Persian
Language
Geographical Research is Published in Persian Fulltext.
Article Type:
Subject:
History
Received: 2023/10/14 | Accepted: 2024/04/22
Received: 2023/10/14 | Accepted: 2024/04/22
Rights and permissions
1- Associate Professor, Department of Geography and Urban Planning, Faculty of Social Sciences, Payam Noor University, Tehran, Iran
2- , Associate Professor, Department of Geography and Urban Planning, Faculty of Literature and Humanities, Gilan University, Rasht, Iran
3- Department of Geography and Urban Planning, Faculty of Social Sciences, Payam Noor University, Tehran
2- , Associate Professor, Department of Geography and Urban Planning, Faculty of Literature and Humanities, Gilan University, Rasht, Iran
3- Department of Geography and Urban Planning, Faculty of Social Sciences, Payam Noor University, Tehran
Abstract (30 Views)
Aims: District 20 of Tehran Municipality is one of the less developed areas due to issues such as a lack of smart infrastructure, lacking a suitable vision for infrastructure development of a smart city. This study aimed to provide an integrated management model of spatial data infrastructure with a smart city approach in District 20 of Tehran.
Methodology: This srudy, conducted in two qualitative and quantitative phases in 2023-2024, focused on a case study of District 20 in Tehran. The statistical population consisted of 16 experts. Data collection tools included semi-structured interviews and questionnaires. In the qualitative phase, components of integrated management of spatial data infrastructure for a smart city were identified using the literature review. After making adjustments based on expert opinions, 12 components were confirmed. In the quantitative phase, structural equation modeling was used to identify the network of relationships and interactions and presenting the final model.
Findings: The component of formulating policies for spatial data infrastructure was identified as the most influential component at the highest level. Components such as the extent of using spatial data, the number of electronic service offices, data updating rates, collaboration and coordination among the three authorities of Rey Governorate, Rey Municipality, and Tehran Municipality towards the production, standardization, and sharing of spatial data, as well as the component of cooperation and coordination with other agencies under the title of data user, were placed as the influential components at the lowest level.
Conclusion: The model showed that the policies and standards defined in the field of spatial data infrastructure has the greatest impact on other components. Therefore, formulating policies and standards in the field of spatial data infrastructure can help achieve other components of smart spatial data management by creating a systematic framework, including collecting, organizing, sharing, and effectively using spatial data.
Methodology: This srudy, conducted in two qualitative and quantitative phases in 2023-2024, focused on a case study of District 20 in Tehran. The statistical population consisted of 16 experts. Data collection tools included semi-structured interviews and questionnaires. In the qualitative phase, components of integrated management of spatial data infrastructure for a smart city were identified using the literature review. After making adjustments based on expert opinions, 12 components were confirmed. In the quantitative phase, structural equation modeling was used to identify the network of relationships and interactions and presenting the final model.
Findings: The component of formulating policies for spatial data infrastructure was identified as the most influential component at the highest level. Components such as the extent of using spatial data, the number of electronic service offices, data updating rates, collaboration and coordination among the three authorities of Rey Governorate, Rey Municipality, and Tehran Municipality towards the production, standardization, and sharing of spatial data, as well as the component of cooperation and coordination with other agencies under the title of data user, were placed as the influential components at the lowest level.
Conclusion: The model showed that the policies and standards defined in the field of spatial data infrastructure has the greatest impact on other components. Therefore, formulating policies and standards in the field of spatial data infrastructure can help achieve other components of smart spatial data management by creating a systematic framework, including collecting, organizing, sharing, and effectively using spatial data.
Send email to the article author