a. a. alesheikh

Traffic accidents are a major public health concern worldwide, causing significant loss of life and property damage. To reduce the number of traffic accidents, it is crucial to identify where and when recurrent accidents occur. These accidents often follow specific spatial and temporal patterns and may form clusters, representing areas of concentrated accidents within a geographical space. Therefore, analyzing traffic accidents in both spatial and temporal dimensions is essential for determining the most effective and sustainable solutions. Isfahan province is among the provinces in the country with high accident rates in Suburban areas. Previous research conducted in Isfahan province has predominantly relied on statistical methods and has not adequately addressed the spatial and temporal aspects of accidents.This study aims to address the gaps in previous research by determining the spatial and temporal patterns of urban traffic accidents in Isfahan province and visualizing these patterns using spatial statistical methods in a GIS environment. The novel aspect of this research lies in utilizing spatial statistical techniques to identify and analyze the spatiotemporal patterns of urban accidents in Isfahan province at different time intervals and intensity levels.

The spatial and temporal patterns of traffic accidents in Isfahan Province were investigated using suburban traffic accident data from March 2017 to March 2019. After collecting the relevant data, performing necessary preprocessing, and preparing the data, the spatial and temporal patterns of traffic accidents occurring on the main roads, highways, and freeways of the study area were analyzed and identified using spatial statistical methods such as the Average Nearest Neighbor test, Spatial Autocorrelation (Global Moran's I), and Optimized Hot Spot Analysis (Getis-Ord Gi* technique) at different levels in a GIS environment.

Since the aim of this study is to identify the spatial-temporal patterns of suburban traffic accidents in Isfahan Province, the spatial distribution pattern of accident events was first examined using the Average Nearest Neighbor and Spatial Autocorrelation (Global Moran's I) methods. The results indicated the presence of a strong clustering pattern in the traffic accident data during the study years in Isfahan Province. Then, an optimized Hot Spot Analysis was performed on the entire dataset of accidents using the Getis-Ord Gi* method. Subsequently, the analysis was conducted on the dataset of each level separately, considering different levels such as time of day, day of week, month, year, and accident severity level. The results of the Getis-Ord Gi* analysis at different levels showed that the majority of hot spots with a 99% confidence level are located on the routes leading to the provincial center, namely Isfahan City, as well as the neighboring populous cities. These areas experience the highest volume of traffic and congestion, and the accident density decreases significantly with increasing distance from the provincial center.

Based on the results of the Spatial Autocorrelation analysis of accidents and the hot spot maps at the studied levels, the results showed that accidents are clustered in some areas of Isfahan Province. Proximity to the provincial center and major populated cities has a significant impact on the concentration of traffic accidents in this region. The frequency of accidents decreases with distance from major urban centers. The results of this study and the insights gained about the spatial and temporal patterns of traffic accidents can be used to develop new strategies, guide transportation managers and stakeholders, make decisions, and take suitable proceedings to effectively improve the safety of accident-prone areas.

Analyzing natural hazards due to economic, environmental, and social effects is necessary for crisis management. However, good analysis comes from good data. In this case, natural hazard databases or inventories that contain all spatial, temporal, and other relevant information for each event are required. To realize that, news and social media, which provide detailed information about the hazards, are two precious resources. Nevertheless, most works manually extracted events from these text resources in the inventory development process. This paper presents a framework for extracting natural hazard events automatically from news stories by leveraging text mining techniques. By implementing the framework for the study region of Iran, we analyzed the spatial-temporal distribution of natural hazards.

According to spatial and temporal coverage of Mehr news agency, we selected this website as the main resource, and for training machine-learning-based models, we used ISNA's news articles. The process starts by mining web pages. All irrelevant records such as the news stories about maneuvers, conferences, and contradicts are removed automatically. Then, standardizing the texts of news articles should be accomplished. In the next stage, the text classification technique determines whether a news story is about a newly occurred event or a current event, in which context about the event the news story is published, and which natural hazards are pointed out in the text. For the first and the second text classification task, we used machine-learning-based models which achieved 0.875 and 0.716 for F-score, respectively. For the third task, we developed leveraged a rule-based model. The results of text classifications are used in the proceeding steps, including toponym recognition and resolution, information extraction, and topic detection and tracking.

