Land Use Investigation and Its Distribution Analysis in Various Districts of Tehran City According to Land Use Planning Standards

Kolbadi Nejad, M.; Sarvar, R.

doi:10.22061/jrsgr.2023.2009

Document Type : Original Research Paper

Authors

Department of Geography and Urban Planning, Islamic Azad University-Science and Research Branch, Tehran, Iran

https://doi.org/10.22061/jrsgr.2023.2009

Abstract

Background and Objectives: In today's world, cities have garnered significant attention as central hubs for social and economic activities. This research aims to enhance urban development and improve the quality of life for Tehran's residents, focusing on land use in Tehran, the capital of Iran and one of the most populous and pressured cities. The primary objective is to evaluate the distribution of various land uses across Tehran and analyze shortcomings that do not align with urban needs and standards. The study particularly delves into issues related to the appropriate number of land uses in different areas of Tehran and the role of recreational land use in urban development.
Methods: To conduct this comprehensive research, Tehran was divided into various zones, with diverse land uses thoroughly examined in each zone. These zoning divisions aimed to provide a more precise understanding of the unique needs of each part of the city for development and productivity. The distribution of land uses across the city was analyzed, and the frequency of each land use at the macro level was measured. Utilizing urban standards as evaluation criteria, a detailed analysis of land use distribution was performed. These standards served as benchmarks for assessment, highlighting areas where the standards fell short. This analytical approach facilitated a better understanding of strengths and weaknesses in urban development. Through this method, land use distribution in the city was evaluated more optimally, leading to recommendations for improvement and optimization.
Findings: The outcomes of this study underscore significant deficiencies in the allocation of land uses across Tehran, signaling a compelling call for refinement and heightened precision in regulation, particularly within the identified areas of 7, 10, 13, 14, 16, and 17. The identified weaknesses in land use distribution offer invaluable insights for urban decision-makers and planners, serving as a clarion call for immediate interventions in the city's development trajectory. The critical nature of this information lies in its potential to guide strategic decisions and policy implementations aimed at rectifying existing imbalances. Furthermore, the research accentuates a growing imperative to prioritize recreational land use within Tehran, shedding light on the inadequacy of current land use patterns to align with established urban standards and the evolving needs of society. The pressing need for increased attention to recreational spaces emerges as a key takeaway, suggesting that the development of such areas within the city could yield tangible enhancements in residents' overall quality of life. The envisaged benefits extend beyond mere leisure, with the potential to foster the creation of green spaces, promoting environmental sustainability, and providing platforms for diverse recreational activities that contribute to a healthier, more vibrant urban community. In essence, the research outcomes advocate for a strategic reevaluation and recalibration of land use policies in Tehran, with a heightened focus on rectifying existing imbalances and proactively addressing the evolving needs of the city's residents.
Conclusion: This research serves as a valuable tool for urban decision-makers and city planners in the development and enhancement of the quality of life for Tehran's residents. The obtained results indicate that optimizing land use and addressing urban needs can contribute to sustainable development and improved living conditions in Tehran.

Keywords

Main Subjects

Geography

COPYRIGHTS
© 2023 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

References

[1] Rousta I, Sarif MO, Gupta RD, Olafsson H, Ranagalage M, Murayama Y, et al. Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability. 2018;10(12):4433.

[2] Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A. Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustainable Cities and Society. 2016;23:94-104.

[3] Jabbari M, Behzadi S. Modelling Effects of Land Use Changes on Traffic based on Proposed Traffic Simulator. Computational Engineering and Physical Modeling. 2019;2(3):61-70.

[4] Shorabeh SN, Varnaseri A, Firozjaei MK, Nickravesh F, Samany NN. Spatial modeling of areas suitable for public libraries construction by integration of GIS and multi-attribute decision making: Case study Tehran, Iran. Libr Inf Sci Res. 2020;42(2):101017.

[5] Motieyan H, Azmoodeh M. Mixed-use distribution index: A novel bilevel measure to address urban land-use mix pattern (A case study in Tehran, Iran). Land use policy. 2021;109:105724.

[6] Behzadi S, Alesheikh AA. Introducing a novel model of belief–desire–intention agent for urban land use planning. Eng Appl Artif Intell. 2013;26(9):2028-44.

[7] Ghasemi K, Behzadfar M, Borhani K. Spatial analysis of leisure land uses in Tehran: Assessing inequity using the MARCOS method within a GIS framework. Heliyon. 2023; 9:e19691.

[8] Behzadi S, Memarimoghadan K. A Belief-Desire-Intention Agent-based procedure for urban land growth simulation. A case study of Tehran Metropolitan Region, Iran. Forum Geografic. 2019;18(1):53-62.

[9] Mousavi Z, Behzadi S. Geo-Portal Implementation with a Combined Approach of AHP and SWOT. International Journal of Natural Sciences Research. 2019;7(1):23-31.

[10] Mousavi Z, Behzadi S. Introducing an Appropriate Geoportal Structure for Managing Wildlife Location Data. International Journal of Natural Sciences Research. 2019;7(1):32-48.

[11] Poorazizi E, Alesheikh A, Behzadi S. Developing a mobile GIS for field geospatial data acquisition. J Appl Sci. 2008;8(18):3279-83.

