دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 2 2026 07 18 Identifying the Climate Regimes of the Caspian Sea Basin Through a Data-Driven Approach: A Pathway to Sustainable Agriculture and Climate Change Adaptation شناسایی رژیم اقلیمی حوضه دریای خزر با رویکرد داده کاوی: رهیافتی برای کشاورزی پایدار و سازگاری با تغییر اقلیم 1 17 107220 10.22059/jphgr.2026.406953.1007911 FA محبوبه مولوی عربشاهی گروه ریاضی کاربردی، دانشکده ریاضی و علوم کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران 0000-0003-1242-4989 علی زالی گروه علوم کامپیوتر، دانشکده ریاضی و علوم کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران 0009-0002-3120-9563 علیرضا خادم دقیق گروه علوم کامپیوتر، دانشکده ریاضی و علوم کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران 0009-0006-8976-7663 Journal Article 2026 02 24 ABSTRACT The Caspian Sea basin is considered one of the regions sensitive to climate change due to its prominent role in ensuring Iran’s food security, economic development, and environmental sustainability. Topographic irregularities, land cover diversity, and proximity to a vast water body have led to the formation of complex and heterogeneous climatic patterns within this basin. Therefore, the precise identification of spatiotemporal climate structures in this region is an essential step toward climate-adaptive management and the planning of water and agricultural resources. This study employs a mixed-methods analytical framework, integrating principal component analysis (PCA) for dimension reduction with K-means and DBSCAN (Density-based spatial clustering of applications with noise) clustering techniques, in order to reveal latent climatic patterns. ERA5 reanalysis data from 1964 to 2024, along with seven key climatic indicators, were used as inputs to the model. Initially, principal component analysis extracted the dominant climatic features and reduced the dimensionality of the data. Subsequently, clustering algorithms divided the basin into distinct subregions that clearly demonstrate the influence of latitudinal gradient, elevation above sea level, and distance from the coast. Validation utilizing the silhouette and Davies–Bouldin indices, along with analysis of variance (ANOVA), confirmed the statistical significance of the distinction among these clusters. Furthermore, correlation analysis with ENSO indices (El Niño–Southern Oscillation and the Southern Oscillation Index) revealed that the El Niño phase is associated with warmer and wetter conditions in the southern half of the basin, whereas the La Niña phase exacerbates colder and drier conditions in the northern parts. The findings serve as an analytical framework for climate zoning, monitoring atmospheric risks, optimizing agricultural calendars, and sustainable management of natural resources in the Caspian basin. Extended Abstract Introduction The Caspian Sea basin, as the largest enclosed body of water in the world, plays an essential role in ensuring food security, preserving biodiversity, and supporting agricultural and industrial activities in the adjacent countries. However, in recent decades, this unique ecosystem has come under increasing hydroclimatic pressures, including marine heatwaves, water level fluctuations, shoreline erosion, eutrophication, and the accumulation of heavy metals. The complex topographic structure and significant climatic contrasts within this basin, together with its high sensitivity to global atmospheric systems such as the El Niño–Southern Oscillation (ENSO), have given rise to spatiotemporal heterogeneities that traditional classification systems (e.g., Köppen–Geiger) are unable to fully represent. Prior studies have chiefly relied on linear and station-based statistical methods or classical classifications, and there exists a significant methodological gap in identifying homogeneous climatic regimes based on multidimensional data without structural assumptions at the scale of the entire basin. Consequently, this study aims to employ a hybrid data mining framework to identify homogeneous climatic regions, investigate the influence of ENSO phases on their stability, and provide practical applications for sustainable agriculture and water resource management in the Caspian Sea basin. Methodology This study utilizes ERA5 reanalysis data with a spatial resolution of approximately 31 kilometers for the period 1964 to 2024, concentrating on the