The results show that although most natural hazards have a specific temporal distribution, the highest total frequency is for a Solar Hijri year's initial and final months. Spatially, the storm occurs more in eastern than western provinces. The diversity of other meteorological hazards, except dust, in northern provinces, is more than in southern ones. Regardless of the fatalities, the highest frequency of reported earthquake events is for southern provinces.

This paper presents a framework for automatically extracting natural hazard events from news stories. The framework leverages several text mining techniques such as text classification and information extraction to develop an inventory of the hazards. We implemented the framework to analyze the natural hazards of Iran from 1390 to 1400 Solar Hijri. Comparing the number of extracted events with the thematic maps published by the National Disaster Management Organization shows that the differences vary with the province. Based on that, the frequency of extracted events for some provinces equals the official statistics; hence the analysis for mined events is generalizable to real-world situations.

In recent years, the population in Iran has been increasing. This growth in the population of large cities multiplies the demand for dwellings. The increase in demand, in turn, has led to the creation of high-rise buildings and numerous complex registration maps in these buildings. The current method for registering and storing various legal boundaries in Iran is based on two-dimensional maps. This method has its limitations for visualizing different registration boundaries that define legal spaces. For this reason, a new method to register the ownership of buildings accurately and efficiently is more than ever needed. With the advancement in the field of 3D modeling, especially Building Information Modeling, a lot of research is being done to record the three-dimensional legal boundaries using these models in different countries. In this article, for the first time, Building Information Modeling was used to record three-dimensional legal boundaries in the Iranian jurisdiction. To do this, first different types of registration boundaries in Iran were identified according to the regulations of the Real Estate Registration Organization of Iran and the building subdivision instructions in Iran. Then, to record these boundaries, Industry Foundation Classes (IFC) was developed and implemented on a prototype, which is a building in Tehran. In the evaluation stage, the registration boundaries in the two proposed methods and the current method were compared using a questionnaire. The results of this evaluation in different criteria showed the high capability of the proposed method in registering and visualization legal boundaries approved by the Real Estate Registration Organization so that more than 80% of the elite community participating in the questionnaire considered this method more appropriate than the current method.

Gazetteers are geospatial dictionaries of geographic names containing triples of place names, geographic footprints, and feature types for named geographic places. As an important element in Geospatial Information Retrieval (GIR), these precious resources should be enriched according to new applications. . Identification and adding new place names to the gazetteer, and keeping it up to date are important issues in the gazetteer enrichment. The main challenge in this era is that in most gazetteers only a top-down approach is considered. Consequently, most local place names are ignored in such gazetteers. In addition, updating gazetteers is a time-consuming and expensive process. Since the emergence of Web 2.0, using volunteered Geographic Information (VGI) and social media in harvesting place names have been attracted the attention of many researchers due to containing local place names and recently created ones. In a similar condition, online property ads published by people contain such place names. This article presents a data-driven method for identifying urban place names including neighborhoods and main streets using online real estate advertisements.

The online real estate ads of four metropolises including Tehran, Mashhad, Isfahan, and Shiraz mined from the Divar website. After n-gram extraction and applying required pre-processes, the n-grams got labeled. To remove outlier points from an n-gram set and consider the scenario that several places can have the same name through a city, the point set of the n-gram get clustered. Based on a set of spatial statistics, the random forest models on housing data of each city trained and then tested on the ads data of other cities.

The results show that either in detecting the main street or neighborhood, the model trained on ads data from one city has a successful prediction on the other ones. For example, the models trained based on the data of Tehran and tested on the data of Mashhad achieved 61% and 74% respectively in identifying street and neighbourhood. However, for some reasons such as imbalancement of datasets, data labeling challenges, and in some cases, identifying non-spatial n-grams due to clustering, precision has been decreased. Also, because of differences in urban patterns and place naming patterns between the cities, the recall has been slightly decreased.

A place can be referenced in two different ways: 1- By calling its name and 2- By coordinate data. Gazetteers are considered a bridge between that two types of georeferencing. According to the importance of these resources in geospatial applications, the enrichment of them is a necessity. For containing local place names, online property listings can be considered as a valuable resource for harvesting toponyms and enriching gazetteers. Regarding to that most users in publishing online property, ads consider a neighborhood or main street name which is well-known for the readers, these place names usually are written without any clue for identifying a location in a text processing manner. The behavior with respect to a set of spatial statistics can be considered as a spatial signature to recognize an n-gram as a neighborhood or street place name.

