Difference between revisions of "ANLY482 AY2017-18 T2 Group 31 Model Buidling and Analysis"
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<b>Modified L Test via Ripley's K Function</b><br> | <b>Modified L Test via Ripley's K Function</b><br> | ||
| − | To determine: | + | To determine: <br> |
1. If the notifications appear to be clustered or randomly distributed in our area of interest<br> | 1. If the notifications appear to be clustered or randomly distributed in our area of interest<br> | ||
2. Minimum radius distance which shows signs of statistically significant clustering | 2. Minimum radius distance which shows signs of statistically significant clustering | ||
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[[File:RKFunctionFormula.png|500px|]] | [[File:RKFunctionFormula.png|500px|]] | ||
</div> | </div> | ||
| + | Number of observed notifications is compared to the number of notifications expected based on Complete Spatial Randomness (CSR)<br> | ||
| + | CSR assumes distribution of points is homogeneous over the study area<br> | ||
| + | Null hypothesis: the spatial points are randomly distributed, using alpha = 0.01<br> | ||
Revision as of 20:02, 14 April 2018
Model Building
Model Building
Modified L Test via Ripley's K Function
To determine:
1. If the notifications appear to be clustered or randomly distributed in our area of interest
2. Minimum radius distance which shows signs of statistically significant clustering
Number of observed notifications is compared to the number of notifications expected based on Complete Spatial Randomness (CSR)
CSR assumes distribution of points is homogeneous over the study area
Null hypothesis: the spatial points are randomly distributed, using alpha = 0.01
