Journal of Sustainable Urban & Regional Development Studies (JSURDS)

Abstract In recent years, the increase in the frequency and severity of floods caused by climate change and excessive urban development has created serious challenges for human societies and the environment. Meanwhile, flood resilience assessment has been proposed as a fundamental solution to reduce losses and increase the ability of natural and human systems to cope with this phenomenon. Geographic Information System (GIS) with its unique analytical and visualization capabilities is an efficient tool for identifying high-risk areas, analyzing effective factors, and planning flood management solutions. This study has comprehensively investigated the factors affecting flooding in Zabol County using modern spatial analysis methods. By using the Random Forest (RF) algorithm and combining 9 key indicators including topography, slope, precipitation, slope direction, watercourse density, geology, land use, topographic wetness index (TWI), and distance from the watercourse, an accurate map of flood risk zoning in the region was prepared. The results showed that topography (importance 0.77) and precipitation (importance 0.68) indicators have the greatest impact on flood occurrence. The presented model has high accuracy with a kappa coefficient of 0.93 and RMSE of 0.15. The analyses show that about 45% of the study area is in the high-risk and very high-risk categories, mainly located in the southern low-lying areas with gentle slopes and close to waterways. This study highlights the importance of integrated flood management approaches with an emphasis on natural and human factors in arid regions.

Abstract Introduction: The phenomenon of land subsidence has caused many problems and dilemmas in agricultural lands, roads, power and energy transmission lines for various reasons, including excessive extraction of groundwater resources and climate change. In the last decade, subsidence has been occurring as a geomorphic hazard in a large part of the plains of Iran, including the Ardabil Plain. The increasing human need for water resources in recent years has led to an increase in the exploitation of surface and groundwater resources. One of the hazards facing the plains of the country is the risk of subsidence.
Methods and Material:  In this study, the annual land subsidence rate in Hamedan-Sanandaj province (Dehgolan-Qorveh and Hamedan-Bahar railway lines) has been investigated using data from the Sentinel 1 satellite and the combined aperture radar interferometry (InSAR) technique. To ensure the accuracy of the results obtained from processing satellite images, hydrological information including the water level of the region's piezometers has been used.
Results and Discussion: Based on the results of interferometric image processing, we are witnessing subsidence in the study area. The observed subsidence range from InSAR processing shows that the highest subsidence has occurred in areas with a high density of wells and thick clay layers. Finally, the results of radar interferometry show the subsidence rate in the Dehgolan-Qorveh area as 0.17, 1, and 1.5 mm per year, respectively. Next, using the GIS spatial information system, the risks of subsidence in the area were analyzed. The results show that important and strategic structures are at risk of subsidence.

Abstract Results and Discussion: The geographical conditions and ecosystem of the study area have played a significant role in the formation and expansion of settlements. Based on the description and analysis of the obtained maps, it can be said that the study areas are located adjacent to and along the path of water resources. The most important indicators of the sites in this region are: an altitude of 1355 to 1710 meters, moderate to poor vegetation cover, irrigated agricultural land use, location between communication routes and proximity to the modern village of Wind Kelkhoran, the suitable topography of the region considering the low numerical range of slopes and slope directions toward the northeast, east, southeast, and south, and the geological features of the region with volcanic rocks ranging from dacite to andesite. Given the importance of rural tourism in the sustainable development of the country and the diversity of historical sites in this region, it is necessary to pay serious attention to this area. Finally, it is recommended that the development infrastructure of this village, such as the construction of accommodation and communication spaces, the introduction of the village's capabilities thru appropriate advertising, and the restoration and revitalization of historical spaces to attract tourists and convert hand-dug spaces into places like the production and sale of local products and handicrafts, etc., are important strategies for tourism development in this region.

