Understanding the underlying structure of vehicle crashes is essential for improving safety on the roads. Past research has found that accident tend to cluster both spatially and temporally. This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of Road Accident in Karaj-Qazvin highway-Iran between 2009 and 2012, at different characters. Spider graphs were adapted to identify temporal patterns of vehicle crashes at different situation. The spatial structures of vehicle crashes were analyzed using Fuzzy Kernel Density and then result was compared with Kernel Density Estimation. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes and spatial pattern of accident at different characters. Characters that influence at spatial and temporal pattern of accident are: Time, Week day, Type of accident (Fatal, Injury and Non-Injury), Type of Collision (Vehicle with Vehicle, Vehicle with Pedestrian, Vehicle with Objects and Overturn), weather situation (Clean, Rain and Snow), Human Behavior (Hurry, Fatigue and Sleepy and Low Inattention). The results show significant differences in spatiotemporal patterns of Road Accident for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies.

Today, the high number of the rural road accidents has shown that accidents at intersections organize high percentage of the total number of accidents. Meanwhile, the geographical information system (GIS) is considered as an appropriate tool for doing spatial analysis and analysis of accidents at intersections. Also, considering that the accident data are massive and non-homogenized, the methods of spatial autocorrelation and kernel estimation can present connected and more real models than samples of current spots in rural road accidents. The purpose of this research is spatial analysis of rural road accidents based on rural intersection or utilizing spatial autocorrelation methods and estimation of kernel density. In the first stage, it considers appropriate criteria for spatial analysis of accidents in the old roads of Karaj-Qazvin in the 1388-1392 periods and were weighted by use of fuzzy analytic hierarchy process. Then, to identify accident prone intersections and investigation of their characteristics from autocorrelation functions of Getis-Ord Gi*, Anselin Local Moran's I and the kernel density estimation was used in order to investigate the spatial autocorrelation, each of the useable parameters in five successive years used as a Moran's I function. The results have shown that there are 26 accident-prone intersections towards Karaj-Qazvin path and 10 accident-prone intersections for returning, from the total intersections of two way path. Also, for path towards Karaj-Qazvin none of the parameters and for returning path the only parameter of accident type contained spatial autocorrelation in the five successive years.

During recent decades, spatial analysis of crime and identifying the spatial factors of crime occurrence has been emphasized. Using statistical and graphic models and tests and also Geographical Information Systems (GIS) are the appropriate methods of crime analysis in urban areas. This study is based on analytic-comparative method and the essential data has been collected through documentary and survey methods. All the violence, fight and brawl crimes which are occurred in Zanjan city during a year have been examined in this study. The spatial patterns of crime distribution in Zanjan city have been identified by using some statistical methods including: Mean Center, Standard Deviation Ellipse, Tests for Clustering, Nearest Neighborhood Index (NNI) and Quartic Kernel Density Estimation.The results of this study showed that spatial distributions of these crimes in Zanjan city are significantly clustered suggesting the existence of hot spots and cool spots in the city area. In order to improv security and reducing crime in these areas, these suggestions should be considered as a priority for people in charge of urban management as listed: Allocating some essential urban services in Bisim area and making balance in the type and composition of urban land use, locating and building police station in the Bisim area in order to increase formal surveillance and at least focusing on economical, social and cultural preventive efforts in this part of city.

In the realm of urban transportation management, identifying critical locations within transportation networks is paramount for efficient urban planning, crisis management, and infrastructure development. This study investigates the effectiveness of the Network Kernel Density Estimation (Net-KDE) method in identifying these critical locations. The Net-KDE method is chosen for its inherent capacity to consider spatial patterns and path length between sample and estimation points, making it well-suited for capturing the complexities of urban transportation networks. The approach is experimented with a series of control maps applied to Tehran city. Overall, Net-KDE provides valuable and agreeable outputs that show its potential. The findings underscore the crucial role of scenario customization in enhancing the method's accuracy. Remarkably, featuring adaptive bandwidth and strategic sampling emerges as the most effective in identifying critical locations. This research has a broader impact on crisis responders, urban developers, and city planners in addition to improving our knowledge of urban network dynamics.

