1. Validation Dr Andy Evans

2. Preparing to model Verification Calibration/Optimisation Validation Sensitivity testing and dealing with error

3. Validation Can you quantitatively replicate known data? Important part of calibration and verification as well. Need to decide on what you are interested in looking at. Visual or “face” validation eg. Comparing two city forms. One-number statistic eg. Can you replicate average price? Spatial, temporal, or interaction match eg. Can you model city growth block-by-block?

4. Validation If we can’t get an exact prediction, what standard can we judge against? Randomisation of the elements of the prediction. eg. Can we do better at geographical prediction of urban areas than randomly throwing them at a map. Doesn’t seem fair as the model has a head start if initialised with real data. Business-as-usual If we can’t do better than no prediction, we’re not doing very well. But, this assumes no known growth, which the model may not.

5. Visual comparison

6. Comparison stats: space and class Could compare number of geographical predictions that are right against chance randomly right: Kappa stat. Construct a confusion matrix / contingency table: for each area, what category is it in really, and in the prediction. Fraction of agreement = (10 + 20) / (10 + 5 + 15 + 20) = 0.6 Probability Predicted A = (10 + 15) / (10 + 5 + 15 + 20) = 0.5 Probability Real A = (10 + 5) / (10 + 5 + 15 + 20) = 0.3 Probability of random agreement on A = 0.3 * 0.5 = 0.15 Predicted APredicted B Real A10 areas5 areas Real B15 areas20 areas

7. Comparison stats Equivalents for B: Probability Predicted B = (5 + 20) / (10 + 5 + 15 + 20) = 0.5 Probability Real B = (15 + 20) / (10 + 5 + 15 + 20) = 0.7 Probability of random agreement on B = 0.5 * 0.7 = 0.35 Probability of not agreeing = 1- 0.35 = 0.65 Total probability of random agreement = 0.15 + 0.35 = 0.5 Total probability of not random agreement = 1 – (0.15 + 0.35) = 0.5 κ = fraction of agreement - probability of random agreement probability of not agreeing randomly = 0.1 / 0.50 = 0.2

8. Comparison stats Tricky to interpret κStrength of Agreement < 0None 0.0 — 0.20Slight 0.21 — 0.40Fair 0.41 — 0.60Moderate 0.61 — 0.80Substantial 0.81 — 1.00Almost perfect

9. Comparison stats The problem is that you are predicting in geographical space and time as well as categories. Which is a better prediction?

10. Comparison stats The solution is a fuzzy category statistic and/or multiscale examination of the differences (Costanza, 1989). Scan across the real and predicted map with a larger and larger window, recalculating the statistics at each scale. See which scale has the strongest correlation between them – this will be the best scale the model predicts at? The trouble is, scaling correlation statistics up will always increase correlation coefficients.

11. Correlation and scale Correlation coefficients tend to increase with the scale of aggregations. Robinson (1950) compared illiteracy in those defined as in ethnic minorities in the US census. Found high correlation in large geographical zones, less at state level, but none at individual level. Ethnic minorities lived in high illiteracy areas, but weren’t necessarily illiterate themselves. More generally, areas of effect overlap: Road accidents Dog walkers

12. Comparison stats So, we need to make a judgement – best possible prediction for the best possible resolution.

13. Comparison stats: time-series correlation This is kind of similar to the cross-correlation of time series, in which the standard difference between two datasets is lagged by increasing increments. r lag

14. Comparison stats: Graph / SIM flows Make an origin-destination matrix for model and reality. Compare the two using some difference statistic. Only probably is all the zero origins/destinations, which tend to reduce the significance of the statistics, not least if they give an infinite percentage increase in flow. Knudsen and Fotheringham (1986) test a number of different statistics and suggest Standardised Root Mean Squared Error is the most robust.

15. Preparing to model Verification Calibration/Optimisation Validation Sensitivity testing and dealing with error

16. Errors Model errors Data errors: Errors in the real world Errors in the model Ideally we need to know if the model is a reasonable version of reality. We also need to know how it will respond to minor errors in the input data.

17. Sensitivity testing Tweak key variables in a minor way to see how the model responds. The model maybe ergodic, that is, insensitive to starting conditions after a long enough run. If the model does respond strongly is this how the real system might respond, or is it a model artefact? If it responds strongly what does this say about the potential errors that might creep into predictions if your initial data isn't perfectly accurate? Is error propagation a problem? Where is the homeostasis?

18. Prediction If the model is deterministic, one run will be much like another. If the model is stochastic (ie. includes some randomisation), you’ll need to run in multiple times. In addition, if you’re not sure about the inputs, you may need to vary them to cope with the uncertainty: Monte Carlo testing runs 1000’s of models with a variety of potential inputs, and generates probabilistic answers.

19. Analysis Models aren’t just about prediction. They can be about experimenting with ideas. They can be about testing ideas/logic of theories. They can be to hold ideas.