Methods and Frequently Asked Questions (FAQs)
more information on specific BRFSS programs, see the
BRFSS FAQs and SMART FAQs.
Technical Maps FAQs:
What is the BRFSS Maps application?
BRFSS Maps is an interactive mapping application that graphically displays
the prevalence of behavioral risk factors at the state and MMSA level. Using
GIS (geographic information systems) mapping technology and BRFSS data, it allows users to visually compare
prevalence data for states, territories, and local areas. Features include
multiple data classification methods, map panning and zooming, related
prevalence tables, downloadable map images, and the capability to download
the BRFSS data in a GIS shapefile data format for more detailed analysis.
What are the data sources for this site?
The information for this site is obtained from the Behavior Risk Factor
Surveillance System (BRFSS) and its Selected Metropolitan/Micropolitan Area
Risk Trends (SMART). Metropolitan/micropolitan statistical area (MMSA)
populations were obtained from the U.S. Census Bureau – 2000 Census. For
more information, see the BRFSS FAQs and
SMART BRFSS FAQs. To access the
data and corresponding documentation, see the
BRFSS Technical Info and Data
or the SMART Technical Documents and Survey Data.
Can I download the GIS data files?
Yes, you can download
the BRFSS data in a GIS shapefile data format. To download the entire data
file for a given year, click the "Download GIS Data" link located
in the Maps menu at the upper left of this page. From the
Download GIS Data page, you may select a data
year. Also see the BRFSS
How were the MMSAs selected? Why are different MMSAs available for different years?
MMSAs with at least 500 completed interviews in the BRFSS data were
selected for inclusion in this project. The MMSAs included in the project met
certain weighting criteria for a given year. Some MMSAs, especially micropolitan areas, may not be able to attain a large enough sample size to be included every year.
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Whom do I contact for more information about my state?
Contact your state health department using the information available on the
State Coordinators page.
The circle representing a certain MMSA falls outside the geographic boundary
of its namesake city. Why is this?
Some MMSAs are geographically large, comprising many cities and counties.
The circle representing an MMSA on the map is placed at the MMSA’s
geographic center, or centroid, occasionally placing the circle outside the actual city
for which the MMSA is named.
For example, the circle representing the Washington-Arlington-Alexandria,
DC-VA-MD-WV MMSA is not located in the map for the District of Columbia, but
is located in northern Virginia, because that is the centroid of
this statistical area. Additionally, because the
Washington-Arlington-Alexandria, DC-VA-MD-WV MMSA encompasses a different
and larger area than the actual boundaries of the District itself, the
prevalence data for the metropolitan division and the district itself will
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Does selecting/deselecting the display of "Outlying Territories" affect
how the data are classified for display in the map?
Yes. By default, the original map that is displayed after selecting a question/answer combination includes only the 50 states and the District of Columbia (DC). The data classification for this map is based only on the data estimates for those 50 states and
Washington, DC. When the Outlying Territories are selected for display, the map refreshes and the data for the three territories are included in the dataset. The data are then classified and displayed based on the 54 state (including
Washington, DC) and territory data estimates. Conversely, when the Outlying Territories are deselected for display, the data classification reverts back to a 51-state (including
Washington, DC) dataset.
What is included in the "Nationwide" designation?
By default, "Nationwide" currently includes all 50 U.S. states and the District of Columbia,
for which data are available, but
not Puerto Rico, Guam, nor the U.S. Virgin Islands.
When I click the Information icon
or the Print/Save
Map link, nothing
happens. What can I do to address this?
The Information and Print/Save Map windows are pop-ups. Pop-ups must be
enabled in your browser, or your pop-up-blocking software must be disabled.
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Technical Maps FAQs
Describe the methodology used to create the maps.
The maps were created by merging BRFSS data, in database format, with
geographic boundary files, called shapefiles. In this manner, the
statistical data in the BRFSS database are spatially referenced with their
associated administrative boundaries (e.g., states and MMSAs). This permits
the data to be mapped and seen. Users can specify
the number of data classes into which the data are categorized, as well as
the statistical method of determining the class break values (e.g.,
equal-interval, quantiles, natural breaks, and standard deviations).
How were the maps projected?
Several different map projections were used to present the information in
- Maps of the Continental United States,
Alaska, and Hawaii are projected to the Albers Equal-Area (Continental
United States) projection.
- Puerto Rico was projected to the Albers
Equal-Area (North America) projection.
- Guam was projected to the World Mercator projection.
Alaska, Hawaii, Puerto Rico, and Guam are not in the same geographic
scale relative to each other, nor to the continental United States in these
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How should one choose a data classification method?
Each method provided in the BRFSS Maps section enables the user
to choose the data classification method that they feel is most appropriate. There is no single best data classification method; each classification method has advantages and disadvantages. When
creating a map, the map user should consider the purpose of the map, the
data distribution (if known), and the knowledge level (i.e., mapping
and statistical awareness) of the intended audience. The following are brief
descriptions of the four data classification methods available to
users of the SMART and BRFSS data used in the BRFSS Map application.
Equal-interval: In equal-interval classifications, the data ranges for all
classes are the same. In other words, the range of the entire dataset is
divided by the desired number of data classes, such that each class occupies
an equal interval along the range of data values. The major advantage of the
equal-interval classification is that the resulting equal intervals may be
easy for many map users to interpret. The major disadvantage of the
equal-interval classification is that the data distribution is not
considered when determining class breaks for the intervals
(only the lower and upper data values are used).
Quantiles: In quantile classifications, an equal number of observations are
placed in each class. For example, if there are 50 observations, 10
observations would be placed in each class of a five-class (quintile)
quantile map. The data are first rank-ordered, and then the appropriate
observations are assigned to each class (class 1, class 2, class 3, etc.).