[12] Behzadi S, Alesheikh AA, Poorazizi E. Developing a genetic algorithm to solve shortest path problem on a raster data model. J Appl Sci. 2008;8(18):3289-93.

[13] Behzadi S, Kolbadinejad M. INTRODUCING A NOVEL METHOD TO SOLVE SHORTEST PATH PROBLEMS BASED ON STRUCTURE OF NETWORK USING GENETIC ALGORITHM. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. 2019.

[14] Shahmoradi A, Behzadi S. Optimum Routing in the Urban Transportation Network by Integrating Genetic Meta-heuristic (GA) and Tabu Search (Ts) Algorithms. Journal of Geomatics Science and Technology. 2020;9(3):145-58.

[15] Babaei M, Behzadi S. ‏ Spatial Data-Driven Traffic Flow Prediction Using Geographical Information System. Journal of Soft Computing in Civil Engineering. 2023;7(4):132-43.

[16] Ioki K, Din NM, Ludwig R, James D, Hue SW, Johari SA, et al. Supporting forest conservation through community-based land use planning and participatory GIS–lessons from Crocker Range Park, Malaysian Borneo. J Nat Conserv. 2019;52:125740.

[17] Behzadi S, Alesheikh AA. Hospital site selection using a BDI agent model. International Journal of Geography and Geology. 2013;2(4):36-51.

[18] Mahjoobi M, Behzadi S. Solar desalination site selection on the Caspian Sea coast using AHP and fuzzy logic methods. Modeling Earth Systems and Environment. 2022:1-9.

[19] Behzadi S, Hamoudzadeh A. Evaluation of effective factors on air pollution using optimized cellular automata: A case study of Tehran. Journal of Engineering Research. 2021.

[20] Jafarian H, Behzadi S. Evaluation of PM2. 5 emissions in Tehran by means of remote sensing and regression models. Pollution. 2020;6(3):521-9.

[21] Chatrsimab Z, Alesheikh A, Vosoghi B, Behzadi S, Modiri M. Land Subsidence Modelling Using Particle Swarm Optimization Algorithm and Differential Interferometry Synthetic Aperture Radar. ECOPERSIA. 2020;8(2):77-87.

[22] Chatrsimab Z, Alesheikh AA, Voosoghi B, Behzadi S, Modiri M, editors. Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran). Dokl Earth Sci; 2020: Springer.

[23] Khaledi S, Behzadi S. Monitoring and Assessing the Changes in the Coverage and Decline of Oak Forests in Lorestan Province using Satellite Images and BFAST Model. Journal of Applied researches in Geographical Sciences. 2020;20(57):265-80.

[24] Ghashghaie S, Behzadi S. Spatial Statistics Analysis to Identify Hot Spots Using Accidental Event Calls Services. Journal of Statistical Research of Iran JSRI. 2019;16(1):121-41.

[25] Behzadi S. An intelligent location and state reorganization of traffic signal. Geodesy and Cartography. 2020;46(3):145-50.

[26] Pasha A, Sorbi A, Behzadi S. Landslide risk assessment in Qazvin-Rasht quadrangle zone (North of Iran). Scientific Quarterly Journal of Geosciences. 2018;27(106):89-98.

[27] Behzadi S, Mousavi Z, Norouzi E. Mapping Historical Water-Supply Qanat Based On Fuzzy Method. An Application to the Isfahan Qanat (Isfahan, Iran). International Journal of Numerical Methods in Civil Engineering. 2019;3(4):24-32.

[28] Jalilzadeh A, Behzadi S, editors. Flood Mapping and Estimation of Flood Water-Level Using Fuzzy Method and Remote Sensing Imagery (Case Study: Golestan Province, Iran). Forum Geografic; 2020: University of Craiova, Department of Geography.

[29] Ghasempoor Z, Behzadi S. Traffic Modeling and Prediction Using Basic Neural Network and Wavelet Neural Network Along with Traffic Optimization Using Genetic Algorithm, Particle Swarm, and Colonial Competition. Journal of Geomatics Science and Technology. 2021;10(3):147-63.

[30] Ghasempoor Z, Behzadi S. Predicting Traffic Data in GIS using Different Neural Network Methods. International Journal of Geography and Geology. 2022;11(2):62-71.

[31] Ghasempoor Z, Behzadi S. Provide an Automated Web-based Platform for Collecting Traffic Data. Journal of Geomatics Science and Technology. 2022;12(1):171-86.

[32] Jalilzadeh A, Behzadi S. Machine learning method for predicting the depth of shallow lakes using multi-band remote sensing images. Journal of Soft Computing in Civil Engineering. 2019;3(2):54-64.

[33] Behzadi S, Jalilzadeh A. Introducing a Novel Digital Elevation Model Using Artificial Neural Network Algorithm. Civil Engineering Dimension. 2020;22(2):47-51.

[34] Norouzi E, Behzadi S. Evaluating machine learning methods and satellite images to estimate combined climatic indices. International Journal of Numerical Methods in Civil Engineering. 2019;4(1):30-8.

[35] Norouzi E, Behzadi S. The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery. Journal of Electrical and Computer Engineering Innovations (JECEI). 2021;9(2):229-38.

[36] Abdollahi A, Behzadi S. Socio-Economic and Demographic Factors Associated with the Spatial Distribution of COVID-19 in Africa. Journal of Racial and Ethnic Health Disparities. 2022:1-13.