spatial correlation

Humans seek locations that offer a high quality of life, which is significantly influenced by environmental factors. Consequently, these factors play a crucial role in determining real estate values. This research aimed to explore the extent to which spatial variations in residential property prices in Tehran were affected by ecological factors. Data were collected from various sources, including real estate websites, meteorological synoptic stations, air quality monitoring stations, a GIS file of Tehran's land use, and the Information and Communication Technology Organization of Tehran Municipality. The data were integrated into a Geographic Information System (GIS), focusing on 400 points representing the geometric centers of neighborhoods in Tehran. These points served as the basis for analytical calculations with information gathered for all relevant variables. Using ArcGIS software and Spatial Analyst Tools, we generated the necessary data. The findings indicated a decreasing desirability of ecological factors from the north to the south of the city corresponding to a decline in housing prices. The correlation coefficient of +0.71 demonstrated a strong relationship between housing prices and desirability of ecological factors. Notably, there was approximately a threefold difference in average housing prices between District 1 (the northernmost district) and District 20 (the southernmost). In conclusion, despite the influence of political and economic factors and the profound changes brought about by modernity, the socio-economic gradient from north to south in Tehran remained aligned with the gradient of ecological desirability, suggesting a shift from natural environmental qualities to the role of capital in shaping real estate values.

Evaluating the health of the environment and its detailed analysis is one of the priorities that city managers should address because it can be very effective in spatial differences in the quality of the environment and housing prices. This article aimed to investigate the status of environmental health indicators and the impact of their spatial differences on housing prices in Hashtpar, a city in Guilan province with an area of about 1,378 hectares and a population of about 55,000 people. The data was collected through observation and survey. To collect the data from 14 components of environmental health, the area was divided into 34 parcels. In each parcel, the data was collected separately. The data with the UTM coordinates of the central point entered into the geographic information system to produce zoning maps using the Kriging tool. The spatial correlation coefficient was calculated by means of TerrSet Geospatial Monitoring and Modeling System software. Findings showed that moderate weather and permanent humidity, diverse and extensive animal life, modern agriculture, eco-tourism, poor waste management, and crossing a road through the middle of the city are the sources of threats to environmental health and consequently, human health. There is a positive correlation of 0.73 between 14 environmental health components and housing price. In the central parts of the city, the components revealed a more favorable situation than the fringes of the city, and housing prices are relatively higher in this part. Enjoyment from safe drinking water, exposed to pesticides, green space, and the beauty of the space are four components with better condition. The conditions of the natural environment and weakness in urban management have caused many weaknesses in the environment's health despite many blessings. Fixing these shortcomings requires careful planning and precise implementation.

Tabriz city in East Azarbaijan province also has a high potential in terms of having natural attractions and suitable climatic conditions. In this research, in order to evaluate the suitability of a suitable ecotourism location using the Analytical Hierarchy Process (AHP) and using the method (OWa) and Geographical Information System (GIS) were first examined according to the situation of the study area and the opinions of nature tourism experts and the evaluation of effective options in evaluating the spatial suitability of ecotourism development and all the options were converted into information layers and finally Using the AHP FUZZY technique with the OWA method in the GIS environment with the combination of 16 layers of information, the spatial compatibility map of the ecotourism development of the study area was prepared. Based on the results obtained from the spatial suitability map of ecotourism development in the study area, 52493.8 hectares or in other words 23.48% of the area is at a very favorable level, 50017.8 hectares or 22.38% of the study area is favorable and 1 51709/ or in other words 23/13 is at the average level. Also, the results of this research showed that the most unsuitable areas for the development of ecotourism include 24659.3 or in other words 11.03% of the entire area. The central region of Tabriz has a high potential for ecotourism. This area is in a very favorable condition in terms of research criteria. Thus, in terms of the most important criteria, i.e. distance from natural landscapes, distance from historical, religious and ancient attractions, and distance from facilities and services, due to its proximity to neighboring cities, including Tabriz itself, as the capital of the province, it has a good status compared to other parts of Tabriz city.

