nearest neighbor

The family has been introduced as a social institution resulting from the marriage bond between a man and a woman and is classified as individual and personality. Sometimes, couples face problems in their marital relations that cause them to have conflicts. A person's general evaluation of the marital relationship and the fulfillment of the personal needs, desires, and wishes of the couple determines the level of satisfaction or dissatisfaction with the marital relationship; on the other hand, disagreement is normal in any marital relationship and marital conflicts are one of the common problems of couples, in today's world. Therefore, some call divorce liberating in these circumstances. Divorce means the collapse of the most important institution of socialization in human society. This phenomenon is one of the realities that the family institution in today's world experiences widely. However, this does not mean that the family has not faced this phenomenon in the past. Nevertheless, divorce, like all other phenomena in today's world, has changed in nature and is more of an optional choice; therefore, in Iran, due to the rapid growth of the population during the past decades, there have been consequences such as increased rent living, marginalization, selling body parts, malnutrition, increased begging in the city, child labor, increased hand-selling, and finally divorce and the collapse of foundations. In order to plan and organize the situation of people experiencing poverty, one must first gain the necessary knowledge of their location and spatial distribution in Iran and then solve this challenging problem of the provinces with detailed planning so that the complications and negative consequences can be avoided. Poverty at the provincial level, including divorce, was prevented or moderated. Usually, in social sciences, the number of marriage and divorce cases and their causes and results are investigated. However, geographical discussions emphasize the location of damage in the country's provinces and its contexts. Hence, the purpose of this research is to investigate the geographical distribution. Divorce is in the provinces of Iran. Therefore, it is tried to investigate the distribution of divorce at the level of the provinces so that the country's administration, social institutions, and charitable organizations can help the target groups with precision and proper planning and take effective steps to reduce the social damage of divorce. 

The lowest divorce rate belongs to Sistan and Baluchistan, Semnan, Chahar Mahal, and Bakhtiari provinces. On the other hand, the highest divorce rate belongs to Tehran, Razavi Khorasan, and North Khorasan provinces. Graphical statistical models were used based on the mean center test and standard deviation ellipse. The average center specifies the central location as the basis of all social damage locations. The standard deviation ellipse, using the standard deviation of the distance of each divorce to the center of the average, determines its dispersion, direction, and position. The average divorce occurred in Isfahan province. The ellipse of the standard deviation of the total divorce under investigation has much accumulation; this shows that the divorce complex is in the direction of the southeast to the northwest, so with a probability of 99%, 70% of the divorce area is 6.368836.

 The results indicated that most divorce spots in the east are in Razavi, South, and North Khorasan provinces due to the size of the provinces and population. Also, most of the cold spots are in the northwestern provinces. It can be said that in the upper and middle classes, the main issue is not economy and livelihood but cultural factors related to changes in society's values and attitudes, including in the field of marriage, giving priority to the family, and having a negative perception of divorce, which conflicts with the research findings. Akhoi Samrin's research in 2016 showed that the lack of love and intimacy is the central category of Al. It is similar to the research findings. The category of not being ready for marriage and not going through the marriage process is one of the causal conditions for collapsing marriages. Cultural issues and economic issues were placed under the intervening conditions and categories such as addiction, inappropriate current family structure, lack of marital compatibility, sexual factors and problems, inappropriate relationship with the main families, family history, and personality problems were among the background factors effective in divorce. The results showed that the use of ineffective communication and emotional strategies led to consequences such as feeling tired, lonely, hopeless, hatred for one's spouse, and finally, the desire for divorce, which conflicts with the research findings. The main limitation of the research is the non-cooperation of the country's civil registration organizations, the General Justice, and other related organizations and not providing information about divorce constant problems in the geographic information system software (Crack). As a result, the country's hot spots and centers of divorce have been formed in this region of the northeast and east of the country, and it is indicative of the high incidence of divorce in this region. Therefore, to take the necessary measures to reduce the phenomenon of divorce in the province, it seems necessary to plan carefully to reduce the factors.

