Sara Haghbayan; Behnam Tashayo; Mehdi Momeni
Abstract
Extended Abstract
Introduction
Today, one of the most complex issues in most countries is the high crime rate and the increase in social anomalies in them. One of these anomalies is residential burglary, which is one of the most widespread crimes in most countries of the world. Because spatial and ...
Read More
Extended Abstract
Introduction
Today, one of the most complex issues in most countries is the high crime rate and the increase in social anomalies in them. One of these anomalies is residential burglary, which is one of the most widespread crimes in most countries of the world. Because spatial and time play a very important and undeniable role in the formation of hot crime spots such as residential burglary therefore, by identifying the spatial and temporal of hot crime spots can be largely prevented. Previous studies have focused more on identifying and analyzing spatial crime hotspots and performing temporal analysis of crimes independently of spatial crime hotspots. However, in order to prevent the occurrence of these crimes in the future, a combination of time and spatial hot crime spots is needed to provide a more complete and accurate analysis. The aim of this study is to provide a systematic method for combining spatial and temporal information of residential burglary. The proposed method is based on spatial analysis and allows investigating the temporal distribution of events in hot crime spots. For this purpose, GIS capabilities have been used to perform statistical and graphical tests to identify and display crime hotspots. The results showed that hotspots follow a spatially clustered and temporally focused pattern. The research findings showed that the highest frequency of burglary is in hot spot No.4 in 2016 August, on Wednesday at 8 am, and the lowest frequency of burglary is in hot spot No.1 in 2018 January, on Sunday at 4 am.
Materials & Methods
The statistical tests used in this study include mean center, standard deviation ellipse test for clustering. The first step in identifying crime hotspots is to use the tests for clustering. For this purpose, in this study, the method of the average nearest neighbor is used. The results of residential burglary test for clustering showed that this crime is a cluster pattern in the study area. After proving to be clustered, graphical methods including point map display and kernel density have been used to display the hot crime spots. The results of the kernel density test cause to the identification and display of four spatial the hot crime spots in the study area.
The data used in this research include information on the time, place and type of crimes in the years 2015, 2016, 2017, 2018. The total number of crimes is 319073, of which 5573 were related to residential burglary, which was used as a statistical population in this study.
Results & Discussion
Statistical analysis was performed over a period of four years, which is equivalent to 48 months and 35064 around the clock for each hot crime spot. The results show that the highest incidence of crime in hot spot No.4 is equivalent to 1172 cases of residential burglary, which of all these four hot spot has a smaller area equivalent to 1117 hectares. Temporal analyzes of hot crime spots were performed annually, monthly, weekly and hourly. The results of the annual analysis of all four hot spots show that the highest rate of residential burglary is in 2016 and the lowest rate is in 2018.
The findings of this study show that the combination of spatial and temporal of hot crime spots analysis lump-sum by temporal analysis regardless of the spatial hot spots in monthly, daily and hourly intervals is significantly different. The combination of spatial and temporal of hot crime spots in the monthly interval shows that the maximum and minimum rates of residential burglary per month are different in these four hot spots. The highest number of residential burglary respectively occurred in hot spot No. 1 in October, in hot spot No. 2 in August, in hot spot No. 3 in June and in hot spot No. 4 in August. However, the results of the statistical analysis of time without considering the spatial hot crime spots show that August is the highest and April is the lowest. Daily statistical analysis shows that the highest number of residential burglary occurs in hot spot No. 1 and hot spot No. 3 on Friday, while in hot spot No. 2 it is Thursday and in hot spot No. 4 it is Wednesday. This analysis is different with a general daily analysis that shows Friday as the highest number of occurrences. Hourly analysis also shows that the peak of residential burglary in all four centers is at different hours; Thus, the peak of residential burglary areas in the study area is in the hot spot No. 1 hour 22, in the hot spot No. 2 hours 17, in hot spot No. 3 hours 12, in the hot spot No. 4 hours 8. However, statistical analysis of the time without considering the spatial hot spot shows the peak of residential burglary at 12 noon.
Conclusion
In this study, a new framework for the simultaneously displaying the pattern of crimes in two dimensions of spatial and time was presented, which can be used to identify the pattern of distribution of spatial and temporal of hot crime spots. The results of kernel density estimation analysis are four spatial-temporal crime hotspots where the spatial hotspot distribution pattern is clustered and the temporal of hot crime spots distribution pattern is focused. The results show that 78% of burglaries occur in these four crime hotspot, which cover only 25% of the total area of the study area. Therefore, by identifying the spatial and temporal of hot spots, crime can be largely prevented. This method is used to identify and display any type of crime in each study area and allows the identification and display of the combination of spatial and temporal hot crime spots.
Abolfazl Sharifi; Mohammad SaadatSeresht Mohammad SaadatSeresht
Abstract
Extended Absrtact
Introduction
Today, With the improvement of UAV technology as a spatial data collection platform, using the UAV photogrammetric method for mapping aims has become more popular. The advantages of this method include cost-effectiveness, speeding up the project process, ...
Read More
Extended Absrtact
Introduction
Today, With the improvement of UAV technology as a spatial data collection platform, using the UAV photogrammetric method for mapping aims has become more popular. The advantages of this method include cost-effectiveness, speeding up the project process, high resolution of spatial data, and production of various spatial products such as orthophoto mosaic, digital surface and ground models, 3D virtual model, and 3D map. From quality point of view, in addition to the network design in UAV photogrammetry projects, the camera and its accurate calibration are essential too. Metric cameras have a strong geometry, and their calibration parameters are known and stable with the smallest possible values. In spite of high accuracy outputs of metric cameras, it is practically impossible to use them in ultra-light public drones due to their high weight, size and cost. Therefore, today, non-metric and unstable digital cameras are conventional in UAV photogrammetric systems.However, many efforts are being made to reduce this weakness by improving the geometric quality of lightweight and inexpensive non-metric cameras. Despite of these efforts, application of non-metric cameras will not yet give us acceptable products without some practical considerations such as reducing flight altitude, increasing image side lap and overlap, and using high density of ground control points, which leads to a significant increase of cost and time. The main problem with these non-metric cameras is the weak geometry of their components that makes a high instability in the camera calibration parameters. This highlights the importance of proper geometric calibration of these cameras.