months of September to November. Seven key climatic variables were employed, including air temperature at 2 meters above ground, dew point temperature, wind components (U and V) at 10 meters above ground, surface pressure, mean sea level pressure, and total cloud cover. Furthermore, ENSO indices, encompassing ENSO and the Southern Oscillation Index (SOI), were obtained from the Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA) of the United States. The data were normalized via the min-max method, and geographic coordinates (longitude and latitude) were added to the feature vector to enhance spatial coherence. PCA was applied to reduce dimensionality and eliminate correlations, and the four principal components were retained that accounted for more than 78% of the data variance. In the clustering stage, the DBSCAN algorithm (with parameters ε = 0.9 and MinPts = 50) was first applied to remove noisy points (approximately 12% of the data), and its output was then transferred as the initial input to the k-means algorithm. Clustering quality was assessed using the silhouette index (coefficient of 0.65), the Davies–Bouldin index (0.58), as well as MSE and EAI indices. The statistical validity of the clusters was confirmed through one-way analysis of variance (ANOVA) and the chi-square test to examine the association with ENSO. Additionally, a 3×3 modal filter was applied for spatial smoothing and to enhance spatial continuity. Results and Discussion The hybrid DBSCAN-K-means framework successfully identified nine climatically distinct and physically meaningful regions across the Caspian Sea basin. The optimal number of clusters was determined by maximizing the silhouette coefficient (0.65) and minimizing the Davies–Bouldin index. The Analysis of variance confirmed significant differences among the clusters in terms of 2-meter temperature and surface pressure (p < 0.001). The spatial pattern of the clusters is clearly governed by latitudinal and elevational gradients: the northern clusters (including the Caucasus and adjacent highlands) exhibit average autumn temperatures of 10–12°C, whereas the southern and southeastern clusters experience temperatures approaching 20°C. Based on their dominant climatic characteristics, these nine clusters can be aggregated into three physical types: northern-mountainous (characterized by lower summer temperatures, higher surface pressure, and stable snow cover); coastal-humid (characterized by thermal moderation due to the sea, high relative humidity, and greater cloud cover); and inland-semi-arid (characterized by higher summer temperatures, distance from moisture sources, and intensified evaporative processes). Seasonal variability analysis indicates that the northern clusters show a high annual thermal range (cold winters and hot summers), whereas the coastal clusters display a more moderate seasonal cycle. The analysis of the association with ENSO revealed that during the El Niño (warm) phase, the frequency of warmer clusters in the southern half of the basin upsurges, with positive temperature anomalies of 1–2°C observed, while during the La Niña (cold) phase, the cold northern clusters expand and lead to the intensification of frost events. The chi-square test confirmed a significant relationship between ENSO phases and the frequency of climatic zones (p < 0.01). Conclusions This study, by mixing unsupervised machine learning algorithms with ERA5 reanalysis data, successfully identified nine homogeneous climatic zones in the Caspian Sea basin, reflecting the complex atmosphere–ocean and topographic interactions. Statistical validation confirmed the high internal coherence and significant differentiation of these zones. The analysis of the ENSO effect demonstrated that El Niño is associated with the expansion of warm and humid conditions in the south, whereas La Niña is associated with the intensification of cold and dry patterns in the north. From a practical perspective, the findings can serve as a basis for designing adaptive cropping calendars, optimizing water resource allocation at the sub-basin scale, and developing early warning systems for climate hazards. The proposed framework supports the transition from traditional static planning to dynamic, climate-adaptive planning in the Caspian Sea basin. It is recommended that forthcoming research studies incorporate additional variables such as soil moisture, actual evapotranspiration, and socio-economic indicators into the model, so that more comprehensive decision support systems can be developed for resilience to climate change. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of Interest The authors declare no conflict of interest. Acknowledgements We are grateful to all the scientific consultants of this paper. حوضه دریای خزر به دلیل نقش برجسته‌اش در تأمین امنیت غذایی، توسعه اقتصادی و پایداری زیست‌محیطی کشور، یکی از مناطق حساس در برابر تغییرات اقلیمی محسوب می‌شود. ناهمواری‌های توپوگرافی، تنوع پوشش زمین و مجاورت با پهنه آبی وسیع، موجب شکل‌گیری الگوهای پیچیده و ناهمگن آب و هوایی در این حوضه گردیده است. ازاین‌رو، شناسایی دقیق ساختارهای فضایی‑زمانی اقلیم در این منطقه، گامی ضروری برای مدیریت سازگار با اقلیم و برنامه‌ریزی منابع آب و کشاورزی به شمار می‌رود. پژوهش حاضر با بهره‌گیری از رویکردی ترکیبی شامل کاهش ابعاد (تحلیل مؤلفه‌های اصلی) و خوشه‌بندی (K-میانگین و DBSCAN)، به کشف الگوهای پنهان اقلیمی پرداخته است. داده‌های باز تحلیل اِرا ۵ در بازه ۱۹۶۴ تا ۲۰۲۴ و هفت شاخص کلیدی اقلیمی (دما، بارش، رطوبت، فشار سطح دریا و غیره) به عنوان ورودی مدل استفاده شدند. ابتدا تحلیل مؤلفه‌های اصلی، ویژگی‌های غالب اقلیمی را استخراج و ابعاد داده را کاهش داد. سپس الگوریتم‌های خوشه‌بندی، حوضه را به زیرمنطقه‌های متمایز تفکیک کردند که به‌وضوح تأثیر گرادیان عرض جغرافیایی، ارتفاع از سطح دریا و فاصله از ساحل را نشان می‌دهند. اعتبارسنجی با شاخص‌های سیلوئت و دیویس-بولدین و آزمون تحلیل واریانس، معناداری آماری تمایز این خوشه‌ها را تأیید کرد. افزون بر این، تحلیل همبستگی با شاخص‌های اِنزو آشکار ساخت که فاز ال‌نینو با گرم‌تر و مرطوب‌تر شدن نیمه جنوبی حوضه همراه است؛ درحالی‌که فاز لانینا، شرایط سردتر و خشک‌تری را در بخش‌های شمالی تشدید می‌کند. یافته‌های این پژوهش می‌تواند به عنوان چارچوبی تحلیلی برای ناحیه‌بندی اقلیمی، پایش ریسک‌های جوی، بهینه‌سازی تقویم کشاورزی و مدیریت پایدار منابع طبیعی در حوضه خزر مورداستفاده قرار گیرد.

https://jphgr.ut.ac.ir/article_107220_525373dc12408217b7c6b1f3ad06b590.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 2 2026 07 18 Identification of Suitable Areas for the Physical Expansion of the Urban and Suburban Areas of Divandarreh Using Geomorphological Documents and Integrated Models شناسایی مناطق مستعد توسعه فیزیکی در پهنه شهری و پیراشهری دیواندره با به‌کارگیری سند ژئومورفولوژیکی و مدل‌های تلفیقی 19 38 107403 10.22059/jphgr.2026.413134.1007929 FA سینا کرمی گروه ژئومورفولوژی، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران 0000-0001-5611-4160 ممند سالاری گروه ژئومورفولوژی، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران حمید گنجائیان گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران. 0000-0001-5611-4160 Journal Article 2026 02 24 ABSTRACT This study aims to identify suitable areas for the physical development of Divandarreh through the preparation of a geomorphological and hazard documentation framework. Due to its geographical location and diverse geomorphological and topographical conditions, the city faces both opportunities and constraints for spatial expansion. Geomorphological landforms not only shape the natural landscape but also directly influence settlement suitability and urban development decisions. In the first stage, a geomorphological document of the urban area was prepared focusing on landforms, dominant processes, and hazards. In the second stage, a comprehensive spatial analysis was conducted to identify development-prone areas in both urban and peri-urban zones. The study utilized Google Earth imagery, MODIS images, a 12.5-m ALOS PALSAR Digital Elevation Model, and a 1:100,000 geological map. Analytical tools included ArcGIS, TerrSet, and SuperDecisions software, along with field observations. Fuzzy Gamma and Weighted Linear Combination (WLC) methods were applied for spatial modeling. Results indicate that low-slope hilly landforms, covering about 39.2 km² (47.2%) of the study area, represent the most suitable geomorphological unit for urban expansion due to minimal active processes and hazards, mainly located in the northern and northeastern parts of the city. According to the Fuzzy Gamma and WLC models, approximately 25.2% and 28.7% (equivalent to 9.1 and 10.2 km²), respectively, have high development potential. The strong quantitative and spatial consistency between the geomorphological document and model outputs confirms the validity of the research and highlights the effectiveness of geomorphological and hazard mapping in urban