![\"Guideline\"](\"https:\/\/blogs.ubc.ca\/rightclickleona\/files\/2015\/11\/Guideline.png\")
<\/a><\/p>\nTo begin, it is important to examine data. \u00a0Evaluate\u00a0the distribution of data, then, take a look at\u00a0central tendencies, the range, and if any outliers exist. \u00a0The way data is distributed often dictates which method will be\u00a0used for classification (Equal Interval, Quantile, Standard Deviation, Natural Breaks or Manual Breaks). \u00a0However,\u00a0the pros and cons of each method must be considered when you want to represent data onto a map. \u00a0For instance, Equal Interval divides the same range between each class, which is more effective when dealing with percentage. Generally, distinction is made within 5 class groups. \u00a0However, since this method does not consider the distribution of data it can falsely convey information. \u00a0For instance, in the map of Data Classification of Housing Cost<\/a>, the lowest group is categorized between\u00a0200 598$ to 761 152$. That’s about a range of 550 000$, which is very high and makes it seem as if the majority of Metro Vancouver is decently\u00a0priced. In contrast, the natural breaks seem to sort data by considering the dispersion. The Jenks-Optimization \u00a0insures homogeneity within classes and heterogeneity between them. Despite that, the downfall is that\u00a0the legend bracket for each class seems aleatory for certain audience, especially if they are unaware of the method. \u00a0Therefore, the ideal data method classification is manual breaks. \u00a0Although, it may appear as subjective as natural breaks, it becomes easier to compare between two cities and facilitates the communication of the data.<\/p>\n