2025-03-04T19:17:42Z) - InclusiViz: Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation [41.758626973743525]
InclusiViz is a novel visual analytics system for multi-level analysis of urban segregation.
We developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI.
The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail.
arXiv Detail & Related papers (2025-01-07T07:50:36Z) - ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions [47.85181608392683]
We employ ValueScope to dissect and analyze linguistic and stylistic expressions across 13 Reddit communities.
Our analysis provides a quantitative foundation showing that even closely related communities exhibit remarkably diverse norms.
arXiv Detail & Related papers (2024-07-02T17:51:27Z) - Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness [6.683051393349788]
This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies.
We validate our approach using both proprietary and public datasets.
Our findings reveal significant discrepancies between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications.
arXiv Detail & Related papers (2024-05-24T08:10:31Z) - Federated Learning for Generalization, Robustness, Fairness: A Survey
and Benchmark [55.898771405172155]
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties.
We provide a systematic overview of the important and recent developments of research on federated learning.
arXiv Detail & Related papers (2023-11-12T06:32:30Z) - Through the Fairness Lens: Experimental Analysis and Evaluation of
Entity Matching [17.857838691801884]
Algorithmic fairness has become a timely topic to address machine bias and its societal impacts.
Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching.
We generate two social datasets for the purpose of auditing EM through the lens of fairness.
arXiv Detail & Related papers (2023-07-06T02:21:08Z) - Beyond Normal: On the Evaluation of Mutual Information Estimators [52.85079110699378]
We show how to construct a diverse family of distributions with known ground-truth mutual information.
We provide guidelines for practitioners on how to select appropriate estimator adapted to the difficulty of problem considered.
arXiv Detail & Related papers (2023-06-19T17:26:34Z) - Fairness meets Cross-Domain Learning: a new perspective on Models and
Metrics [80.07271410743806]
We study the relationship between cross-domain learning (CD) and model fairness.
We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks.
Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
arXiv Detail & Related papers (2023-03-25T09:34:05Z) - The Association Between SOC and Land Prices Considering Spatial
Heterogeneity Based on Finite Mixture Modeling [1.933681537640272]
Even within a district, there are multiple sections used for different purposes.
Land prices can be managed by adopting the spatial clustering method.
Policymakers and managerial administration need to look for information to make policy about land prices.
arXiv Detail & Related papers (2022-11-15T23:18:06Z) - Understanding Interpersonal Conflict Types and their Impact on
Perception Classification [7.907976678407914]
We use a novel annotation scheme and release a new dataset of situations and conflict aspect annotations.
We then build a classifier to predict whether someone will perceive the actions of one individual as right or wrong in a given situation.
Our findings have important implications for understanding conflict and social norms.
arXiv Detail & Related papers (2022-08-18T10:39:35Z) - This Must Be the Place: Predicting Engagement of Online Communities in a
Large-scale Distributed Campaign [70.69387048368849]
We study the behavior of communities with millions of active members.
We develop a hybrid model, combining textual cues, community meta-data, and structural properties.
We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment.
arXiv Detail & Related papers (2022-01-14T08:23:16Z) - Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
arXiv Detail & Related papers (2021-10-05T12:51:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.