equal interval data classification


The advantage of the equal interval classification method is that it creates a legend that is easy to interpret and present to a non-technical audience.

The user determines the number of classes. In these cases, datasets that may not be overly disparate may appear in the output graphic. If we decide to classify this data into five equal interval classes, the range of each class will cover a population spread of 9,184,730 / 5 = 1,836,946. For example, as shown in the figure Equal Interval Classification for 1997 US County Population Data, almost all the counties are assigned to the first (yellow) bin.

The equal interval (or equal step) classification method divides the range of attribute values into equally sized classes. oral dogs biopharmaceutical applying bcs predict pitfalls drugs classification criteria absorption challenges system fig This method is best for data that is evenly distributed across its range. Want to adapt books like this? Your email address will not be published. The maximum number of letters in a state is 14 as in North or South Carolina. These data are freely available on the US Census website (http://www.census.gov). The equal interval classification method divides attribute values into equal size ranges. geopandas choropleths equal choropleth Its completely based on the data and which values fall in their respective bin. If we want to show the number of letters for a state in a choropleth map, we can start counting letters for each state. This means that Ohio falls in the 4-7 bin and California belongs in the 8-11 bin. The process of data classification combines raw data into predefined classes or bins. Your email address will not be published. The natural breaks (or Jenks) classification method utilizes an algorithm to group values in classes that are separated by distinct breakpoints. Each of these methods presents the data differently and highlights different aspects of the trends in the dataset. Including too many classes can make a map look overly complex and confusing. It is often helpful to tweak the classes following the classification effort or change the labels to some ordinal scale such as small, medium, or large. The latter example, in particular, can result in a map that is more comprehensible to the viewer. The method is best suited for data that conforms to a normal distribution. Learn more about how Pressbooks supports open practices. On the other hand, too few classes can oversimplify the map and hide important data trends. The equal interval classification method is best used for continuous datasets such as precipitation or temperature. The number of records that fall into each bin will differ. Introduction to Geographic Information Systems by adamdastrup is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. 25 Map Types: Brilliant Ideas to Build Unbeatable Maps, 50 Map Projections Types: A Visual Reference Guide [BIG LIST], What are Map Projections? The primary disadvantage of the quantile classification methodology is that features placed within the same class can have wildly differing values, mainly if the data are not evenly distributed across its range. And that is perfectly OK because its not misleading. Why Are Great Circles the Shortest Flight Path? The primary disadvantage is that specific datasets will end up with most data values falling into only one or two classes, while few to no values will occupy the other classes. In the county population example, the mean is 85,108, and the standard deviation is 277,080. We can see how there is an uneven number of states in each grouping because of how the data is being grouped with this classification system. The Natural Breaks figure shows the natural breaks classification for the 1997 US county population density data. Monmonier (1991) noted that different classification methodologies could significantly impact the interpretability of a given map as the visual pattern presented is easily distorted by manipulating the specific interval breaks of the classification. Although other methods are available (e.g., equal area, optimal), those outlined here represent the most commonly used and widely available. The advantage of this method is that it often excels at emphasizing the relative position of the data values (i.e., which counties contain the top 20 percent of the US population). Finally, the standard deviation classification method forms each class by adding and subtracting the standard deviation from the mean of the dataset. Therefore, it is incumbent upon you, the cartographer, to select the method that best suits the needs of the study and presents the data in as meaningful and transparent a way as possible. 1.2 Spatial and Temporal Science of Geography, 1.4 Spatial and Temporal Mapping Concepts, 2.3 Datums, Coordinate Systems, and Map Projections, 8.4 Spatial Interpolation for Spatial Analysis. Required fields are marked *. Figure Quantiles shows the quantile classification method with five total classes. As there are 3,140 counties in the United States, each class in the quantile classification methodology will contain 3,140 / 5 = 628 different counties. The map above takes the tables with the 3 groupings and shades each break based on the number of letters in the state name. A second disadvantage is that comparing two or more maps created with the natural breaks classification method can be challenging because the class ranges are particular to each dataset. This method is best used with unevenly distributed data but not skewed toward either end of the distribution. For example, in the case of the 1997 Census Bureau data, county population values across the United States range from 40 (Yellowstone National Park County, MO) to 9,184,770 (Los Angeles County, CA) for a total range of 9,184,770 40 = 9,184,730. Introduction to Geographic Information Systems, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. In addition to the methodology employed, the number of classes chosen to represent the feature of interest will also significantly affect the ability of the viewer to interpret the mapped information. Accordingly, class 1 is characterized by a range of just over 150,000, while class 5 is characterized by over 6,000,000. For example, if you create 5 classes with attribute values from 0-100, the 5 classes will be 0-20, 21-40, 41-60, 61-80, and 81-100. These methodologies break the attribute values down along various interval patterns. 10 Topographic Maps From Around the World, 3 Wildfire Maps: How to Track Real-Time Fires Around the World. Most effective classification attempts utilize approximately four to six distinct classes. Unlike quantile classification, the number of records that fall into each category (or bin) will differ. While problems potentially exist with any classification technique, a well-constructed choropleth increases the interpretability of any given map. Choropleth maps are thematic maps shaded with graduated colors to represent some statistical variable of interest.

The quantile classification method places equal numbers of observations into each class. In these examples, we will use the US Census Bureaus population statistics for US counties in 1997. These classes may be represented in a map by some unique symbols or, in the case of choropleth maps, by a unique color or hue (for more on color and hue, see Chapter 8 Geospatial Analysis II: Raster Data, Section 8.1 Basic Geoprocessing with Rasters). A choropleth map using equal interval classification will emphasize the amount of an attribute relative to one another. One potential disadvantage is that this method can create classes containing widely varying number ranges. (And Why They Are Deceiving To Us), Esri JavaScript API Examples: 15 High-Tech Webmaps and Webscenes. Indeed, the classification methodology and the number of classes utilized can result in wildly varying interpretations of the dataset. The following discussion outlines the classification methods commonly available in geographic information system (GIS) software packages. In addition, the opposite can also happen, whereby values with small range differences can be placed into different classes, suggesting a broader difference in the dataset than exists.

The minimum number of letters is 4, such as Iowa or Ohio. Therefore, as shown in the figure on Standard Deviation, the central class contains values within a 0.5 standard deviation of the mean, while the upper and lower classes contain values of 0.5 or more standard deviations above or above the mean. Although seemingly straightforward, several different classification methodologies are available to a cartographer. We can group the data into three classes as follows: 4-7, 8-11, and 12-14. Here is a color-coded table grouping US states by the length of the state name: Now, that we have each US state categorized into 3 equal interval breaks, we can generate a choropleth map with these groupings. In the example above, 100 is the maximum, and 0 is the minimum with 5 classes. In conclusion, several viable data classification methodologies can be applied to choropleth maps. Equal intervals divided the categories equally based on the minimum and maximum values. The ColorBrewer has a nice tool for color advice.