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Volume 39, Issue 3 (2024)
GeoRes 2024, 39(3): 269-277 |
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Received: 2024/06/6 | Accepted: 2024/07/8 | Published: 2024/07/18
Received: 2024/06/6 | Accepted: 2024/07/8 | Published: 2024/07/18
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Khaki M, Fathi M, Mallaei M. Analysis of Environmental Factors on the Spatial Structure of Industrial Estates in Alborz Province Using the ELECTRE III Method. GeoRes 2024; 39 (3) :269-277
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1- Department of Architecture, Faculty of Technical Engineering, Shamal University, Amol, Iran
2- Department of Architecture, Borujerd Branch, Islamic Azad University, Borujerd, Iran
3- Department of Industrial Management, Ershad-Damavand University, Tehran, Iran
2- Department of Architecture, Borujerd Branch, Islamic Azad University, Borujerd, Iran
3- Department of Industrial Management, Ershad-Damavand University, Tehran, Iran
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Background
Spatial analysis, using quantitative data and tools such as GIS, examines spatial patterns and the structural organization of phenomena. In Iran, the improper placement of industrial parks has created challenges, highlighting the need for precise spatial analysis to improve site selection and the performance of these areas.
Previous Studies
Previous studies have examined various aspects of spatial analysis in industrial parks. Fernández and Ruiz (2009), using the AHP model, identify environmental and economic factors as the most important criteria for site selection. Jusuf et al. (2014) have identified three spatial patterns—clustered, block, and hybrid—in industrial parks. Xue et al. (2023), relying on GIS, analyze the spatial structure of the Shichuan Industrial Park in China. Antunes (2023) also studies spatial patterns in contemporary industrial buildings. Jiang et al. (2024) have used space syntax to examine the external form of creative industrial parks in China and offered suggestions for improvement. Additionally, Han et al. (2022) confirm a significant relationship between spatial structure and functional performance in industrial parks. Fathi et al. (2023) have demonstrated that spatial structure parameters such as integration and depth have varying effects on employee productivity.
Aim(s)
The aim of this article is to determine spatial priorities within the internal structure of industrial parks in Alborz Province using the Analytic Hierarchy Process (AHP) method.
Research Type
This study is applied in terms of its objective and belongs to the category of quantitative and descriptive research in terms of methodology.
Research Society, Place and Time
The research population consisted of 14 experts in urban planning and spatial analysis who participated in a Delphi panel. The study was conducted in two industrial towns, "Simin Dasht" and "Baharestan," located in Alborz Province, Iran, during the year 2023.
Sampling Method and Number
The sampling method in this study was purposeful sampling of the Delphi type. In this approach, 14 experts and specialists related to spatial analysis and industrial park planning were selected as members of the Delphi panel and participated in four rounds of evaluation and consensus-building.
Used Devices & Materials
In this study, UCL Depthmap 10 software was used for spatial analysis to extract space syntax indicators such as integration, depth, choice, and connectivity. Additionally, an axial map of the industrial park street networks was prepared at a 1:2000 scale. For the analysis and prioritization of criteria, the SANNA multi-criteria decision-making model was employed within the Excel environment. The main data collection tool was a Delphi questionnaire, which was completed in four rounds by 14 experts.
Findings by Text
The analysis conducted in this study consisted of two main parts: first, the examination of space syntax indicators within the selected industrial parks, and second, the analysis and prioritization of environmental and spatial parameters influencing their physical structure.
In the first phase, using space syntax analysis techniques, axial maps of the Simindasht and Baharestan industrial parks were generated (Figures 3 and 4). These maps analyzed four main indicators: integration, depth, choice, and connectivity. Each pathway in the maps was color-coded from warm to cool tones that warm colors represented higher values, while cooler tones indicated lower values. This analysis offered more precise insights into the spatial configuration and accessibility of each park.
