Beyond simple descriptive statistics, we also wanted to look at simple correlations12 between contextual variables and outcomes. Resulting values range from 1 (perfect linear correlation) to –1 (perfect inverse linear correlation), with 0 indicating no correlation (random correlation). Contextual variables included land area, population, income, governmental structure, and other similar items. The outcome variables included level of cooperation, the quality of planning data and the quality of outreach, the level of final agreement reached, and other similar “outcomes” of the RTP process.
Complete details are shown in Table 9, Appendix D.
Surprisingly, neither 1997 population nor absolute population change were closely correlated with any of the outcome variables. However, population growth rate was moderately but negatively correlated with several important variables, such as the degree of cooperation with air quality districts (-0.67) and several “level of agreement” variables:
12 Sometimes called Pearson correlations. A basic description of the statistical techniques used in this study is provided in Appendix D.
• Local governments (-0.76)
• Environmental groups (-0.75)
• Business community (-0.65).
An obvious, albeit speculative, explanation for this is that higher population growth rates may put additional strains on the transportation system and may create more opportunities for conflict in balancing transportation, air quality, economic development, and other types of goals. Higher average population density in an MPO, in contrast, produced almost the opposite result, with MPOs reporting greater levels of agreement and cooperation with local government and environmental and pro-business groups. This may be due to an underlying relationship between population growth rates and density; the lowest-density metropolitan areas are generally growing faster than higher-density areas.
Educational attainment was measured by the Census Bureau’s estimate of the percent of the population over 25 years of age who had graduated from college.
There were positive correlations here with level-of-agreement variables with local governments (0.87), environmental groups (0.64), business groups (0.84), and highway agencies (0.62).
Per capita income had a mixed relationship to the other variables. It was negatively correlated with agreement on project priorities (-0.65) and quality of the outreach process (-0.66), but positively correlated with level of agreement with business groups (0.79) and transit operators (0.60).
Several variables had no significant correlation with other outcome variables.
They included the following:
• Number of governments in the MPO;
• Number of lane-miles of highway;
• Number of highway lane-miles per capita;
• Year the MPO was formed;
• Number of governing board members;
• Current number of agency functions;
• Time needed to complete plan;
• Quality of planning data on nonmotorized modes
• Quality of safety/accident data
• Quality of data on other “management systems;”13and
• Percentage of funding budgeted for operations and maintenance.
As noted earlier, we initially expected that the percent of MPO population in the most populous city would be correlated with several outcome variables.
This turned out to be untrue; the only variable it correlated with was degree of cooperation with counties. Even then, it was a fairly weak positive correlation (0.60) that may have little practical significance.
Most MPOs have several advisory committees that allow citizen representatives greater opportunity to participate in the MPO process and may help diffuse opposition to particular projects. The number of advisory committees did seem to play a positive but modest role in increasing the level of agreement with local governments (0.65) and environmental groups (0.63).
Several of the “quality of data available” questions appeared to be closely related to outcome variables. The quality of land use/demographic data was correlated with the degree of cooperation with the U.S. DOT, but not with other outcomes. The quality of traffic projections seemed to be the most influential variable, with significant correlations with the degree of cooperation with all other agencies except counties and transit operators.
Developing credible traffic (and transit) forecasts requires broad agreement on input assumptions and devotion of considerable MPO resources to the travel demand forecasting function. Another interesting result was that the quality of transit projections was not correlated with the degree of cooperation of transit operators (r of just 0.20) or the level of agreement of environmental advocacy groups (r=0.10).
Other conclusions from the simple linear correlations include:
• Quality of data on lifestyle/structural change data was correlated with the quality of the outreach process (0.83), state DOT cooperation (0.68), and success in improving the quality of transportation (0.73).
• Better economic projections were related to the degree of cooperation with cities (0.62), state DOTs (0.68), and the quality of the outreach process (0.79).
13 As initially required under ISTEA but later made optional.
• Better demographic projections were correlated with the degree of cooperation with transit operators (0.61) and marine ports (0.91).
• Quality of data on the impacts of technology was correlated with several outcomes, including the cooperation with local governments (0.62), cooperation with air quality agencies (0.79), cities (0.72), California congestion management agencies (0.90), and airports (0.68).
• Quality of freight/goods movement data was correlated with only one variable: the degree of cooperation with marine ports (0.83).
• Better air quality data resulted in several positive results: better cooperation with local governments (0.77), with cities (0.76), with state DOTs (0.68), and marine ports (0.81). It also was correlated positively with the quality of the outreach process (0.71).
Not unexpectedly, the greater the relative importance of adding highway capacity in the future, the less the degree of cooperation there seemed to be with transit operators (-0.84). An unexpected result was that greater emphasis on added road capacity also was correlated with reduced cooperation with street and highway agencies (0.69).
Cooperation with counties seemed improved with the number of official public meetings held (0.77) and the relative amount of time used for developing the vision statement (0.79), but these two inputs were not correlated with other variables.