Materials & Methods
So far, several distortion models have been used to calibrate the metric cameras such as Brown model with a maximum of 12 parameters, including principal distance, principal point coordinates, lens radial and decentering distortions and affinity. These parameters are simultaneously estimated in a bundle adjustment with self-calibration process. Therefore, it can be said that this model considers fixed physical parameters for geometric modeling of the camera by which many images acquired in a photogrammetric block. If non-metric camera geometry is not modeled by a dynamic model with local spatial and temporal distortion parameters, some local systematic errors remain in the image observations. These systematic errors cause the estimation of unknown parameters in the least square adjustment is biased. Though this solution significantly improves the result of non-metric cameras in UAV photogrammetry, some errors in the 3D reconstruction remain yet due to low strength of observation equations set which comes from dynamic nature of the camera distortion model.The dynamic image distortions lead to parallax in stereoscopic vision and horizontal/vertical steps in the boundaries of connected 3D models. This paper proposes a post-processing method to reduce dynamic image distortions after conventional self-calibration of a non-metric camera with Brown model. The proposed method is based on local modeling of the image residuals using a finite element method. The data used in this study are photogrammetric drone images taken by ILCE-7RM2T, FC6310 and FC300S cameras. The proposed algorithm has been implemented in MATLAB programming environment and Agisoft Metashapesoftware has been used for initial processing.
Results & Discussion
As mentioned, the proposed algorithm is a post-processing task which reduces the image residuals and increases the geometric compatibility of 3D stereovision models.One of the critical indicators in the photogrammetric mapping production line is the quality of stereoscopic vision and the study of the vertical steps between connected 3D models. Because, photogrammetric map production requires stereo vision and the amount of model steps is used as a criterion for evaluation of image geometric distortion level. It can conclude that the use of the above idea is very effective in non-metric cameras with high geometric instability. The results of our experiments performed on the UAV photogrammetry data with low camera geometric stability indicate a60% reduction in the vertical steps of the models in stereoscopic vision and a 70% reduction in image residuals. This leads to a higher geometric quality of digital-elevation, 3D model, orthophoto, and map with 3D stereoscopic vision process. On the other hand, using this algorithm for non-metric cameras with higher geometric stability has a lower effect on the results. In our experiments, it was shown the vertical steps between 3D models can be reduced by 15% to 20%. However, there are still consecutive stereo models with quick steps in this type of camera, which will improve the geometric errors in stereoscopic vision if we ignore the computational costs.
Conclusion
The results of our experiments performed on the UAV photogrammetry data with low camera geometric stability indicate a 70% reduction in image residuals and a 60% reduction in the vertical steps of the models in stereoscopic vision. In this paper, the behavior of image residuals, the rate of model step reduction, and processing time in different dimensions of the distortion grid were investigated, and the grid dimensions of 150 to 200 pixels were recommended to apply the proposed method. Suggestions for further research are summarized in three sections. First, various factors such as the weight of observations and the weight of constraint equations can affect the estimation of the distortion grade, which can be estimated from the VCE method. Another point to consider in completing the proposed solution is to apply the temporal dependence between distortion grids in consecutive images. Also, although the proposed method uses the idea of finite elements as post-processing, it is more accurate to estimate this grid of distortion at the same time as the bundle adjustment.
Saeed Farzaneh; Mohammad Ali Sharifi; Amir Abdolmaleki; Masood Dehvari
Abstract
Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. ...
Read More
Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. Many parameters have an impact on the precision and accuracy of their information. Atmospheric friction is one of the most principal forces on satellites, which may cause deviation and falling of satellite on a short period. From the beginning of aerospace missions, many efforts have been done to determine atmospheric friction by geodesists, e.g., empirical models of atmosphere neutral density. Because of the complex nature of atmosphere behavior and also data limitations, these models may have low accuracy. So, there is a need for methods to improve the accuracy of empirical models by means of combining observations of atmospheric density to predict its future state. Materials & MethodsAlong with the extension of computer science, new reliable algorithms have been introduced which are able to predict a time series; Artificial Intelligent (AI) and Neural Networks (NN) are the best of these methods. These simple algorithms are inspirations of the human brain and its ability to learn and have been used in many different scientific fields. In these techniques without any requirement for constructing complex modeling, the relation between input and output will be provided only using weight and bias vectors during the training procedure. Simple Neural Networks are memoryless meaning that the value of time-series in previous can’t be used for predicting the future value of time series and therefore some important dependency of signal values with time will be lost. A Recurrent Neural Network (RNN) has been implemented to overcome this issue. RNN’s can store some important information of the values of the time series in the previous steps in a chain-like structure and using this information for predicting the next value of time series that will improve the accuracy of prediction. In this study, the Long Short-Term Memory (LSTM) Neural Network which is a kind of Recurrent Neural Network’s has been implemented to predict the scale for correcting atmospheric density of numerical models. The data of Grace Accelerometer observation in the 6 first month of the year 2014 have been used for training the LSTM for univariate training. Also, the LSTM has been trained in multi-variants mode once with using the coefficient of atmospheric correction expansion up to degree 2 and once with using sun geomagnetic information along with information of k_p index. Results & DiscussionAfter training the LSTM network, by using the estimated parameters of the model, the zero degrees coefficient of harmonic expansion for a scale factor of correcting atmospheric density has been predicted in periods of 7, 14, 30, 60, and 90 days. The results of the univariate model show that the lower RMSE (Root Mean Square Error) is obtained about 0.054 in the period of prediction of about 14 days. Also, the results show that the multi-variants model with input data of sun geomagnetic information and k_p index has lower RMSE values in considered prediction periods compared to the other modes and the lowest RMSE is about 0.03 and belongs to the prediction of about 7 days. For evaluation of LSTM parameters in the obtained results, the predictions have been implemented with various