Abstract Perhaps one of the biggest and most dangerous diseases for urban life in the current century is the Covid-19 pandemic. Spatial pattern analysis and risk analysis is a suitable tool in the diagnosis and weather of epidemics and can be understood and help public health diseases. Spatial analysis methods of Geographic Information System (GIS) were used to examine the relationship between the initial statistics of the Covid-19 virus data and the analysis and analysis of spatial patterns in 62 neighborhoods of Khoy city. Therefore, using spatial statistics analysis tools, the spatial distribution pattern was examined and analyzed. The research method is descriptive-analytical; the aim of this study is to use spatial statistics methods to analyze and analyze the use of the Covid-19 spatial virus from the beginning of 2010 to the end of 2023. In order to analyze and analyze spatial patterns, spatial autocorrelation statistics, Moran cluster analysis, global local cluster analysis and identification of hot spots were used. The spatiotemporal pattern showed that the global Moran autocorrelation is highly clustered and less than 1% is likely to be a random cluster pattern. Local Moran autocorrelation statistics for hot and cold spots showed that the northwestern neighborhoods of Khoy city have a high-high clustering (aggregation) feature and the neighborhoods of the western part of Khoy city are hot spots with a 99% confidence level and the southern neighborhoods in the Valiasr township are cold spots for COVID-19 with a 99% confidence level. Comparison of multiscale geographic modeling with other models showed that apart from the performance index with a negative effect; the social and economic, environmental, communication network and physical indicators have a positive effect. Results The degree of dependence index (DOD) of the research data improved with 90% spatial dependence and the R^2 coefficient from 58% to 64% in the multiscale regression model.

Abstract Background and Objective: Despite the widespread implementation of road traffic restrictions in Iran during the COVID-19 pandemic, there is little quantitative evidence regarding the spatial effectiveness of these policies at the intra-provincial scale. The present study aimed to analyze the impact of traffic restrictions on the spatiotemporal dynamics of COVID-19 in Kurdistan Province and to test five quantitative hypotheses.
Methodology: In this study, a balanced panel of 10 counties of Kurdistan Province was constructed for the period 2019–2022, and spatial autocorrelation was examined using the Global Moran's I index. A spatial lag panel model with random effects and a K-nearest neighbor weight matrix was estimated. The policy variable (the proportion of months with travel bans) was extracted from the announcements of the National COVID-19 Taskforce and entered into the model in the form of interaction terms with traffic and migration variables. Direct, indirect, and total effects were calculated through matrix inversion (I − λW)⁻¹, and the robustness of the results was assessed using six different spatial weight matrices.
Results and Findings: The Global Moran's I index was non-significant for all years (p-value > 0.05); however, the spatial lag model revealed a strong and significant spatial autoregressive coefficient (λ = 0.812, p < 0.001) (confirming H2). The coefficient for "bus travel" was positive and significant (β = 0.100, p = 0.004), whereas the coefficient for "private car travel", contrary to expectations, was negative and significant (β = -0.0073, p < 0.001) (partially confirming H1). The interaction terms of restrictions with car and bus traffic were nonsignificant (rejecting H3). The "restrictions × migration" interaction was positive and significant (β = 0.271, p = 0.045) (confirming H4). Hypothesis H5 was not tested due to the absence of daily mortality data. For all variables, spillover effects outweighed direct effects (for main road density: direct effect = 2.275 versus indirect effect = 5.706). The results remained robust across the six weight matrices. Hence, uniform road traffic restrictions, in the absence of essential travel management, shifted the disease transmission pathway from public travel to exempted migrations. The strong spatial autocorrelation and the predominance of spillover effects necessitate the design of regional and coordinated inter-county interventions. Policy evaluation in regions with a small number of spatial units requires advanced spatial models, and simple tests such as Moran's I are insufficient.