Figure 3. Space syntax analysis of the Simindasht industrial park
Figure 4. Space syntax analysis of the Baharestan industrial park
In the second phase, pairwise comparison methods were applied to prioritize the influencing criteria. Among space syntax indicators, spatial depth received the highest relative weight, identifying it as the most influential factor in shaping the spatial structure of the parks. It was followed by integration, connectivity, and choice, respectively.
In the physical structure category, building density received the highest weight, indicating its key role in shaping the form and spatial layout of industrial parks. It was followed by visual connectivity, greenness, and architectural quality, with the latter having the lowest weight.
In the geomorphological category, climate ranked highest by a considerable margin, followed by topography and soil texture, highlighting climate as the most significant factor affecting site selection and development of the parks.
Within the access and connectivity group, internal infrastructure (e.g., utility networks and basic facilities) had the highest weight, emphasizing the importance of infrastructure development for improved spatial performance. This was followed by the internal road network and external connectivity of the parks.
It is worth noting that the inconsistency ratio in all pairwise comparisons was below 0.1, indicating high reliability and accuracy of the results. Furthermore, in the Expert Choice software, the final weight of each criterion group was set to 1, reflecting the relative importance of indicators within each category.
Afterward, using the SANNA multi-criteria decision-making model, the final weights of the parameters, regardless of their initial groupings, were calculated, and a standardized spatial structure map of the industrial parks was generated (Figure 5). The criteria were graded into five levels of desirability, ranging from “Highly Desirable” to “Highly Undesirable” (Table 5).
Figure 5. Standardized spatial syntax map of Simindasht and Baharestan industrial parks.
Table 5. Decision matrix of environmental parameters in the spatial structure of industrial parks in Alborz
Subsequently, the overall final weights of the parameters at the macro level were presented (Table 6). Among the environmental factors, the street network, grain size, and visual connectivity received the highest weights. Within the space syntax indicators, integration and depth demonstrated the greatest influence.
Table 6. Decision matrix of environmental parameters in the spatial structure of industrial parks in Alborz Province
Finally, the environmental parameters were classified into five levels of desirability based on their spatial syntax metrics and spatial roles (Table 7). The results indicated that the street network and visual connectivity were rated as “Highly Desirable,” while architectural quality and greenery were categorized as “Undesirable.” Overall, the Simindasht Industrial Park demonstrated a more favorable spatial condition compared to Baharestan.
Table 7. Desirability of environmental parameters relative to spatial syntax parameters in Simindasht and Baharestan industrial parks
Main Comparisons to Similar Studies
Compared to similar studies, the present research shows relative similarity with two key studies in the field of spatial syntax analysis of industrial parks. First, the findings related to visual connectivity align with the results of Jiang et al. (2024), who identified visual convergence as a key factor in the structure of industrial parks. Second, the classification structure of factors in this study differs from the tripartite model of Xue et al. (2023) (spatial, industrial, service), indicating innovation in the categorization approach of the present research. From the perspective of the road network, although most studies such as Antoro et al. (2024), Hou and Li (2013), and Han et al. (2022) have focused more on traffic aspects, the spatial evaluation results of this article are close to the study by Genc et al. (2020), which addressed the ring spatial pattern. Moreover, the role of landscape and visual connectivity in this study is consistent with the findings of Yuhong et al. (2011) and Sjaifuddin (2020), although their focus was primarily on environmental factors. Regarding the physical dimensions and architectural quality, the present article aligns with the study by Ferm et al. (2021), which concentrated on the industrial parks of London, with the difference that no similar study has been found in Iran.
Suggestions
It is recommended that future studies analyze the spatial structure of industrial parks in other provinces as well, to enable comparison and ranking of all parks in this regard. Additionally, it would be beneficial to incorporate complementary methods, such as observation-based visual analysis, for examining the internal areas of industrial parks in upcoming research.
Conclusion
Three groups of factors including Access and Connectivity, Physical Structure, and Geomorphology were identified as the most important. However, this ranking does not hold consistently among the sub-parameters, where the highest priorities were assigned to Visual Connectivity, Road Network, and Soil Texture. Between the two industrial parks studied, the spatial quality of Simindasht Industrial Park was found to be higher than that of Baharestan Industrial Park based on the parameters examined in this research.