Foot-and-Mouth (FMD) is a viral and contagious disease that endangers both domestic and wild animals and this disease has caused a lot of social and economic damage to the livestock industry in Iran. Considering the endemism of this disease in Iran, the main purpose of this study is to investigate the temporal trend, discover spatial and spatio-temporal clusters and determine the impact of environmental factors and model the disease in order to better understand the spatio-temporal epidemiology of FMD in Iran. In this study, data related to the incidence of disease as well as environmental variables including meteorological variables, vegetation and topography of the region were used. This study was performed on 12442 cases of registered diseases in the years 1396-1387 in the city for the whole of Iran. First, general auto correlation indices including Moranchr('39')I index, General G index and Ripleychr('39')s K index were used to investigate the distribution of the spatial pattern of the disease. Local Moran and the Gatis-Ord G* index were used to detect hot and cold spots. Then, with the help of the points of occurrence and time of the disease, spatial and spatio-temporal clusters of the disease were discovered. Spearmanchr('39')s correlation and linear regression analysis were used to evaluate the type and severity of correlation between environmental parameters and disease incidence. The results showed that the maximum incidence is in spring (44%) and the minimum incidence is in summer (15%). The months of Ordibehesht, Farvardin, Khordad and Dey, with a total of 6650 cases, were the most riskful months of the year, respectively. Autocorrelation tests showed the distribution of FMD clusters for every 10 years studied. The results of the local Moran index and the Gatis-Ord G* showed that the foci of this disease are still active in Iran and parts of Semnan, Tehran, Markazi, Fars, Lorestan, Khorasan Razavi and East Azerbaijan provinces are hot spots of FMD. In spatial scan, 8 clusters were identified, which confirmed the results of Moran and the Gatis-Ord G* index. In the spatio-temporal scan, 5 clusters were discovered that were consistent with the results of the spatial scan. Spearman analysis showed that there is a positive correlation between the incidence of the disease and vegetation, which is decreasing from the beginning to the end of the year and has a negative relationship in winter. Also, a positive and increasing correlation was observed with wind speed and precipitation from the beginning to the end of the year. In addition, a positive correlation between the incidence of the disease and altitude and a negative relationship with the direction of the slope was calculated. Linear regression analysis was also used to evaluate the effect of variables, which resulted in confirming Spearman correlation outputs. The results of the model showed that vegetation and topography are the most important environmental factors that affect the prevalence of snow fever in the region. The results of this study identify areas that are at higher risk and need more planning and attention to control the disease.

The deployment of new technologies in digital devices and communications has increased social networkschr('39') popularity and pervasiveness. Geo-tagged content has connected cyberspace to the real world by adding a new dimension to social networks. Among geo-tagged content available on social networks, geo-tagged images show userschr('39') communication and interaction with the environment in a better way. Because users frequently photograph regions of interest, these images can be used in many applications, including discovering regions of interest. Compared to traditional methods such as censuses and surveys, geo-tagged images benefit from saving time and expense to discover and analyze regions of interest. Therefore, researchers can use them in urban management and tourist recommendation. The purpose of this study is to discover the region of interest using geo-tagged data. Also, extracting appropriate semantic information and analysis in different contexts to identify regions of interest and understand the reason for their attractiveness is another goal of this research. This paper uses the Flickr geo-tagged images taken from New York City between 2015 and 2018. In the preprocessing phase, noise and data redundancy was removed. Then the data were clustered by the HDBSCAN method, and adjacent clusters that were similar in terms of text tags were merged. As a result, 106 regions of interest were identified. At the next step, a concave surface was fitted to points by α-shape method, and semantic information including distinguished labels, names, and categories, was selected for regions of interest. Finally, attractive regions were analyzed based on the type of visitors, users’ sentiment and the number of visits in different contexts. The evaluation results show the discovery of regions of interest in different shapes, dimensions, and densities. Our result corresponded for 66% with TripAdvisorchr('39')s top attractions, while for the simple DBSCAN method this value was 53%. In regions that overlapped with TripAdvisor attractions, the naming was 76% similar.

