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Land use/land cover (LULC) change contributes significantly to the formation of hotspot dust sources (HDSs). The Tigris-Euphrates Basin (TEB), a major source of dust storms globally, has experienced severe LULC changes. This study assessed the hazardous HDSs within LULC change categories (2000–2020) in TEB by classifying them into from very low to very high hazardousness classes based on the frequency, intensity, and continuity of dust events. The study calculated frequency using the autocorrelation function plot, investigated intensity based on the aerosol optical depth (AOD), and considered continuity by counting consecutive days when a dust event emitted dust with an AOD value ≥ 0.4. The study found that the most frequent dust events occurred in bare ground and rangeland. Highly intense HDSs occurred in croplands, while dried water beds and seasonal marshes contained more continuous dusts. Also, bare ground included the highest number of hazardous HDSs. The spatial distribution of HDSs showed that the northwestern region of Lake Therthar and the northern deserts of Muthanna Governorate significantly contributed to the formation of hazardous HDSs due to drought and vegetation cover conditions of the region. Sequential pattern mining of the HDSs within LULC changes revealed that transitions between rangeland and bare ground generated the most hazardous HDSs. Conversely, rangeland and bare ground to croplands and rangeland to seasonal marsh were the least hazardous frequent changes. Our observations confirmed that rapid LULC changes, tree cutting, and the drying up of water bodies can exacerbate hazardous dust emissions in the TEB.

Foot-and-mouth disease (FMD) is a highly contagious viral disease that affects cloven-hoofed animals such as sheep, cattle, goats, and buffalo. It is recognized as one of the most destructive animal diseases, primarily attributed to its significant financial impact. We used a machine learning model to predict FMD occurrence at the county level in Iran from 2008 to 2018 using historical data on cases of FMD, so the monthly occurrence of FMD per county was predicted using spatial and temporal lags using the Random Forest (RF) classifier. The cost-sensitive balancing and Borderline Synthetic Minority Oversampling Technique (Borderline SMOTE) methods were also used to handle the imbalanced classification. The results revealed that after applying the cost-sensitive balancing method, the model’s performance, as measured by the Area Under the Receiver Operating Characteristic (ROC) Curve, was 81%. However, when we implemented the Borderline SMOTE Oversampling technique, the method performance increased from 81 to 88%. We also found that our proposed model’s performance is better than when we use climate-related features to predict disease occurrence. The methodologies and findings of this study provide valuable insights for decision-makers to make informed decisions, mitigating county-level FMD occurrences and preventing economic losses.

The Covid-19 epidemic led to loss of the lives of many people in the world and had a very negative impact on the mental and physical health of humans. One of the effective ways to preventive strategies regarding is to study the impact of climatic parameters. This research introduces a new spatiotemporal methodology to explore the association between Covid-19 and hourly data of weather. This methodology developed based on machine learning using unsupervised clustering method. Six counties considered for finding association and the cities that have similar climatic temporal changes clustered and compared with cities that have similar number of Covid-19 cases. For this goal, a new model is developed for finding similarities between clusters, which indicates the association between weather and Covid-19. The result shows similarities are about 57% for wind speed, 63% for temperature, 63% for surface pressure, and 42% for elevation. Then result evaluated sing Kendall’s tau_b and Spearman’s rho which shows the proposed methodology has an acceptable result.

The learning similarity between users and points of interests (POIs) is an important function in location-based social networks (LBSN), which could primarily benefit multiple location-based services, especially in terms of POI recommendation. As one of the well-known recommender technologies, Collaborative Filtering (CF) has been employed to a great extent in literature, due to its simplicity and interpretability. However, it is facing a great challenge in generating accurate similarities between users or items, because of data sparsity. Traditional similarity measures which rely on explicit user feedback (e.g., rating) are not applicable for implicit feedback (e.g., check-ins). In this study, we propose multi-criteria user–user and POI–POI similarity measures, based on the dominance concept. In this regard, we incorporate geographical, temporal, social, preferential and textual criteria into the similarity measures of CF. We are interested in measuring POI similarity from a location perspective, by taking into account the influence of the dominance concept on multiple dimensions of POIs. To evaluate the effectiveness of our method, a series of experiments are conducted with a large-scale real dataset, collected from the Foursquare of two cities in terms of POI recommendation. Experimental results revealed that the proposed method significantly outperforms the existing state-of-the-art alternatives. A further experiment demonstrates the superiority of the proposed method in alleviating sparsity and handling the cold-start problem affecting POI recommendation.

