scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

scholarly journals Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

2014 ◽  
Vol 11 (8) ◽  
pp. 2401-2409 ◽  
Author(s):  
W. J. Fu ◽  
P. K. Jiang ◽  
G. M. Zhou ◽  
K. L. Zhao
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Forest Ecosystem ◽  
Forest Litter ◽  
Zhejiang Province ◽  
Moran’S I ◽  
Carbon Density ◽  
Southeastern China ◽  
Moran's I ◽  
Density Values

Abstract. Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south–north) × 6 km (east–west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha−1 to 8841.3 kg ha−1, with an average of 1786.7 kg ha−1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

Analisis Moran’s I, Geary’s C, dan Getis-Ord G pada Penerapan Jumlah Penderita Kusta di Kabupaten Gowa

2020 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
S. Sukarna ◽  
Wahidah Sanusi ◽  
Hafilah Hardiono
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Analysis Method ◽  
Moran's I ◽  
Surrounding Areas ◽  
Geary’S C ◽  
Quadrat Analysis

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I  dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

scholarly journals Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals The spatial pattern of forest cover changes in Lithuania during the second half of the twentieth century

2015 ◽  
Vol 22 (4) ◽  
Author(s):  
Daiva Juknelienė ◽  
Gintautas Mozgeris
Keyword(s):  
World War Ii ◽  
Forest Cover ◽  
Agricultural Land ◽  
Moran’S I ◽  
Sources Of Information ◽  
Forest Cover Change ◽  
Moran's I ◽  
Local Patterns ◽  
Forest Cover Changes ◽  
Global And Local

The trends of forest cover change in Lithuanian municipalities are introduced in the current paper. Two sources of information on the forest cover in 1950s and today (2013) were used in this study: (i) a geographic forest cover database developed using historical orthophotomaps based on aerial photography, which was carried out in the period just after the World War II, and (ii) the information originating from the State Forest Cadaster and referring to the year 2013. These two layers were compared using GIS overlay techniques. The data was made available for the analyses aggregated up to the municipality level. The Global Moran’s I statistic and Anselin Local Moran’s I were used to identify global and local patterns in the distribution of forest cover characteristics in Lithuanian municipalities, respectively. The  main finding of this study was that the  proportion of the  forest cover in 1950 was 26.5%, i. e. notably differing from the official statistics – 19.7%. The proportion of the forest cover increased in all municipalities during the period 1950–2013. The largest increase in forest cover proportion was in the areas less suitable for agriculture. The relatively largest areas of new forests were identified in the south-eastern part of Lithuania, the deforestation was relatively slowest around less forested municipalities, while the afforestation was relatively slowest around the agricultural Pakruojis municipality. Deforestation was most commonly associated with the forest transformation into agricultural land, less often into scrublands or waters.

scholarly journals Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Huling Li ◽  
Hui Li ◽  
Zhongxing Ding ◽  
Zhibin Hu ◽  
Feng Chen ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Cumulative Number ◽  
Moran's I ◽  
Spatial Statistical Analysis ◽  
Global Concern ◽  
And Cluster Analysis ◽  
Global Moran’S I

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

scholarly journals Exploring spatial patterns of cardiovascular disease in Sweden between 2000 and 2010

2018 ◽  
Vol 46 (6) ◽  
pp. 647-658 ◽  
Author(s):  
Mohammadreza Rajabi ◽  
Ali Mansourian ◽  
Petter Pilesjö ◽  
Daniel Oudin Åström ◽  
Klas Cederin ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Moran’S I ◽  
Scan Statistic ◽  
Moran's I ◽  
Mortality And Morbidity ◽  
Admission Rates ◽  
Geographical Patterns ◽  
Causes Of Mortality

Aims: Cardiovascular disease (CVD) is one of the leading causes of mortality and morbidity worldwide, including in Sweden. The main aim of this study was to explore the temporal trends and spatial patterns of CVD in Sweden using spatial autocorrelation analyses. Methods: The CVD admission rates between 2000 and 2010 throughout Sweden were entered as the input disease data for the analytic processes performed for the Swedish capital, Stockholm, and also for the whole of Sweden. Age-adjusted admission rates were calculated using a direct standardisation approach for men and women, and temporal trends analysis were performed on the standardised rates. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I, together with Kulldorff’s scan statistic were applied to explore the geographical patterns of admission rates. Results: The rates followed a spatially clustered pattern in Sweden with differences occurring between sexes. Accordingly, hot spots were identified in northern Sweden, with higher intensity identified for men, together with clusters in central Sweden. Cold spots were identified in the adjacency of the three major Swedish cities of Stockholm, Gothenburg and Malmö. Conclusions: The findings of this study can serve as a basis for distribution of health-care resources, preventive measures and exploration of aetiological factors.

scholarly journals Significant spatial patterns from the GCM seasonal forecasts of global precipitation

2019 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Wei Zhang ◽  
Yongyong Zhang ◽  
Xiaohong Chen
Keyword(s):  
Spatial Patterns ◽  
Global Climate ◽  
Predictive Performance ◽  
Global Climate Models ◽  
Moran’S I ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Moran's I ◽  
Anomaly Correlation ◽  
Global Precipitation

Abstract. Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

scholarly journals Kemiskinan dan Ketimpangan Pembangunan kabupaten/Kota di Provinsi Lampung

2021 ◽  
Vol 10 (1) ◽  
pp. 31-45
Author(s):  
Resha Moniyana ◽  
Ahmad Dhea Pratama
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Research Area ◽  
Moran’S I ◽  
Poor People ◽  
High Poverty ◽  
Spatial And Temporal Patterns ◽  
Moran's I ◽  
Different Characteristics ◽  
Poverty Levels

The analysis results used in the problem of poverty are increasingly developing as the understanding of the problem of poverty becomes more complex in the spatial and temporal patterns, seeing the patterns and characteristics of a phenomenon with spatial imaging and study of patterns is the main objective of this study by looking at the pattern of the percentage of poor people and the level of inequality. The method used is processing Moran's I spatial data, Moranscatterplot and LISA, testing development inequality with the Williamson Index, The research area covers 15 districts/cities in 2015-2019. Spatial linkages The percentage of poor people between districts/cities in Lampung Province has a positive Moran's I value, has a spatial pattern with the same characteristics and is clustered. Development inequality is negative Moran's I, Development inequality has a spatial pattern with different characteristics in 2015 -2019. Poverty analysis indicates that during the 5-year study period, 5 districts in Lampung Province were still trapped in high poverty levels, The results of regional development inequality with the Williamson index indicate 3 regions with high levels of inequality, 4 areas of moderate inequality and 8 regions with low levels of inequality.

scholarly journals Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

2022 ◽  
Vol 14 (2) ◽  
pp. 291
Author(s):  
Zhengyu Wang ◽  
Yaolin Liu ◽  
Yang Zhang ◽  
Yanfang Liu ◽  
Baoshun Wang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Land Subsidence ◽  
Urban Areas ◽  
Spatial Scales ◽  
Moran’S I ◽  
Anthropogenic Factors ◽  
Spatial Correlations ◽  
Moran's I ◽  
Average Value ◽  
Gwr Model

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

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