Introduction In just a few decades, urban areas across the world in both developed and developing countries have become increasingly deteriorated. In other words, it is a worldwide phenomenon. Deterioration and urban decay are created and aggravated by many factors and variables and considered as one of the most severe environmental and socio-economic problems of recent times. These areas were occupied by poor immigrants and low income groups. Thus, the areas are faced with physical deterioration, economic and environmental problems. Undesirable changes in urban environments make living conditions more difficult for citizens. ...

Background and Objectives: The incidence of colorectal cancer has increased significantly in Iran in recent decades. The pattern of occurrence varies in different populations. A study was conducted to perform a spatial analysis of colorectal cancer and some of its risk factors in Iran using GIS. Methods: The data of this descriptive-analytic study included colorectal cancer incidence as a dependent variable and physical activity, Body Mass Index and smoking as independent variables recorded by the Cancer Department, Center for Non-Communicable Diseases Management, the Ministry of Health and Medical Education and the care system for non-communicable disease risk factors according to province and gender in 2009. Data was analyzed using the ArcGIS 10. 3 software and spatial correlation analysis, hot spots analysis, and geographic weighted Regression model. Results: The spatial relationship between the disease and some of its risk factors was confirmed by the model of Geographical weight Regression, according to which the northern and central provinces had the highest risk of colorectal cancer compared to other regions of the country. Conclusion: The results of this study showed that spatial analysis could be useful in identifying disease patterns, prioritizing the factors affecting it, and controlling the disease through strategic planning and interventions.

The main objective of this paper is to study the effect of government size and good governance on human development in different countries in 2000, 2005 and 2010. For this purpose, Geographical weighted Regression (adaptive spatial kernels) was used for data analysis considering significance of Moran statistic and spatial correlation of Human Development Index. Results (obtained utilizing ArcGis 9.3 software) indicate that on average, government size and good governance have an overall positive impact on human development index. Specifically, the effect of government size shows a decline between 2000, 2005 and 2010, whereas, the effect of good governance shows an increase from 2000 to 2005 and a decline from 2005 to 2010 respectively. In the case of Iran, findings indicate that regional impact coefficient of government size is positive in 2000 and 2005, while it is negative in 2010. The effect of good governance in Iran is positive with an increase in effect intensity. We also analyzed the effects of government size and good governance specifically on four groups of countries in terms of the level of development.

Malaria is a vector born disease caused by protozoan parasites of the genus Plasmoclium that causes 300-500 million new infections and 1 million deaths annually. Before any anti-malarial campaign in Iran, about 60% of the population were living in malaria endemic areas and 4 to 5 million of them had malaria infection each year. By starting of the anti-malaria campaign, the number of infections decreased so that the nwnber of malaria infected in Iran was 3000 people in 2009, 0.65 of them lived in Sistan & Baluchistan province. In the present study, for spatial modeling of the Annual Parasite Incidence (API) of malaria in Sistan & Baluchistan province, we applied a Geographical Weighted Regression (GWR) model to predict API by there independent climatic factors. The results indicated that the most important climatic factor for explaining API in Sistan & Baluchistan is annual rainfall as spatial factor, which is more important in the southern parts of the study area such as Chabahar and Nikshar. Temperature and relative humidity are of the second and third prioriies, respectively. The importance of these two climatic factors is higher in northern portion of the province. This model explained only 0.48 of API spatial variation (R2=0.48). Therefore, the non-climatic factors such as socioeconomic and lifestyle and the neighborhood position of this province with Afghanistan and Pakistan should also be considered in epidemiological survey of malaria in Sistan & Baluchistan province.