The ever-increasing emergence of Aedes albopictus as one of the most significant vectors of arboviruses like Zika, chikungunya, and dengue requires deeper studies in new areas using environmental factors. Almost 2 billion people in tropical and subtropical zones are exposed to the vector. Because of the vector’s tendency to reproduce in a variety of habitats, including urban, suburban, and rural areas, the species is spreading rapidly wherever a set of climatic factors is available. Iran as a country of diverse climates and biomes with more than 80 million population is highly prone to the disease. Hence, this study aims to monitor the risk probability of the mosquito’s presence according to the environmental parameters in Iran. In this research, we classified each parameter based on the appropriate conditions for the breeding and activity of the vector. To calculate the weight of each parameter, we applied analytical hierarchy process (AHP) as an expert-based decision-making method and risk map generated using spatial analysis. Finally, to classify the values of the risk map and finding the most important risk areas, we performed a grouping analysis. The result showed that 5 coastal counties in Guilan province in the north and 6 counties in Khuzestan province in the southwest of Iran were ranked first and second riskiest places, respectively. Due to the semi-tropical climate of the coastal areas, they record a suitable pattern of contributing parameters for the presence of the vector. These findings help for the public health policymakers to control the invasion of Aedes albopictus to estimate the related disease occurrence.

Iran, as an endemic country of Crimean-Congo hemorrhagic fever (CCHF), has been suffering from severe health issues and substantial economic burdens imposed by the disease. We analyzed monthly and yearly spatial and temporal distributions of CCHF to better understand the epidemiology of the disease in Iran. A cross-sectional survey was performed on 1027 recorded cases between 2000 and 2014. Global Moran’s Ianalysis was applied to statistically evaluate the spatial pattern of the disease. Additionally, spatial and space-time scan statistics were used to study the presence of possible spatial and space-time hotspots. Global Moran’s Ianalysis proved that the incidence of the disease is strongly clustered in Iran (p<0.01). Purely spatial scan statistics identified that there were three clusters in the eastern, southern and western parts of the country. Through space-time analysis, we found that the highest incidence of CCHF occurred in the eastern parts of the country between 2006 and 2012. Monthly clusters, which include cities with lower (average) temperatures, had been occurring in relatively short periods. The distribution of CCHF incidence in this country is spatially and temporally clustered. The majority of the clusters emerged during the critical years of 2009 and 2013. Summer is the predominant period for the formation of CCHF clusters.

Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping(CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

The main purpose of any facility location is to select the optimal places that satisfy project’s goals. In location problems, the object is usually to optimize a function - objective function - that defines the problem conditions and efficient decision parameters. Numerous methods are proposed to challenge the location facility issues. In this paper, we consider a class of location/allocation problem that can assume more realistic conditions in real-life applications. This problem is an extension to the well-known capacitated multi source Weber problem. A new method that uses two genetic algorithms is used to solve the problem efficiently. The first, External GA, solves the location problem while the second, Internal GA, solves the allocation problem. A case study was designed to assess the feasibility of the proposed solution. The results indicate that the new approach is optimal, efficient and successful.

The main purpose of any facility location is to select the optimal places that satisfy project's goals. In location problems, the object is usually to optimize a function - objective function - that defines the problem conditions and efficient decision parameters. Numerous methods are proposed to challenge the location facility issues. In this paper, we consider a class of location/allocation problem that can assume more realistic conditions in real-life applications. This problem is an extension to the well-known capacitated multi source Weber problem. A new method that uses two genetic algorithms is used to solve the problem efficiently. The first, External GA, solves the location problem while the second, Internal GA, solves the allocation problem. A case study was designed to assess the feasibility of the proposed solution. The results indicate that the new approach is optimal, efficient and successful.

Numerous classification algorithms have been developed, many of which are highly specific and only solve a reduced class of problems. Maximum likelihood classification (MLC) is the most widely used classification method. The underlying assumption in performing MLC is that the prior probability of land cover is equal. However, a priori occurrence probability has a crucial effect on classification results. The objective of this paper is to improve the accuracy of MLC using a priori information in a knowledge-based system. The mathematical formulations and the strategy are presented. Estimates of a prior probability through crop areas, crop calendar, and some a priori probability about agricultural practices have been used in augmenting the probability of pixels. An industrial agricultural field, Moghan Plain in northwestern Iran, has been selected for testing. A total of 176 ground truth points were identified and measured in the field, and 323 fields were used for accuracy evaluation. Prior probabilities were estimated based on transition matrices of five successive years. Overall classification accuracy based on conditional prior probabilities increased from 53.2% to 66.7% relative to MLC. Knowledge about harvesting time is formalized using a normalized difference vegetation index (NDVI) map from an advanced spaceborne thermal emission and reflection radiometer (ASTER) image. The overall accuracy was then increased to 72.3%. Object-based classification is used to determine the crop type of agricultural fields for which the geometry is constrained. Overall accuracy is then raised to 88.7%.