Arboriculture & Urban Forestry 37(4): July 2011 155 Figure 1. Mean change in communities with local urban and com- munity forestry activity within the 50 United States between 1997 and 2002. (Bars are standard error of the mean, activity catego- ries rank communities in a state from rudimentary activity in proj- ect to sustained activity with infrastructure sufficient to maintain status quo of the U&CF program, composite is the sum of all four activity categories in a state, no significant change in the number of communities within a state between 1997 and 2002.) amount of federal money provided to state U&CF programs (FedMoney), and the number of communities receiving financial assistance (FinAsst) were not significant (t-value < 1, and p > 0.25, in all cases). The StaGrant indicator was correlated with ActDiff, but not significant in the regression model. The indica- tors in the final model had a positive effect on activity change except FTE which offered a negative effect on activity. States with more communities had fewer staff proportionally scaled to communities per state, which may be the reason for this find- ing. Staffing level was positively correlated and strongly cor- related with FinAsst, TechAsst, FedMoney, and StaMoney. = 0.451, p < 0.000). In addition, stepwise multiple regres- sion approaches (forward, backward, and stepwise selection) with all exploratory variables in the final a priori models did not add these three into the final a priori models. Subsequent- ly, no additional variables are supported for addition in the fi- nal a priori model with the exception of FTE, as noted earlier. Validation Models Similar outcomes were detected in the validation models as found with the final a priori model on ActDiff (data not shown). The models found a similar explanation of the variance with consistent parameter strength and sign. Overall, the validation models provide evidence supporting the final a priori models. DISCUSSION Results from this study have implications with developing policy and direction for state and federal U&CF programs. During the study period, an increase in local urban forestry activity occurred. A major finding suggests technical assistance is a strong expla- nation of increased local urban forestry activity. Less certainty was found with state money allocated to the state U&CF program Table 3. Initial and final a priori models testing the relationship between indicators of technical assistance, financial assistance, grants, staffing level, and state U&CF program money sources on change in local U&CF activity within a state. Model variablesz B Initial Model All Indicators (R2 (Intercept) FinAsst TechAsst TechFreq FedGrant StaGrant FedMoney StaMoney FTE -0.360 -0.233 0.638 0.145 0.062 0.023 7.209E-6 8.025E-5 -9.291 Final a priori Model (R2 (Intercept) TechAsst TechFreq StaMoney FTE = 0.506, R2 -0.298 0.620 0.142 7.875E-5 -9.480 1997 and 2002 within a state. y Unstandardized coefficients Std. Error = 0.523 R2 0.281 0.153 0.085 0.077 0.077 0.000 0.000 3.480 0.130 0.075 0.000 2.504 Standardized coefficients Beta -0.127 0.765 0.231 0.116 0.056 0.038 0.262 -0.640 0.743 0.227 0.257 -0.653 t-test Statistics t-value -1.868 -0.829 4.162 1.695 0.815 0.299 0.177 1.379 -2.670 Sig.y adj = 0.403, std. error of est. = 0.159, F(8,32) = 4.378, p < 0.001) 0.193 0.071 0.413 0.000 0.100 0.421 0.767 0.860 0.177 0.012 adj = 0.451, std. error of est. = 0.152, F(4,36) = 9.215, p < 0.000) 0.156 -1.915 4.757 1.904 1.691 -3.785 0.063 0.000 0.065 0.099 0.001 Correlations Zero-order 0.023 0.478 0.340 -0.075 0.322 -0.062 0.250 -0.014 0.478 0.340 0.250 -0.014 Partial -0.145 0.593 0.287 0.143 0.053 0.031 0.237 -0.427 0.621 0.302 0.271 -0.534 z Dependent Variable: ACTDIFF = scaled change in the number of communities demonstrating rudimentary to advanced urban and community forestry activity between Independent variables regarded as significant with t-value probability <0.25 in the initial model, and <0.10 in the final model. Exploratory Model Subsequent exploration of additional independent variables (OtherAgn, EnabLeg, Agency, Coordin, ProgYear, FundAdeq, Council, and StraPlan) on ActDiff in the refined final a priori model occurred (data not shown). None of these additional ex- ploratory independent variables from Table 2 significantly added to the explanation of ActDiff when added individually to the final a priori model (t-value < 1.5, and p > 0.15, in all cases). The exploratory variables ProgYear, EnabLeg, and Stra- Plan were possible indicators (p < 0.25) when tested against ActDiff in an alternate model. However, these final explorato- ry models offered an inferior explanation of ActDiff (Adj R2 = 0.129, p = 0.044) compared to the final a priori model (Adj R2 ©2011 International Society of Arboriculture
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