Window sizes. The results show that by increasing windows size, the RMSE of the prediction will be reduced and the lowest RMSE was for prediction of 7 days with a window size of about 90 days. For the purpose of more evaluation, with the predicted atmospheric densities correction coefficient, the orbit of GRACE satellites has been propagated and the calculated position and velocity of satellites have been compared with the real orbit data. The results show that the lower RMSE will be provided with the prediction of 7 days with an RMSE for position and velocity of about 50 meters and 0.15 m/s respectively. ConclusionIn this study, due to the complex nature of the atmosphere, the LSTM Neural Network has been used for modeling and predict the zero-order scale for correcting atmospheric densities harmonic expansion. For training the network, the data of Grace Satellites Accelerometer in the 180 days of the year 2014 have been used. The LSTM has been in univariate and multi-variant models. In the multi-variants model, once with using the coefficient of atmospheric correction expansion up to degree two and once with using sun geomagnetic information along with information of k_p index the network have been trained. The period of prediction was considered of about 7, 14, 30, 60, and 90 days.The results show that the LSTM is capable to predict the correction coefficient in considered periods with a mean RMSE of about 0.05 for zero-order degree. Also, the results show that the lowest RMSE was for the 7 and 14 days of prediction and by increasing the window size of LSTM the RMSE will be decreased. The results of calculating the position of GRACE satellites position and velocity using predicted correction coefficients with real data show that the lowest RMSE was for prediction of 7 days for implemented method.
Sara Khanbani; Reza Shahhoseini
Abstract
Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can ...
Read More
Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can face a different challenge; most of the CD method from a high-resolution image requires training procedure to overcome this challenge. In this paper, an unsupervised (without needing training process) CD algorithm proposed from the high-resolution image. In this method spatial and spectral features extracted from bi-temporal images of the studied area. Difference images generated from high information content features. Then generated different images mapped into spherical space. The Primary change map created using implemented multi-thresholding method on created spherical space and the second change map created using hierarchical clustering regularized by Markov random field method. The final change map created by integrating the result of primary and secondary change maps. The final change map shows an overall accuracy of 92.56% in the studied area. Data and methodsThe data used in this paper is a subset of the main data with dimensions of 2000 * 2000 from an urban area in the city of Mashhad. These images corresponded to the two periods of 1390 and 1395 and were taken with UAV. The orthoimage is related to the first time with a spatial resolution of 6 cm and the second image is taken with a pixel size of 10 cm. In this paper, in order to detect of change of high-resolution images, first, the input images are registered in terms of spectral and spatial, and then feature images are extracted from each input image separately. In the next step, the differences images corresponding to high information content feature images are calculated. . The optimal difference images are mapped to the spherical space using selected statistical methods and in order to better analysis of the results. Otsu multi-thresholding method implemented on r component of sphere space. In the next step, the optimal difference image mapped to a spherical space is divided into non-overlapping blocks with the same dimensions; a cumulative hierarchical clustering method is applied for each block separately. In this case, the computational volume and space proposed in the hierarchical clustering method are reduced. The results of the cumulative clustering of the blocks are merged together and then the Markov random field method is used in order to regularize the results of the cluster in order to reduce noise.In final clustering, the class values below the lowest Otsu threshold are known as unchanged pixels with high reliability and the values above the maximum threshold are determined as changed pixels. The class of middle interval is unknown. For determining, the class of middle interval the corresponded output of hierarchy clustering regularized with a random Markov field is used. In the last step, a vegetation and shadow mask is used for final post-processing. Results and discussionIn order to an accurate assessment of the proposed method on the mentioned study area, a ground truth image with 11073 pixels has been used as a ground test image. The proposed method has shown an overall accuracy of 92.56 in the study area. The accuracy of detecting changed pixels shows 81.61% and the accuracy of detection unchanged pixels shows 92.77%. The false alarm percentage is 0.21 percent and the missed alarm accuracy is 0.0723 percent. For comparative evaluation, the proposed method is compared with the change vector analysis algorithm. In this section, the selected features in the feature extraction section are entered in the change analysis algorithm, and then the multi thresholding algorithm and shadow analysis used to create the final change map. This method has shown increasing the alarm in comparison with the proposed method. The accuracy of changed and un-changed pixels in the change vector analysis method is equal to 52.98 and 89.24%, respectively. Comparing these results with the results of the proposed method shows the efficiency of the proposed method. ConclusionIn this paper, the new unsupervised change detection method presented based on the combination of multi thresholding and the hierarchical clustering algorithm. Compared to supervised methods that require training data, this method does not require training data. In this method, textural and spatial-spectral features are extracted from images with high spatial resolution, which covers the discussion of the importance of neighborhoods in images with high spatial resolution. In the next step, the extracted features that have a high information content are selected, which helps to reduce the redundancy of the information. The contrast images of the features with high information content are created to differentiate the location of the changes. Spherical computing space is considered as the basic computing space. In order to create a binary change map, two analyzes have been performed on the spherical computational space. First, the Otsu multi-thresholding method has been applied. The values of the smaller and larger thresholds have definite classes. But the value of the middle interval needs to be further analyzed using the hierarchical clustering method. In this section, the middle pixel class is examined, and then a final adjustment is performed using Markov field and shadow and vegetation analysis in order to post-process and prevent false changes. In this paper, the parameters of changed accuracy – unchanged accuracy - overall accuracy - false and missed alarms have been used to evaluate the accuracy of the proposed method with a ground accuracy map. In order to make a comparative study, the proposed method is compared with the change vector analysis method of the created feature space. The results show the efficiency of the proposed method.
