Journal of Sustainable Urban & Regional Development Studies (JSURDS)

Abstract چکیده
تحلیل الگوی پراکنش فضایی قیمت مسکن در شهر اردبیل با استفاده از GIS، موضوعی است که به بررسی توزیع جغرافیایی قیمت مسکن و عوامل مؤثر بر آن می‌پردازد. با رشد شهرنشینی و افزایش تقاضا برای مسکن، درک نحوه توزیع قیمت‌ها در سطح شهر به برنامه‌ریزان و سیاست‌گذاران کمک می‌کند تا تصمیمات بهینه‌تری اتخاذ کنند. در این تحقیق، مسأله اصلی شناسایی الگوهای فضایی قیمت مسکن و ارتباط آن با متغیرهای اقتصادی، اجتماعی و کالبدی است. هدف اصلی این مطالعه، تحلیل پراکنش فضایی قیمت مسکن و تعیین عوامل کلیدی تأثیرگذار بر آن در شهر اردبیل است. روش تحقیق به‌صورت توصیفی-تحلیلی بوده و از ابزارهای GIS برای تحلیل فضایی داده‌ها استفاده شده است. داده‌های مورد نیاز از منابع رسمی و میدانی جمع‌آوری و با روش‌های آماری و زمین-آماری مورد پردازش قرار گرفته‌اند. یافته‌ها نشان می‌دهد که: قیمت مسکن در شهر اردبیل از الگوی خوشه‌ای تبعیت می‌کند و مناطق با قیمته‌ای بالا و پایین به‌صورت متمرکز در نقاط خاصی از شهر قرار دارند. عواملی مانند دسترسی به مراکز خدماتی، کیفیت محیطی و ویژگی‌های کالبدی ساختمان‌ها تأثیر معناداری بر قیمت مسکن دارند. نتایج تحقیق بیانگر آن است که تحلیل فضایی قیمت مسکن می‌تواند به درک بهتر نابرابری‌های شهری و تدوین سیاست‌های مسکن کمک کند. این مطالعه به برنامه‌ریزان شهری پیشنهاد می‌کند از ابزارهای GIS برای پایش مستمر تغییرات قیمت مسکن و شناسایی مناطق محروم استفاده کنند.

Abstract The optimal site selection for sensitive facilities such as prisons is a significant challenge in urban and regional management. This study aims to identify optimal locations for a prison in Neyshabur city by integrating Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods. In terms of purpose, the present study is applied, and in terms of methodology, it is descriptive-analytical. Through library and field studies, 14 effective criteria for prison site selection were identified and categorized into two groups: natural criteria (elevation, slope, aspect, soil type, distance from fault lines, distance from surface waters) and human-security criteria (distance from communication routes, urban boundaries, villages, military centers, industrial centers, passenger terminals, and gas pipelines). The required data were obtained from relevant organizations and processed in a GIS environment. For data analysis, the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP) methods were used. In the AHP method, after forming the pairwise comparison matrix and calculating the criteria weights, an inconsistency rate of 0.03 was obtained, indicating the reliability of the results. Accordingly, the criteria of 'distance from the city boundary' (0.18) and 'distance from military centers' (0.16) received the highest weights. The results showed that based on the ANP method, 6.49% (500 hectares) of the study area was classified as 'highly suitable', 43.87% (3380 hectares) as 'suitable', and 24.53% (1890 hectares) as 'moderately suitable'. In total, 74.89% of the city's area (5770 hectares) possesses favorable conditions for prison construction. The 'highly suitable' zones are primarily located in the northern and central parts of the city, characterized by gentle topography, balanced access to main roads, and a safe distance from settlements. This zoning can provide a scientific basis for decision-makers and planners in the site selection of a prison in Neyshabur city.

