Agriculture is regarded as one of the most fundamental economical basis in our country, hence estimating the amount of crops is highly regarded. In this study, estimating the amount of wheat stubble and its energy have been assessed using satellite images. SPOT images were employed to estimate the wheat farms in Hamedan and Bahar area (Hamedan province) during the harvesting time and peak of greenness. The wheat farms were then extracted based on the wheat index derived from green and red bands after pre-processing. Four different vegetation indices, the Normalized Difference Vegetation Index (NDVI), the Simple Ratio (SR), the Perpendicular Vegetation Index (PVI) and the Soil Adjust Vegetation Index (SAVI) were evaluated after images pre-processing. The procedure followed by sampling. Wheat farms were extracted using similar bands.OLS and GWR were employed to estimate the biomass based on NDVI index and the results indicated an improvement of GWR over OLS based on AIC and criteria. The results of AICc for SAVI in fixed and adaptive kernel bandwidth were estimated 614.7 and 615.7 respectively and for NDVI equal to 615.1 and 615.6.The results also indicated the value for both SAVI and NDVI in fixed and adaptive kernel bandwidth equal to 0.71 and 0.70 respectively. The results also revealed the significant non-stationary state in relationships.

So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is Geographically weighting Regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local Regressions. In order to evaluate annually precipitation spatial variation, this study was conducted in Gilan province which precipitation is distributed non-uniform due to different environmental conditions. The results of Geographically weighting Regression method were compared with another interpolation methods including global polynomial, local polynomial, inverse distance weighting (IDW), spiline, kriging and co-kriging and. In this study, average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Cross validation technique was used to assessment accuracy of each interpolation methods. The result showed that Geographically weighting Regression method had minimum error with RMSE=147 and had significant difference with the kriging method which was in the second rank with RMSE=187. Finally the best method for mapping isohyets in Gilan province is Geographically weighting Regression method.

Introduction and Objective: Biodiversity has a very important role in the sustainability and self-regulation of ecosystems and is used as an indicator to compare the ecological status of forest ecosystems. Corticolous lichen are one of the most common components of biodiversity in the forest community. The high diversity of Corticolous lichenes in an area indicates the biodiversity and sustainability of an ecosystem. One of the most important approaches to interpreting and tracking spatial variations of biodiversity is to use a Regression model. The aim of this study is to model the diversity of Corticolous lichen species. Materials and Methods: This research was carried out in section 2 of Shurab of Golband forestry projects in Noshahr city (Mazandaran province). Firstly, 54 samples were collected using rotating forest and selective sampling method. Then the Corticolous lichenes species in the parts were identified. Spatial location of all sample plots was recorded using GPS. All skin lichens were collected in each sample plot. Collected specimens were identified using valid lithological sources as well as laboratory methods. In this study, to determine the biodiversity in the next step, the values of Shannon Wiener and N1-Hill diversity indices and J-Pilo uniformity index were calculated for each of the sample plots. Then, a map of geographical and topographic factors affecting diversity including distance from road and distance from waterway and slope, height, wetting index, flow strength index and erodibility factor was prepared. Weighted geographical Regression and Ordinary Least Squares for modeling were used. Results: In this study, 17 species of lichens belonging to 14 genera and 11 families were identified. The results showed that the Weighted geographical Regression for Shannon Wiener, N1 Hill and J Pilo indices based on the coefficients of explanation coefficient and the modified Akaike information criterion had better results than the Ordinary least squares Regression. The amount of lichen diversity based on Shannon-Wiener and N1 Hill indices was calculated with a range from 1. 24 to 2. 98 and 2. 06 to 6. 99, respectively, and the amount of J Pilo uniformity index was 0. 205 to 0. 830. Also, the results of Moran I index showed that the spatial correlation in dermatological lichens is significant and their distribution pattern is clustered. Conclusion: In general, the results of this study showed that the geographical Weighted Regression method has a relatively good capability in modeling the spatial diversity of bark lichen species in forest stands. This Regression model can be used to model lichen diversity.

Ordinary linear Regression (OLR) is one of the most common statistical techniques used in determining the association between the outcome variable and its related factors. This method determines the association that is assumed to be true for the whole study area – a global association. In the field of public health and social sciences, this assumption is not always true, especially when it is known that the relationship between variables varies across the study area. Therefore, in such a scenario, an OLR should be calibrated in a way to account for this spatial variability. In this paper, we demonstrate use of the Geographically Weighted Regression (GWR) method to account for spatial heterogeneity. In GWR, local models are reported in which association varies according to the location accounting for the local variation in variables. This technique utilizes geographical weights in determining association between the outcome variable and its related factors. These geographical weights are relatively large (i. e. close to 1) for observations located near Regression point than for the observations located farther from the Regression point. In this paper, we demonstrated the application of GWR and its comparison with OLR using demographic and health survey (DHS) data from Tanzania. Here we have focused on determining the association between percentages of acute respiratory infection (ARI) in children with its related factors. From OLR, we found that the percentage of female with higher education had the largest significant association with ARI (P = 0. 027). On the other hand, result from the GWR returned coefficients varying from-0. 15 to-0. 01 (P < 0. 001) over the study area in contrast to the global coefficient from OLR model. We advocate that identifying significant spatially-varying association will help policymaker to recognize the local areas of interest and design targeted interventions.

