Transcript Powerpoint - Herbie Huff

What are the Drivers of Privatization in
LA County?
Mean in
GSWC
Percent 35%
neither
White
nor
Asian
Standard
Deviation
in GSWC
Mean out of Standard
GSWC
Deviation
out of
GSWC
20%
28%
23%
The Model Automates Comparison
between GSWC-Served Areas and
Places with Public Water Utilities
Starts with the
Census Places
shape file,
which already
has race and
GSWC marker
Creates two
selections: places
that are served by
GSWC and places
that are not
Calculates
mean and
standard
deviation for
percent
nonwhite
nonasian.
Outputs two
tables, each
containing the
summary
statistics
Metadata
Skills
1.
2.
3.
4.
Inset Map, Color Graduated Symbol
Color Graduated Symbol, Joined Excel Data from USGS on water
usage to County Layer
Color Graduated Symbol
Color Graduated Symbol
5.
Clipped Aqueducts and Dams to CA from a US data set; Made an
attribute sub-set selection to exclude minor streams and exported
selection as a new layer; Made a field query for conditional labeling
6.
Water utilities: Geocoded from CA-EDD database using LA
County Street File for address locator, manually added zip codes.
Clipped census places to LA County, joined demographic data
from American Fact Finder. Converted labels to annotation. Added
inset map.
Created GSWC polygon file using information from GSWC
website. Used select by location with an active selection in LA
County to select dams in LA County and export them as a new
layer.
Used select by location to find rivers originating in areas with high
precipitation, i.e. “full” rivers. Exported as a new layer, then created
1 mile buffer around those rivers.
Used a model to compare race data inside and outside of GSWC
service areas. Added hatching to represent GSWC service areas.
The model output as a .dbf file and this is the information from
that file.
Metadata sheet.
7.
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11.