Abstract Background and Objective: Location selection is one of the strategic elements in the development of a country's defense infrastructure, requiring precise analysis of environmental, climatic, and human factors using modern spatial approaches. Therefore, the objective of this research is to investigate the role of climatic and geomorphological factors affecting the site selection of military garrisons in Moghan Plain, located in Ardabil Province, by utilizing remote sensing and GIS capabilities.
Methodology: To fulfill the research objective, 14 relevant criteria were selected, including precipitation, temperature, actual evapotranspiration, elevation, slope, aspect, vegetation cover, land use, distance from rivers, faults, roads, cities, villages, and geology. The required spatial layers were derived from remote sensing data sources such as Sentinel-2 imagery, the 12.5-meter ALOS PALSAR Digital Elevation Model, and global climate datasets including TerraClimate, and were processed within the Google Earth Engine (GEE) environment. Subsequently, training sample points representing suitable and unsuitable locations were defined, and the Random Forest (RF) algorithm was trained and applied to produce the final suitability zoning map.
Results and Findings: The variable importance analysis indicated that proximity to cities and villages, slope, vegetation cover, and distance from geological faults were the most influential factors in military base site selection within the study area. The final suitability map classified the region into five categories: very suitable, suitable, moderately suitable, unsuitable, and very unsuitable. Notably, portions of the northern and eastern sections of the Moghan Plain were identified as very suitable zones. This study demonstrates that the integration of machine learning algorithms with remote sensing data offers a robust and efficient approach for spatial analysis and supports strategic decision-making in the defense and military planning sectors.
Conclusion: The findings of this study can support the optimization of military base site selection by integrating strategic, environmental, and security-related criteria. Such an approach has the potential to enhance military operational efficiency, strengthen crisis management capabilities, reduce infrastructure development costs, and minimize vulnerability to natural hazards.

Abstract Background and Objective: Multi-criteria decision-making methods combined with Geographic Information Systems (GIS) enable the simultaneous evaluation of various environmental, infrastructural, and security criteria, assisting in the optimal selection of locations for the establishment of military bases. Accordingly, the aim of this study is to locate military bases in Meshginshahr County using the Analytic Network Process (ANP) model and GIS.
Methodology:To achieve the research objective, a survey method was first employed by designing a questionnaire to collect expert opinions on the factors influencing the siting of military bases in the study area. In the next step, necessary data layers were digitized based on existing maps, and a database was created in the ArcGIS environment. Data weighting was carried out using the ANP method. Finally, the zoning map of the study area was prepared by integrating and overlaying the selected layers.
Findings and Results: The results indicated that 15.67% of the county's area falls within the "highly suitable" class and 17.97% within the "suitable" class, mainly located in the central and eastern parts of the county. In contrast, 25.55% is categorized as "unsuitable" and 21.48% as "highly unsuitable," primarily found in the mountainous and steep western and southern regions.This study demonstrated that integrating multi-criteria decision-making models with GIS spatial analysis can significantly assist in optimizing the site selection of military bases while minimizing environmental and security conflicts.

Abstract Background and Objective: In this study, the Analytic Network Process (ANP) model was employed within a Geographic Information System (GIS) framework to zone Ardabil County for the purpose of optimal military site selection. The primary objective was to identify and prioritize the most suitable areas for the establishment of military bases, taking into account environmental, climatic, and human-related criteria.
Methodology: This research is of an applied type and has been conducted using a descriptive-analytical method. For this purpose, fourteen important indicators including altitude, slope, slope direction, geology, vegetation, temperature, humidity, soil, distance from road, village, fault, land use, distance from waterway were classified into three main groups (minor, human and environmental). First, using the ANP model and Super Decisions software, the weight of each of them (using a questionnaire and 30 experts and news) was calculated based on pairwise comparisons and internal and external relationships among them. Then, using the GIS environment, spatial information layers related to each indicator were prepared and processed, and the final weight of each layer was integrated using the Raster Calculator tool. In the next step, the final zoning map of the garrison location was divided into five classes from completely suitable to completely unsuitable.
Results and Findings: Results indicated that approximately 30% of the study area falls within the "highly suitable" and "suitable" classes, mainly located in the central and western parts of the county. In contrast, about 30% of the area was found to be "moderately suitable," predominantly covering the northern and central regions. Additionally, 40% of the area was classified as "unsuitable" or "highly unsuitable," with most of these areas situated in the northern and southern parts of the county. The findings of this research can serve as an effective decision-making tool for defense institutions and urban management authorities in the optimal placement of military facilities.