planning and resilience enhancement. Extended Abstract Introduction Iran’s geographical and geological setting places the country among the ten most disaster-prone regions in the world with respect to natural hazards. disasters such as earthquakes, floods, droughts, soil erosion, and landslides continuously threaten cities and human settlements, causing extensive damage. Among Iranian provinces, Kurdistan Province, due to its diverse geomorphological characteristics, mountainous structure, and numerous drainage basins, is considered one of the most sensitive areas with respect to hydro-geomorphological hazards. Studies indicate that a significant portion of the province’s land is exposed to seismic risk, severe soil erosion, and destructive floods, with the increasing trend of flood occurrence in recent decades resulting in substantial social and economic losses. Divandarreh city, as one of the major cities of Kurdistan Province, has experienced rapid physical expansion due to its geographical location, residential attractiveness, and rural-to-urban migration. If such development proceeds without adequate consideration of natural and geomorphological conditions, it may lead to increased urban vulnerability and the intensification of hazards. The prevalence of floods, landslides, slope instability, and soil erosion, combined with uncoordinated physical development, highlight the necessity of revising urban planning and management approaches in Divandarreh. Geomorphology, through its focus on landforms and governing processes, plays a crucial role in guiding sustainable urban development and provides a scientific basis for site selection, hazard zoning, and risk reduction. Given that urban development in Iran—particularly since the 1960s—has often occurred with limited integration of environmental sciences, many cities, including Divandarreh, face serious environmental and hazard-related challenges. Therefore, examining the physical development of Divandarreh through a geomorphological and hazard-oriented approach, aimed at identifying suitable, restricted, and prohibited development areas and preparing a realistic environmental framework, is essential for achieving sustainability and enhancing urban resilience. Methodology A diverse set of data and tools was employed to achieve the research objectives. Library-based data were used to compile the theoretical framework and identify regional natural hazards through the review of theses, scientific articles, books, and relevant research projects. Statistical data included climatic and demographic information, collected from relevant organizations and institutions. Visual and spatial data consisted of Google Earth and Landsat satellite imagery, geological maps, and a 12.5 m resolution ALOS PALSAR digital elevation model, which were utilized for spatial and geomorphological analyses. Field surveys were conducted to validate and complement the collected data and to assess the actual conditions of the study area. Regarding research tools, physical instruments such as GPS devices and digital cameras were used for field data collection, while conceptual tools included various software platforms and analytical models. Google Earth Engine was applied to generate vegetation density maps; ArcGIS was utilized for spatial mapping and analysis; TerrSet software was employed to implement the WLC model; SuperDecisions was used to execute the ANP model; SPSS was applied for statistical calculations; and Google Earth was utilized for monitoring and visual assessment of the study area. The research was conducted in two main stages: first, the preparation of a comprehensive geomorphological and hazard framework for Divandarreh city and its surrounding area, and second, the identification of areas suitable for physical urban development. Results and Discussion The results of geomorphological and spatial analyses indicate that the natural structure of Divandarreh city plays a decisive role in shaping the physical development pattern of residential areas. The dominance of steep hilly terrains over more than 94% of the study area, together with the presence of deep river valleys and floodplains, reflects unstable geomorphological conditions and a high level of natural hazard risk. These characteristics increase the sensitivity of the urban core and its periphery to hazards such as flooding and slope movements, significantly