Mohsen Pourkhosravani; Ali Mehrabi; Sadegh Karimi; Mina Azizi
Abstract
Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global ...
Read More
Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global warming caused by greenhouse gas, acid rain, environmental problems and threats to human health, lack of fossil energy sources and rising energy consumption have increased interests in renewable energies. Solar energy has been used as a source of renewable energy for a long time. As one of the safest, most efficient and most economical sources of energy, it has the potential to become the main energy source in the near future (Dincer, 2000: 157). Due to the high number of sunny days, Iran is among the countries receiving the highest level of solar radiation in the world. With 240 to 250 sunny days per year, approximately 80 percent of the country receives an average annual solar radiation of 4.5 - 5.4 kWh / m² (Moghadam et al, 2011: 107). In this regard, the present study seeks to evaluate and monitor radiant energy reaching the surface of Sirjan basin. Materials & MethodsThe study area, Sirjan Basin, is located between 28 degrees and 46 minutes and 50 seconds to 29 degrees and 58 minutes and 1 second northern latitude, and 55 degrees and 11 minutes and 20 seconds to 56 degrees and 32 minutes and 40 seconds eastern longitude. It includes 18481 square kilometers with an average altitude of 1710 meters above sea level. Descriptive-analytical method has been used in the present applied research. Data are collected using library and documentary research methods (from information and statistics offered by different organizations) or extracted from satellite images. Solar radiation energy reaching to the surface of the study area has been evaluated using three methods including Angstrom experimental model, Solar Analyst method in GIS and Remote Sensing. Results & DiscussionAngstrom experimental model indicates that the maximum amount of energy directly received by the basin at low latitudes (28 degrees and 50 minutes) is 73370-73436 watts per square meter. This decreases as we move toward higher latitudes reaching 72836-72903 watts per square meter in the northern parts of the basin (latitude 29 degrees and 50 minutes). Monitoring solar radiation energy reaching the surface with GIS Solar Analyst (solar radiation analysis method) shows that the lowest amount of radiant energy reached the surface in January (between 14000 to 144039 watts per square meter). Also, the maximum amount of radiant energy reached the surface in July (between 111000 to 252000 watts per square meter). Remote sensing technique also shows that the amount of instantaneous radiation received in Sirjan basin reaches its minimum during winters and only a limited part in the west of the study area receives 4.498 to 8.436 watts per square meter. The maximum amount of instantaneous radiation received in summers is 597.6 to 845.6 watts per square meter, which is received in a large part of the west, northwest and southwest of the basin. ConclusionMonitoring radiant energy reaching the surface of Sirjan basin using experimental Angstrom model shows that the highest level of energy received in the southern parts of the basin is around 733370 to 73436 watts per square meter. This is reduced moving toward the northern parts of the basin. Moreover, solar radiation analysis method (Solar Analyst in GIS) shows that the highest amount of solar energy in Sirjan Basin is received in July with 200000 to 252000 watt-hours per square meter , June with 170000 to 248341 watt-hours per square meter, May with 190000 to 247627 watt-hours per square meter and August with 190000 to 234500 watt-hours per square meter, respectively. These values are recorded in eastern, northeastern and southeastern parts of the basin. Results indicate that the eastern half of the basin in which the cities of Balvard, Tekiye, Saadatabad and Pariz are located, receives the highest amount of solar radiation energy especially in summer. Remote sensing technique shows that the highest amount of instantaneous radiation received in summer is 597.6to 845.6 watts per square meter which is recorded in the western, northern, northwestern, southern and southern parts of the region including the villages of Pariz, Saadatabad, Balvard in the central strip and Khatunabad, Mahmoudabad, Najafabad, Malekabad and Golestan. The same is also recorded in other seasons, though with a decreasing trend. The highest level of instantaneous radiation is received in these parts of the basin.
Abolfazl Ghanbari; Sadra Karimzadeh; Sedighe Taraneh
Abstract
Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and ...
Read More
Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and etc. Manifested as severe differences between living standards in different parts of cities, these affect the quality of urban life. Quality of life is considered to be one of the most important dimensions of sustainable urban development. The desire to improve the quality of life in a particular space, for a particular individual or group is one of the main concerns of planners. Failure to identify factors affecting the quality of life in various human settlements will have unexpected and unfortunate consequences. With a decrease in citizens' life satisfaction, society will gradually lose its productive and capable labour force. The present study primarily seeks to find a way to objectively study and evaluate the quality of life in urban areas using remote sensing technology and GIS. Therefore, it investigates the quality of life in Zahedan and identifies possible factors improving life quality. Methods and MaterialThe present study applies a descriptive-analytical methodology. Statistical data were collected from census data of Iranian Statistics Center and maps were retrieved from Zahedan detailed plan-related service centers. Satellite images were also used. The present study applies four indicators to study the quality of life: economic, social, and environmental indicators along with access to service providing centers. Cronbach's alpha method was used in SPSS to determine the reliability of the questionnaire resulting in a coefficient of 0.723 for the previously mentioned indicators which shows high reliability of the instrument. The validity of the questionnaire was also investigated using experts' opinions. Collected data and factor analysis for economic and social variables were performed using SPSS. Criteria were weighted using Super Decision software and ArcGIS was used to combine and model the layers. Satellite images were retrieved from Google Earth Engine. Results and DiscussionIn order to investigate the socio-economic inequalities affecting quality of life, 16 parameters were extracted from the available census data and used to assess the socio-economic situation in the study area. Correlated parameters were combined using factor analysis to represent a single index. A specific name was then assigned to each factor. Indicators were normalized and aligned for the modeling stage. Fuzzy membership functions (Large, Small and Liner) were used to normalize the indicators in ArcGIS. Each index is then multiplied by the weight obtained from ANP method, and integrated using GAMMA fuzzy command. Spatial distribution of urban blocks in the central parts of the first district ranked higher in terms of economic and social indexes of life quality. Environmental indexes and access to service providing centers have a more desirable status in the second district. Parameters such as economic participation rate , housing status, air pollution and health centers had the largest impact on quality of life. Moran's spatial autocorrelation index shows a cluster pattern for quality of life in the study area. ConclusionFinal results show that access to service providing centers has the largest impact on quality of life. In general, the second district ranks higher than the first district in terms of quality of life. This city faces various economic and social limitations, while having access to many facilities: Recent droughts, universities and higher education institutions, mutual borders with neighboring countries and a large number of immigrants from Afghanistan. It is also facing hot and dry climate, a decrease in vegetation cover and an increase in temperature level. The freeway located in the western part of the study area, urban expansion toward the western parts, increased constructions and increased urban density due to proximity to university centers and finally heavy traffic have caused air pollution. Also, public service centers are not evenly distributed. These are some of the most important causes of low quality of life in the study area.
