This paper investigates the correctness of the spatiotemporal coverage as a suitable criterion for performance evaluation of a border patrolling algorithm. This criterion, which determines the probability of the intruder observation, shows the coverage of different points of the border during a period of time. Spatiotemporal coverage depends on the effective velocity of the intruder which means the magnitude of the instantaneous velocity perpendicular to the border. In the previous research, the maximum of the average velocity of the intruder is used to calculate the spatiotemporal coverage. Computing this criterion in this manner can’t show the effect of the behavior change of the intruder on the spatiotemporal coverage and wont provide a correct evaluation of the border patrolling capability of the system. This paper tries to represent a better assessment of the coverage using probability density function for the intruder's uncertain behaviors in selecting different passage times and speeds. Simulation result shows that the calculation of the spatiotemporal coverage with this approach provides a better evaluation of aerial surveillance capability in comparison to the traditional methods of coverage evaluation.

Brucellosis is one of the most prevalent bacterial zoonotic diseases considered as a public health problem in Golestan province (north-east of Iran), but its spatial pattern remains unclear. Hence we evaluated the spatial analysis of brucellosis disease in Golestan province during 2015-2017. In this study, we explored the Spatial and Spatiotemporal clusters by using scan-statistic to consider influencing factors. In addition, logistic regression and Pearson’s chi-square tests were used to analyze the clusters' zones and compare them with others. We used GIS to determine unites' (Golestan cities) coordinate centers and visualize the location of the clusters. Results revealed that the geographical distribution of brucellosis in Golestan province was affected by several spatial and spatiotemporal clusters. Constituent units of both spatial and spatiotemporal clusters were the same, but the identified time period of spatiotemporal clusters was from January 2015 to June 2016. The main influencing factors were in contact with livestock and dairy hygiene. This study can assist health authorities to plan more effectively to control diseases by highlighting the high-risk areas and behaviors.

Coronary artery disease (CAD) is the most common cause of mortality and disability around the world. Determination of the health status and spatial and temporal patterns of the disease prevalence has a major role in health planning. This study aimed to investigate the spatiotemporal pattern of CAD in Ardabil Province.
This descriptive and cross-sectional study was performed using the data belonging to 60156 patients with history of hospital admission due to CAD in Ardabil Province during 2010-2015. Hospitalization Rate (HR) was calculated after classification of the data in terms of location and date of hospitalization. The spatiotemporal and demographic variables, including age, gender, type of CAD, periods and duration of hospitalization were analyzed by descriptive statistics, ANOVA and repeated measures in SPSS v23 statistical software. Spatial distribution maps were drawn for each month and year in ArcGIS 10.2.
Of 60156 hospitalized patients with CAD diagnosis, men (58%) were more in number than women (42%). The majority of hospitalized patients fell in the age range 60-69 years (25.3%), and 92.40% of patients were hospitalized for less than 7 days. An ascending pattern was observed in temporal hospitalization trend in Khalkhal and a descending trend in Ardabil. The lowest hospitalization rate, however, was observed in Kosar city despite the ascending trend. During the research period, the greatest decline in hospitalization rate was observed in temporal trend in Bilasuvar, Meshkinshahrand Namin cities, and the largest increase was observed in Germi, Pars-Abad and Kosar cities.
By using the comprehensive technology, GIS, spatiotemporal distribution and CAD trend during 2011-2015 were modeled in Ardabil province. Spatiotemporal trend of CAD in Ardabil province and allied cities indicated the necessity of paying more attention and studying community for further prevention of the disease. Community-based interventions should be implemented for prevention of risk factors of CAD during childhood and adolescence especially in the districts with high risk.

Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.

Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated ​​and that of the observed values ​​were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.

The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.

The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.

It is clear to all that update precipitation data with adequate spatiotemporal resolution is step one for each research on precipitation. To doing this research Iran daily precipitation data with spatial resolution 0.25 degree extracted from Aphrodite data base during 1/1/1951 to 31/12/2007. One time spatial matrix in dimension 20818× 2491 has been created that located days on the rows and pixels on the columns. For each calendar days precipitation long term mean has been calculated and one new matrix in dimension 2491 × 366. By applying cluster analysis with Ward merging method regionalization of precipitation has been done. To detection optimum numbers of clusters, different inter groups variance quality control indices used. Amount of indices for different clusters have been calculated. The results show that regionalization of Iran precipitation into 8 regions is optimum. Amount of precipitation for each months of year in different regions calculated. Disperse of precipitation regions are corresponds with reality. It seems that Aphrodite precipitation data base has adequate accuracy to analysis of spatiotemporal precipitation on Iran.