limiting the availability of land suitable for sustainable development. The outputs of multi-criteria decision-making models, including fuzzy gamma, WLC, and ANP, reveal that parameters such as slope, lithology, and distance from watercourses exert the greatest influence on land suitability for physical development. Accordingly, the northern parts of the urban area and sections along the Divandarreh–Saqqez transportation corridor exhibit the highest development potential due to gentler slopes, more favorable elevations, and greater distances from high-risk zones. In contrast, the southern parts of the city, steep valleys, and floodplains were identified as highly vulnerable areas where physical development would entail serious risks. The consistency among the results of different models confirms the reliability of the findings and demonstrates that the integrated application of geomorphological analysis and spatial decision-making models provides an effective approach for identifying safe zones for urban development. Conclusion The findings demonstrate that physical development of residential areas in Divandarreh city, if undertaken without adequate consideration of geomorphological characteristics and natural hazards, may result in undesirable consequences such as increased risk, environmental damage, and urban instability. The high diversity of landforms, the predominance of steep terrains, and the presence of flood-prone and landslide-susceptible areas underscore the necessity of adopting a preventive, science-based approach to urban planning. The integration of remote sensing data, GIS techniques, and multi-criteria decision-making models revealed that only a limited portion of the urban area and its surroundings—particularly the northern zones and areas along the Divandarreh–Saqqez axis—possess relatively suitable conditions for physical development. Consequently, concentrating urban expansion within these areas can reduce natural hazards while promoting safer and more sustainable development. Moreover, the preparation of a geomorphological and hazard-based framework, as the main outcome of this research, serves as a strategic tool for urban managers and planners by facilitating informed development guidance, controlling physical expansion, and reducing urban vulnerability. Ultimately, this study emphasizes the importance of integrating geomorphological assessments and risk evaluation into higher-level urban planning documents and offers a practical model applicable to other cities with similar natural conditions. Funding There is no funding support. Authors’ Contribution Sina Karami: Writing the initial draft, compiling the introduction, collecting data and sources, and final editing the text. Mamand Salari (corresponding author): Research design, developing the methodology, analyzing and interpreting data, supervising the research process Hamid Ganjaian: Processing spatial data, preparing maps and graphic outputs, implementing spatial models. Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. این پژوهش با هدف شناسایی مناطق مستعد توسعه فیزیکی شهر دیواندره بر پایه تدوین سند ژئومورفولوژیکی و تحلیل مخاطرات طبیعی انجام‌شده است. تنوع فرم‌ها و فرایندهای ژئومورفیک و شرایط توپوگرافیک منطقه، در کنار ایجاد ظرفیت‌های توسعه، محدودیت‌ها و تنگناهایی را نیز برای گسترش کالبدی شهر به همراه دارد. ازاین‌رو، در گام نخست سند ژئومورفولوژیکی پهنه شهری با تمرکز بر فرم‌ها، فرایندها و مخاطرات غالب تهیه شد و سپس با رویکردی تلفیقی، مناطق مناسب توسعه در مقیاس شهری و پیراشهری شناسایی گردید. داده‌های مورداستفاده شامل تصاویر ماهواره‌ای، مدل رقومی ارتفاعی 5/12 متری ALOS PALSAR و نقشه زمین‌شناسی 1:100000 بود و تحلیل‌ها با بهره‌گیری از نرم‌افزارهای ArcGIS, TerrSetو Super Decisions و مدل‌های گامای فازی و WLC انجام شد. نتایج سند ژئومورفولوژیکی نشان داد فرم تپه ماهوری با شیب کم که حدود 2/47 درصد (معادل 2/39 کیلومترمربع) از محدوده مطالعاتی را دربر می‌گیرد، به دلیل کمترین میزان فرایندهای فعال و مخاطرات، مناسب‌ترین بستر توسعه شهری است و عمدتاً در بخش‌های شمالی و شمال شرقی قرار دارد. همچنین بر اساس مدل‌های گامای فازی و WLC به ترتیب حدود 2/25 و 7/28 درصد (به معادل 1/9 و 2/10 کیلومترمربع) از این فرم دارای پتانسیل بالای توسعه است که از نظر مکانی با نتایج سند ژئومورفولوژیکی همخوانی دارد. در مجموع، یافته‌ها بیانگر کارآمدی اسناد ژئومورفولوژیکی و مخاطراتی در برنامه‌ریزی و ارتقای تاب‌آوری شهری و قابلیت استفاده از نتایج به عنوان پایگاه داده مدیریتی برای توسعه آتی شهر است.

