spatial data

The purpose of this research was to advocate for the creation of child-friendly spaces in the Sardaran neighborhood of Urmia, which holds a traditional and historical significance. Using the "right to the city" model, the study recognizes that the presence and interaction of different age and user groups (commercial, residential, religious, office) are crucial for improving the quality of spaces. This research is descriptive-analytical in terms of the research method and practical in its purpose. The research sample consists of 135 observations selected from 4 different land uses that comprise the most significant percentage of existing land uses in the Sardaran neighborhood. The collected data is categorized into spatial arrangement models, questionnaire responses, and field impressions. The spatial layout model involves extracting the overall depth map from the axial map of the area. By analyzing the topological relations, the continuity and integrity of the area's accesses were represented in maps. The research results indicate that local access areas such as Sahaba, shishegarkhaneh, and Heshmati Alley have the highest connectivity (10) and strong connections with the immediate neighborhood. The information collected suggests that real estate plots in these areas have great potential for development. There is also a focus on promoting child-friendly spaces to cater to the interests of all age groups in the neighborhood. This includes enhancing neighborhood spatial relations, encompassing residential and commercial spaces, to foster social interactions among residents of all ages despite the limited local facilities.

Clustering is a vital technique for revealing structures and discerning groupings within extensive datasets, particularly in spatial data analysis, where the primary objective is to segregate data into clusters with shared characteristics. Artificial neural networks are established tools for clustering large and multidimensional datasets. This research focuses on clustering census block data, encompassing 21 socio-economic variables and access to services relevant to sustainable urban development. The study employs the Neural Gas (NG) network without spatial parameters. Then, it introduces the geographic coordinates of census blocks as spatial parameters, comparing the outcomes of the two approaches (NG & CNG). The NG algorithm, a prevalent choice for clustering high-dimensional data, and its spatially enhanced version, the Contextual Neural Gas (CNG) algorithm, were employed in clustering Isfahan city's census blocks. Results indicated a notable distinction in the clusters derived from the implementation of the NG and CNG algorithms. Clustering with the NG algorithm yielded heterogeneous clusters, whereas the CNG algorithm produced homogeneous clusters benefiting from spatial parameters. Evaluation of clustering quality, performed by calculating the average Silhouette coefficient for census blocks, showed the superior performance of the CNG algorithm, attaining a silhouette coefficient of 0.29 compared to the NG algorithm's -0.02. This research affirmed the positive impact of spatial parameters on creating homogeneous clusters within the urban environment. Leveraging the CNG algorithm and extracting homogenous areas based on sustainable development variables contributed to streamlined urban planning and management. The clustering of census blocks using variables related to sustainable urban development and a location-based approach using the CNG algorithm is one of the innovations of this research.

The increasing volume and dimensions of spatial data have made self-organizing neural networks a prominent tool for analyzing large and multi-dimensional datasets. Clustering, an approach for extracting knowledge from big data, aims to group similar data into clusters. This research focused on clustering socio-economic data of census blocks associated with urban sustainable development using self-organizing neural networks with and without spatial parameters referred to as SOM and Geo-SOM, respectively. Both algorithms employ the same clustering process but differ in the inclusion of spatial parameters, specifically the geographic coordinates of block centroids, in the Geo-SOM algorithm. The SOM and Geo-SOM algorithms were trained and applied to cluster the data. The resulting clusters exhibited distinct dissimilarities, demonstrating that clustering census block data solely based on non-spatial attributes leads to heterogeneous and incongruent clusters, whereas incorporating spatial parameters yields homogeneous and congruent clusters. Evaluation of the results using Silhouette coefficient indicated that Geo-SOM outperformed SOM in clustering the data with average Silhouette coefficients of -0.02 and 0.27 for SOM and Geo-SOM, respectively. Comparison of the outcomes highlighted the positive impact of incorporating spatial parameters on clustering socio-economic data.

