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rail/com~,nity conflicts and to provide the basis for evaluating remedial <br />actions. Estimates of current problem magnitudes are presented in Exhibit <br />3. These figures are estimates of community-wide problem magnitudes and <br />are subject to the limitations of the data and methods used to develop <br />them. They are representative of the order of problem magnitude in each <br />case; they are not preCiSe measures of problem size or intensitY- <br /> The estimates presented in Exhibit 3 reveal a wide range of <br /> experience among the communities on an absolute scale. On a per capita <br /> bast~, however, the relatlve magnitude of the problems is less diverse. <br /> Also, the rank order of communities by problem magnitude changes when per <br /> capita rather than absolute statistics are used, as can be seen in <br /> Exhibit 4. For example, in terms of absolute problem magnitude, Moorhead <br /> has the highest number 'of estimated vehicle delays, about 20 times more <br /> than the com~anitY with the smallest number of potential vehicle delays <br /> (Hebron). In per capita terms, Casselton has the highest number of <br /> estimated delays--two and one-half times the estimate for Moorhead, which <br /> has the lowest per capita estimate- Similar comparisons may be made <br /> between the absolute and per capita magnitude estimates of other problems. <br /> These comparisons illustrate the importance of using per capita <br /> as well as absolute estimates when evaluating problem severity'in <br /> communities- It suggests that'a co~mintty with a seemingly small absolute <br /> problem magnitude may be as adversely affected by rail/commanitY conflicts <br /> as a community with a significantly higher absolute problem magnitude. , <br /> Thus, absolute estimates alone are insufficient to asseSS the relative <br /> problem magnitude amnng communities- <br /> <br />6 <br /> <br /> <br />