Zahra Rezaee; Mohammad Hasan Vahidnia
Abstract
Extended abstract
Introduction
Population growth, urbanization and land use change in recent decades have made floods one of the most devastating natural disasters in the world. Therefore, understanding this phenomenon, its effects and methods used to deal with it is considered to be among the most ...
Read More
Extended abstract
Introduction
Population growth, urbanization and land use change in recent decades have made floods one of the most devastating natural disasters in the world. Therefore, understanding this phenomenon, its effects and methods used to deal with it is considered to be among the most important issues crisis management planners and policymakers in urban and rural areas should pay attention to. Iran faces many natural disasters among which flood is one of the most serious ones. Monitoring and controlling accidents, assessing damages and providing relief are among the main concerns of government and crisis management experts. Continuous monitoring before the occurrence, and accurate assessment during and after the event can decrease damages to human and natural resources. Preventing flood related hazards, organizing and managing flood water in channels and ultimately improving channels require identifying and determining flood zones.
Materials & Methods
Agent-based modeling (ABM) provides simulation and abstract systems used to identify patterns of land forms in the study area. As a new approach, agent-based modeling is used to develop simulation tools for complex phenomena in various fields such as natural disasters, biological studies and relief provision in flood occurrences. In fact, agent-based modeling (ABM) has been increasingly used to confront the risk of flood and its challenges in recent years. The present study applies fuzzy inference approach (using parameters affecting the occurrence of flood and remote sensing data) and agent-based modeling to prepare a flood risk map and provide a deterrent solution for flood risk management and decision making before the occurrence. In the fuzzy inference system, various maps are prepared showing parameters affecting the occurrence of floods such as slope, soil type and rivers. Then, Fuzzy Overlay model is used to define the flood risk zones and overlay the fuzzy parameters. The present study applies fuzzy gamma operator with a coefficient of 0.8 in the final fuzzy overlay calculation.
Results & Discussion
Comparing the results obtained from overlaid maps reveals that most flood plains are located in areas covered with Affisols (clay-rich soil) and low-lying arable lands and orchards. In agent-based modeling, GIS plugin of NetLogo was used to investigate the flood phenomenon based on the digital elevation model of the area. In this model, raindrop cycle was simulated in the DEM raster layer of Gilan. DEM layer can be used to calculate the slope (vertical angle) and slope direction (horizontal angle) of the ground surface. Simulated images shows the movement and accumulation of agents along the rivers and their surroundings and in low altitude areas. Analysis confirms the risk of floods in rivers and low-lying areas. Finally, georeferenced images of points in risk of possible flood (agents in the slopes of the study area), land use map and soil cover map can be overlaid to evaluate the obtained results. Results indicate that the highest number of agents (white markings on the map) are located in agricultural land use covered with Affisols while a relatively moderate number of agents are located in agricultural lands covered with Inceptisols. As previously mentioned, these agents simulate the amount of runoff accumulation due to atmospheric precipitation. Results indicate that precipitation models simulated using artificial intelligence lead to almost the same result Fuzzy analysis method shows (regarding the prediction of flood occurrence).
Conclusion
Finally, these two approaches are compared and their functions are examined. It should be noted that multi-criteria methods such as fuzzy inference approach has a higher level of complexity and accuracy, while methods based on artificial intelligence and agent-based modeling are faster. On the other hand, agent-based modelling method use relatively ready programs and thus has a lower level of complexity. The level of accuracy in this method is also lower than the fuzzy logic method.
Fariba Moghani Rahimi; Ahmad Mazidi; Hamid Reza Ghafarian Malamiri
Abstract
Abstract ExtendedIntroductionStudying land cover changes has a very long history which coincides with the beginning of human life. Following the formation of societies, primitive humans began to change the cover of wasteland to form suitable lands for agriculture and animal husbandry. More than half ...
