Journal of Spatial Analysis

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Landslides, as one of the most dangerous natural hazards in mountainous regions, continuously threaten human infrastructure, especially roads and transportation routes. Their occurrence often results in significant loss of life and property, making it crucial to study and assess landslide hazards for effective zoning. The purpose of this research is to zone the landslide hazard along the Masal to Gilvan road using a neural network algorithm. The neural network algorithm is recognized as one of the most effective machine learning models, capable of solving complex problems in prediction and classification despite its simplicity. For this zoning analysis, nine influencing factors were considered: (1) geology, (2) vegetation cover, (3) slope, (4) land use, (5) distance from the road, (6) slope aspect, (7) elevation, (8) distance from fault lines, and (9) distance from rivers. The data were prepared, preprocessed, and then entered into MATLAB 2018. A neural network model was designed and implemented with 9 input neurons, 8 hidden neurons, and 1 output neuron. The results indicated that the four most influential factors, ranked by weight, were: slope (0.24), vegetation cover (0.17), distance from fault lines (0.14), and geology (0.11). Final validation using the ROC curve showed that the AUC values were 0.854 for the training phase and 0.971 for the testing phase, both of which reflect highly favorable results. The error rate was found to be very low.
 

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Drought is one of the most important challenges faced by pastoralists around the world. This phenomenon can have significant negative effects on livestock health, production, and livelihoods. However, pastoralists can adapt to drought and reduce its negative effects by adopting various strategies. Semi-nomadic people in Darbandi, Kalat-Naderi County, have been facing drought since 2007 due to their livestock farming. Since livestock farming has profound impacts on the lifestyle and livelihoods of these communities, the present study examines their experience in facing drought and identifies their management strategies in these conditions. The research method is qualitative and the research tool is in-depth interviews with 20 semi-nomadic people in Darbandi, Kalat-Naderi. Sampling was purposeful and carried out until theoretical saturation was reached to ensure that a wide range of perspectives and experiences were collected. The data from the interviews were analyzed using a qualitative grounded theory approach to extract key patterns and concepts. According to the findings, the semi-nomadic Darbandi people of Kalat County have adopted various strategies in the face of drought, which are classified into four categories: rangeland and grazing management strategies, livestock nutrition management, water consumption management, and livelihood diversification. These results can be used as a basis for formulating better policies in the field of crisis management and rural development. Also, these results can be used for more effective planning to reduce the vulnerability of nomads to drought.

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Aging is one of the most prominent indicators of demographic decline that most modern societies experience. At this stage of demographic decline, alongside a decrease and stabilization of mortality rates, birth rates also sharply decline. The development of technology and the mechanization of tasks, the improvement of quality of life and health-related indicators, individual-centered lifestyles, and increased economic inflation are significant factors in this issue. Iran is also among the countries on the verge of entering the stage of demographic decline. However, the intensity of this trend varies in different regions of the country. This article examines and analyzes the state of aging in the neighborhoods of the metropolis of Tehran. This research falls into the category of applied research in terms of purpose and is descriptive-analytical in terms of method. The research is based on the census data from 2016 and utilizes spatial statistical analyses. The positive values of Moran's autocorrelation analysis for each of the indices: aging (0.664), old-age dependency ratio (0.644), youth ratio (0.653), aging ratio (0.664), and aging index (0.665) in the neighborhoods of Tehran indicate a clustered pattern. This means that the issue of aging is more acute in some neighborhoods and areas of Tehran. Accordingly, the density of the elderly population is higher in most neighborhoods of the central and northern parts of the city. The final result shows that the distribution of the elderly space follows the logic of the social macro-ecology of Tehran and is relatively consistent with its natural-social topography. Furthermore, the spatial analysis of aging in the neighborhoods of this city shows that although all neighborhoods generally grapple with the issue of aging, planning and management should be based on the patterns and nature of the spatial distribution of this issue.
 

