If you like Python code, see the attached script "SuperRegionLine":# Description # ----------- # This script will take a line featureclass that contains overlapping line features and build a # planarized feature class (with no overlapping lines) and a one-to-many look up table. This # is useful to examine the relationships of the overlapping features. For example, for a segment # of stream that has a seperate features for each specie of fish, count how many species share # that particular segment of stream. Optionally you can calculate a MIN, MAX, SUM, MEAN, or MEDIAN # statistic for any numeric field in the inputFC and propogate it to the output featureclass # (for example, the MIN "SURVEY_YEAR" value of the overlapping features). Also optionaly, you can use the # concatenate feature to concatenate coincident feature field values. For example, useing the fish analogy, # the calculated concatenated field value could be something like "Salmon, Trout, Scum Sucker". The code isn't completely bullet proof yet, but the basic algorithms are sound. Should work for you...
# Description # ----------- # This script will take a line featureclass that contains overlapping line features and build a # planarized feature class (with no overlapping lines) and a one-to-many look up table. This # is useful to examine the relationships of the overlapping features. For example, for a segment # of stream that has a seperate features for each specie of fish, count how many species share # that particular segment of stream. Optionally you can calculate a MIN, MAX, SUM, MEAN, or MEDIAN # statistic for any numeric field in the inputFC and propogate it to the output featureclass # (for example, the MIN "SURVEY_YEAR" value of the overlapping features). Also optionaly, you can use the # concatenate feature to concatenate coincident feature field values. For example, useing the fish analogy, # the calculated concatenated field value could be something like "Salmon, Trout, Scum Sucker".
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