Land surface temperature is a significant factor affecting thermal variation and balance in global studies. In the last two decades, the great necessity for LST data in environmental studies and land resource management activities has made the measurement of LST as a major scientific debate. Discovering the Spatial heterogeneity of land surface temperature and analyzing the key factors and specific effective Spatial relationships that are affected by time series have great importance in land management. The aim of this study is to analysis of land surface temperature driving factors and Spatial heterogeneity using Spatial regression models. To review this issue, daily LST maps were prepared by the radiative transfer equation method using Landsat 7 and 8 data for 2002, 2013, and 2021 years in Bojnord city. The analysis of land surface temperature in areas where barren lands prevail requires nighttime temperature data. Therefore, MODIS night LSTs were also prepared as auxiliary maps. Pearson correlation, Spatial autocorrelation, ordinary least square, and geographically weighted regression models were used for data analysis. Then, the performance of the models was compared using the coefficient of determination and the Akaike information criterion. The results showed that the GWR approach had a better prediction accuracy and a better ability to describe Spatial non-stationarity than the OLS approach. The Spatial response of LST and different influencing variables from 2002 to 2021 showed that the development of green space plays an important role in modulating land surface temperatures. Since LST is influenced by various variables, including topography, climatic and atmospheric variables, and vegetation, therefore, understanding Spatial relationships and analyzing the areas with high LST can be useful as a way forward in the planning strategies.

Erosive soil processes in arid ecosystems create local heterogeneities and associated ecological and hydrological diversities within the landscape. Spatial heterogeneity exhibits simultaneous opposing degrading and developing conditions with varying degrees of resilience. While it would have been expected that heterogeneity-induced response diversity should increase the ecosystem resilience, highly heterogeneous ecosystems promote irreversible shifts. The major question is whether heterogeneity accelerates dryland degradation or provides an opportunity for increasing sustainability. To understand this paradox, recent studies were reviewed to answer (1) the causes of Spatial heterogeneity in patterns of soil biotic-abiotic properties; (2) how heterogeneity simultaneously exhibits seemingly opposite effects in dryland dynamics through the emergence of resilience thresholds. Until heterogeneity can retain multiple resilience thresholds, it will have facilitative effects on resilience of the landscape. When the distance between fragments exceeds a dispersal threshold, the disappearance of resilience thresholds promotes destructive effects of the heterogeneity, stimulating irreversible transitions. It is hoped that this review, in emphasizing the importance of the relationship between erosive soil disturbances and soil biotic-abiotic variables in the dynamics of Spatial heterogeneity can provide an effective basis to quantify critical heterogeneity thresholds as an early warning sign for anticipating the future evolution trend of landscape.

Background: The suicide incident has had an increasing trend in Iran over the past years. This study mainly aimed to investigate and visualize the Spatial variations of registered suicide cases at the province level. A two-step modeling approach was employed in order to estimate the relative risks (RRs) and model the age of fatal suicide across provinces in Iran. Study design: An applied ecological study. Methods: This study used the suicide death data recorded by the Iranian forensic medicine organization from March 21, 2016, to March 20, 2018. Furthermore, a Bayesian Spatial approach-Besag, York, and Mollie (BYM) model-was applied to estimate the RR of suicide across provinces in Iran. Results: This risk was found to be significantly higher than the average in both men and women in the west of Iran. For women, higher population density (mean: 0. 003; 95% CrI: 0. 001-0. 005) and lower urbanization rate of provinces (mean:-0. 025; 95% CrI:-0. 038,-0. 012) were associated with increased RR of suicide. Based on the log-normal model fitted to the data, the overall mean age of the fatal suicide at the national level was 34 years. Conclusions: The magnitude of gender and age differences was quantified, and many Spatial variations were identified in suicide mortality across provinces in Iran. Given the heterogeneity in suicide mortality risk among different subgroups of age and gender, our findings point to the urgent need in developing gender-and age-specific suicide prevention strategies. Moreover, efficient allocation of healthcare resources for suicide prevention can be attained by targeting provinces with higher risk.

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.