Validating crash locations for quantitative spatial analysis
Validating crash locations for quantitative spatial analysis - theprinciplesofdating com
Enfin, l’impact des dates d’entraînement sur la quantité des changements prédits par chaînes de Markov est analysé.
Overall agreement may also be obtained by using statistical indices, such as Chi-square or Kappa (Pontius 2002).
In addition, data weighting and transformations provide helpful information regarding model performance.
Gómez Delgado and Tarantola (2006) propose a sensitivity analysis to test model stability.
In addition, the fidelity of the spatial patterns and the congruency of the simulation maps from different modelling tools are tested.
Finally, an error analysis is conducted that focuses on the magnitude of allocation errors and the magnitude of errors in predicted land use / cover classes.
These authors use several indices to measure the variability of model results based on changing input parameters.
Gomez Delgado and Barredo (2005) describe a method to assess risk when using model outputs and Jokar Arsanjani (2012) focus on model data and drivers of uncertainty. A validation of a hard prediction results from a comparison made between simulated and observed LUCs.
According to Coquillard and Hill (1997), model validation includes the following progressive steps: verification (also called internal validation, Does the model work accurately?
), calibration (Does the model correctly simulate a known situation?
Eastman validation is more common and uses more developed statistical tools.
These statistical tools focus on the following aspects: accuracy of quantity, LUCC components, landscape pattern, model congruence in the sense of similar results between different models, and error analysis.
Rykiel (1996) distinguishes between “conceptual” and “operational” validation.