2024-11-21 Moku Loa Hawaii Island Sierra Club Testimony (1)

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Vce The initial analysis included an overall view of development, an earlier era of development (1-975-95), and post-1995 recent development patterns for both of the dependent variables(see Appendix 1: Comparing Post 1995 Regression Factors with the 1975-1995 Regression Factors for a discussion of these results). The results show the top io variables—that is,the top io of the hypothesized desirability factors—that influence each of the analyses, along with the absolute value of each of the standardized coefficients. The coefficient values allow ranking the variables from most to least influential. Detailed analysis information is included at the end of this report. The standardized regression coefficients with the io highest absolute beta values for the post- 3.995 period were converted into CommunityViz weighting factors normalized to the scale o— so,where o is no correlation and io is the highest correlation of any factor(though less than 1). Candidate factors with lower beta coefficients, below the top ten,were ignored for the desirability score. A cutoff of io factors was chosen for a few reasons. One was to keep the most significant factors in the mix. The top io account for the majority of the causal influence of all factors tested. Additionally, there was a benefit to limiting the number of movable parts for testing. Even with io, it is challenging to understand the interplay of all inputs and the individual effect on the overall score. The goal here is to incorporate both sophistication and manageable interactive parts, and io is a reasonable number for that. Some factors are negatively correlated, and some factors are inversely correlated. For instance, distance values that correlate to growth are often inverse: nearer, smaller distance values are more desirable and further, larger distance values are less desirable. In the table below, negative and inverse correlations are indicated by a negative beta value score. Understanding this, many factors below make intuitive sense. The slope factor for example suggests that as land gets steeper(slope increases),the likelihood of development decreases. Some results are not always intuitive, however. Statistically,for example, it is found that parcels that are closerto old lava flows are more desirable for non-residential development than those far away(i.e. Distance 2 Lava FlOw179O). Some positively correlated cases also benefit from some explanation. For example,the strongest factor for residential development is Dlstance2VOIcanOHazard. This is a positively correlated factor meaning that as distance increases away from volcano hazards,the likelihood of development also increases. Trend Scenario and Land Use Allocation Technical Report 8