Acknowledgments: None reported by the authors.
Ethical Permission: The authors adhered to scientific ethical principles and guidelines such as integrity, confidentiality, honesty, and others.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Khaki M (First author), Main Researcher (35%); Fathi M (Second author), Introduction Writer/Discussion Writer (30%); Mollaei M (Third author), Methodologist/Statistical Analyst (35%).
Funding: None reported by the authors.
Spatial analysis, using quantitative data and tools such as GIS, examines spatial patterns and the structural organization of phenomena. In Iran, the improper placement of industrial parks has created challenges, highlighting the need for precise spatial analysis to improve site selection and the performance of these areas.
Previous Studies
Previous studies have examined various aspects of spatial analysis in industrial parks. Fernández and Ruiz (2009), using the AHP model, identify environmental and economic factors as the most important criteria for site selection. Jusuf et al. (2014) have identified three spatial patterns—clustered, block, and hybrid—in industrial parks. Xue et al. (2023), relying on GIS, analyze the spatial structure of the Shichuan Industrial Park in China. Antunes (2023) also studies spatial patterns in contemporary industrial buildings. Jiang et al. (2024) have used space syntax to examine the external form of creative industrial parks in China and offered suggestions for improvement. Additionally, Han et al. (2022) confirm a significant relationship between spatial structure and functional performance in industrial parks. Fathi et al. (2023) have demonstrated that spatial structure parameters such as integration and depth have varying effects on employee productivity.
Aim(s)
The aim of this article is to determine spatial priorities within the internal structure of industrial parks in Alborz Province using the Analytic Hierarchy Process (AHP) method.
Research Type
This study is applied in terms of its objective and belongs to the category of quantitative and descriptive research in terms of methodology.
Research Society, Place and Time
The research population consisted of 14 experts in urban planning and spatial analysis who participated in a Delphi panel. The study was conducted in two industrial towns, "Simin Dasht" and "Baharestan," located in Alborz Province, Iran, during the year 2023.
Sampling Method and Number
The sampling method in this study was purposeful sampling of the Delphi type. In this approach, 14 experts and specialists related to spatial analysis and industrial park planning were selected as members of the Delphi panel and participated in four rounds of evaluation and consensus-building.
Used Devices & Materials
In this study, UCL Depthmap 10 software was used for spatial analysis to extract space syntax indicators such as integration, depth, choice, and connectivity. Additionally, an axial map of the industrial park street networks was prepared at a 1:2000 scale. For the analysis and prioritization of criteria, the SANNA multi-criteria decision-making model was employed within the Excel environment. The main data collection tool was a Delphi questionnaire, which was completed in four rounds by 14 experts.
Findings by Text
The analysis conducted in this study consisted of two main parts: first, the examination of space syntax indicators within the selected industrial parks, and second, the analysis and prioritization of environmental and spatial parameters influencing their physical structure.
In the first phase, using space syntax analysis techniques, axial maps of the Simindasht and Baharestan industrial parks were generated (Figures 3 and 4). These maps analyzed four main indicators: integration, depth, choice, and connectivity. Each pathway in the maps was color-coded from warm to cool tones that warm colors represented higher values, while cooler tones indicated lower values. This analysis offered more precise insights into the spatial configuration and accessibility of each park.
Figure 3. Space syntax analysis of the Simindasht industrial park
Figure 4. Space syntax analysis of the Baharestan industrial park
In the second phase, pairwise comparison methods were applied to prioritize the influencing criteria. Among space syntax indicators, spatial depth received the highest relative weight, identifying it as the most influential factor in shaping the spatial structure of the parks. It was followed by integration, connectivity, and choice, respectively.
In the physical structure category, building density received the highest weight, indicating its key role in shaping the form and spatial layout of industrial parks. It was followed by visual connectivity, greenness, and architectural quality, with the latter having the lowest weight.