Read More
Abstract ExtendedIntroductionStudying land cover changes has a very long history which coincides with the beginning of human life. Following the formation of societies, primitive humans began to change the cover of wasteland to form suitable lands for agriculture and animal husbandry. More than half of the world's population recently lives in cities, urbanization and urbanism is rapidly increasing, and this trend will continue to reach its peak. Due to their extensive coverage, reproducibility, easy-access, high accuracy and reduction in necessary time and expenses, remote sensing data are generally considered a preferred method used to study land cover, vegetation, and their changes. Many researchers have shown an interest in land cover change in different cities of the world. The history of land cover studies dates back to the early nineteenth century and the studies performed by von Thünen (1826). Von Thünen have determined the economic benefits of different land covers based on their distance from the central city and found an optimal distribution for production and land cover in the form of a series of concentric circles. Land cover changes due to human activities are considered to be an important topic in regional and development planning. Since land cover changes and urban development in the study area have not been previously studied, Landsat time series satellite imagery and a combination of Landsat 7 and 8 panchromatic and multispectral bands were used to identify and detect changes in land cover and urban development in the urban areas of Abarkooh from 2000 to 2020. Materials & MethodsSatellite remote sensing data are used in the present study (Landsat 7 and 8 multi-temporal satellite images collected in 2000, 2010 and 2020). 3 images were retrieved from US Geological Survey website and used in the present study. Raw remote sensing images always contain errors in geometry and the measured pixel values. The former category is called geometric errors and the latter is called radiometric errors. Atmospheric corrections were performed for all images used, and stripping in the imagery collected in 2010 image was also corrected. For image enhancement and extraction of more information from the images, false color composites were used (5-4-3 infrared, red and green bands) for Landsat 8 and Landsat 7 (3-4-3 near infrared, red and green bands) images. Using this technique, vegetation is shown in red. Compared to other methods, Gram-Schmidt based pan sharpening method produced higher spatial resolution images of the study area and thus was used to combine the selected images. Maximum likelihood method is considered to have the highest efficiency among various supervised classification methods. Results & DiscussionThis method assumes the presence of a normal distribution for all training areas. The accuracy of this classification has to be calculated following the classification. To do so, the kappa coefficient and overall accuracy of each class were calculated in ENVI5.3. The results are shown in the error matrix. Overall accuracy is the average of classification accuracy. The kappa coefficient calculates the accuracy of classification as compared to a completely random classification. Based on the available data, spatial resolution of the images and the information researcher has access to, 5 classes of training data (urban constructed space, roads, barren lands, arable lands, and gardens) have been selected for each image. Results obtained from the maximum likelihood classification method in ENVI5.3 environment were changed into the vector format and then used as a shape file in GIS environment. After compiling the land database, land cover maps and its changes were extracted in three periods and the area of each land cover class was determined. Each of the land cover maps, 5 classes with different colors are determined and shown. To ensure the accuracy of the classification, the accuracy of the classification has been evaluated. ConclusionThe resulting kappa coefficient for 2000 and 2020 equaled 86% and overall accuracy equaled 89%, while for 2010 kappa coefficient equaled 90% and overall accuracy equaled 92%. Thus, the error rate is small and acceptable. Finally, post-classification comparison method was used to investigate the nature of changes. 13 square kilometers of land cover were investigated in the present study. To identify the exact type of land cover changes, categorized images collected in these years were compared. Total area of residential land use showed an increasing trend: a total 4.25 square kilometers in 2000 (32.69 percent of the total area under study) has reached 5.58 square kilometers (42.92 percent) in 2020. Overall area of arable land use did not change much in the period of 2000 to 2010. However, a declining trend was observed in 2020 changing a part of this land use into residential and barren lands. Results of satellite image processing and classification indicate that supervised classification and maximum probability algorithm were close to ground realities and had an acceptable accuracy. In general, results indicate that significant amounts of vegetation and agricultural lands have been converted into urban areas and thus, planning for urban growth in these areas should be in favor of preserving gardens and agricultural lands.
Asyeh Namazi; Sayed Ahmad Hosseini; Sohrab Amirian
Abstract
Extended AbstractIntroductionAs a land use specially designed for physical activity, recreation and leisure, sports facilities are considered to be a public space vital for the society, improving health and well-being of the community. Therefore, special attention should be paid to the pattern of distribution, ...
Read More
Extended AbstractIntroductionAs a land use specially designed for physical activity, recreation and leisure, sports facilities are considered to be a public space vital for the society, improving health and well-being of the community. Therefore, special attention should be paid to the pattern of distribution, easy access to these land uses, and spatial organization of these facilities in accordance with the pattern of road networks. Accordingly, the pattern in which sport facilities are distributed across urban areas can have a direct impact on the desired operational efficiency of the city. Therefore, optimal site selection and easy access to sports facilities are of great importance for a healthy city and a healthy community. A huge difference between per capita sports areas and the standard per capita or imbalanced distribution of sports facilities in the region may result in reduced interest in physical activities and threaten the health of individual and society. Accordingly, the present study has evaluated per capita sports spaces in Kashan, and the spatial distribution of these facilities. The average time required for accessing these spaces has also been measured in accordance with the local road network and the total area each facility serves. Finally, an optimal model has been proposed for sports related land use in Kashan. Materials & MethodsThe present descriptive-analytical study is applied in nature and uses ArcGIS and SuperDecisions software to analyze its descriptive and spatial data. To provide an optimal model, 11 indicators including area each land use serves, its quality, urban land use, population density, health centers, educational centers, distance from faults, distance from urban waterways, fuel centers, distance from industries, parks and green spaces were identified based on expert opinions. The importance of each indicator was also determined based on expert opinions using the ANP model, and weighted linear combination was used to combine the previously mentioned indicators in GIS. A brief summary of the models used are presented in the following section. Results and discussionThe nearest neighbor algorithm is used to evaluate the spatial distribution regardless of the total area of each sports facility. Results indicate the presence of a completely clustered distribution (P = 0.000 and Z = -3.368) at the level of 99%. Finally, the relative weight of each criteria is combined with the relative weight of each option obtained from ANP model using the weighted combination in GIS to reach an optimal model for site selection. The resulting value actually indicates the necessity of new sports facilities. In other words, higher values show higher priority and as it is shown, about 40% of the total area of Kashan is potentially appropriate for new sports facilities while about 60% of the city area is not suitable for such facilities. ConclusionOptimal site selection maximizes the efficiency of sports facilities and improves the quality of services for those using the areas. Therefore, the present study aims to evaluate the area each sports center is serving and provide an optimal model for site selection in Kashan. In 2016, Kashan had a population of about 304 thousand people and about 202 thousand meters of sports related land use. Thus, there was a 0.67 square meters per capita sports related land use in Kashan. Finally, 11 indicators were combined using the weighted combination to reach an optimal model. Results showed that about 40% of the total area of Kashan is potentially appropriate (relatively appropriate and completely appropriate) for new sports centers while about 60% of this urban area is not suitable for construction of such facilities. Moreover, results proves the efficiency of spatial statistics used to evaluate spatial distribution of land uses. As it is shown in the present study, GIS can provide an optimal model for site selection using practical indicators and appropriate data analysis methods. In general, results indicate that sports facilities in Kashan are not generally in a good condition in terms of per capita and distribution pattern which confirms the fact that these issues were not considered important in the process of site selection.