Land subsidence is one of the environmental hazards that threatens most countries today, including the majority of Iran's plains (Ranjabr and Jafari, 2010). Damages caused by subsidence can be direct or indirect. Infrastructural effects are direct and indirect effects of subsidence, but economic, social and environmental effects are indirect effects of subsidence (Bucx, et al., 2015). The environmental effects of subsidence are related to other effects of subsidence, including the infrastructural, economic and social effects of subsidence. The southwest plain of Tehran is considered one of the most important plains of Iran due to its large areas of residential, agricultural and industrial lands from various aspects, especially economic, political and social. The subsidence of the Tehran plain was first noticed by the measurements of the country's mapping organization in the 1370s. Since 2004, the responsibility of investigating this phenomenon in the plains of Tehran was entrusted to the Organization of Geology and Mineral Explorations of the country. Although several researches have been done in the field of subsidence factors, amount and zoning. In the field of estimation of subsidence and changes in water level, spatial correlation of subsidence with changes in water level and estimation of vulnerability due to subsidence according to the density of population, settlements and facilities in the southwestern plain of Tehran has not been done.

In the current research, we will analyze and estimate the spatial regression of the subsidence phenomenon by InSAR technique with water level changes from 2005 to 2017, as well as the environmental effects of subsidence in the southwest plain of Tehran by using Quadratic analysis method (O’Sullivan and Unwin, 2010). The criteria map of the current research is overlapped using the ANP method (Ahmedabadi and Ghasemi, 2015) weighting and finally with the SAW method (Emaduddin et al., 2014) in the Arc GIS 10.8 software, and the vulnerability map due to land subsidence in the study area is prepared.

The average subsidence in 12 years is about 9.9 cm per year. Average subsidence has occurred in four main zones. Maximum and minimum subsidence have been observed in B (near the Sabashahr) and D (in east of plain) zones respectively. The results of the interpolation of the depth of the underground water in the study area indicate that the general trend of increasing the depth from the south (10 meter) to the north (more than 90 meter) of the plain. The results of spatial correlation showed that there is a significant direct relationship between the spatial layer of the average subsidence rate of Tehran Plain and the spatial data of the underground water level, and the R value is equal to 0.61. The distribution map of the underground water depth of the study area in the form of Quadrat analysis shows that in the main part of the plain, the depth of underground water is at an average level. The general trend of changes in the level of underground water is decreasing from northwest to southeast and is in 5 levels. The distribution of the networks shows that the rivers have three linear trends from north and northwest to south; their dispersion is mostly in the center of the study area. The flood rate is higher in the central plain networks. In study area, there are important arterial roads such as Tehran-Qom highway, Tehran-Saveh highway and Tehran Azadegan highway. The southern and northeastern areas of the study area are urban settlements such as Islamshahr, the 18th and 19th districts of Tehran Municipality and other residential areas such as Sabashahr. The major part of the region has fertile soil and the occurrence of subsidence can have negative effects on the fertility and texture of the soil in the study area. The results of vulnerability analysis due to subsidence show that there are 5 vulnerability classes in the study area including very low, low, medium, high and very high.

All in all most of the study areas (central, northern and western networks) are in medium, high and very high vulnerability. About 14,600 hectares of the study area are in medium vulnerability. Which is continuous from the west to the east of the study area. Most of the urban infrastructures are moderately vulnerable to subsidence. About 17,000 hectares of the southwestern plain of Tehran are very vulnerable. That more than half of the area of ​​this area is covered by settlements and urban infrastructures. Therefore, the phenomenon of subsidence causes irreparable damage to the settlements and infrastructures in the southwest plain.

