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Advantages of Choropleth maps

Very common, easy to understand & interpret. Easy to make (especially in ArcGIS)


Disadvantages of Choropleth maps

Easy to misinterpret due to their easy in creation, even if it is not appropriate for the data


What is a Choropleth ideal for?

Things that are smooth or evenly distributed throughout the enumeration unit. Also units that are similar in size & shape


Should data be standardized or raw/count data for Choropleth Maps

Data should be standardized (ratios, percentages, densities)


How many classes are ideal for Choropleth maps

5-9 classes


2 important considerations for choropleth maps

classification method & number of classes


5 common data classification methods for choropleth maps

Equal intervals (all class intervals are the same), natural breaks (class intervals determined by big breaks in data), quantiles (same # of enumeration units in each class), mean/standard deviation, nested means)


3 common color schemes for choropleth maps

Sequential, part-spetral, & diverging


Map where symbols are scaled in proportion to some data that occur at point locations

Proportional or Graduate Symbol Maps


What kind of data is ideal for proportional or graduate symbol maps?

True point data where data is actually measured at a point. Also, conceptual point data (data collected for an area, but can be conceived to occur at point [ex: population of McLean County]. Data are typically raw/count data


Maps that are ideal for displaying variation within an enumeration unit

Dot maps


What type of data is common for dot maps

Raw Count data (Ex: total population, number of acres of corn harvested, etc.)


Used to restrict where dots are placed (ex: no dots in urban areas or water bodies for corn harvested)

Limiting attributes


Used to construct where dots are placed (ex: all dots for corn harvest to be placed in areas of cropland)

Related attributes


Automated mapping & GIS software such as ArcGIS will randomly placed dots within enumeration units

Ancillary Data