Abstract Background and Objective: Optimal land use and physical organization of cities based on efficiency, environmental protection, social justice, and equitable access to services are among the primary goals of urban planning. Therefore, the location and spatial distribution pattern of urban land uses should be aligned with meeting the needs of all social groups, aiming to improve the welfare and comfort of citizens. In this context, qualitative and quantitative evaluation of land uses in the spatial structure of the city and examining their locational suitability across defined zones are of particular importance. Pharmacies are among the units that play a significant role in meeting the diverse needs of citizens in the fields of health, treatment, and wellness. Due to this role, their spatial distribution pattern greatly impacts the level of pharmaceutical service coverage. Given the inter-neighborhood nature of pharmacies, this study investigates their spatial distribution at the level of the municipal areas of Rasht city.
Methodology: To achieve the research objectives, various spatial analysis models within the ArcGIS environment were used, including hot spot analysis, frequency weighting, spatial autocorrelation analysis, population density in relation to the number of pharmacies, and Thiessen polygon analysis.
Findings and Conclusion: The results indicate a high frequency weight of pharmacies in areas 1-3, 3-2, and 2-2; a statistically significant condition in district 2-1 based on high Z-scores in hot spot analysis; a rate of 7.27 pharmacy units per 10,000 people in district 2-2; the representation of proximity-based pharmacy access through Thiessen polygon analysis; and finally, the outcome of spatial autocorrelation analysis showing a random distribution pattern of pharmacies across the areas of Rasht. Each of these findings reveals aspects of spatial distribution patterns across the city, which are discussed further in the full article.

Abstract Background and Objective: The spatial distribution of urban services is a key factor in quality of life and the realization of spatial justice. Centers offering goods and services related to visual health, in addition to their economic role, are also important from a public health perspective. With population growth and the physical expansion of Ardabil city, the need for proper organization of these services has increased. The aim of this study is to investigate the spatial distribution pattern and service coverage of eyeglass retail units in the city of Ardabil using the relevant capabilities and tools in the ArcGIS environment.
Methodology: This research follows a descriptive–analytical approach, and data were collected through both documentary studies and field surveys. The statistical population includes all visual health service centers across Ardabil city. Spatial analysis was conducted using geographic tools within the ArcGIS environment.
Results and Findings: Out of a total of 38 identified units, 34 are located in Region 1, while Regions 4 and 5 lack any such services. Districts 1-1 and 2-1 show the highest concentration. Cluster analysis revealed that District 1-1, particularly Neighborhood 08-1, constitutes a hotspot with a 99% confidence level, indicating that service density there significantly exceeds the city’s average. This high concentration can lead to congestion and unequal access. In contrast, in the fourth to seventh rings away from the city center, no units have been deployed in these areas. The Nearest Neighbor Index (0.215602) confirms a clustered distribution pattern. Therefore, targeted expansion of these services in underserved areas is essential to promote spatial equity and improve access for all citizens.

