One bit i think is missing is weighting for each area. In the above model, each of the 6 scores is given weight - 16.6%.
Just to add another spanner in the works which will make the model more accurate is to adjust scores to put more emphasis on differing factors, eg cbd might be more important than proximity to water.
This can either be worked out by yourself to fit your own personal criteria, or you could use trial + error by using known values to find which fits your pre-determined answer the most accurately.
eg. Changing:
CBD - 17%
Water - 17%
Trees - 17%
Schools - 17%
Transport - 16%
Local Facitilities - 16%
to:
CBD - 25%
Water - 10%
Trees - 15%
Schools - 15%
Transport - 19%
Local Facitilities - 16%
(numbers totally made up).
Just to add another spanner in the works which will make the model more accurate is to adjust scores to put more emphasis on differing factors, eg cbd might be more important than proximity to water.
This can either be worked out by yourself to fit your own personal criteria, or you could use trial + error by using known values to find which fits your pre-determined answer the most accurately.
eg. Changing:
CBD - 17%
Water - 17%
Trees - 17%
Schools - 17%
Transport - 16%
Local Facitilities - 16%
to:
CBD - 25%
Water - 10%
Trees - 15%
Schools - 15%
Transport - 19%
Local Facitilities - 16%
(numbers totally made up).