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Deforestation is one of the primary challenges and environmental threats facing forest ecosystems, including the Hyrcanian forests, and occurs under the influence of various natural and anthropogenic drivers. This study aimed to model the probability of deforestation occurrence within the Loveh forest management district located in northern Iran. The dataset comprised 104 documented deforestation points and 14 explanatory variables, derived through spatial analysis using GIS and environmental, topographic, and anthropogenic data. To assess the relationships among variables and predict the likelihood of deforestation, two statistical models were employed: logistic regression and the Generalized Additive Model (GAM). The results revealed that the GAM outperformed the logistic regression model, achieving a higher Kappa coefficient (0.84) and Area Under the Curve (AUC) value (0.956), and providing a more realistic spatial distribution of deforestation risk. The most influential variables included distance from roads, slope, wind effect, and elevation. Based on the GAM output, approximately 20% of the study area was categorized as high and very high risk. These findings underscore the pivotal role of access infrastructure, human pressure, and climatic factors in accelerating deforestation processes. The results of this study can serve as a scientific basis for prioritizing conservation interventions, reassessing road development policies, and enhancing spatial planning for sustainable forest management in northern Iran.
 

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Introduction: Assessing the vulnerability and pollution of the aquifer is necessary for the management, development and allocation of land use, quality monitoring, prevention and protection of groundwater pollution. The purpose of this research is to identify and analyze the qualitative vulnerability of the Mashhad plain aquifer in order to monitor and manage underground water resources and prevent its future pollution.
Methodology: Mashhad plain is located in the northeast of Iran between Binaloud and Hezarmasjed mountains and in the watershed of the Kasfroud river, and its area is 2527 square kilometers. In this research, the vulnerability of the Mashhad Plain aquifer was evaluated with DRASTIC and SI models, and ArcGIS was used to analyze the parameters and prepare the vulnerability map. DRASTIC model is one of the overlap and index methods. In this method, the seven measurable parameters for the hydrogeological system include the depth of the groundwater level(D), net recharge(R), aquifer environment(A), soil environment(S), topography(T), Impact of the unsaturated Zone(I) and hydraulic conductivity(C) is used. The ratings for the sub-layers of each criterion vary from one to ten depending on their impact on the vulnerability potential. In SI method, five parameters of groundwater depth(D), net recharge (R), aquifer lithology(A), topography(T) and landuse(LU) are used for aquifer vulnerability. After preparing the SI model layers and weighting each of the layer classes using the functions available in the ArcGIS, the sensitivity index is obtained from the weighted sum of the mentioned parameters.
Conclusion: Study area is divided into four zones with very low vulnerability(21.85%), low(32.09%), medium to low(31.05%) and medium to high vulnerability(14.59%). Also, based on the results of the SI model, the study area is divided into five areas with very low vulnerability(0.4%), low(24.63%), medium to low(23.98%), medium to high(18.71%) and high vulnerability(32.25%). In general, the vulnerability of the aquifer increases from the southeast to the northwest.For verification, statistical method and calculation of correlation coefficient between vulnerability maps and TDS layer was used in TerrSet software and the results showed that both DRASTIC and SI models have high accuracy in zoning the vulnerability of Mashhad plain aquifer, so that the correlation coefficient of vulnerability maps with index The quality of TDS in Drastic model is (0.996) and in SI model (0.995); Therefore, the results of the following research can be used in environmental assessments and analysis of various pollutions and can be used as a basis for management decisions.

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Objective: Investigating the occurrence of land subsidence in the country and the extent to which rural settlements are exposed to the phenomenon of land subsidence.
Methods: The present study was based on library surveys and studies, field observations and impressions. Using information received from the National Mapping Organization, a map of the country's subsidence zones and the degree of risk of each zone, including very low, low, medium, high and very high risk, was drawn, and the aforementioned maps were compared with the location of the villages.
Results: According to the analysis of the available data, 302 villages are at very high risk, 768 villages are at high risk, 834 villages are in the medium risk zone, and 573 villages are in the low risk zone. In terms of percentage weight, about 4 percent of the country's villages are at medium to very high risk of subsidence, of which 1,904 villages are at medium to very high risk, and 573 villages are at low risk.
Conclusions: The highest provincial distribution of villages at risk of subsidence in the country with a very high degree is in the provinces of Alborz, Tehran, Khorasan Razavi, Qazvin, Kerman, Golestan and Hamedan, and the highest provincial distribution with a high degree is in the provinces of West Azerbaijan, Isfahan, Alborz, Tehran, Khorasan Razavi, Semnan, Qazvin, Kerman, Golestan, East Azerbaijan, Hamedan and Yazd. Also, the highest provincial distribution of villages at risk of medium-level subsidence is in the provinces of East and West Azerbaijan, Isfahan, Alborz, Tehran, Semnan, Qazvin, Kerman, Golestan, Mazandaran, Markazi, Hormozgan, Hamedan, and Yazd.