https://jphgr.ut.ac.ir/article_107403_a17adcdd9d562ceec4ea4da7046f61d5.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 2 2026 07 19 Urban Ecological Security: A Bibliometric and Systematic Review with an Emphasis on Spatial Analysis Methods امنیت اکولوژیکی شهر: یک مرور نظام‌مند از نوع کتاب سنجی با تأکید بر روش‌های تحلیل فضایی 39 58 107177 10.22059/jphgr.2026.412243.1007925 FA معصومه مقبل گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران 0000-0001-9393-9954 سید عرفان مومن پور گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران 0000-0003-1122-2373 Journal Article 2027 02 25 ABSTRACT Rapid urbanization, alongside increasing human pressures on natural resources and urban ecosystems, have created significant challenges for the environmental sustainability of cities. This study aims to analyze the knowledge structure, research trends, and main thematic areas related to urban ecological security, with a particular emphasis on the application of spatial analysis methods.Conducted through a bibliometric approach and systematic review, scientific data were extracted from the Scopus citation database and analyzed using Biblioshiny and VOSviewer tools. A total of 411 sources were reviewed, encompassing research articles, review papers, conference proceedings, and books related to urban ecological security, resilience, and spatial analysis methods. The results indicate an increasing and interdisciplinary trend within this field. Keyword network analysis revealed core themes including urban planning and resilience, spatial analysis technologies such as Geographic Information Systems (GIS) and remote sensing, ecological risk assessment, and environmental pollution. Furthermore, temporal analysis of keywords reflects a shift in research concentration from environmental pollution toward the adoption of advanced technologies and intelligent spatial analysis methods. Despite the expansion of urban ecological security studies, the most research works remain limited to examining individual dimensions or indicators. Comprehensive studies integrating ecological, social, and spatial dimensions simultaneously and systematically leveraging remote sensing data, urban big data, and advanced spatial analytical techniques are still scarce. The findings suggest that the simultaneous application of spatial analysis methods, such as GIS and remote sensing, combined with urban planning approaches, can effectively enhance environmental sustainability, optimize natural resource management, and reduce ecological risks in cities. Extended Abstract Introduction Rapid urbanization in recent decades has exerted growing pressure on natural resources and urban ecosystems, posing serious challenges to the environmental sustainability of cities. Habitat degradation, rising environmental pollution, climate change, and excessive land consumption are among the factors that weaken the ecological security of urban areas. In this context, the concept of urban ecological security has emerged as a strategic framework for maintaining a balance between urban development and environmental protection, attracting growing attention from researchers and urban planners. The application of spatial analysis methods and data‑driven technologies such as Geographic Information Systems (GIS), Remote Sensing (RS), and intelligent modeling approaches provides effective tools for understanding spatial relationships between ecological factors and human activities. Despite the considerable growth of studies in this field, a comprehensive understanding of its scientific structure, thematic focus, and research trends remains limited. Identifying patterns in scholarly performance related to urban ecological security and examining the role of spatial technologies in advancing this field are essential for urban managers and researchers. Such insights can contribute to more effective policymaking, improved management of environmental risks, and the design of urban models aligned with sustainability principles. Therefore, a systematic bibliometric review of this field can reveal future research directions, knowledge gaps, and scientific strengths. The main objective is to systematically analyze scientific trends, knowledge structures, and research orientations in the field of urban ecological security with an emphasis on spatial analysis methods. The secondary objectives consist of examining the historical trend of scientific publications, identifying leading countries, analyzing author collaboration networks, detecting conceptual keyword clusters, and determining influential journals in this domain. In addition, the study seeks to identify theoretical and practical gaps that hinder the development of comprehensive and integrated models of urban ecological security. Methodology This research adopts a bibliometric approach. Data were extracted from the Scopus citation database and include a set of English-language articles related to the concepts of “urban ecological security,” “spatial analysis,” “GIS,” and “remote sensing.” The analysis process was conducted using the Biblioshiny and VOSviewer software packages. In the first stage, key bibliometric indicators were examined —including publication trends over time, geographical distribution of research