One of the important conditions for optimal use of land is obtaining information about landuse patterns and their changes over time. Landuse is usually defined based on human use of land, emphasizing the role of land in economic activities. Today, remote sensing technology is considered as the main element in landuse monitoring. The aim of the current research is to extract landuse maps for the years 2000 and 2021 in FirozabadKhalkhal region and to investigate the changes made in the studied time period in the region using the images of ETM and OLI sensors of Landsat. Also, checking the capability of basic pixel and object-oriented methods for landuse classification is another purpose of this study. In the current research, the object-oriented technique nearest neighbor algorithm and the vector machine method supporting the pixel-based algorithm have been used for landuse classification. Then, to verify the accuracy of these two methods, the overall accuracy and Kappa were extracted. The results of this evaluation show the high accuracy of the object-oriented method in extracting land use classes. Based on the results of the detection of landuse changes in the studied time period, the highest amount of changes occurred is related to the use of good pasture to poor pasture with a value of 51.72 square kilometers, followed by forest to good pasture with a value of 30.11 and the lowest changes It is related to the use of pasture and water with the amount of 0.03 square kilometers. The reasons for these changes are the increase in population, indiscriminate grazing of livestock, incorrect and illegal use of different lands. The use of more parameters such as scale, shape, compactness, color, texture, smoothness criterion and pattern for landuse classification in the object oriented technique can be considered as an innovation of the present study.

Land use classification has always been one of the most important applications of remote sensing, and for this reason, different methods have been developed. With the passage of time, more advanced methods with higher accuracy were created, which increased the accuracy and performed better in extracting classes that were spectrally closer to each other. Algorithms for detecting changes in remote sensing images are divided into two categories: pixel-based and object-oriented, based on the minimum processing unit. The purpose of this research is to evaluate and prepare a land use map of the Nirchai watershed in Ardabil province using the object-oriented method. Land use classification including segmentation of image data was done using multi-scale segmentation algorithm in eCognition software environment. Then these parts were selected and classified and evaluated using the object-oriented nearest neighbor algorithm. The results showed that the object-oriented classification has an overall accuracy of 99 and a kappa coefficient of 0.88%, which indicates the high accuracy of the object-oriented method in classification. Also, the land use map showed that the use of water areas and poor pastures occupied the least (70 hectares) and the most (8069 hectares), respectively.

With the advent of remote sensing technology, huge volume of remotely sensed data is now availablein different areas. As the fastest and the most cost-efficient method, satellite data is available for both researchers and responsible authorities seeking to produce land use (LU) maps. Compared to traditional methods, object based image analysis (OBIA) techniques use more comprehensive datasets,including geometric information (shape and placement of phenomena), digital elevation models, andvarious spectralindicesfor LU classification.Therefore, different OBIA methods have been widely used forclassification of satellite imageriesin different regions. Despite large amount of researches performed in this area, little attention has been paid to the systematic comparison ofdifferent object-based methods. Therefore, examining different techniques used for object-based processing of satellite imageries in diffrent situations can be considered as an appropriate research field for researchers.  The present studyexamines some powerful OBIA classification techniques such as threshold, nearest neighbor algorithm and fuzzy object based classification to determine the most suitable OBIA algorithm for classification of Ikonos satellite images.  

An Ikonos satellite imagery was used in this studywhich included red, green, blue and near-infrared bandswith spatial resolution of 4 m and a1 m resolutionpanchromatic band.Object based classification can be implemented in three general phases: segmentation, classification, and accuracy assessment.The present study has appliedmulti-resolution segmentation method in the segmentation phase. Three techniques ofthreshold, nearest neighbor algorithm and fuzzy based OBIA were also used for classification. 

The present study takes advantage of various features to extract land use classesfrom Ikonos satellite imageswith high level of accuracy.Textual information (Grey Level Co-occurrenceMatrix), mean of the imagery’s spectral bands, geometry (shape, density and asymmetry), and normalized difference vegetation index (NDVI)were among these features.Compared to threshold method,nearest neighbor algorithm withoverall accuracy of 92% and kappacoefficient of 0.9hada higher level of accuracy.Also, FOS algorithm was used to optimize the nearest neighbor technique. This algorithm optimizes intervals between the training samples using secondary information provided by the user.The eighteenth dimension, which contains the mean of spectral bands3 and 4, vegetation index, brightness, length to width ratio, indices of shape, compactness, asymmetry, texture information (homogeneityand contrast), were determined by FOS algorithmas the best dimension for extracting each LU classes. Finally,featuresproposed by FOS algorithm were used for image classification in nearest neighbor method.This optimizing process is considered to be one of the main reasons for superior performance ofnearest neighbor technique compared to threshold method.  

