1. Spatial Clustering
Yogi Vidyattama

2. 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

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

4. 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

5. Children in Jobless household

6. Children in Jobless household

7. Children in Jobless household

8. Children in Jobless household

9. SYDNEY
Ethnicity

10. MELBOURNE
Ethnicity

11. Cluster of HDI 1999

12. Cluster of HDI 2008

13. Moran’s “Arrow” plot of HDI 1999-2008

14. 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

15. Software
OpenGeoDa
AURIN PORTAL

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

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

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