A Spatial Analysis:
Mapping Rooftop Solar Potential & Solar Equity in Los Angeles
Madeline Wander
UP 206: Introduction to Geographic Information Systems
Final Project Write-Up
Professor Leo Estrada
Teaching Assistants: Nic Jay Aulston & Peter Capone-Newton
March 18, 2011
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Introduction
With its abundance of sunshine and large amounts of rooftop space, Los Angeles is a prime
location to cultivate solar energy. A rooftop solar feed-in tariff (FiT) program would enable Los
Angeles residents and business owners to install rooftop solar panels and sell the power
generated back to the electrical grid.1 This type of program would not only reduce tenant and
owner utility costs, but it would help Los Angeles meet its ambitious renewable energy goals
and create an estimated 11,000 new jobs.2 Since the costs of solar module production are
increasingly inexpensive, the time is ripe for the City of Los Angeles to adopt such a program.
The Program for Environmental & Regional Equity (PERE) at USC—which I work for—is
collaborating with the UCLA Luskin Center for Innovation and the Los Angeles Business Council
(LABC) to create a vision for a rooftop solar FiT program in Los Angeles. In addition to the clear
environmental and economic benefits, we aim to better understand the equity considerations
of such a program. Specifically, we will identify rooftop solar capacity of multi-family
residential, industrial and commercial buildings in Los Angeles and demonstrate the benefits of
such a program to low-income tenants, homeowners and workers.
Through a spatial analysis using GIS, this study explores the following research questions:
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Where does multi-family, industrial and commercial rooftop solar potential exist in Los
Angeles, and where is solar potential clustered?
What are the income levels and unemployment rates of the neighborhoods surrounding
potential solar sites?
Where are the photovoltaic (PV) solar installation training programs in relation to
potential solar sites?
Which potential sites would maximize benefits to low-income tenants, homeowners and
workers trained to install rooftop solar?
See the UCLA Luskin Center for Innovation’s website: http://luskin.ucla.edu/news/school-public-affairs/uclaresearch-solar-energy-prompts-coalition-campaign-rooftop-solar.
2
This number is based on the UCLA Luskin Center for Innovation’s proposed ten-year 600 Megawatt rooftop solar
FiT program for the City of Los Angeles. See the UCLA Luskin Center’s 2010 report, “Bringing Solar Energy to Los
Angeles: An Assessment of the Feasibility and Impacts of an In-basin Solar Feed-in Tariff Program,” at
http://luskin.ucla.edu/content/bringing-solar-energy-los-angeles.
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Map Layouts
This section describes each map layout, details methodologies used to create each layout, and
explains the data analysis and sources behind each layout.
Although we are conducting an analysis to understand the equity considerations of a rooftop
solar FiT program for the City of Los Angeles, it is important to understand the benefits of such
a program at the regional scale as well. Therefore, this report’s spatial analysis is at the county
level.
Layouts 1 – 5 each represent individual elements of indices (Layouts 6 – 10) identifying the
optimal locations for rooftop solar installation that would maximize social equity benefits of
this type of program.
Layouts 1 & 2: Multi-Family and Industrial/Commercial Solar Opportunities in LA County
Layout 1 (left) displays the spatial
distribution of the number of
multi-family buildings—or
“opportunities”—with rooftop
solar potential by Census block
group. Most multi-family solar
potential falls within the City of Los
Angeles, with some pockets of
solar potential in the south central
part of LA County outside of the
city boundary.
Layout 1
Based on parameters outlined in
the UCLA Luskin Center’s 2010
report “Bringing Solar Energy to
Los Angeles,” this study only
identifies multi-family buildings
with the capacity, or “system size,”
to hold 50 kilowatts (kW) or more
of rooftop solar PV. Indeed, the
UCLA Luskin Center claims that Los
Angeles has the potential to
generate 1,000 megawatts (MW)
of solar energy from projects with
system sizes of 5-10 kW, 1,500
MW from projects with system
sizes of 10-50 kW, and 1,800 MW
from projects with system sizes 50-
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500 kW. However, the 2010 report also states that the most viable program would install solar
panels on large commercially owned rooftops that can take advantage of federal tax incentives.
Thus, this study only examines multi-family buildings with system sizes of 50 kW or more.
