Arboriculture & Urban Forestry 46(6): November 2020 Table 4. continued Name of method (Reference) (Country) (Coding) USDAFS Method 2 (Pokorny et al. 2003) (USA) (USDAFS 2) Tree Risk Assessment Qualification (TRAQ) (Dunster et al. 2017) (USA) (TRAQ) This method endorsed by the ISA is wholly verbal or ordinal in the description of risk and has 3 assess- ment criteria. To allow analysis, scores were allocated for the “Likelihood” components (“Impact” and “Failure”), ranging from 1 to 4, which were multiplied. The product was then re-ranked, with scores of 2 to 6 being unlikely and re-ranked as 1, while scores > 12 were very likely and re-ranked as 4. These were then multiplied by the “Consequences” factor, again scored from 1 to 4, giving a Risk Rating (RR) range from 1 to 16. In both multiplications, it is impossible to obtain scores of 5, 7, 10, 11, 13, 14, or 15. The final risk rating scores were quantified and aligned to 4 categories: 1 to 4 = Low, 6 to 8 = Moderate, 9 to 12 = High, and > 12 = Extreme. Brief description and derivation of method 411 The second USDAFS used 2 categories, “Targets” and “Defects” (each covers a different range), which were multiplied to create a “Hazard Rating.” This method can produce a zero score, and the “Hazard Rating” ranged from 0 to 6. No definition or quantification of the output score values was provided. to the different methods to allow comparison of their risk ratings (Table 5). If clear guidelines on the mean- ing of the output were not provided, the rating was based on the width of the method’s risk scaling. For example, a Matheny and Clark score of 4/12 was des- ignated a risk scale rank of 3 or lower (because it was only 1 above the minimum possible score), while a score of 7/12 was designated a risk scale rank of 6 (or medium/moderate). For wholly verbal or ordinal meth- ods such as TRAQ, points were assigned to the verbal descriptions provided to allow analysis. The results were directly compared using descriptive statistics and correlation analysis. The “standardised score” is a method permitting comparisons amongst different methods’ outputs. The conversion of raw scores to units of standard deviation is often termed “z-scores” (Urdan 2005). Minitab™ (Minitab 2006) was used to generate the standardised scores using the default “subtract mean from raw value and divide by standard deviation” method: where X is the raw score, µ is the mean, and ϭ the standard deviation. Positive values are those above the mean, and neg- ative values those below the mean. The standardised score represents the number of standard deviations. Hence, a standardised score of −1 is 1 standard deviation below the mean. Whilst all of the qualitative methods passed this test for normality (p > 0.05), the 4 proba- bilistic methods failed (p < 0.05) due to their inherent logarithmic nature. The raw output values from these methods were log-transformed and then passed the test for normality. RESULTS The results are presented in two parts. The first pres- ents data on the general outcomes of the 2 sensitivity analyses for all methods, while the second part pres- ents the data on the analysis of each method separately. Comparison of Sensitivity Analyses for All Methods The 1 rank or 25% change produced a range of out- comes that readily identified inputs that had a significant influence on outcomes. This approach can be extended by undertaking a 2 rank or 50% change to each input. A limitation occurs with methods that do not use an even range across the input scales (e.g., 1, 2, 3, and 4 versus 1, 3, 7, and 10). The second sensitivity approach (@Risk multivariate stepwise regression) more suc- cessfully identifies the range of variations found with methods. Matched input scales, such as MandC or probabilistic methods using true probabilities such as QTRA, rather than scaled probabilities, such as Kenyon and QTRA W, will produce even changes. ©2020 International Society of Arboriculture
November 2020
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