The number of classes also determines the specific type of quantile map
(three classes = tertile; four classes = quartile; five classes = quintile).
Two major advantages of the quantile classification are that it is useful
for ordinal data (because the data are rank-ordered) and that it can help
facilitate map comparisons (as long as the same number of classifications is
used for all maps). The major disadvantage of the quantile classification is
that it does not consider how the data are distributed. Therefore, if the
data have a highly skewed distribution (e.g., many outliers) this
classification will force data observations into the same class (either the
lowest or highest, in this case) where this may not be appropriate; as a
result, the quantile classification may give a false impression that there
is a relatively normal data distribution.
Standard Deviations: In standard deviations classifications, the data are
assigned to classes based on where they fall relative to the mean and
standard deviations of the data distribution. The major advantage of this
classification method is that by using the mean as a dividing point, a
contrast of values above and below the mean is readily seen. This
method only works well for a dataset that is normally distributed. An even
number of classes should be used, such that the mean of the data serves as
the dividing point between an even number of classes above and below the
mean. The major disadvantage of the standard deviations classification is
that it requires a basic understanding of statistical concepts, and hence
may be difficult for some map users to interpret.
Natural Breaks: In this classification method (also variously known as
Optimal Breaks and Jenks’ Method), the data are assigned to classes based
upon their position along the data distribution relative to all other data
values. This classification uses an iterative algorithm to optimally assign
data to classes such that the variances within all classes are minimized,
while the variances among classes are maximized. In this manner, the
data distribution is explicitly considered for determining class
breaks; this is the major advantage of the Natural Breaks classification
method. The major disadvantage is that the concept behind the classification
may not be easily understood by all map users, and the legend values for the
class breaks (e.g., the data ranges) may not be intuitive.
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How do I enter the break points for the "Custom"
class breaks method?
The values that are entered in the text boxes correspond to the upper
value of each data class. For example, assume that the lowest data value
for a particular dataset is "15.0." If you enter "25.0" in the first text
box and "35.0" in the second text box, then the data ranges for the two
lowest data classes will be "15.0 to 25.0" and "25.1 to 35.0." The number
in the rightmost text box is automatically entered and cannot be manually
changed; this value corresponds to the maximum value for the dataset,
which by default corresponds to the upper value of the last data class.
Because there are two datasets being simultaneously
mapped (i.e., states and MMSAs), how do I interpret the legend, and to which
dataset are the class breaks assigned?
Class break values are determined for the states' data and are
concurrently applied to the states' and MMSAs' data. Therefore, when
displayed simultaneously, the class breaks are the same for both states
and MMSAs. The exception to this rule is for the last class (e.g., the
fifth class in a five-class map): the upper values for each dataset are
usually different; therefore, the data range for the last class varies
between the states and the MMSAs. However, the lower data value for states
and MMSAs in that class are identical. The opposite applies for the first
(lowest) data class. In this case, the lower data values usually differ
between states and MMSAs; however, the class break point is identical for
the upper range of the first class. To use one legend for both
states and MMSAs, the values for the first class are indicated as, for
example, "£ 23.0" (the upper value of the lowest class). By inference,
the lowest values for the states and MMSAs are lower than this stated
value, but are not specifically depicted in the legend. Similarly, the
legend label for the last class is, for example, "³ 55.2" (the lower
value of the last class). By inference, the highest values for the states
and MMSAs are higher than this stated value, but are not specifically
depicted in the legend. The reason that class breaks for both states and
MMSAs are determined based upon the dataset for the states is that in the
majority of question and answer combinations, the range of the MMSAs
prevalence estimates is greater than that for the states. If the class
breaks were determined for the dataset with the greater range (i.e., MMSAs),
it is conceivable that when these class breaks are applied to the states'
data, there may be classes to which no states are assigned (usually the
first and the last classes). In order to avoid this issue, the breaks are
determined for the states' data, and then applied to the MMSAs. This
usually results in a map in which MMSAs and states are assigned to and
depicted for all data classes.
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How were the color schemes chosen for the maps?
Color schemes were chosen based upon the number of data classes, the
types of data being mapped (e.g., sequential or diverging data),
consideration of the display devices to be used for the resulting maps
(e.g., computer CRT monitor, computer LCD monitor, LCD projector, and print
copy), and the need to avoid colors that cannot be differentiated by individuals with impaired
color-vision (e.g., red-green color combinations).
The two color schemes for the BRFSS Maps were selected by
consulting ColorBrewer (http://www.colorbrewer.org*), an online tool for
selecting color schemes.
The color scheme chosen for natural breaks, quantile, and equal interval
maps is the Sequential Oranges scheme. This scheme works well with ordinal,
interval, and ratio data, such as the prevalence data in the BRFSS. The
color scheme chosen for the standard deviation maps is the Diverging
Purple-Orange scheme. This scheme emphasizes the natural midpoint of a
diverging dataset (e.g., the mean) and the diverging values from the mean
(e.g., positive and negative standard deviations). The color schemes are
automatically selected based upon the user's statistical classification
method selection for categorizing the data.
Where can I find additional GIS resources?
The following Web sites feature reference maps with background data. You can
use these resources to compare other sociodemographic data to health
NationalAtlas.gov includes maps at the county, state, and other geographic
levels for a variety of data including income, crime rates, cancer
mortality, election results, race/ethnicity, population density, etc.
American Factfinder shows 2000 Census (race, household size, Hispanic
ethnicity, age, sex, household and family structure, income, education,
commuting, ancestry, etc.) and other Census Bureau data sources via both
reference and thematic maps. Maps can be created at the block, census tract,
county, MSA, city, ZIP code equivalent, state, and other geographic levels.
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