Leveraging Open-Source and Crowdsourced Data to Evaluate Spatial Justice in Cultural Planning 

dc.contributor.authorAbdelgawad, Norhan Tareq Ahmeden
dc.contributor.committeechairMisra, Shalinien
dc.contributor.committeememberSanchez, Thomas W.en
dc.contributor.committeememberWagle, Paroma Subodhen
dc.contributor.committeememberWernstedt, Kris Fredericken
dc.contributor.departmentPublic Administration/Public Affairsen
dc.date.accessioned2025-10-17T08:00:08Zen
dc.date.available2025-10-17T08:00:08Zen
dc.date.issued2025-10-16en
dc.description.abstractWith the rise of data-driven planning and decision-making, fueled by the abundance of digital information, data are increasingly being positioned as a "common language" for interrogating the built environment. These claims perceive data as neutral representations of reality, overlooking the social, political, and institutional contexts that shape them. However, digital data can vary widely in quality, completeness, and classification standards, raising concerns regarding their accuracy, applicability, and effectiveness in examining just planning outcomes. Focusing on cultural planning as a domain and Los Angeles as a case study, I examined the utility of data for evaluating the fair distribution of cultural resources. This research addressed the following question: What can open-source and crowdsourced data reveal about the equitable allocation of cultural resources? To address this question, I developed a socio-ecological framework that synthesized Henri Lefebvre's (1991) Spatial Triad Theory and Anthony Giddens' (1986) Structuration Theory for evaluating justice in planning contexts. This framework identified three different dimensions and three types of justice. (1) The institutional dimension, conceptualized as the conceived, is centered around understanding the institutional decisions and strategies that shape cultural planning and have procedural justice implications. (2) The environmental dimension, which is interpreted as the perceived focuses on the physical manifestation of resources represented as data points and can have distributional justice implications. (3) The experiential dimension, conceptualized as the lived, which addresses the meaning and significance of these resources to individuals, which can impact participatory justice. This framework provided the opportunity to bridge theory and practice by operationalizing meta-theories to create a diagnostic adaptive tool with actionable steps for examining just planning processes and outcomes. Guided by this framework, I conducted two studies to: (1) compare digital data obtained from institutional and crowdsourced sources in terms of quality and 'fitness of use' in justice research; and (2) to compare community perceptions of cultural resources with digital data representations. The first study focused on the perceived dimension, viewing data as representations of both institutional and crowdsourced physical cultural infrastructure. In this study, I examined and compared data quality and representations from institutional city and county sources with crowdsourced OpenStreetMap. The findings revealed differences across the datasets, with significant discrepancies in spatial patterns, cultural asset classification, and descriptive detail. Recognizing the trade-offs involved in selecting a dataset for justice research in cultural planning, this analysis highlighted the need for the integration and critical examination of institutional and community-sourced data with insights from the community. The second study focused on the lived dimension of the social ecological framework emphasizing the lived experiences of the community. Taking a critical Geographic Information Science (GIS) perspective, I leveraged cultural mapping as a tool for critical data inquiry and integrated Kevin Lynch's (1960) notion of imageability as an analytical lens to identify perceived discrepancies between community insights and digital datasets. The study provided a systematic approach for the multi-level examination of cultural resources, highlighting the conceptual fuzziness in the classification of cultural resources, spatial discrepancies between community perceptions and commissioned artworks, and overlooked dimensions such as accessibility and engagement, which are crucial for cultural dataset development. Integrating theory and methods from Sociology, Urban Planning, Data Science, Geography, and Environmental Psychology, this dissertation bridged theory and practice by developing and applying a diagnostic framework to examine the utility of different types of data in justice-oriented research in cultural planning. In doing so, this dissertation made theoretical and methodological contributions, spanning institutional, environmental, and individual levels of analysis.en
dc.description.abstractgeneralThere has been a surge of interest in using digital data for decision-making in urban planning. Data are said to be neutral and show the objective truth. However, data can be influenced by the viewpoints of the persons or organizations that created them, which can bias or distort reality. With the abundance of digital data, it is not always clear which data should be used and what the implications of their use are for the fair distribution of resources. To address this problem, I focused on cultural planning as a research domain and the City of Los Angeles as a case. My main question was: What can the different types of data (open-source and crowdsourced) reveal about the fair distribution of cultural resources? To address this question, I used the principles of Social Ecology, a meta-analytical framework to study the relationship between people and their environment (Stokols et al., 2013; Misra and Stokols, 2012), and synthesized two theories: (1) Spatial Triad Theory by philosopher Henri Lefebvre (1991); and (2) Structuration Theory by sociologist Anthony Giddens (1986). I developed a social ecological framework for evaluating justice in planning contexts. This framework identified three different dimensions and highlighted how they can be used to study three types of justice. (1) The institutional dimension focused on planning decisions (who makes the decision?) which can influence procedural justice (rules and regulations). (2) The environmental dimension focused on art infrastructure and is usually measured using data points and affects distributive justice (the fair distribution of cultural resources). (3) The individual dimension which considered the experiences and meaning of resources to the community, which affects participatory justice (the meaningfulness of cultural resources). Using this framework, I created a diagnostic tool with clear steps that can be applied in any urban planning context to evaluate the justice implications of planning decisions. I applied this framework in two studies: (1) to compare digital data obtained from institutional and crowdsourced sources in terms of quality and 'fitness of use' in justice research; and (2) to compare community perceptions of cultural resources with digital data representations. The first study focused on examining differences between different datasets from the government and OpenStreetMap (OSM) show the distribution of art in the city. The results revealed several differences between the datasets, specifically spatial distribution patterns, how cultural assets were labelled, and descriptions used to identify materials, locations, and the type of cultural asset. These differences showed that selecting a dataset for decision-making comes with trade-offs, whether it is using an incomplete dataset that has descriptive detail or using one with a large number of datapoints but that does not include artwork titles. For that reason, to conduct research on a complex topic like justice, one should use multiple datasets that provide a more complete picture of the problem and can tell us about the organizations or people who created them. It is also important to use the insights of community members through interviews to evaluate the efficacy of cultural planning strategies and to understand the data. My second study focused on comparing community experiences and perceptions of cultural resources with digital data. To conduct this study, I used a cultural mapping approach and used Kevin Lynch's (1960) concept of imageability. I created a systematic approach to study the differences between community perceptions of cultural resources and digital data from the government and OSM. I was able to explore these differences at multiple levels from the city level to the individual level. The results showed differences in how cultural resources are defined and where cultural resources are located. The findings revealed the different ways in which people interact with cultural resources, which can be helpful in creating digital data that reflects community perceptions. My dissertation translated theory to practice by developing a tool that can be used to examine which type of data are useful in justice research. In doing so, I contributed to the literature by developing a new theory and applying it to inform just decision-making in cultural planning.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:44801en
dc.identifier.urihttps://hdl.handle.net/10919/138229en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectCultural Planningen
dc.subjectSpatial Justiceen
dc.subjectOpen Government Dataen
dc.subjectCrowdsourced Dataen
dc.subjectCultural Mappingen
dc.subjectSocial Ecologyen
dc.subjectCritical GISen
dc.subjectMuralsen
dc.subjectGiddens' Structuration Theoryen
dc.subjectLefebvre's Spatial Triad Theoryen
dc.subjectData Qualityen
dc.subjectImageabilityen
dc.titleLeveraging Open-Source and Crowdsourced Data to Evaluate Spatial Justice in Cultural Planning en
dc.typeDissertationen
thesis.degree.disciplinePlanning, Governance, and Globalizationen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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