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Volume 39, Issue 2 (2024)
GeoRes 2024, 39(2): 139-148 |
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Received: 2023/10/14 | Accepted: 2024/04/22 | Published: 2024/05/25
Received: 2023/10/14 | Accepted: 2024/04/22 | Published: 2024/05/25
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Morsousi N, Nasiri Hendehkhaleh E, Hosseini M. An Integrated Management Model of Spatial Data Infrastructure with a Smart City Approach in District 20 of Tehran. GeoRes 2024; 39 (2) :139-148
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1- Department of Geography and Urban Planning, Payam-e Noor University, Tehran, Iran
2- Department of Urban Planning, Faculty of Art and Architecture, University of Guilan, Rasht, Iran
2- Department of Urban Planning, Faculty of Art and Architecture, University of Guilan, Rasht, Iran
Abstract (671 Views)
Aims: District 20 of Tehran Municipality is characterized as a less developed region attributable to factors such as insufficient smart infrastructure and the absence of a coherent vision for smart city infrastructure advancement. The primary objective of this research was to develop a comprehensive management framework for spatial data infrastructure integrated with a smart city approach within District 20 of Tehran.
Methodology: This investigation, carried out between 2023 and 2024, was divided into qualitative and quantitative phases, with a particular focus on District 20 in Tehran. The study's sample size comprised 16 experts, and data collection methods encompassed semi-structured interviews and questionnaires. During the qualitative phase, the components essential for managing spatial data infrastructure in the context of a smart city were identified through an extensive review of existing literature. Subsequently, following adjustments based on expert feedback, a total of 12 components were validated. In the quantitative phase, structural equation modeling was employed to delineate the network of relationships and interactions, culminating in the presentation of the final model.
Findings: The formulation of policies for spatial data infrastructure was recognized as the most influential component at the highest echelon. Factors such as the utilization extent of spatial data, the quantity of electronic service centers, data update frequencies, cooperation and synchronization among the authorities of Rey Governorate, Rey Municipality, and Tehran Municipality in the realms of spatial data production, standardization, and distribution, along with the element of collaboration and synchronization with other entities categorized as data users, were positioned as influential components at the lowest tier.
Conclusion: The policies and criteria established within the domain of spatial data infrastructure possess the most significant influence on other elements.
Methodology: This investigation, carried out between 2023 and 2024, was divided into qualitative and quantitative phases, with a particular focus on District 20 in Tehran. The study's sample size comprised 16 experts, and data collection methods encompassed semi-structured interviews and questionnaires. During the qualitative phase, the components essential for managing spatial data infrastructure in the context of a smart city were identified through an extensive review of existing literature. Subsequently, following adjustments based on expert feedback, a total of 12 components were validated. In the quantitative phase, structural equation modeling was employed to delineate the network of relationships and interactions, culminating in the presentation of the final model.
Findings: The formulation of policies for spatial data infrastructure was recognized as the most influential component at the highest echelon. Factors such as the utilization extent of spatial data, the quantity of electronic service centers, data update frequencies, cooperation and synchronization among the authorities of Rey Governorate, Rey Municipality, and Tehran Municipality in the realms of spatial data production, standardization, and distribution, along with the element of collaboration and synchronization with other entities categorized as data users, were positioned as influential components at the lowest tier.
Conclusion: The policies and criteria established within the domain of spatial data infrastructure possess the most significant influence on other elements.