Abstract Background and Objective: The spatial distribution of urban services and facilities, particularly in the field of health and healthcare, is a key issue in urban planning. Pharmacies, as critical units for providing medications and health services, must be spatially distributed in a way that ensures spatial justice and equitable access for all citizens. Therefore, both qualitative and quantitative assessments of land use within the city’s spatial structure and their locational suitability across defined zones are of great importance. In recent years, Ardabil has experienced rapid population growth and physical expansion, intensifying the need for the development and equitable distribution of pharmaceutical services. The aim of this study is to examine the pattern of distribution, spatial dispersion, and location allocation of pharmacies across the districts of Ardabil city using the capabilities and tools of ArcGIS.
Methodology: This research is descriptive-analytical in nature and the required data were collected through documentary and field methods. The statistical population includes all active pharmacies in the city of Ardabil. Considering the trans-neighborhood operation of pharmacies, the analyses were conducted at the scale of urban areas. To examine the spatial pattern of distribution and dispersion of pharmacies, several analytical capabilities were used in the ArcGIS environment; including hot spot analysis (Getis-Ord Gi) to identify statistically significant clusters*, Moran's spatial autocorrelation index (Moran's I) to measure the degree of spatial dependence, and nearest neighbor mean analysis to identify the type of cluster or dispersed pattern in the spatial distribution of pharmacies. Spatial data were collected using GPS and transferred to the ArcGIS environment via Google Earth. The simultaneous combination of these analytical tools along with the high accuracy of spatial data is the main innovation of this research compared to previous studies.
Results and Findings: In terms of quantity, District 2-1, with 57 pharmacies, represents the main concentration center of pharmacies in Ardabil, whereas Districts 2-3, 2-4, and 1-5 each have only one pharmacy. The results of Hot Spot analysis indicated high Z-values and a significant hot spot in District 2-1 with a 99% confidence level, while other districts did not exhibit similar patterns. The Moran’s I index (0.0808) suggests a random distribution pattern of pharmacies across the city districts. However, the average nearest neighbor ratio (0.49) indicates a clustered pattern in the spatial distribution of pharmacies within Ardabil. The study reveals a spatial concentration of pharmacies in certain districts of Ardabil, particularly in District 2-1, leading to unequal access across the city. Central districts benefit from better service coverage due to their proximity to healthcare centers, while peripheral areas face shortages. Although this clustering improves efficiency in some areas, it undermines spatial equity. The research highlights the need to revise location policies for pharmacies and calls for a more balanced distribution to enhance access, improve service efficiency, and promote equity in the urban health system.

Abstract Background and Aim: Urbanization is a socio-spatial process that is directly related to the expansion of urban areas and the growth of urban population. Developed countries such as the United States, Japan, and the United Kingdom have already completed their urbanization processes. The urban spatial structure has a significant effect on the sustainable development of cities. One of the important stages of urban spatial structure analysis is the identification of urban centers.
Methods:  This research is of applied type and was done in Tabriz city in 1402. The data collection method is library and document. The investigated indicators included educational centers, banks, health centers, offices, sports venues, commercial centers, green spaces, tourist places, religious places, military places and parking lots in Tabriz city. After preparing the urban blocks and the desired data, the prepared information was added to all the urban blocks of Tabriz in ArcGIS software. Then, based on expert opinions, the appropriate range of distances from the desired facilities was determined using a 5-point Likert scale. In this research, the network analysis method was used to identify the central cores of the city.
Findings and Conclusion: According to the results of network analysis, the city had two big central cores in the city center and 2 sub-cores around the city. The final result was that the city of Tabriz was forming the next nuclei and becoming a multi-nuclear city.
 

Abstract Background and Objective: The accurate locating and design of industrial areas can be considered a robust step taken to improve the environment, develop industries, and accurate and logical growth of urban and rural areas. In this research, the number of pollutants is measured during the 30 days of November 2023 to examine and analyze the impact of industrial uses of Tabriz City on its air pollution, and its location and placement in Tabriz have increased the air pollution indicators. The mentioned indicators are revised and examined based on the collected data and available data of research through Geographic Information System (GIS) and using library and analytical methods such as the IDW method.
Materials and Methods: the variables examined in this study included pollutants and their types over the 30 days of November 2022 in Tabriz Metropolis, which is done through library and data collection methods. Carbon monoxide (CO), Sulfur Dioxide (SO2), Particulate Matter (PM2.5) and (PM10), Nitrogen Dioxide (NO2), and Ozon were collected based on the point method in Kajvar, Hakim Nezami, Azerbaijan Square, Hotel Marmar, Namaz Square, Rah Ahan, Bagheshmal, Abresan, and Behdasht Stations. These data are prepared and expressed as pollutant distribution maps for each pollutant in the form of descriptive maps through ARC GIS software based on the Inverse Distance Weighted (IDW) method.
Findings and Conclusion: The results show that the location of industrial areas in Tabriz does not affect the air pollution rate of Tabriz, and has no negative effect on increasing air pollution in Tabriz.