outputs, citation counts, and scientific collaboration among countries and authors. In the second stage, network analysis was performed to identify thematic structures and conceptual clusters through keyword co‑occurrence mapping. Subsequently, source analysis was conducted to determine the most influential journals, and a Three‑Field Plot (author–keyword–source) analysis was utilized to explore relationships among key scientific elements. Findings and Discussion The results indicate that studies related to urban ecological security have experienced significant growth in recent years and developed an increasingly interdisciplinary character. China leads in the number of scientific publications in this field, followed by the United States and several European countries. International research collaborations have grown steadily, and technology‑oriented academic institutions contributed substantially to the advancement of knowledge in this area. The keyword co‑occurrence network analysis identified four major thematic clusters: Urban resilience and spatial planning: focusing on concepts such as urban resilience, spatial planning, and smart city, highlighting the relationship among resilience, urban spatial structure, and ecological security. Spatial and data‑driven technologies: encompassing GIS, remote sensing, machine learning, and spatial modeling, play a central role in monitoring and assessing environmental changes. Ecological risk assessment and environmental pollution: centered on concepts such as ecological risk assessment, environmental monitoring, and water pollution control. Policy and socio‑ecological dimensions: reflected in concepts such as urban governance and socio‑ecological systems. Temporal analysis of keywords also indicates a shift in research focus—from traditional topics such as “pollution” and “risk” during the 2015–2018 period toward more technology‑oriented concepts such as “machine learning” and “remote sensing” in recent years. This transformation reflects a transition from descriptive approaches to analytical and predictive methodologies. In the journal analysis, Sustainability (Switzerland) was identified as the most productive and influential journal in this field, followed by Sustainable Cities and Society and Frontiers in Ecology and Evolution. The Three‑Field Plot analysis revealed strong connections among the keywords urban resilience, GIS, and remote sensing and journals focused on sustainability studies, indicating the central role of spatial analysis in urban ecological research. Conclusion This study, through a comprehensive bibliometric analysis, demonstrates that urban ecological security is an expanding and interdisciplinary research field in which spatial analysis technologies are gaining increasingly prominent. The findings indicate that recent scientific directions are moving toward the application of artificial intelligence, machine learning, and the integration of remote sensing data with ecological and social indicators. The integration of spatial analysis with urban resilience models can contribute to improved environmental crisis management, more accurate prediction of ecological risks, and the promotion of urban sustainability. Future studies are recommended to concentrate on developing integrated frameworks that simultaneously incorporate the natural, social, and economic dimensions of cities. Furthermore, the use of multi‑source spatial data and intelligent tools for analyzing urban networks can enhance data‑driven decision‑making and policymaking processes. Overall, this bibliometric review provides a clear picture of the scientific landscape and future directions of research on urban ecological security with a spatial analysis perspective. As such, it may serve as a foundation for scholars seeking to design innovative models for sustainable and resilient cities. Funding There is no funding support. Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. هدف این پژوهش تحلیل ساختار دانشی، روندهای پژوهشی و محورهای اصلی مطالعات مرتبط با امنیت اکولوژیکی شهر با تأکید بر کاربرد روش‌های تحلیل فضایی است. این پژوهش با رویکرد کتاب سنجی و مرور نظام‌مند انجام‌شده است. داده‌های علمی از پایگاه استنادی Scopus استخراج و با استفاده از ابزارهای تحلیلی Biblioshiny و VOSviewer مورد تحلیل قرار گرفتند. تعداد کل منابع مورداستفاده برابر با 411 مورد بود. این منابع شامل مقالات پژوهشی، مقالات مروری، مقالات همایشی و کتاب‌های مرتبط با موضوع امنیت اکولوژیکی شهری، تاب‌آوری و روش‌های تحلیل فضایی بودند. نتایج نشان داد که مطالعات این حوزه دارای روندی افزایشی و ماهیتی میان‌رشته‌ای هستند. تحلیل شبکه کلیدواژه‌ها چند محور اصلی شامل برنامه‌ریزی شهری و تاب‌آوری شهری، فناوری‌های تحلیل فضایی مانند سیستم‌های اطلاعات جغرافیایی و سنجش از دور، ارزیابی ریسک‌های اکولوژیکی و آلودگی‌های محیطی را آشکار ساخت. همچنین روند زمانی کلیدواژه‌ها نشان‌دهنده حرکت پژوهش‌ها از تمرکز بر آلودگی‌های محیطی به سمت کاربرد فناوری‌های پیشرفته و روش‌های هوشمند تحلیل فضایی است. یافته‌های پژوهش نشان می‌دهد که استفاده توأمان از روش‌های تحلیل فضایی مانند سیستم‌های اطلاعات جغرافیایی (GIS) و سنجش از دور همراه با رویکردهای برنامه‌ریزی شهری، به‌طور مؤثری می‌تواند به بهبود پایداری محیطی، بهینه‌سازی مدیریت منابع طبیعی و کاهش مخاطرات زیست‌محیطی در شهرها کمک کند.

https://jphgr.ut.ac.ir/article_107177_3dcafedd266a6209767f3d853f68c47b.pdf