Binning in GIS


In GIS it is often difficult to present point-based data because in many instances there are several different points and data symbologies that need to be shown. As the number of different data points grows they can become complicated to interpret and manage which can result in convoluted and sometimes inaccurate maps. This becomes an even larger problem in web maps that are able to be depicted at different scales because smaller scale maps need to show more area and more data. This makes the maps convoluted if multiple data points are included.

In many maps there are so many data points included that little can be interpreted from them. In order to reduce congestion on maps many GIS users and cartographers have turned to a process known as binning. Binning is defined as the process of grouping pairs of locations based on their distance from one another. These points can then be grouped as categories to make less complex and more meaningful maps.

The Binning Process

Binning is a data modification technique that changes the way data is shown at small scales. It is done in the pre-processing stage of data analysis to convert the original data values into a range of small intervals, known as a bin. These bins are then replaced by a value that is representative of the interval to reduce the number of data points.

Binning in ArcGIS

In ArcGIS, binning is considered a two-step process. The first step is to identify pairs of points. Once these pairs are formed they should then be grouped together so that they have a common distance and direction from one another. Take for example the locations of six ranger stations in a national park with respect to a popular tourist attraction. In the first step of binning the six ranger station locations would be paired with the location of the tourist attraction – a central point. On a map similar colors for the links can then aid in pairing similar bin distances or ranger station locations in this example.

The matching of similar distances and directions continues for all possible pairs in binning. According to ArcGIS Help 10.1 in the pairing process, the number of pairs increases rapidly with the addition of each new bin location. As a result only the average distance and semivariance for all the pairs in that bin are plotted as a single point on the map (ArcGIS Help 10.1).

The second step of binning in ArcGIS is the actual technical grouping of the pairs that have common distance and direction. Each point has a common origin and the distance and direction for the pairs are based off of that origin point to form the bin. According to ArcGIS Help 10.1 for each bin users must form the squared difference from the values for all of the linked pairs. The distance between these linked pairs are then averaged and multiplied by 0.5 to give an empirical semivariogram instead of multiple points to clutter the map (ArcGIS Help 10.1). Each of the empirical semivariogram values in the bins can then be color coded to form an easy to understand semivariogram surface (ArcGIS Help 10.1).

Binning with QGIS

Anita Graser, who runs the popular Free and Open Source GIS Ramblings blog, has an older post that describe the process for binning using QGIS.  The post entitled, “Mapping Density with Hexagonal Grids“, runs users through the process of taking a layer of tree locations and creating a hexagonal grid showing tree density in Vienna, Austria.

Binning with D3

There are a host of geographic data visualization plugins that extend the capabilities of making maps with D3 and the d3.hexbin plugin enables hexagonal bining.  In the example map below, hexagonal binning is used to display and categorize 3,000 locations of Walmart stores in the lower 48 states of the U.S.  Median store age is shown by color with black for older stores and blue for younger store locations. (More: Making Maps with D3)

Hexagonal binning map showing the locations of 3,000 Walmart stores in the United States.

Hexagonal binning map showing the locations of 3,000 Walmart stores in the United States. Click on map for D3 page.

Different Binning Techniques

Binning itself is a general term used to describe the grouping of a dataset’s values into smaller groups (Johnson, 2011). The bins can be based on a variety of factors and attributes such as spatial and temporal and can thus be used for many different projects. Rectangular and hexagonal binning are the simplest methods of binning.


Rectangular binning is the simplest binning method and as such it heavily used. In addition, in many applications binning is done using a technique called hexagonal binning. This technique uses hexagon shapes to create a grid of points and develops a spatial histogram that shows different data points as a range or group of pairs with common distances and directions. In hexagonal binning the number of points falling within a particular rectangular or hexagon in a gridded surface is what makes the different colors to easily visualize data (Smith, 2012).

Hexagonal binning was first developed in 1987 and today “hexbinning” is conducted by laying a hexagonal grid on top of 2-dimensional data (Johnson, 2011). Once this is done users can conduct data point counts to determine the number of points for each hexagon (Johnson, 2011). The bins are then symbolized differently to show meaningful patterns in the data.

Proportional symbols choropleth maps are another type of map to that uses binning. Like hexbinning, proportional symbol and choropleth maps group similar data points together to show a range of data instead of many individual points. Symbology is then used to make the data meaningful.

Multivariate binning is another binning method that is very complex. In this method there can be many different variables consisting of different types of data. Like other binning methods the data is typically grouped with the sum or average of the data. Different types of symbology (such as size, shape and color) can also be used to represent this data as well.

Examples of using GIS to bin point location data for South Africa, Nebraska, and Kenya. Source: MapBox

Examples of using GIS to bin point location data for South Africa, Nebraska, and Kenya. Source: MapBox

Benefits of Binning

Because of the plethora of data types available and the wide variety of projects being done in GIS, binning is a popular method for mapping complex data and making it meaningful. Binning is a good option for map makers as well as users because it makes data easy to understand and it can be both static and interactive on many different map scales. If every different point were shown on a map it would have to be a very large scale map to ensure that the data points did not overlap and were easily understood by people using the maps.

According to Kenneth Field, an Esri Research Cartographer, “Data binning is a great alternative for mapping large point-based data sets which allows us to tell a better story without interpolation.  Binning is a way of converting point-based data into a regular grid of polygons so that each polygon represents the aggregation of points that fall within it.”

By using binning to create categories of data maps are easier to understand, more accurate and more visually appealing.


ArcGIS Resources. (n.d.). “Binning Empirical Semivariograms.” ArcGis Help 10.1. Retrieved from: (8 August 2014).

Field,Kenneth. (8 June 2012). “Using a Binning Technique for Point-Based Multiscale Web Maps.” ArcGIS Resources. Retrieved from: (8 August 2014).

Johnson, Zachary Forest. (18 October 2011). “Hexbins!” Retrieved from: (8 August 2014).

Smith, Nate. (25 May 2012). “Binning: An Alternative to Point Maps.” Mapbox. Retrieved from: (8 August 2014).

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