List of Articles Spatial relationship

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  1 - The spatial relationship between climatic factors and air pollution in the last 10 years of Tabriz city
  Fahimeh Banasaleh Mohammad Ebrahim Ramazani Ziba Beheshti
  Air pollution poses a significant threat to many cities in Iran, with Tabriz the bustling metropolis, being particularly affected. Factors such as rapid population growth, rural migration, industrial expansion, vehicular density, topographical features and natural facto More

  Air pollution poses a significant threat to many cities in Iran, with Tabriz the bustling metropolis, being particularly affected. Factors such as rapid population growth, rural migration, industrial expansion, vehicular density, topographical features and natural factors have collectively contributed to Tabriz becoming one of Iran’s most polluted cities. Beyond human-related factors, geographical factors including location, topography, and temperature inversions also play a crucial role in exacerbating air pollution in Tabriz. In this research, descriptive research methods drawing from meteorological and air pollution data sources were employed to investigate the spatial relationship between climatic factors and air pollution in Tabriz. By collecting and analyzing information from meteorological stations in Tabriz and air quality measurement stations related to Tabriz, we created integrated maps using Geographical Information System (GIS) software. These maps visually depicted pollution distribution and zoning. According to the results of the research, a significant spatial relationship and correlation exist between meteorological parameters and air pollution parameters. Additionally, the distribution of air pollution parameters across the surface of Tabriz city reveals that the highest pollution levels, attributed to polluting gases such as SO2, NO2, O3, and CO, occur in the western and central parts of the city. Conversely, suspended particles contribute to the most pollution in the eastern side of Tabriz. Furthermore, the seasonal quality index indicates that summer and spring experience the least air pollution, while autumn and winter exhibit the highest levels. Manuscript profile
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  2 - Fire risk zoning in urban areas using logistic regression method (Case study: Kashan city)
  Mohammad Amin Vakilalroaya Saeed Malmasi Mojgan Zayeem Dar Mahnaz Mirza Ebrahime Tehrani
  10.30495/girs.2022.685583
  Background and Objective Fire risk management is a global issue, and urban safety policies must take it seriously. One of the fields of research for controlling urban fires is to identify critical fire points in the region; Insufficient knowledge of these points will ca More

  Background and Objective Fire risk management is a global issue, and urban safety policies must take it seriously. One of the fields of research for controlling urban fires is to identify critical fire points in the region; Insufficient knowledge of these points will cause the occurrence and spread of fire in various areas and uses, delays in controlling it, and causing financial losses and personal injury as well as environmental pollution. Fire risk zoning with the aim of being used in planning and management in urban fire control has not been considered in the study area of this research and no research has been done in this field in the form of research and study plan. The purpose of this study is to determine and identify the criteria for fire risk zoning in the study area, to create a fire risk map based on the logistic regression method and compliance with the fire reality map, and also to present fire management programs and fire crisis management in Kashan.Materials and Methods The steps and techniques used in this study were performed in six steps. The first step is to identify the effective criteria and indicators. Using library studies, information obtained from authoritative articles, and also through the Delphi method in order to collect the opinions of experts, the Likert scale was used. In the second step, the screening of criteria was done in accordance with the purpose. The effective criteria in this study are vulnerable factors including (population density, industrial units, commercial-warehouse, high-rise buildings, old tissue, and fuel station) as well as the capacity of reducing factors. (Fire station, roads, and hydrant valves). In the next step, the layers were standardized using fuzzy logic. At first, the distance function was performed on the criteria in SELVA IDRISI to determine the distance from each phenomenon. Then, by the fuzzy method, all the criteria determined in the range of zero to 255 were standardized. The type of function used in the fuzzy logic approach is linear, and the selection of the function type and thresholds was based on a review of sources and expert opinion. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. In the fifth step, a Fire hazard map was prepared using logistic regression. After determining the validity of the logistic regression model using the specified indicators, the fire risk zoning map of Kashan was drawn. In the last step, Chi-Square, ROC, and Pseudo R Square were used to validate the logistic regression model.Results and Discussion Advantages of using the logistic regression model In addition to modeling observations, it is possible to predict the probability of each person belonging to each of the levels of dependent variables and the possibility of directly calculating the odds of variables using the maximum likelihood of maximum model coefficients. Also, compared to other statistical techniques, multivariate such as multiple regression analysis and diagnostic analysis, the dependent variable can have only two variables, one is the probability of an accident and the other is its non-occurrence. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. The output of the logistic regression model has coefficients between 0 and 1, with a probability higher than 0.5 of a value of one (occurrence of fire) and a probability of less than 0.5 of a value of zero (no occurrence of fire) and thus a boolean map of risk is generated. This logarithmic change causes the predicted probability to be continuous in the range of 0 to 1, and the output of the model to be presented as a spatial prediction map of the probability of destruction. Then, in the logistic regression equation, this layer was introduced as a dependent variable and the effective parameters in fire zoning were introduced as an independent variable. After entering the data into the logistic regression statistical model, model coefficients were extracted using effective parameters in IDRISI software. After determining the validity of the logistic regression model using the specified indicators, a fire risk zoning map of Kashan was drawn. Finally, in terms of fire risk potential, the study area was divided into 5 classes: very low, low risk, medium, very high, and high risk. The area of each of the 5 classes was obtained in hectares and the percentages were 87475.47, 4669.03, 132115, 1116.33, 788.96 hectares, and 90.94, 4.85, 2.19, 1.16, 0.82 percent, respectively.Conclusion The value of 0.95 obtained from ROC indicates a very high correlation between the independent and dependent variables. The value of the qi index is twice equal to 110836.07; Since its value is much higher than the threshold value, then the null hypothesis of all coefficients is also rejected. The value of the PR2 test in this study was 0.47, so the logistic regression model had an acceptable fit. Manuscript profile
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  3 - Explaining the evolution of Iranian traditional house spaces based on distance measurement method of plan similarity vector
  reza babakhani ali keifari
  10.22094/soij.2021.1930842.1424
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