Amer Nikpour; Hamid Amoniya; Sahele Shokri
Abstract
Extended Abstract
Introduction
Sprawl is the process of rapid population growth and spreading of urban developments on undeveloped land near a city with a direct impact on the spatial development which in recent years has become one of the major challenges of cities around the world. Growing population ...
Read More
Extended Abstract
Introduction
Sprawl is the process of rapid population growth and spreading of urban developments on undeveloped land near a city with a direct impact on the spatial development which in recent years has become one of the major challenges of cities around the world. Growing population trend and substantial changes in land use have made scientific and accurate planning a vital requirement for the management of this phenomenon. Accurate planning can help managers and spatial planners achieve sustainable urban and rural development. The present study seeks to enhance understanding about spatio-temporal processes of urban growth and development in Babolsar, identify general factors affecting the formation and spatio-temporal changes of the city and also inform managers and decision makers of the trends and growth patterns to help them in accurate planning, designing and managing. In order to achieve these goals, detailed information about the physical structure of the region in different time periods are collected, changes and spatial dispersion of the study area are observed, and information about the physical growth of the city is also obtained.
Material and Methods
The present study applies descriptive-analytical method to examine population growth and physical expansion of the city. After selecting the geographical area, satellite images captured in 1990, 2000, 2010 and 2020 were obtained from the US Geological Survey (USGS) web site. To calculate Shannon's entropy, the study area was divided into 25 regions based on the distance from central core of the city. Then, total area of each region and each zone (marked in each region for each period) were calculated. Thus, the necessary information was prepared to determine the trend of physical expansion and development of Babolsar city from 1990 to 2020. Shannon's entropy model not only has no limitation regarding the number of areas, but also has a high level of flexibility regarding the types of divisions used for the study area.
Results and discussion
These maps show that Babolsar has always grown both spatially and demographically from 1990 to 2020. The relative entropy of Shannon was calculated for each period and each region, and resulting coefficients show that not only is the rate of sprawl high in Babolsar, but it has always exhibited a sharply increasing trend during the last three decades especially from 2010 to 2020. Since examining expansion and dispersion require a careful consideration of population changes and trends, population of the study area was calculated for each year and its relationship with sprawl was examined. Findings indicate that sprawl has increased along with population increase. According to Holdern model and results obtained in the present study, population is the most important factor affecting physical growth of Babolsar city. It has played an especially powerful role from 1990 to 2000. Three main patterns of spatial development and sprawl can be identified in Babolsar: 1) strip or linear growth pattern spreading the city along the main transportation artery further away from the urban core. 2) Leapfrog development pattern which occurs when developers skip over land to obtain cheaper land further away from cities and thus create separately, singularly, discontinuously developed settlements. 3) Continuous low-density pattern developed due to excessive use of land for urban purposes along the outskirts surrounding the city. Gradual development in this pattern support infrastructure such as water, and energy and road network.
Conclusion
Studies indicate that sprawl in Babolsar city has had destructive effects on the environment and high quality agricultural lands around urban and rural settlements. Especial attention of Iranian society to its northern culture and the concept of "pleasure utopia" which has been assigned to the Southern Coast of the Caspian Sea are considered to be the most important reasons for urban sprawl in this city and other similar cities. Rapid increase in the number of villas built by indigenous and non-indigenous people has resulted in the destruction of high quality agricultural land and irreparable socio-economic damages. Currently, real estate trading, even in the villages of northern region, has not only intensified the sprawl, but also has changed and dissolved the traditional land use systems turning previous land owners into janitors. Other influential factors affecting sprawl in Babolsar and similar cities in the northern region of Iran include inefficient government policies in land and housing section, failure to meet the goals of urban and rural projects, population growth, real estate trade, development and construction codes incompatible with the realities of society, ambiguity in the laws and regulations governing construction within the legal limits of cities, lack of protection for government-owned land and properties, lack of proper supervision in construction projects.
Hasan Sinaei; Mohammad Saliqe; Mehri Akbari
Abstract
Extended AbstractIntroductionPrecipitation is considered to be one of the most important elements of climate. It affects the distribution of other climate elements and thus, has played a prominent and significant role in recent studies especially those focusing on global climate change. Due to ...
