The most widely used classification method for statistical mapping is Jenks’s natural breaks. However, it has been found that natural breaks is not good at classifying data which have scaling property. Scaling property is ubiquitous in many societal and natural phenomena. It can be explained as there are far more smaller things than larger ones. For example, there are far more shorter streets than longer ones, far more smaller street blocks than bigger ones, and far more smaller cities than larger ones. Head/tail breaks is a new classification scheme that is designed for values that exhibit scaling property. In Digital Elevation Models (DEMs), there are far more lower elevation points than higher elevation points. This study performs both head/tail breaks and natural breaks for values from five resolutions of DEMs. The aim of this study is to examine advantages and disadvantages of head/tail breaks classification scheme compared with natural breaks. One of the five resolutions of DEMs is given as an example to illustrate the principle behind the head/tail breaks in the case study.The results of head/tail breaks for five resolutions are slightly different from each other in number of classes or level of details. The similar results of comparisons support the previous finding that head/tail breaks is advantaged over natural breaks in reflecting the hierarchy of data. But the number of classes could be reduced for better statistical mapping. Otherwise the top values, which are very little, would be nearly invisible in the map.A main conclusion to be drawn from this study is that head/tail breaks classification scheme is advantaged over natural breaks in presenting hierarchy or scaling of elevation data, with the top classes gathered into one. Another conclusion is when the resolution gets higher; the scaling property gets more striking.

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Map generalization is a traditional cartographical issue which should be particularly considered in today’sinformation age. The aim of this study is to find some characteristics about head/tail breaks which worksas generalization method compared with the well known Topfer’s method. A questionnaire survey wasconducted to let 30 users choose either of the series maps of both methods and the reason(s) for thatchoice. Also to test their understanding of the series maps histograms were added for them to match.Afterwards the sample results were analyzed using both univariate and bivariate analysis approaches. Itshows that the head/tail breaks method was selected by 58%, compared with 38.7% of Topfer’s method,because of its simplicity. By checking the correctness of histogram question it also shows that those whowell understood answers choose the head/tail breaks rather than the Topfer’s method. However in somecases, where the amount of geographical features is relatively small, Topfer’s method is more selectedbecause of its informative characteristic and similar structure to the original map. It was also found that inthe comparison the head/tail breaks is more advantageous in line feature type generalization than in arealfeature type. This is probably because Topfer’s method changes its minority selection rule to half selectionin line feature type, whereas the head/tail breaks keeps the scaling property. Any difference between thetwo tested scales, Finland level and Helsinki level, is not found in this comparison study. However, futurework should explore more regarding this and other issues.