Dasymetric mapping is for phenomena that have uneven distribution that has been improve from the choropleth mapping technique. For determine the cause of uneven distribution, this technique where used other geographical factors. The symbols not follow the boundaries in the dasymetric map. The objective of the dasymetric mapping is to show uniform quantities regardless of unit area boundaries. The dasymetric map is used the intersection of two layer in the same database to get more precise of spatial data. It uses the same type of data as Choroplethic mapping, but involves some analysis about districts. For example, it not assumes homogeneity within districts.
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Choropleth mapping is means place and value. It content two components which is base map and attribute data (statistical data). This method is one of the most popular of quantitative thematic maps. The symbol is following the boundaries in the choropleth mapping. The objective of this mapping is to show the quantities within administrative unit areas. It employs area symbols to show the spatial distribution of the geographic phenomena. The problems with this method are making an easy way to slant data to suit the purpose of map by adjusting the values. It also by creating the illusion of rapid breaks where data varies continuously and gradually in the real world. Finally it allows small areas to cover the large region data.
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George Jenks developed this optimization system. The goal is forming groups that are internally homogeneous while assuring heterogeneity among classes. This has proven to be a very useful method, next to natural breaks - but requires computing power to perform. A statistical approach based on “Min & Max” of data variance. Data variance – how much data values vary in magnitude among each other. Start with a single class: range (a single class) = max data value – min data value. Introduce another group whereby. Minimize within group variance (member data values closer in value). Maximize between group variance (difference in group averages as great as possible).
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Standard deviation classification method finds the mean value, and then places class breaks above and below the mean at intervals of either 0.25, 0.5 or, one standard deviation until all the data values are contained within the classes. Values that are beyond the three standard deviations from the mean are aggregated into two classes; greater than three standard deviation above the mean and less than three standard deviation below the mean.
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It is a manual data classification method that divides data into classes based on the natural groups in the data distribution. It uses a statistical formula (Jenks' Natural Breaks) that calculates groupings of data values based on data distribution, and also seeks to reduce variance within groups and maximize variance between groups.
This method is based on a subjective decision and is the best choice for combining similar values. However, since the class ranges are specific to the individual dataset, it is difficult to compare a map with another map and to choose the optimum number of classes, especially if the data is evenly distributed.
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Classification Quantitative Data is the determining of class intervals and class boundaries in that data to be mapped and it depends in part on the number of observations. Most of the maps are designed with 4-6 classifications .There are five classification methods for making a graduated color or graduated symbol map. All these methods reflect different patterns affecting the map display. They are:
