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**[Dasymetric Mapping and Areal Interpolation -- Definition and
Purpose]{.underline}**

There are problems associated with mapping demographic data by
administrative areal units such as census boundaries. A choropleth
mapping representation gives the impression that population is
distributed homogenously throughout the areal unit, even when portions
of the areal unit are uninhabited. Dasymetric mapping, as a solution, is
a type of areal interpolation where data from one set of geographic
source zones are transferred to a set of target zones based on ancillary
information used to aid the interpolation. The following application
interpolates census data to a surface representation of population
density (population/grid cell) based on the principles listed below.

**[Dasymetric Mapping Tool - Software Capabilities and
Usage]{.underline}**

**Input Data Needs:**

-   Population Layer -- Any demographic data in a geo-spatial format
    that has one population value representing each areal unit
    (polygon). (Example -- US Census Data by block, block-group, tract,
    county, etc.)

-   Ancillary Layer -- A land use or land cover-derived raster layer
    that has been re-classified into four classes representing
    inhabited/uninhabited areas into density stratification. (Example
    -- (1) High-Density Residential, (2) Low-Density Residential, (3)
    Non-Urban inhabited, (4) Uninhabited)

**Empirical Sampling:** To model the relative difference in population
density between the 'inhabited classes' we use an empirical sampling
approach. The grid cell with the higher inhabited class has a higher
population density than a grid cell with a low or non inhabited class
code based the empirical measurement. In order to determine this
relative difference between classes, the population density values are
sampled for each 'inhabited class'. The sampling process selects all
block-groups that have a specified 'percent cover' (this threshold is
set by the user within the tool -- Example 80%) by a single 'inhabited
class' that is equal to or exceeds that threshold. The total population
and area are calculated to find the aggregated density. This sampling
technique provides a value that can be used as a 'population density
fraction' which is the percentage of a block-group's total population
that should be assigned to each 'inhabited class' within the
block-group.

**Areal Weighting:** The 'population density fraction' must be adjusted
by the percentage of the block-group's total area that each 'inhabited
class' occupies. A ratio is calculated for each 'inhabited class'
representing the percentage of area that an 'inhabited class' actually
occupies within a block group to the expected percentage of 33.3%. The
area ratio is used to adjust the 'population density fraction'
accounting for the variation of both the population density and area for
the different 'inhabited classes' for each block group.

**Preset Classes:** If you have an ancillary class that you know is
'uninhabited' (i.e. water, non-residential developed, uninhabited
wetlands or forest, etc.) you may "preset" these classes to "0", so no
data may be distributed to these zones.

**Output: \* Output destination folder must be empty when running the
software application. Program will not overwrite or add to existing
files in output folder, resulting in an error.** The "Dasymetric Mapping
Tool" outputs a raster grid named "Dasymetric Result" representing
population/grid cell. This data has been transferred from the source
population data (census) to the target land use/land cover data zones by
means of areal interpolation. The grid cell size can be determined by
the user and is dependent on the scale you and the input data available.
There is no limit to how small the grid cell size can be, however
computation time may be affected.

**Symbolization:** The raster grid named "Dasymetric Result" will have a
field that has all of the new density values. The field is named "New
Density" and the user must modify the symbolization to visually
represent the results. The recommended symbolization is to use the
'classified' data range with the desired amount of classes. Another
recommended option is to exclude all of the zero values by going to
Classification -- Data Exclusion Properties. Enter "0" into the
exclusion value field and choose your legend properties for all zero
values.

[For Full Equations: see research by Jeremy Mennis
(click)](http://astro.temple.edu/~jmennis/research/dasymetric/dasydetails/dasydetails.htm)

**[Further Questions Please Contact:]{.underline}**

**Mike Gould -- USGS**

<mgould@usgs.gov>

4336. \-- or \--

**Rachel Sleeter - USGS\
**<rsleeter@usgs.gov>

650-329-4373