In this research, three OBIA methods including threshold technique, nearest neighbor algorithm and fuzzy based OBIA algorithm were compared based on their capability in producing land use map from Ikonos satellite image. Identical ground control pointsof the study areawere used to classify and compare the results of these three OBIA classification methods.Finally, the best classification algorithmwas determinedbased on thevalues of accuracy assessment metrics including overall accuracy and kappa coefficient. Results indicate thatwith overall accuracy of 97%, and kappa coefficient of 0.95, fuzzy based OBIA classification algorithm has thehighest accuracy as compared to nearest neighbor algorithm and threshold method. Generally, the accuracy of fuzzy based OBIA classification method largely depends on the selection of appropriateclassification parameters and suitablealgorithm to obtain membership degrees.Investigating membership degree of effective parameters in the classification and using parameters with maximum degree of membership are considered to be two main reasons for achieving this high accuracy. Results of the present study indicate that fuzzy based OBIA techniqueis the best algorithm for classification ofIKONOS satellite images in the study area, andareas with similar conditions. This findingcanguide researchers and organizations producingLU map from IKONOS satellite imagery. Finally, investigating different techniques using satellite imageries (imageries with different spatial resolution, and received from areas with different land uses) is considered to be an appropriate area of study for OBIA researches.

Despite of widespread usage of Global Positioning System (GPS), this system is considered inefficient for indoor areas. Although the most prominent positioning system is Global Positioning System, this system uses some electromagnetic waves which are unable to pass thick obstacles such as concrete roofs and trees [1]. Thus, it cannot be considered as a robust infra-structure for indoor positioning purposes. Since, other signal networks like Wireless Local Area Network (WLAN) can be an appropriate alternative for indoor spaces. In addition, widespread usage of mobile smart instruments has provided the possibility of ubiquitous system’s development.
Several methods have been proposed to obtain indoor positions which are generally based on received radio waves from fixed points. Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Location fingerprinting can be used in this case. It is noteworthy that some of these methods are not really appropriate for indoor areas which maybe contain complex structure [2]. Time of Arrival, Time Difference of Arrival and Angle of Arrival methods use triangulation techniques so direct lines of sight are desired for them. And also acquisition of accurate time and angle of received signal without professional instruments, which are usually expensive, sounds almost impossible. Furthermore, for most of indoor areas such as commercial centers and museums direct line of sight is rarely available and signals are likely to be affected by multipath phenomena [3].
In recent years methods based on Inertial Measurement Units (IMU) have been proposed and programmed [4], [5]. These methods which are usually called Pedestrian Dead Reckoning (PDR) often employ sensors such as Gyroscope, Accelerometer and Magnetic sensors to obtain the position of the client [6]. It can be regarded as an important limitation along the objectives of the Ubiquitous systems. Such systems are restricted to clients equipped by platforms having these expensive modern sensors. Therefore, the methods using WLAN signals are usually preferred for location based services.
WLAN Fingerprinting can be regarded as a most appropriate technique that uses signal strength as an identification parameter, which can be simply obtained. Furthermore, fingerprinting does not have any special infrastructure to establish and it can be conveniently laid out. In order to apply this method there are several ways to recognize the pattern of signals received from active transmitters. Stochastic method, Artificial Neural Network and K-Nearest Neighbor methods are some of classic pattern recognition techniques [7] that were investigated in this study. In this article these three methods were scrutinized and relatively compared, eventually an enhanced method has been offered. After using several data sets in order to assess the pattern recognition techniques, the proposed method got the first rank of the accuracy and also other techniques were ranked based on the accuracy.
One of the most important differences between indoor positioning systems might be utilizing of various algorithms to recognize the spatial pattern. In this study, three popular classic methods including Probabilistic algorithm, Nearest Neighbor and Artificial Neural Network were investigated. The flowchart presented in Figure 1 has depicted the major steps of the study.
Figure 1. The flowchart of the study.
This study focuses on Nearest Neighbor in Signal Space method as the most accurate method among all and tries to enhance the output accuracy of the method. NNSS Method computes the difference between received signal strength in a point from each transmitter and the received strength of that signal in the rest of the sample points (Equation 1).
Where Sij be jth sample point of the database from ith transmitter and Si received signal strength from ith hotspot in online phase and also for m hotspots and n sample points, i= 1,2,…,m and j=1,2,…,n [8].
By applying this formula, the most likely sample point as the location of the observer can be obtained. Since the number of sample points in the design of the model in offline phase is limited and the distance between two adjacent sample points is constant in the whole model, the accuracy might be affected. Regarding these limitations, in order to increase the output accuracy of the system, the medium of first and second candidate location points was proposed as the position of the user. After applying this change, the highest accuracy was acquired (Figure 4). The study area was the third floor of the building of Geomatics faculty of K.N.Toosi university of Technology (Figure 2). For this building with dimensions of 70×14 meters, totally 6 hostspots with reasonable distribution, covering the whole area, were taken into account. The best distance between each adjacent pair was 0.9 m and for each sample point four directions were observed and recorded in to the database and also JAVA programming language was chosen to develop the user friend software. Figure 3 depicts an instance of the database.
Figure 2. Plan of the study area.
Figure 3. A part of the produced database.
In order to evaluate the accuracy of each method, observations in the online phase were categorized in 6 separate classes containing 10, 20, to 60 obser-vation in each class. Based on the output results of the system, although the accuracy of Artificial Neural Network raised up to 2.7 m by increase in the number of observations, it showed the worse accuracy among all methods. Probabilistic and KNN methods with final accuracy of 1.8 and 0.9 meters respectively were more accurate than ANN. Our extended Nearest Neighbor method was the most accurate method almost in all sets of observations. In the first observation class, ANN with 3.6 m, KNN and Probabilistic methods with 2.7 m were not really reliable to locate the position of the user, however, extended KNN with 1.5 m seemed more acceptable than the rest of methods (See Figure 4).