The expansion of cities in recent years has led to an increase in uncontrolled construction around historic buildings and as a result has caused problems such as moisture leakage into the foundations of historic buildings. Isfahan province with more historic buildings It is the first city in 400 years that the sewerage network has been implemented. In recent years, some of these works have been exposed to moisture from old and dilapidated facilities. Therefore, overcoming this problem requires designing a spatial model based on smart technology in the operation of the municipal sewage network.

This model was designed based on the combination of the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) based on the Getis ord GI* index. After combining the results of this combination with statistical analysis, using non-parametric Kendall and Spearman tests, wastewater accidents and their probability of occurrence in the context of historical regions were evaluated.

This model is capable of evaluating previous sewage accidents and predicting the possibility of future accidents in the historical regions of the cities. Such a model has an empirical nature and as a result, can be updated in the event of an accident in the future. The combination of the Analytical hierarchy process and the Getis ord Gi* index is a special feature of this research.

In the study area, soil type, inappropriate infrastructure, worn sewer pipes, small diameter pipes, and population density were identified as the most important causes of accidents in the sewerage network.

Consumption of non-renewable resources such as oil, gas and coal is increasing every day. But when these resources run out, we have to look for renewable energy such as solar, wind and geothermal. Solar energy is a clean energy that can reduce the production of environmental pollution and is the beginning of reducing carbon from human life. One of the most important issues in the construction of solar power plants is to determine the places that have a high potential for solar energy. This research has been done in order to locate solar power plants using multi-mechanical decision-making methods of participation in Fars province. In the initial studies on the region and the current state of solar power plants, and then using the resources and opinions of experts, 10 effective criteria, including climatic, geomorphological and economic criteria in the location of solar power plants have been selected. Criteria information layers are prepared in GIS environment and then the best-worst and Denp methods are used to weigh the selected criteria. In the first method, two criteria of sunshine hours and distance from roads and in the second method, the criteria of sunshine hours and distance from the fault have been performed as the most important and the least best criteria, respectively. Due to the different methods of these two techniques, the results of its losses have been used for the final weight of the criteria. After weighing the criteria, zoning has been done and 8 places have been proposed for the construction of a solar power plant in Fars province. In the last step, using Coopras and TOPSIS methods, the places whose proposals are ranked and for the final results, the integration of these two methods with the ranking technique is used. The methods used in this research are programmed in Linux and MATLAB software environments. Finally, the No. 8 reality site in Zarrin Dasht city of Fars province has been selected as the best location for the construction of a solar power plant.

In recent years, the shared content on the web has had significant growth. A great part of these information are publicly available in the form of semi-strunctured data. Moreover, a significant amount of these information are related to place. Such types of information refer to a location on the earth, however, they do not contain any explicit coordinates. In this research, we tried to georeference the semi-structured resources on the web using machine learning. To this end, we leveraged the advertisements related to real state domain in the city of Tehran, Iran, published in Divar website. In order to extract the advertisesments from the website, a crawling approach was chosen. In addition, to assign coordinates to advertisements, we used Random Forests algorithm. The results show that using this approach, the advertisements can be georeferenced at the precision of neighborhoods. The resulting presicion from this approach is about 2 km and 6 km in latitude and longitude directions, respectively. Moreover, the results demonstrate that price of the property has higher importance relative to other variables considered in this study. It can be concluded that the price of properties in Tehran shows stronger spatial pattern in North-South direction than East-West direction.