In the geomorphological category, climate ranked highest by a considerable margin, followed by topography and soil texture, highlighting climate as the most significant factor affecting site selection and development of the parks.
Within the access and connectivity group, internal infrastructure (e.g., utility networks and basic facilities) had the highest weight, emphasizing the importance of infrastructure development for improved spatial performance. This was followed by the internal road network and external connectivity of the parks.
It is worth noting that the inconsistency ratio in all pairwise comparisons was below 0.1, indicating high reliability and accuracy of the results. Furthermore, in the Expert Choice software, the final weight of each criterion group was set to 1, reflecting the relative importance of indicators within each category.
Afterward, using the SANNA multi-criteria decision-making model, the final weights of the parameters, regardless of their initial groupings, were calculated, and a standardized spatial structure map of the industrial parks was generated (Figure 5). The criteria were graded into five levels of desirability, ranging from “Highly Desirable” to “Highly Undesirable” (Table 5).
Figure 5. Standardized spatial syntax map of Simindasht and Baharestan industrial parks.
Table 5. Decision matrix of environmental parameters in the spatial structure of industrial parks in Alborz
Subsequently, the overall final weights of the parameters at the macro level were presented (Table 6). Among the environmental factors, the street network, grain size, and visual connectivity received the highest weights. Within the space syntax indicators, integration and depth demonstrated the greatest influence.
Table 6. Decision matrix of environmental parameters in the spatial structure of industrial parks in Alborz Province
Finally, the environmental parameters were classified into five levels of desirability based on their spatial syntax metrics and spatial roles (Table 7). The results indicated that the street network and visual connectivity were rated as “Highly Desirable,” while architectural quality and greenery were categorized as “Undesirable.” Overall, the Simindasht Industrial Park demonstrated a more favorable spatial condition compared to Baharestan.
Table 7. Desirability of environmental parameters relative to spatial syntax parameters in Simindasht and Baharestan industrial parks
Main Comparisons to Similar Studies
Compared to similar studies, the present research shows relative similarity with two key studies in the field of spatial syntax analysis of industrial parks. First, the findings related to visual connectivity align with the results of Jiang et al. (2024), who identified visual convergence as a key factor in the structure of industrial parks. Second, the classification structure of factors in this study differs from the tripartite model of Xue et al. (2023) (spatial, industrial, service), indicating innovation in the categorization approach of the present research. From the perspective of the road network, although most studies such as Antoro et al. (2024), Hou and Li (2013), and Han et al. (2022) have focused more on traffic aspects, the spatial evaluation results of this article are close to the study by Genc et al. (2020), which addressed the ring spatial pattern. Moreover, the role of landscape and visual connectivity in this study is consistent with the findings of Yuhong et al. (2011) and Sjaifuddin (2020), although their focus was primarily on environmental factors. Regarding the physical dimensions and architectural quality, the present article aligns with the study by Ferm et al. (2021), which concentrated on the industrial parks of London, with the difference that no similar study has been found in Iran.
Suggestions
It is recommended that future studies analyze the spatial structure of industrial parks in other provinces as well, to enable comparison and ranking of all parks in this regard. Additionally, it would be beneficial to incorporate complementary methods, such as observation-based visual analysis, for examining the internal areas of industrial parks in upcoming research.
Conclusion
Three groups of factors including Access and Connectivity, Physical Structure, and Geomorphology were identified as the most important. However, this ranking does not hold consistently among the sub-parameters, where the highest priorities were assigned to Visual Connectivity, Road Network, and Soil Texture. Between the two industrial parks studied, the spatial quality of Simindasht Industrial Park was found to be higher than that of Baharestan Industrial Park based on the parameters examined in this research.
Acknowledgments: None reported by the authors.
Ethical Permission: The authors adhered to scientific ethical principles and guidelines such as integrity, confidentiality, honesty, and others.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Khaki M (First author), Main Researcher (35%); Fathi M (Second author), Introduction Writer/Discussion Writer (30%); Mollaei M (Third author), Methodologist/Statistical Analyst (35%).
Funding: None reported by the authors.
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