    In order to calculate the erosive power of rainfall, high-resolution precipitation data are necessary for rainfall erosion evaluation. However, collecting the required data on kinetic energy of the rainfall particles and precipitation rates with short-term temporal resolution is a time-intensive task, particularly in developing countries, and the collected data are difficult to process. Rain sensors provide valuable information on the rate and intensity of rainfall, but fail to adequately represent irregular and inconsistent spatial distribution of the precipitation when evaluating the precipitation rate as those perform point measurements of precipitation. Under such circumstances, evaluation of precipitation rate from satellite data provides an alternative approach to the problem, which makes it possible to estimate precipitation rate and its spatial distribution across large areas. All around the world, several research works have been performed to estimate soil erodibility factor using the precipitation product of TRMM sensor, while no research has used such products for erosion and erodibility studies in Iran. Given that a limiting factor for estimating rainfall erosive power across large areas in Iran has been the lack of required data on precipitation intensity or precipitation rate, the present research can provide an approach to address such limitations. This study is aimed at monitoring the precipitation and hence evaluating and monitoring soil erodibility factor using precipitation products of TRMM sensor and comparing the results with those of terrestrial stations     In this research, in order to use Modified Fournier Index (MFI) to estimate corrosive rate during 2000, 2005, 2010, and 2015, monthly precipitation products data of TRMM3B24 sensor was retrieved from http://apdrc.soest.hawaii.edu for all months of the considered years. Then, using the Fournier index and the equation proposed by Renard and Ferimvend, the erosive rate for the entire country was extracted for the four years considered in this study. In order to verify and evaluate the accuracy of the precipitation products of TRMM sensor, the monthly precipitation data collected from 45 terrestrial stations were used, and interpolation technique was used to develop precipitation and erosion maps based on the terrestrial data. Accuracy of the precipitation products of TRMM sensor was verified based on root-mean-square error (RMSE) and coefficient of determination (CD) of the annual precipitation at the pixel position of the synoptic stations. Results of evaluating annual precipitation rates indicated among the monitored years, the 2010 had experienced the lowest level of precipitation, while the 2000 was the year with the highest precipition level. A review on rainfall erosion maps indicated that, in 2000, the areas of the country with the highest erosion rates corresponded to the Middle Zagros and Caspian areas. The country’s erosion map in 2005 closely resembled that in 2000, the erosion rates in the southern Kerman and northern Hormozgan were significantly lower than 2005. In 2010, when mean annual precipitation exhibited a low, the Chabahar Area exhibited the lowest erosion rate across the country, because of the intrusion of a seasonal air mass in June. In 2015, once again, maximum erosion rate across the country corresponded to those in 2000 and 2005, as determined by increased precipitation rate. A comparison between annual precipitation data collected from TRMM sensor and synoptic stations showed that, during all of the four years, an adequately good R2 value was established between the data from TRMM and that from terrestrial stations. The highest value of R2 between the TRMM and terrestrial stations data was obtained for 2000 (0.86), while the lowest R2 value was that of 2015 (0.73). The obtained value of RMSE showed that, in 2000 (the year with the highest precipitation rate among the monitored years), the value of RMSE was 152 mm. For this year, the difference between the peak participation estimated from the two methodologies was 406 mm, which was mainly related to the four stations with the highest precipitation rates. In 2005, the difference between the peak participation estimated based on the data from the two sources was 543 mm, with a RMSE of 205 mm. Also in this year, the difference between minimum precipitation values was highest, as compared to the other years considered in this study. In 2010, the difference between the peak participation estimated based on the data from the two sources was 533 mm, with the RMSE reduced to 129 mm. Spatial resolution includes terrestrial dimensions of each pixel of the image and determines accuracy of the image. Terrestrial dimensions of each pixel of the precipitation products of TRMM is 25 km by 25 km. Given the nature and definition of the pixel, it is the smallest spatial unit with its most important characteristic being the consistency across the entire pixel. Accordingly, the 625-km2 area of each pixel of an image from TRMM takes only one numerical value (DN) which is an average value of the precipitation across the entire 625-km2 area. Therefore, accuracy of each pixel in recording the precipitation depends on the variations of precipitation across the mentioned area. Terrestrial stations provide point estimations of precipitation, and interpolation technique is used to prepare erosion and precipitation maps from terrestrial stations. Accordingly, the higher the number of points included in the process of interpolation, the more flexible will be the resultant interpolated map. Given the fundamental differences between precipitation measurements by satellite sensors and classic terrestrial stations, it is difficult to firmly express that the data from terrestrial stations shall be taken as reference data and the remote sensing data shall be verified against the terrestrial data, because even in point measurements, there are cases where the precipitation condition in points near the measurement point is significantly different from that at the measurement point.