Leptospirosis is a common zoonosis disease with a high prevalence in the world and is recognized as an important public health drawback in both developing and developed countries owing to epidemics and increasing prevalence. Because of the high diversity of hosts that are capable of carrying the causative agent, this disease has an expansive geographical reach. Various environmental and social factors affect the spread and prevalence of the disease. The combination of epidemiology and Geospatial Information System plus using Ecological niche modeling provides the ability to identify areas at risk of disease, then predict the risk map of the disease for other regions by using relevant environment variables, and prevent and eventually eradicate the disease by conducting constructive activities such as increasing public awareness with education. In this study, using land use, environmental, and climate variables and taking advantage of the occurrences of the disease between 2009 and 2018, the risk level of Leptospirosis was modeled in three provinces of Gilan, Mazandaran, and Golestan based on ecological perspective. For modeling, highly correlated variables and also variables with high multicollinearity were identified and omitted. Because in ecological modeling regions to represent the absence of disease is required in addition to the presence and since these areas are not available, the second objective of this study was to evaluate the efficacy of different methods of generating pseudo-absence data in modeling leptospirosis. Finally, more accurate modeling of the prevalence of the disease in the northern provinces of the country can be obtained. Therefore, after selecting suitable variables for modeling, first, based on five methods (completely random generation of points in the study area, applying physical constraints with buffer at two radii of 5 and 10 km the generating points outside of designated buffer, applying environmental constraints by implementing two models of one-class support vector machine and BIOCLIM and generating points in unsuitable areas defined by these two models) pseudo-absence points representative of disease absence points in the study area were produced. Next, four models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine were deployed to produce the disease risk in the study area. BIOMOD2 package in the R programming language was applied for modeling. The results showed that applying physical constraints with buffers yields the most reliable performance in comparison to the other three methods. Finally, the constructed model that performed best in TSS Statistics (with values of 0.76, 0.87, 0.84, 0.82 for Models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine) was considered as the final output. Between all deployed models, Artificial Neural Network delivered the worst performance and had the most unstable results. Based on the risk-map of leptospirosis, central regions of Mazandaran and Gilan province, especially rural areas of Layl, Asalam, Eslam Abad, Chahar-deh, and Lafmejan have very high values of risk. Measures need to be made to reduce the high rate of Leptospirosis incidence in these regions. Furthermore, yearly precipitation was considered the most influential variable for the distribution of Leptospirosis.

In recent years, with the advancement of positioning systems, access to a large amount of movement data is provided. Among the methods of discovering knowledge from this type of data is to measure the similarity of trajectories resulting from the movement of objects. Similarity measurement has also been used in other data mining methods such as classification and clustering and is currently, an important and challenging topic for many researchers in the field of geospatial information systems. Although uncertainty is an inevitable issue in the field of geospatial information systems, so far little attention has been paid to this issue especially in the field of measuring the similarity of trajectories. One way to cope with the uncertainty in the observations and definitions of the problem, is to use fuzzy theory. In this study, two methods of sim1 and sim2 based on Longest Common Subsequence (LCSS) and Edit Distance on Real Sequence (EDR) methods, respectively, have been introduced to deal with uncertainty in measuring similarity of trajectories and improving their performance using fuzzy theory. The proposed methods use a fuzzy membership function based on the distance between the points of two trajectories to determine the degree of matching of every pair of points on two trajectories based on which the similarity of the two trajectories is calculated. In order to evaluate these two methods, two experiments have been performed on the real and synthetic trajectories of personal cars. Experimental results show that sim1 and sim2 are similar to LCSS and EDR in terms of sensitivity to noise, increasing and decreasing sampling rate and have better performance in terms of sensitivity to displacement. For example, the mean percentage change of similarity to distance variations for the four thresholds of 5, 10, 25, and 50 meters for LCSS is 0.02, 0.97, 0.66, and 0.23 but for sim1 and sim2 is 0.41 which is proportional to rate of changes in reference trajectory.

The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MAXENT) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10.6.1 and ENVI 5.3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MAXENT as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2,3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43.5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11.9%, NDVI with 4%, average temperature with 1.3% and displacement with 1.1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0.956 and 0.955, respectively.

With the ever increasing of unplanned urbanization، the quality of citizen`s life are lessening. Understanding the process of urban growths facilitates Sustainable Urban Planning and Urban Management. The spatial patterns of urban modeling can make a useful perspective on how cities develop under different social، economic and environmental factors. So far، many efforts have been carried out in urban growth modeling using a cellular automat، but to create a reliable and credible model، some issues still remain unsolved. The complexity of the urban development process، the numerous variables and the variety of rules have caused the calibration of cellular automation models to be one of the challenging issues in the simulation of urban growth. In this study a new model for urban growth modeling is devised. The model integrates the advantages of cellular automation and genetic algorithm to predict Shiraz urban development between 1990 and 2000. Effective parameters in this process are urban neighborhoods، slope، distance to main roads and undeveloped areas. The chromosome`s contribution of genes to each parameter is then estimated. Optimum results have been exploiting، and finally the overall accuracy and Kappa index were used to evaluate the model. The overall accuracy and the final kappa index obtained were equal to 0. 91837 and 0. 68406 which indicate that 91. 837% of the cells were simulated true and the simulation results were 68. 406 % better than the random simulation.