Today, tourism industry is one of the major sources of income in the world and meanwhile is of the factors effective on the cultural exchanges between countries and as the largest service industry in the world has a very special place which through causing dramatic changes on the land feature, will change the political, economical, cultural and the manner and method of people’s life.While, the residential centers are considered as one of the main elements in development of this industry. Accordingly, the objective of this study is investigate the role of the main elements of the Islamic Iranian city on locating the residential centers. The method of this study is analytical-functional method. In order to classify and analyze the data by the use of ANP models, the nearest neighbor, geographically weighted regression, Moran and network analysis models have been used. For data analysis, ARC/GIS and Super Decisions soft wares have been used. The results showed that 82 percent of the residential elements in the past have been located 5 minutes away from the basic elements of the city, but this rate with a dramatic change has reached to 68 percent in the recent years. This eccentricity in recent years in most of the Iranian cities, and even in the world, can be due to the repulsive factors in the center and also the attractive factors in the margin of cities, so upon the assessment of the experts, it was determined that in Zanjan, the public costs with the effect coefficient 0.38 was the most important index and the land price as the most important sub-criteria with weight 0.49 has had the most effect on locating the residential centers.

Routing issues have many applications in GIS. The main goal in routing is to find the best route passing a series of determined positions. This process has an effective role in many of spatial decision makings. A lot of studies have been accomplished about the optimization of different kinds of routing problems. Traveling Salesman Problem is one of the very old issues in applied sciences. Emerging of new technologies such as GIS caused a lot of new applications for the TSP. A lot of new approaches have been suggested using these technologies to solve TSP. Evolutionary algorithms are one of the methods have been used to solve various optimization problems. Researches have shown that combining local search methods with the genetic operators can lead to better results in solving TSP. In this paper، a new and innovative method is presented to solve this routing problem. In this method using the concept of mass center for a set of station points a growing convex polygon is assigned to them. During this process a reliable solution، which is a route، is produced. Besides، a local search method based on the genetic algorithm and a nearest neighbor algorithm has been implemented to evaluate the results of the new developed method in solving TSP. Evaluation of the results of the genetic method، local search and nearest neighbors indicate that the new method can provide acceptable solutions during very short time.