Read More
Extended AbstractIntroductionPrecipitation is considered to be one of the most important elements of climate. It affects the distribution of other climate elements and thus, has played a prominent and significant role in recent studies especially those focusing on global climate change. Due to its geographical location, Iran climate is affected by various climate elements. As one of the most important components of general atmospheric circulation, jet streams affect the quality of precipitation (amount, intensity, temporal and spatial distribution, etc.). Jet streams are relatively narrow bands of strong wind traveling across very long distances at high altitudes of the troposphere (tropopause) forming a hypothetical wind tunnel. Materials & MethodsThe study area surrounds southwestern Iran including provinces of Khuzestan, Ilam, Chaharmahal and Bakhtiari, Kohkiluyeh and Boyer Ahmad, Bushehr and Fars. The present study applies statistical methods and synoptic climatology. Based on the library research, representative days were selected in accordance with the following conditions: 1. Precipitation must have occurred in cold season, since cold season events generally show various patterns due to multiple weather systems affecting precipitation in Iran. 2. A significant percentage of the total annual precipitation must have occurred on the specified day (for example, a precipitation in the 90th percentile in each station). 3. Precipitation must be pervasive (i.e. recorded in more than 70% of the stations in the study area). Three representative days, December 17th 2006, November 25th 2014, and January 17th 1996 were thus selected with the highest precipitation volume over a 30-year statistical period (1989-2018). Two climate databases (precipitation data collected from meteorological stations in the capital city of the previously mentioned provinces and NCEP/NCAR climate data separated based on a 2.5 degree pattern) were used for synoptic analysis of these precipitation events. First, daily precipitation recorded by synoptic stations of southwestern Iran on each of these days was obtained. Then, climatic parameters such as geopotential altitudes of 500 and 850 hPa, jet streams occurring at an altitude of 300 hPa, specific humidity at the 1000 hPa level and values of omega component (measuring upward and downward movement of air flow) at the 500 and 850 hPa levels have been used to explore the relationship between these precipitations and jet streams in troposphere. Finally, GrADS was used to map the previously mentioned parameters. The relationship between precipitation occurrences across different stations of southwestern Iran and troposphere jet streams was exhibited based on an analysis of jet stream maps, moisture flows and other climatic parameters at various atmospheric levels. Results & DiscussionThe relationship found between previously mentioned precipitation events and tropospheric jet streams shows that in each of these events, the jet stream is a westerly wind meandering toward southwest or northeast in the study area and extending throughout North Africa and the Middle East. Central core of the jet stream was traveling above the study area with a speed of 35 to 60 meters per second. The present study indicates that in these three days of heavy precipitation, the jet stream axis has affected the study area in a southwest-northeast direction. Moreover, a cyclone is located at the 850 and 500 hPa levels approximately over eastern Mediterranean whose eastern side extends across southwestern Iran. Southwest-northeast direction of jet stream axis and eastern side of the Mediterranean Sea cyclone being extended toward the study area intensified instability in the lower atmospheric levels of the study area. Negative omega values at the 850 and 500 hPa levels (from -0.15 to -0.8 Pascal-second) indicates severe atmospheric instability in the study area. ConclusionEvery year, southwestern regions of Iran face intensive, and pervasive rainfalls resulting in severe floods and damaging agricultural products, gardens, roads, facilities, industries, etc. The present study indicates that Mediterranean cyclones, westerly winds across the lower atmospheric levels, and the subtropical jet stream meandering in the southwest-northeast (in the meridian direction) direction across the upper atmospheric levels affects the study area. Precipitation in this region is mainly supplied by the moisture coming from warm southern seas (Red Sea, Arabian Sea, Sea of Oman, Persian Gulf, etc).
Yousef Alipour; Naser Bayat; Ali Osanlu
Abstract
Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran ...
Read More
Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran climate is affected by various patterns of sea level pressure such as subtropical high-pressure, Siberian high-pressure, Monsoon low-pressure, the Mediterranean low pressure, Black Sea low pressure and Sudan low pressure during warm and cold seasons. These patterns have changed in different time series leaving adverse effects such as decreased precipitation and increased temperature, while probably changing Iran climate from semi-arid to arid and causing climate hazards. Having enough information on the temperature characteristics and its future trends, it is possible to decide on macro politics and a comprehensive method for the management of an area. Therefore, the present study aims to detect trends and significant changes in air temperature at the 1000 hPa level. Materials & Methods45 ° to 64 ° Eastern longitude and 45 ° to 64 ° latitude were selected to study temperature changes at the 1000 hPa level in Iran. In this study, temperature data of 1000 hPa level recorded in a 70-year statistical period (1950 to 2020) and data retrieved from NCEP/NCAR with a spatial resolution of 2.5 by 2.5 degrees have been used to prepare time series and necessary maps. The Kendall Man test was used to analyze the trend of time series. The 70-year statistical period (1950 - 2020) was divided into 10 decades and average seasonal temperature was used. Results & DiscussionThe average temperature of Iran at the 1000 hPa level is rising by 1.34° C per century and its standard deviation has reached its maximum value in recent decades. In the last two decades of the statistical period, 30 ° C contour line has approached Iran from southwest. Temperature trend at the 1000 hPa level is investigated in 4 different seasons of Iran.Summer: according to the Mann-Kendall test, average temperature in summer shows a significant trend and has increased by 0.2 ° C every decade.Autumn: time series of temperature data in autumn shows a significant trend and the slope of the regression line (temperature) has increased with a rate of 0.0451 ° C every decade.Winter: average temperature has decreased at the beginning of the study series and increased at the end of the series. 15.26 ° C and 8.18 ° C (in 1966 and 1972) were the highest and the lowest average temperature recorded in winter, respectively.Spring:The average temperature in Iran has increased by 0.197 ° C every decade. In this 70-year statistical period, average temperature of Iran in this season was 24.37 ° C with the highest annual average temperature recorded as 27.18 ° C in 2008 and the lowest annual average temperature recorded as 21.83 ° C in 1972 and 1992. ConclusionAverage temperature in Iran is raising with a rate much higher than the global average (0.74 ° C per hundred years), due to wide fluctuations in the general circulation patterns of the atmosphere and changes in sea level pressure pattern. Thus, it can be predicted that the temperature in southern Iran may reach over 60 ° C by the end of the century threatening southern riparian provinces with dangerously rising water level and the risk of drowning. Wildfires will still be common in Iranian forests, the number and intensity of floods will increase sharply, and water resources will reach a critically low status.