Knowledge of supernatural microphysical properties and revealing its relationship with the spatial temporal distribution of precipitation can significantly increase the accuracy of precipitation predictions. The main purpose of this study is to reveal the relationship between the Cloud microphysical structure and the distribution of precipitation in Khuzestan province. In this regard, first 3 inclusive rainfall events in Khuzestan province were selected and their 24-hour cumulative rainfall values were obtained. The rainfall event of 17December2006, was selected as a sample of heavy rainfall, 25 March 2019, as a medium rainfall case, and finally 27 October 2018, as a light rainfall case. Microphysical factors of clouds producing these precipitations were obtained from MODIS (MOD06) cloud product. These factors included temperature, pressure, and cloud top height, optical thickness, and cloud fraction. Finally, by generating a matrix with 64000 information codes, and performing spatial correlation analysis at a confidence level of 0.95, the relationship between the Cloud microphysical structure and the spatial values and distribution of selected precipitates was revealed. The results showed that in the case study of heavy and medium rainfall, the spatial average of 24-hour cumulative rainfall in the province was 36 and 12 mm, respectively. A fully developed cloud structure with a cloud ratio of more than 75% and a vertical expansion of 6 to 9 thousand meters, with an optical thickness of 40 to 50, has led to the occurrence of these widespread and significant rainfall in the province. While in the case of light rain, a significant discontinuation was seen in the horizontal expansion of the cloud cover in the province and the cloud cover percentage was less than 10%. In addition, the factors related to the vertical expansion of the cloud were much lower, so that the height of the cloud peak in this rainfall was between 3 to 5 thousand meters. The results of this study showed that in heavy and medium rainfall cases, a significant spatial correlation was observed at a confidence level of 0.95 between MOD06 Cloud microphysical factors and recorded precipitation values, while no significant spatial correlation was observed in light rainfall case.

Landslide, as an important natural hazards, causes damage to infrastructure and leads to economic, social and human losses. In this regard, determining the critical zones of landslides occurrence can be used in planning for damage reduction. The aim of the current research is analyzing and identifying landslide hot spots using Getis-Ord algorithm in Gharnaveh watershed, Golestan province. Therefore, the frequency and characteristics of landslides has been analyzed in different classes of slope, aspect, soil and land uses. The area, length, width, depth and height of the precipice of landslide features have been considered as the basis in hot spot analysis. The results showed that the landslide hot spots are located in the eastern part of the study area, which includes high altitude classes, rangelands and moderate slopes. Results showed that the rangeland and forest land uses, loess soils and 50-75% slope class and the northern aspect had the highest number of landslides. Also, the non-significant landslide points using the Getis-Ord method and considering landslide area criterion located in the middle and downstream of Gharnaveh watershed. Determining the landslide hotspots and affecting factors through the spatial analysis in GIS provides the defining thresholds in the landslide’s occurrence. The analysis of the landslide hotspots can be a basis for spatial planning, and risk reduction. The implemented approach can be used in the evaluation of the spatial autocorrelation of natural hazards, and in combination with the areas prone to multiple environmental hazards can predict the risk and severity of damages in the future.

Realizing structural characteristics of the urban street network has a crucial role in understanding dynamic and transforming events in a city. One of the most effective structural characteristics of street network is street network centrality which regarding former studies has a substantial effect on some events namely, distribution of activities along streets especially commercial and service activities that have a significant effect on the formation of motorized and pedestrian traffic flow throughout the city. Thus, considering street network centrality strongly improves the outcomes of urban land use and traffic planning. The current article aims at explaining the relationship between street network centrality and location selection of commercial and service activities. Firstly, it reviews key concepts related to street network centrality; specifically, focuses on metric centrality in the urban street network. Secondly, street network centrality of Qom city is modeled using Multiple Centrality Assessment (MCA) method in terms of centrality indices of intermediary, global and local closeness. Finally, datasets of street network centrality and location of activities transform to one scale unit using Kernel Density Estimation (KDE) to calculate and analyze the degree of spatial correlation between them. Correlation between mentioned variable layers is measured using Pearson’s correlation coefficient as well as spatial correlation index (SCI) which defined by the authors. Results indicate that there is a direct and high correlation between the selected street centrality indices and location selection of commercial and service activities in Qom. The highest correlation coefficients are for intermediary and local closeness centrality respectively. Global closeness centrality has the third place in terms of the correlation coefficient. As a conclusion, findings of this paper confirm that street network centrality has a significant effect on location selection of commercial and service activities in Qom city; the activities choose locations with better network centrality.

Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive mountain glacier surface velocities. In this research, we have conducted a comprehensive assessment of Alam-Chal glacier surface changes (include displacement and velocity), all based on remotely-sensed data. All datasets include aerial photos and satellite images were ortho rectified, normalized and co-registered. By using an aerial photograph collected in 1955 as a baseline and comparing it against a 2003 image collected by the SPOT satellite, the glacier retreat, in direct response to changes in local climate conditions were extracted. Furthermore, we have assessed short-term changes over two-time scales (1988-2003, 2003-2005),using an aerial photo acquired in 1988, a 2003 SPOT image, and a high-resolution Quick Bird image collected over the study area in 2005. We have derived accurate glacier surface velocity vectors (RMSE~2m), based on an FFT-based image cross-correlation technique. Our results point to the capability of the proposed method in accurately retrieving glacier surface changes at a high level of spatial detail, which is important for studies of regional climate change.

Groundwater is the main water resource for agricultural demands in arid and semiarid regions. An Sustainable agriculture requires a precise management and scheduling on utilizing these resources which in turn needs adequate knowledge about spatial variability of groundwater level for a given time period. As traditional interpolation methods do not consider spatial correlation between groundwater level observations, geostatistical methods are used for spatial variability analysis and mapping of groundwater level. In this study first a semivariogram is used to investigate the spatial correlation of groundwater level data from 59 observation wells in Kuhpayeh-Sagzi aquifer (Esfahan Province). Semivariogram analysis was performed for groundwater level data from 2002 to 2009 and for wet (Ordibehesht) and dry (Mehr) seasons. Also Kriging is used to interpolate groundwater level data over the study area and the results are compared with those achieved by inverse distance weighing method. Geostatistical analysis is performed using GS+. Based on results obtained in this study, the best semivariogram model for groundwater level for all time periods was spherical. Moreover the ratio of nugget effect (C0) to Sill showed that groundwater level data are strongly correlated over space for all years and for both wet and dry seasons. The smallest range of influence (36850 m) was found for wet season in 2002-2003 and the largest range of influence (40220 m) was found for dry season in 2007-2008. The cross-validation results showed the better performance of ordinary Kriging in comparison to inverse distance weighting. Based on the generated maps, the lowest groundwater level was seen in southeast and highest groundwater level was seen in center, south and southwest of the study area.

One of temperature estates is hot day occurrence. In order to recognize the mechanism of this estate, surveying its spatial behavior is crucially important. In current paper it has been attempted to recognized widespread hot days and their spatial distribution by using statistical methods. Accordingly maximum temperature database of Iran during 1962-2008 has been used. The analyses result has illustrated 1537 widespread hot days. Most of these days have been occurred during winter and spring seasons. In order to recognize distribution patterns and spatial autocorrelation, Global and local Moran statistic and also Getis- Ord G* statistic has been used. Using Global Moran has been revealed that in 99% confidence interval, cluster pattern governs the hot days frequency as well as hot days temperature. Frequency clusters has been raised in centre of Iran while clusters of the mean of temperature has been occurred in southern (from west to east) and northwest margin of the country. However the regions that experienced maximum frequencies of hot days do not happened in regions with hottest day’s temperature mean. Generally hot days regions are determined by humidity, latitude and elevation. The hot spots are located in south, southeast and cold spots are founded in high lands and northwest of Iran.

Due to deep، complex and everlasting interaction between precipitation and climatic elements-factors، there are changes and varieties in both time and space dimensions. So that climate experts and related scientists take their attentions to this phenomenon. An approach to do this kind of investigations is to describe spatial variations based on spatial statistics. In this paper the main object is to introduce principles of spatial statistics as well as describing general behaviors of Iran precipitation along the space. Accordingly daily precipitation of 1437 synoptic، climatology and rain gage stations during 1961/3/21 to 2004/12/31has been used. First of all the Kriging technique has been used to investigate precipitation distribution. In the next step three characters including spatial distribution، spatial correlation and regression models have been done. The results show that central mean has occurred at 34º 41` N and 50º 58`E. the spatial variation in central mean is occurred in path of the main systems that bring the precipitation to Iran. Spatial correlation shows 12 KM distance for significant correlation. The best fitted model shows slop and its direction the most affective spatial factors on precipitation.