Abstract Background and Objective: Land use changes represent a critical environmental challenge, significantly impacting natural resources, ecosystems, and hydrological processes. This study aims to comparatively evaluate the performance of two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—for land use mapping and analyzing temporal changes between 2015 and 2024 in the Samian Watershed, Ardabil Province, with an approximate area of 4236 km².
Methodology: Satellite imagery from Landsat 8 and 9, along with Sentinel-2, were utilized within the Google Earth Engine platform for land use classification. The RF and SVM classifiers were applied to produce land use maps consisting of eight classes: water, residential, irrigated agriculture, rainfed agriculture, snow, forest, dense rangeland, and sparse rangeland. Accuracy assessment was conducted using confusion matrices and related accuracy metrics. Global datasets (Dynamic World and GHSL) were employed for sampling and model training.
Results and Findings: Comparative analysis revealed that the RF algorithm outperformed SVM, achieving an overall accuracy and Kappa coefficient exceeding 99%. Significant land use changes were observed during the study period, including a notable increase in irrigated agriculture and residential areas, alongside a decrease in rainfed lands, snow cover, and surface water bodies. Overall, due to its high accuracy and stable performance, RF is recommended as the superior method for monitoring land use changes within big data environments such as Google Earth Engine.               

Abstract Background and Aim: The COVID-19 outbreak began in late 2019 and rapidly spread globally. The main objective of this study is to investigate the spatial distribution of the coronavirus disease in the five districts of Ardabil city.
Methods and Material: The research is applied in terms of purpose and descriptive-analytical in terms of nature and method. Spatial statistics methods in Arc GIS software were used to analyze the results. The data collection method was library-based, and the statistical population of the study consisted of individuals infected with coronavirus disease in the five districts of Ardabil city in 2020. Statistical methods including central mean, standard deviation ellipse, and nearest neighbor analysis were used to investigate disease distribution patterns.
Results and Discussion: The results show that the spatial distribution of coronavirus disease in different districts of Ardabil city was heterogeneous. In district one, the values are (z-score: 7.72) and (p-value: 0.000), and the distribution of patients was observed as dispersed and regular with a high concentration in the central part of the city. In district two, the values are (z-score: -4.96) and (p-value: 0.00001), showing a clustered pattern with the disease spreading from southwest to northeast. In district three, the values are (z-score: -0.52) and (p-value: 0.6013), and the distribution of patients was random in the northeast direction. In district four, the values are (z-score: -1.96) and (p-value: 0.094), with a clustered distribution of patients from south to north. In district five, the values are (z-score: -3.24) and (p-value: 0.0011), exhibiting a clustered pattern spreading from east to west.

Abstract Background and objective: The cultural and historical artifacts left from various historical periods in the city of Khalkhal, along with its favorable geographical and strategic conditions, have undoubtedly highlighted its importance in the region. This Township, an area among valleys and mountains with abundant water resources, suitable pastures, and high elevation above sea level, has been a cradle of civilization and a center of population since ancient times. The locational conditions and ecosystem of the region had made it one of the most suitable places for the gathering and establishment of ancient settlements. In this context, the aim of this research is to examine the role of climatic and geomorphological factors of the region on the location of settlement sites using remote sensing and GIS capabilities. By examining and analyzing settlement patterns, one can study and analyze the relationship between sites and these patterns, as well as the overall geographical conditions of the region.
Methodology: The research method was descriptive and analytical, and the data collection tools were field and library methods. In line with the research objective, initially, 7 important settlement sites were selected as samples and the basis for the study. Factors affecting the site selection of the areas, such as the location of the areas in relation to water sources, elevation above sea level, vegetation cover, distance of the areas from roads and villages, distance from the city, soil type, regional faults, and land slope, were selected. The required layers were extracted using remote sensing data and processed in the Google Earth Engine environment.
Findings and conclusions: The results of the findings showed that a direct relationship exists between settlement patterns and sites, and the patterns have played a significant role in the formation of settlements. Most sites are located near and along water sources. An altitude of 1603 to 1960 meters, moderate vegetation cover, location along communication routes, proximity to or within villages, within urban areas, and a slope of 0 to 15 percent are among the most important indicators of the settlement patterns of sites from this period. Additionally, the soil types of the three sites are brown steppe soils, and in terms of the sites' positions relative to the regional faults, most of them are located at a distance of 2992 to 6791 meters. It is suggested that in future projects near these sites, attention should be paid to these factors.