The aim of the present study is to analyze the spatial distribution and to show the spatial patterns of advanced producer services (APS) in Ardabil; one of the middle-sized cities in Iran. Sample of the study comprises 2100 activity units in four groups of advanced services, including banking and financial services, healthcare and remedial, real estate, and insurance services. Documentary and field methods are applied for collecting and processing the data. Spatial statistical techniques such as Kernel Density, Average Nearest Neighbor, the local Moran statistics, Cluster and Outlier spatial analysis, Hot Spots and Geographically Weighted Regression (GWR) methods have been used in GIS environment for data analysis, density estimation, distribution pattern analysis and conceptualization of spatial relationships. The results show that, insurance and real estate consulting services are enjoying the highest levels of coverage around the city. The highest densities of ASPs belong to center and south parts of the city. The spatial pattern of the advanced services is spread in type. Spatial pattern and spatial distribution of insurance services and real estate consulting are dispersed around the city, but the spatial pattern of banking, health and remedial services are clustered. The gravity or central point of advanced services locates at the geographical center of the city and the movement path of the advanced services follows a north-south route. There is a significant relationship between population density and some kinds of advanced services. Finally, some recommendations are presented based on the findings of the study.

Sustainable management of wildlife and natural habitats outcome of spatial, quantitative and qualitative studies of wildlife populations and habitats. Which highlights the importance of proper maintenance of existing data and organizing them to increase lifetime of such data to avoid duplication of resampling operations and to save budgets of wildlife conservation and management.So according to the advantages of using database management system for maintenance, usage and management of data, in this study we aim to design and implement spatial database of Khuzestan province wildlife. In order to design and implement the spatial database we used PostgreSQL/PostGIS object-relational database. According to results of our study, PostgreSQL/PostGIS (open source database management system) known as a viable option for the development of wildlife spatial data management capabilities. Also our study indicates that the designed management database system can support all possible queries related to wildlife spatial data, this system can also provide a solution to solve some problems related to management of large volumes of wildlife scattered data. According to the success of this research, we emphasized the necessity of attention to organize and manage the wildlife information and considered it issue in macro polices of Environmental Protection Organization of Iran. Finally, we pointed the necessity of creating and developing a universal database for the wildlife spatial data of Iran.

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Worldview satellites were used. Pieces with different land applications were cropped from these images to serve as test images. The assessment of the metrics tested on these images resulted in the categorization of metrics into three groups as per their capability for displaying spectral and spatial distortions. The first group included methods that functioned on the basis of noise for overall assessment of images with respect to their noise; these methods included ERGAS, MSE, PSNR, WSNR, and SNR indices. The second group were those aligned with Spectral Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

Information is the main ingredient of scientific research and an efficacious research depends on information processing. Naturally, such a tool and environment with such dimensions and features creates fundamental changes in the realm of scientific researches.
Geographic information system (GIS) provides an appropriate context for the simulation of any situation and space. It also creates the necessary 3d environment for the constant investigation and evaluation of urban plans with its analytic ability in modelling. Except for data used in the analysis of environmental and systematic information, other geographic information will be integrated into the selected site model according to the subject. As an instance, information necessary for urban management includes:Digital aerial imagery and satellite pictures, precipitation data and isohyet points, information about humid and arid lands, soil and its resistance, map of radiologic pollution, water/soil/weather pollution, digital earth model (DEM), vegetation and typology, topographic information and slope, population information and density, access network information, digitalized information of infrastructure and facilities, the situation of watersheds and aquifers in the area (identifying streams and rivers), ground and underground waters, geologic and geotectonic situation of the area, information related to the known faults in the area.
Other information like water table information, depth of underground water, geography of surface, etc. are evaluated according to the subject and used in urban planning, locating, designing and constructing.
After collecting the mentioned information, saving and organizing are performed in GIS environment and data base platform.
In geographic information systems, spatial data and descriptive information are stored as a coverage for vector and raster data. Features of each information model should be considered before being integrated in the selected site system.
The structure of geographic information base is constructed in 2 different ways:a) Stratification of information, spatial model of data digitalization, overlapping
b) Objective method based on spatial position of the phenomena, smartification of the phenomena
In the process of evolution, automation reached from digital production of graphical elements and the ability to integrate, aggregate, regulate, correct, distinguish and synchronize to create spatial and geographic data base and smartification of the graphic components. So that in addition to the ability of producing digital information, it has the necessary conditions for selection and logical decision making.

Remote sensing is important as a new technology for collection of geographic data for GIS on the one hand and as user's reference spatial data for scientific analysis on the other. In this paper, emphasis is placed on the importance of linking remote sensing raster data to GIS vector data to create IGIS. The application of IGIS is of great value in a variety of applications, such as information classification and environmental modeling. It is evident that remote sensing and IGIS complement each other, and both progressed independently and separately, especially in the early days. With linking the technology, concepts and theories of both in IGIS, more advanced and up-to-date information systems can be created for use in real applications. Almost all projects that currently use satellite data or deal with environmental data benefit from development of IGIS.