Detection of pathogenic factors, identify the spatial accumulation of disease cases and finding its distribution pattern are of urgent need in the field of public health and disease management and control. Leptospirosis is a zoonotic disease which occurs worldwide but is most common in tropical and subtropical areas with high rainfall. Wet and mild weather conditions in the northern provinces of Iran have put these areas at high risk for Leptospirosis incidence. The main objectives of this study are to investigate the annual pattern of Leptospirosis distribution, identify the spatial and spatio-temporal clusters of the disease and generate the annual predictive map of spatio-temporal distribution of Leptospirosis at the district level in the Northern provinces of Iran. In this study, Leptospirosis incidences, census data and topographical and climate factors have been used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis. The Leptospirosis incidences are continuously recorded by the Center for Disease Control and Prevention of Ministry of Health of Iran. The population census count estimates for period 2009-2014 were obtained from the Statistical Center of Iran. Topographical data were used to generate the altitude, slope and aspect maps. Climate data such as average temperature, average humidity, annual rainfall and number of freezing days were used to model other affecting parameters. Global clustering methods including Moran’s I and general G indices were applied to investigate the existence of spatial autocorrelation between cases of Leptospirosis and also analyze the annual spatial distribution of the existing patterns. Results of both Moran’s I and general G indices indicated meaningful persistent spatial autocorrelation between Leptospirosis cases and highly clustered distribution of Leptospirosis. Additionally, presence of spatial clusters of Leptospirosis and detection of high risk areas of disease were investigated using the local Moran’s I and local G indices. The results of the local Moran’s I and local G indices identified significant spatial clusters of Leptospirosis cases located in central, north-eastern and western parts of Guilan, Mazandaran and Golestan provinces, respectively.Geographically weighted Regression (GWR) and multilayer perceptron neural network (MLP) models were used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis and modelling the relation between the distribution of Leptospirosis cases with topographical and climate factors. Performance of GWR and MLP models were compared using Kappa coefficient, RMSE, MAPE and R2 measures. The evaluation results showed that the MLP model was able to predict the incidence rate of Leptospirosis in 2014 for each district with acceptable accuracy. MLP was able to model the relationship between Leptospirosis incidence and factors better than GWR. Additionally, results of both GWR and MLP models showed that average humidity and annual rainfall are most important affecting factors on Leptospirosis incidence in the Northern provinces of Iran, respectively. Such predictive maps can be used to provide essential guidelines for planning of effective control strategies and identification of high risk areas of Leptospirosis which should receive more preventive measures from policy makers and healthcare authorities.

Equitable access to land and its optimal use are one of the main components of sustainable development. The speed and the growth rate of land prices are related to the regional situation or land. Understanding the factors affecting land prices in Babolsar is the main goal of this research in order to plan the Balance of prices. GIS software is used to extract and classify the data of this research. Our statistical population in this research is residential land plots in the city's neighborhoods. In order to determine the price of land, 330 land plots were taken as an example. The geographic weight Regression model was also used to analyze the role of seven effective factors on land prices. The results showed that the average price of land in Babolsar is 1337000 Toman. Thirteen neighborhoods of the city were lower than the average and in contrast, nine neighborhoods of the city had a higher price level than the city average. Based on the results obtained from the weight Regression model, the local determination coefficient (Local R2) for the dependent variable of land prices in Babolsar is between 0. 57 and 0. 79 Which shows good fit and high accuracy. As we move from the coastline to the southern parts of the city, the value of the land will be reduced. Neighborhoods close to the beach such as Ali Abadimir, Nokhostvaziri, Parking, Ketiben and ValiAsr and. . . will have a higher price tag than other neighborhoods. In return, the southern neighborhoods of the city such as Miandasht, Ghaemie, Bibi Sarrozeh and parts of Hemmatabad and Yur mahalebala based on the estimated local land values, will have a lower price range than other areas of the city. The proximity to the coastline and the Proximity to the city center factor are among the most influential factors in the final value of land prices.

Statement of the Problem: In the last two decades, the concept of social capital has been emphasized through its relationship to fundamental social components including awareness, participation, trust, cohesion and social networking for sustainable development of communities, particularly rural communities. Therefore, social capital is essential for achieving sustainable rural development. Purpose: The purpose of this study was to evaluate the impact of social capital in sustainable development of rural settlements and its spatial analysis in Bojnourd County. Methodology: The research method in this study is descriptive-analytical and its purpose is fundamental. Documentary and field methods have been used to collect the data. The sample population is 22 villages with over 20 households in Bojnourd County. From a total of 4849 households in rural areas of the sample, using Cochran formula, the sample size of 298 households were selected by random sampling. To test the conceptual model of research and to investigate the impact of social capital on sustainable development of rural settlements, partial least squares technique and Smart PLS software and Geographically Weighted Regression model were used. Result: The coefficients of T among the main variables of the study were above 2. 58, meaning a significant and indirect relationship; thus, social capital has a positive and significant effect on sustainable development of rural settlements. According to total coefficients, social network with coefficient of 0. 575 has the highest and social cohesion with coefficient of 0. 046 has the least effect on sustainable development of rural settlements. The results of spatial analysis using GWR model showed that social capital impact factor on sustainable rural development in Izman-e-Paieen and Miyanzou villages was highest and in total 36. 6% of villages and 41% of rural population of Bojnourd County had an impact factor between 0. 871 up to 0. 885. Innovation: This study was the first attempt to discuss the spatial analysis of the impact of social capital on sustainable development through Geographically Weighted Regression.