Exploring the spatial patterns of vegetation fragmentation using local spatial autocorrelation indices

2019 ◽  
Vol 13 (02) ◽  
pp. 1 ◽  
Author(s):  
Pedzisai Kowe ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
Timothy Dube
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Local Spatial Autocorrelation

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

The local spatial autocorrelation and the kernel method for identifying black zones

2003 ◽  
Vol 35 (6) ◽  
pp. 991-1004 ◽  
Author(s):  
Benoı̂t Flahaut ◽  
Michel Mouchart ◽  
Ernesto San Martin ◽  
Isabelle Thomas
Keyword(s):  
Spatial Autocorrelation ◽  
Kernel Method ◽  
Local Spatial Autocorrelation

scholarly journals Variability of spatial patterns of autocorrelation and heterogeneity embedded in precipitation

2018 ◽  
Vol 50 (1) ◽  
pp. 215-230
Author(s):  
Dedi Liu ◽  
Qin Zhao ◽  
Shenglian Guo ◽  
Pan Liu ◽  
Lihua Xiong ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Spatial Interpolation ◽  
Kendall Test ◽  
Test Methods ◽  
Distributed Hydrological Model ◽  
Interpolation Methods ◽  
Increasing Trend ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation of precipitation data is an essential input for hydrological modelling. At present, the most frequently used spatial interpolation methods for precipitation are based on the assumption of stationary in spatial autocorrelation and spatial heterogeneity. As climate change is altering the precipitation, stationary in spatial autocorrelation and spatial heterogeneity should be first analysed before spatial interpolation methods are applied. This study aims to propose a framework to understand the spatial patterns of autocorrelation and heterogeneity embedded in precipitation using Moran's I, Getis–Ord test, and semivariogram. Variations in autocorrelation and heterogeneity are analysed by the Mann–Kendall test. The indexes and test methods are applied to the 7-day precipitation series which are corresponding to the annual maximum 7-day flood volume (P-AM7FV) upstream of the Changjiang river basin. The spatial autocorrelation of the P-AM7FV showed a statistically significant increasing trend over the whole study area. Spatial interpolation schemes for precipitation may lead to better estimation and lower error for the spatial distribution of the areal precipitation. However, owing to the changing summer monsoons, random variation in the spatial heterogeneity analysis shows a significant increasing trend, which reduces the reliability of the distributed hydrological model with the input of local or microscales.

The impact of spatial heterogeneity on selection: a case study on Pinus pinaster breeding seedling orchards

2008 ◽  
Vol 38 (1) ◽  
pp. 114-124 ◽  
Author(s):  
Rafael Zas
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Genetic Gain ◽  
Pinus Pinaster ◽  
Spatial Models ◽  
Selection Methods ◽  
Family Selection ◽  
The Impact

Although failure to account for spatial autocorrelation has been dramatic in some forest progeny trials, little attention has been paid to how this issue may affect selections within the trials. The effects of spatial autocorrelation of height growth on the estimation of genetic gain and on the spatial distribution of the selected trees were studied in four Pinus pinaster Ait. progeny trials that were rogued using different selection methods and intensities. When selections are based on unadjusted original values, selected trees tend to be located in the best microsites and are unlikely to be the most genetically superior. This resulted in a loss of genetic gain that varied between 10% and 20% and sometimes exceeded 30%. Differences in the loss of gain among different selection methods and intensities were minor and followed no clear pattern. Selecting on the basis of a conventional model resulted in spatial patterns of the retained trees that were clearly aggregated in all cases. However, selections based on spatially adjusted data resulted in random spatial patterns, except with family selection because of the use of multiple-tree plots. Because clumping of the retained trees may seriously affect the quantity and quality of the seed crop, breeders are strongly encouraged to use appropriate spatial models for roguing breeding seedling orchards.

Spatial Epidemiology Of Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma In The State Of Georgia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1683-1683
Author(s):  
Catherine Bulka ◽  
Loretta J. Nastoupil ◽  
Jeffrey Switchenko ◽  
Kevin Ward ◽  
Rana Bayakly ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Cancer Registry ◽  
Census Tract ◽  
Hot Spots ◽  
Research Funding ◽  
The State ◽  
Lymphocytic Leukemia ◽  
Cold Spots

Abstract Background Exploring spatial patterns of disease incidence allows for the identification of areas of elevated or decreased risk. For chronic lymphocytic leukemia and small lymphocytic leukemia (CLL/SLL), which have poorly understood etiologies, identifying spatial patterns through cluster analysis may provide insight about potential environmental and socio-demographic risk factors. Methods In order to investigate the spatial patterns of CLL/SLL incidence among adults (≥ 20 years), we linked cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry (a CDC-supported a statewide population-based cancer registry collecting all cancer cases diagnosed among Georgia residents since 1995) with population data from the 2000 U.S. Census. CLL/SLL cases were aggregated to the census tract level. CLL/SLL incidence in Georgia was standardized indirectly by age, sex, and race to national rates obtained from SEER*Stat software. Choropleth maps were created to depict the ratio of observed to expected incidence (standardized incidence ratios [SIR]) by census tract using ArcGIS. Spatial Empirical Bayes smoothing was performed on the SIR values using GeoDa 1.01. To assess spatial correlation of SIRs, we conducted global and local cluster analyses by calculating global Moran’s I and local Moran’s I (also known as Local Indicators of Spatial Autocorrelation [LISA]) values. Cluster analyses were repeated, stratifying by age (20-59 years, 60+ years), sex, and race (Caucasian and African American). P-values less than 0.01 were considered statistically significant. Results 765 incident CLL/SLL cases occurred among adults residing in Georgia between 1999 and 2008 (Table 1). There was a positive spatial autocorrelation for cases of CLL/SLL age 60 and older indicating these cases were geographically clustered (p = 0.0010) (Table 2). The LISA cluster map of the smoothed standardized incidence ratios shows the locations of “hot-spots” (high-high clusters) and “cold-spots” (low-low clusters) with clustering of high smoothed SIRs was found in the metro-Atlanta area, Albany, Macon, and outside of Augusta while cold-spots were mostly in the southern region of the state. Conclusions Despite the low number of cases of CLL/SLL in Georgia during the 10-year period studied, we found evidence of spatial clustering among adults 60 years and above. Hot-spots of smoothed SIRs were located in the metro-Atlanta area, Albany, Macon, and near Augusta, but these varied when stratified by age, sex, and race, suggesting confounding or effect modification that warrants further investigation. Disclosures: Flowers: Spectrum: Research Funding; Celgene: Consultancy, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Genentech BioOncology: Consultancy; Sanofi: Research Funding; Janssen: Research Funding; Abbott: Research Funding.

scholarly journals Local Spatial Autocorrelation in a Biological Model

2010 ◽  
Vol 30 (4) ◽  
pp. 331-354 ◽  
Author(s):  
Robert R. Sokal ◽  
Neal L. Oden ◽  
Barbara A. Thomson
Keyword(s):  
Spatial Autocorrelation ◽  
Biological Model ◽  
Local Spatial Autocorrelation
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