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Using Maps in NJSHAD

NJSHAD query results and indicator reports that provide data by county or municipality display a map. The NJSHAD maps are a type of map called "choropleth" maps. This page describes choropleth maps and the type of grouping ousing in the choropleth maps on NJSHAD.

Choropleth maps display data for predefined geographic areas. The areas on a choropleth map are shaded or patterned to reflect values of a variable such as population density or birth rate. Choropleth maps are an easy way to visualize differences and patterns across geographic areas.

One challenge presented by choropleth maps is that, by forcing the data into discrete geographic zones, the underlying data distribution can be obscured or misrepresented (either purposefully or accidentally). It will help to understand the methods used to group the map data. For the most part, data classification involves two basic issues: 1) identifying the number of groups and 2) identifying how to assign geographic areas to each group. If too few groups are used, a choropleth map may obscure subtle gradations in a spatial distribution. Too many categories are also unlikely to reveal any existing spatial patterns because a viewer can be visually overwhelmed. (Most map readers find difficulty in distinguishing among more than seven classes; Kraak and Ormeling 2003).

Different types of classification can be used to assign geographic areas to groups. Some grouping methods are better suited than others for different data types. When selecting a grouping method, the underlying data distribution should first be explored. The classification type used in NJSHAD is Jenks natural breaks, described below.

Choropleth maps have an inherent weakness, in that they require the aggregation of data into geographic areas (e.g., counties) that do not necessarily correspond exactly with the data's underlying spatial distribution. To maximize the effectiveness of such a map, the data grouping method should strive to balance between several goals. After classification, each group should contain an appropriately apportioned share of observed data values. The resulting map should also faithfully represent spatial patterns without excluding extreme high or low values. The resulting map should also endeavor to approach the data's statistical surface (a three-dimensional data representation in which the z-coordinate is proportional to the data value) as closely as possible (Kraak and Ormeling 2003).

Finally, choropleth maps do possess other limitations. Small geographic areas that contain a large number of cases (e.g., cities) tend to impose a smaller visual impact and attract the viewer's attention less than large (e.g., rural) geographic areas which may be sparsely populated. Another common error is the use of raw data counts, which represent magnitude, when a choropleth is more appropriate to the use of normalized values that produce a map of rate, density, concentration, or the like, by geographic unit (Monmonier 1991:22-23). For this reason, maps are disabled for the "Count" measure in query modules.

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The Jenks Natural Breaks method, also referred to as the Jenks Optimization method or the goodness of variance fit (GVF), is a data classification method designed to determine the best way to classify features using natural breaks in data values. The method was developed with the intention of dividing data into relatively few data classes (seven or fewer) for mapping purposes. Jenks Natural Breaks iteratively compares the sums of the squared difference between observed values within each class and the class means. The best resulting classification identifies breaks in the ordered distribution of values that minimizes the variance within classes and maximizes the variance between classes (Jenks 1967).

The Jenks Natural Breaks method is well suited to the creation of choropleth maps because it identifies real classes within the data, resulting in maps that can accurately portray data trends. This is a good choice for datasets that are multi-modal, but, this method is not recommended for data that have a low variance. Also, this classification is data-specific and is not useful for comparing multiple maps built from different datasets.

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  1. Kraak, Menno-Jan, and Ferjan Ormeling. 2003. Cartography: Visualization of Geospatial Data. Longman Group, United Kingdom.
  2. Mark Monmonier. 1991. How to Lie with Maps. University of Chicago Press.
  3. Jenks, George F. 1967. The Data Model Concept in Statistical Mapping, International Yearbook of Cartography 7: 186-190.