Search pattern and coverage scenario are two important concepts, in using UAVs in border patrolling problem. It is necessary to define a quality evaluation method to compare different pattern under different implicational conditions to reach optimal coverage by a group of UAVs. The evaluation method calculates probability of seen intruders and shows the quality of coverage in any part of the border. This paper introduces a new tool which is called spatiotemporal virtual plane to evaluate coverage scenario and select proper pattern by considering criteria such as flight time, coverage percentage, and coverage uniformity. Along with UAVs and intruder's relative motions, their motion histories and probability of identification of intruders would be analyzed by spatiotemporal virtual plane. The results show that, this analysis method has powerful ability to evaluate scenarios based on qualitative and quantitative parameters.

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visualstimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns,which contain perceptual information gathered from sensory input. A high-density Electroencephalograph(EEG) device consisting of 48 electrodes with a spacing of 5mm was utilized to measure frontallobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimulirepresenting different levels of salient responsiveness were measured to determine if mild stimuli can bediscerned from more extreme stimuli. AM/PM response patterns were detected between mild and moresalient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AMpatterns had the highest number of incidents detected in the middle of the frontal lobe. Through thiswork, we can expand our encyclopedia of neural signatures to object recognition, and provide a broaderunderstanding of quantitative neural responses to external stimuli. The results provide a quantitativeapproach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to objectperception.

Spiral wave is a particular spatiotemporal pattern, observed in a wide range of complex systems such as neuronal network. Appearance of these waves is related to the network structure as well as the dynamics of its blocks. In this paper, we propose a new modified Hindmarsh-Rose neuron model. The proposed model uses a hyperbolic memductance function as the monotonically differentiable magnetic flux. An external electromagnetic excitation is also considered in the model. Firstly, we study the dynamics of the proposed neuron model through bifurcation diagram and Lyapunov spectrum, in two cases of no excitation and periodic excitation. The bifurcation diagram shows the property of antimonotonicity, which has not been observed in the previous models. Then a square network is constructed and we investigate the spatiotemporal pattrens. By varying the parameters values, spiral waves are observed in specific ranges. The formation of these waves depends on the interaction of all parameters simultaneously.

Knowledge of the spatiotemporal variations of river contamination from surface water enhances management decisions. This study assessed water quality parameters from 2000 to 2020 in four seasons in the Zayandehrud River of Iran. Principal Component Analysis (PCA) was utilized to decrease the dimensionality of water surface pollutants. Then, general linear models (GLMs) were applied to determine the impact of sampling stations along the river, season, year, and different land uses (agriculture, pasture, urban, and water) on the selected water quality parameters. The best model was selected using the Akaike information criterion (AIC) method, and the Wald test results indicated that the station factor significantly influences PC1 and PC2. The most positive effect was noticed in areas between urban (1000-meter buffer) and PC1, agriculture land use (10000-meter buffer) and PC2. In addition, the most significant damaging consequence was detected between agriculture (100-meter buffer) and PC1, pasture (1000-meter buffer) and PC2. This paper revealed that a combination of statistical techniques for evaluating spatiotemporal variation in water resources could benefit integrated environmental management.

Climate variables and their fluctuations dramatically affect terrestrial ecosystems and their variations. Vegetation indices have been used in numerous studies to investigate the relationship between ecosystem changes and climate parameters. In this study, GIS based spatiotemporal analyses were applied to model the relationship between vegetation variations based on the EVI-MODIS and its response to land surface temperature (LST) and rainfall in Mazandaran province during the period of 2000-2016. The LST parameter was derived from the MODIS data and rainfall parameter was achieved via meteorological station data in the region. Correlation and linear regression analyses were used to study the relationship between spatiotemporal enhanced vegetation index (EVI) and two climatic parameters. The findings indicated that the EVI had a rising trend over the study period which was mostly due to the increase in paddy fields. There was also a significant spatial correlation between EVI and LST which was significant and direct in the winter months and reversed during summer. The tabulate area analysis showed that throughout the winter months the spatial distribution of the highest EVI pixels matched to the maximum temperature pixels (20 to 27 ° C), while during June to September, the maximum EVI values were related to the areas in which the LST was less than 25 °C. Although there was no significant relationship between EVI/MODIS and rainfall in studied area, they reached a peak with a lag time of 1/5 to 2/5 months in the spring. The final results showed that the temperature is the main EVI climate factor in region and MODIS products have high potential to reveal the spatiotemporal dynamics of vegetation, the impact of human factors and its relation with the climatic factors of temperature and rainfall in the region.