Promoting social justice, optimal resource allocation, price fixing and improving income distribution are of the main objectives and Philosophy of existence of subsidies' payment in all countries of the world. Also in the fifth and sixth socio-economical development plan of Iran, the aim is that the government through targeting the obvious and hidden subsidies, pays the obtained resources of this policy to the goal groups. The purpose of this study is to recognize the impact of targeting subsides plan on the life of villagers of the central villages of Nurabad mamasani township. The research method is descriptive –analytical one and the necessary data was gathered through analyzing and distribution of questionnaire among the goal groups, The under study statistical community consisted of 4, 494 rural households in three villages of Nurabad township and the sample size was determined 353 households by using Cochran formula. According to the indices of the subject matter, 7 questions out of 15 questions were analyzed which were related to the impact of subsidies on changing consumption patterns in rural households. The results obtained from one-sample t-test indicate that by implementing the policy of targeted subsidies, a favorable trend has been created in the consumption pattern and such improvement is more obvious in poor and low income rural areas. Findings from field surveying and information obtained from the questionnaires and spatial analyzing by using Kernel density function and Hot spot analysis in GIS environment shows that improvement of consumption pattern and promoting the welfare of rural households in Jozar village is significant due to the productivity of its rural areas and less dependent on urban areas and the villages of Javid mahori and bakash Yek, due to their dependence and closing of rural households to Nurabad township and increase of costs, improvement of consumption patterns is not so meaningful and is evaluated as medium. Also it is determined that in the studied villages, the cash subsidies is useful and effective for those in need and poor rural communities, but for high-income groups is evaluated as average.

in geographical studies of rural areas, due to the diversity of place and space phenomenon, it is not possible to study as single and individual. Therefore, the first step is to classify these phenomena in similar categories. In particular, factor analysis as a powerful tool against this problem, is responsible to develope solutions to reduce and analyze the factors. In this paper, 38 indicators were used for leveling villages of Tonekabon city, which was reduced using factor analysis to four factors.Today, he use of geographic information systems in the spatial analysis of rural development and using statistical models and tests of graphics benchmark in order to provide spatial data base, sorting, spatial display of information, the integration of spatial information and analysis is common.The research method in this study is analytical-synthetic method and the data analysis as qualitative and quantitative were made using inferential statistical methods using SPSS and ArcGIS10 softwares. The results show that economic and physical factors, with special quantity of 17.305, is lonely able to calculate 33.769% of the variance And has the most effect among the four factors and is known as a major factor in leveling the villages. Also the best model for estimating the density of development in the region, is kernel model which was selected as the best model with determinant factor of 0.71. According to this model, the development density is in the villages of northwest part of the town.

Kernel method is one of the most common nonparametric density estimation and recently B-spline is used for estimation of a probability density function. These two methods in some how depend on selecting a smoothing parameter that has an important effect on precision of the estimators. In this paper, we consider kernel and B-spline methods of density estimation and smoothing parameter selection for these two methods. Then, the accuracy of the obtained estimators is compared by their mean square errors. Also, the effect of the number and dispersion of data on precision of estimators are studied. The results show that for a symmetric probability density, if the dispersion of data increases, the precision of both estimators decreases, while, for an asymmetric probability density function, the precision of the estimators increases for dispersion data.

Background: Breast cancer is the most common cancer in women. An accurate and reliable system for early diagnosis of benign or malignant tumors seems necessary. We can design new methods using the results of FNA and data mining and machine learning techniques for early diagnosis of breast cancer which able detection of breast cancer with high accuracy. The aim of this study was to diagnosis of breast cancer using non-parametric kernel density estimation.Methods: In this study, 699 samples of benign and malignancy with 9 characteristics from WBCD and 569 samples of benign and malignancy with 30 characteristics from WDBC were used. Then, a model based on non-parametric kernel density estimation was proposed for classification of WBCD and WDBC data.Results: The results of non-parametric methods showed that Gaussian kernel method based on Euclidean distance with accuracy %97.93 has the highest accuracy on WDBC data and Gaussian kernel based on Euclidean distance and k-nearest neighbor methods with accuracy %98.17 has the highest accuracy compared with other methods on WBCD data for breast cancer disease.Conclusion: The result of this study showed that non-parametric kernel density estimation based classification can be used for breast cancer diagnosis with high accuracy.