Background and Aim: Environmental and climatic conditions in different geographical areas provide the basis for certain diseases. Skin cancer is one of the most common types of cancer, with a different incidence rate in geographical areas. The aim of this study is to determine the effects of climate and environmental factors on skin cancer and to map the geographical distribution of skin cancer in Iran.Methods: This study was performed using data of patients with skin cancer, population and data of climatic and environmental factors that affect skin cancer incidence. In this study, after calculating the incidence of skin cancer rate for the whole country, we used the Geographically Weighted Regression model to establish a Regression relationship between climate and environmental data and the incidence of skin cancer. The coefficient of detection between the map of incidence of skin cancer and its model map was calculated.Results: Correlation coefficients showed that sun UV and relative humidity had the highest positive and negative correlation with the incidence of skin cancer, respectively.The southern, eastern and central regions of Iran had the highest incidence of skin cancer rate and the northern and northwestern coasts of Iran had the lowest incidence rate.Validating of actual incidence rate map and the modeled incidence rate map indicated a coefficient of detection of 0.71.Conclusion: All of the climate and environmental parameters in this study contributed to in the incidence of skin cancer.

Background and Objectives: Spatial data mining techniques offer optimal efficiency in scenarios demanding thorough examination and extraction of results from extensive data sources. Emergency calls, due to their gravity and the involvement of rescue and emergency forces, present a scenario well-suited for geographical data mining. Typically, environmental science and geography researchers employ models such as ordinary least squares (OLS) Regression to understand spatial relationships between variables. However, OLS has limitations, particularly at the local scale, prompting the utilization of Geographically Weighted Regression (GWR) in this study to address these shortcomings.Methods: This study employs OLS and GWR methods to analyze the relationship between the high volume of emergency calls in Dallas, USA, and the influencing factors. Various statistical tests were employed for evaluation. Dependent variables include the number and dispersion of emergency calls, while independent variables encompass population, education levels, peak call hours, and distance from the city center. Spatial-statistical analysis and mapping were conducted using ArcGIS Pro software.Findings: Results indicate that population, education levels, distance from the city center, and peak call time respectively exert the greatest influence on the occurrence of emergency calls. In the OLS method, Koenker and Jarque-Bera indices, assessing model stationarity and residual normality respectively, did not yield satisfactory results. Evaluation of both OLS and GWR models revealed an R^2 value of approximately 0.61 for GWR and 0.41 for OLS, suggesting greater proximity to reality in the GWR model. Spatially, the weight of population parameter is higher in central city areas, while the weight of peak call time parameter is more pronounced in northern, southern, and western regions. Additionally, the weight of education level parameter is higher in southern parts of the city.Conclusion: Collectively, the identified factors exhibit a cumulative effect on the occurrence of emergency calls, enabling prediction of future occurrences. Leveraging these insights, appropriate tools can be devised for optimal management and control of regional issues.

Introduction: In 2022, gastric and breast cancer had a mortality rate of 6. 8 cases, ranking fourth despite intervention efforts. A 2020 study by the International Agency for Research on Cancer found global variations in incidence rates. This study examines key risk factors and preventive measures [1-2]. Materials and Methods: This applied ecological study uses the MAGWGPRS (Multivariate Adaptive Geographically Weighted Generalized Poisson Regression Spline) model, integrating MARS and GWGPR, to analyze cancer registry data. The model identifies geographic variations and hotspots in cancer risk. Data sources include pathology reports, death records, biopsy data, and a non-communicable disease risk factor survey. The dataset comprises patient age, location, cancer case counts, and relevant risk factors. Analysis is conducted using R, with ArcGIS for map visualization. Results: According to the International Agency for Research on Cancer, key risk factors for stomach cancer include obesity, smoking, physical inactivity, poor nutrition, age, and population density. The MAGWGPRS model, a Geographically Weighted Model, identifies regional variations in these factors by weighting observations based on distance using a kernel function and optimizing the model with the GCV criterion. Our analysis highlights vegetable consumption, smoking, low physical activity, and age as the primary determinants of gastric cancer risk. Conclusion: Our model identifies vegetable consumption, smoking, low physical activity, and aging as significant risk factors for gastric cancer. Further research is needed to refine obesity risk based on BMI criteria. The MAGWGPRS model is a valuable tool for identifying high-risk regions, enabling targeted interventions and prioritizing key risk factors across diverse geographic areas.