Data classification 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 however with more observations you have to choose a large number of classes but too many classes are also not good, since it makes the map interpretation difficult. There are four classification methods for making a graduated color or graduated symbol map.
| Property | Value |
|---|
| p:abstract
| - Data classification 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 however with more observations you have to choose a large number of classes but too many classes are also not good, since it makes the map interpretation difficult. There are four classification methods for making a graduated color or graduated symbol map. All these methods reflect different patterns affecting the map display. (en)
|
| p:hasPhotoCollection
| |
| rdfs:comment
| - Data classification 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 however with more observations you have to choose a large number of classes but too many classes are also not good, since it makes the map interpretation difficult. There are four classification methods for making a graduated color or graduated symbol map. (en)
|
| rdfs:label
| |
| skos:subject
| |
| foaf:page
| |
| p:redirect
| |
![[RDF Data]](http://dbpedia.org/statics/sw-cube.png)
