Quantile local spatial autocorrelation

2019 ◽  
Vol 12 (2) ◽  
pp. 155-166
Author(s):  
Luc Anselin
Keyword(s):  
Spatial Autocorrelation ◽  
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 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

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

Robust local Spatial Autocorrelation Analysis of Massive Vessel Movements

2019 ◽  
Vol 91 (sp1) ◽  
pp. 306 ◽  
Author(s):  
Myeong-Hun Jeong ◽  
Dong Ha Lee ◽  
Tae Young Lee ◽  
Jung Hwan Lee
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Local Spatial Autocorrelation
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