Spatial Clustering Yogi Vidyattama.

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Presentation transcript:

Spatial Clustering Yogi Vidyattama

Main methodology Local Moran Index of Spatial Autocorrelation where: Zi = deviation of point i to the mean Wij = the spatial weights matrix m2 = total variance ( ) Weight matrix The nature of spatial relationship: contiguity

Methodology Weight matrices Describes the nature of the spatial relationships Rook-Contiguity based As opposed to Queen-Contiguity based Distance based

What it has been used for Concentration area of disadvantage Joblessness Overcrowded housing Less developed region Ethnic group: Ancestry, Language Why? Spill over effect Intertemporal effect Public service/ infrastructure distribution Further analysis necessary Changes over time Different behaviour/attitude Impact on regression

Children in Jobless household

Children in Jobless household

Children in Jobless household

Children in Jobless household

SYDNEY Ethnicity

MELBOURNE Ethnicity

Cluster of HDI 1999

Cluster of HDI 2008

Moran’s “Arrow” plot of HDI 1999-2008

Other used Disease Crime Natural disaster and its impact Contagion, connected topography Crime Vulnerable target, location of criminal Natural disaster and its impact Flood, fire, earthquake

Software OpenGeoDa 09.09.12 AURIN PORTAL

Step in AURIN portal Choose or import your dataset Spatialise your dataset Create your weight matrix

Step in AURIN portal (2) Calculate the Local Moran’s I statistics Visualise the result

Analyse in AURIN portal Percentage is mainly used but comparing the result to the result in number is often important