To create Layout 1 (previous page), I used data from the UCLA Luskin Center and ESRI. I
conducted the following selection by attribute to obtain the multi-family parcels with system
sizes of 50 kW or more:
Residential Parcels: SolarParcels_20110113; "USE_TYPE" = 'Residential'
Multi-Family Parcels: SolarParcels_20110113_Residential; "USE_DESCRI" = 'Five or more apartments‘;
"USE_DESCRI" = 'Four Units (Any Combination)‘; "USE_DESCRI" = 'Rooming Houses‘; "USE_DESCRI" =
'Three Units (Any Combination)‘; "USE_DESCRI" = 'Two Units‘
Multi-Family Parcels > 50 KW: SolarParcels_20110113_Residential_MF; "SYS_SIZE" > 49.9
I then spatially joined the multi-family
parcels with system sizes of over 50
kW to Census block groups, summing
on number of parcels per block group.
Last, I classified the data using the
Natural Breaks (Jenks) method and
divided the data into five classes. The
number of parcels per block group
ranges from zero to 66.
Layout 2 (right) displays the spatial
distribution of the number of
industrial and commercial buildings
with rooftop solar potential by Census
block group. Most
industrial/commercial solar potential
falls outside the City of Los Angeles.
Industrial/commercial solar potential
appears clustered along corridors in
the southeastern part of the LA
County.
For reasons outlined above, this study
only identifies industrial and
commercial buildings with system
sizes of 50 kW or more.
To create Layout 2, I used that same
data sources and methodology as in
Layout 1, and conducted the following
Layout 2
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selection by attribute:
Commercial and Industrial Parcels – Layer: SolarParcels_20110113; "USE_TYPE" = 'Commercial';
"USE_TYPE" = 'Industrial'
Commercial and Industrial Parcels > 50 KW – Layer: SolarParcels_20110113_Ind_Comm; "SYS_SIZE" > 49.9
Again, using the same process and classification method as in Layout 1, I summed the number
of industrial/commercial parcels per block group and divided the data into five classes. The
number of parcels per block group ranges from zero to 486.
Layout 3: Median Household Income by Census Block Group in LA County
To identify which areas contain
low-income homeowners and
tenants who would most benefit
from energy savings generated by
a rooftop solar FiT program, Layout
3 (left) displays the spatial
distribution of median household
income by Census block group in
LA County. As expected, the lowest
income block groups mostly
concentrate in the central part of
the City of Los Angeles, with lowincome pockets reaching across
the southeastern part of LA
County.
Layout 3
To create Layout 3, I used data
from the U.S. Census Bureau
American Community Survey (ACS)
2005-2009 and ESRI. I classified the
data the using the Quantile
method—so each class has the
same number of features—and
divided the data into four classes.
The data ranges from far below
poverty level ($21,954 for a family
of four in LA County) to above
$200,000.
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Layout 4: Unemployment Rate by Census Block Group in LA County
To identify which areas contain
high unemployment that would
most benefit from jobs created by
a rooftop solar FiT program,
Layout 4 (right) displays the
spatial distribution of
unemployment rates by Census
block group in LA County. Similar
to Layout 3 (previous page), the
greatest levels of unemployment
fall within the southeastern part
of LA County. However, it appears
that similar amounts of high
unemployment fall inside and
outside the City of Los Angeles.
Additionally, unemployment rates
are slightly more dispersed than
the lowest median household
incomes, which appear more
concentrated.
To create Layout 4, I used data
from the U.S. Census Bureau ACS
2009 and ESRI. Similar to Layout 3,
I classified the data the using the
Layout 4
Quantile method and divided the
data into four classes. The data
ranges from 0% to well above California’s current rate of 12.4 percent.
Layout 5: Distance of Each Block Group to Closest PV Solar Installation Training Program
To identify areas in which workers have the most access to PV solar installation training
programs, Layout 5 (next page) displays the distance of each block group to the closest training
program in LA County. Nearly all the training programs are located in the southern part of LA
County. Most programs are located outside of the City of Los Angeles, and spread fairly evenly
across the southern part of the county.
To create Layout 5, I used data from the U.S. Census Bureau ACS 2005-2009 and ESRI. I also
gathered locations of the 21 training centers in the LA County Community College system, as
well as one additional program (East Los Angeles Skills Center). I then geocoded all the training
centers and created an original data layer (PV Solar Installation Training Programs in Los
Angeles County).
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Next, I converted each block group
to its centroid point, and used the
“Closest Facility” function in
Network Analyst to find the
distance between each block group
and the closest training program. I
then assigned each block group
polygon its distance to the closest
training program, classified the
data using the Natural Breaks
(Jenks) method, and divided the
data into four classes. The data
ranges from 0 to 36.1 miles.
(Network Analyst was unable to
find the distance between the
closest training program and 10 [of
over 6000] block groups.)
Layout 5
Layouts 6 – 7: Solar Equity Indices for Potential Multi-Family and Industrial/Commercial Solar
Sites
To identify the optimal locations where a rooftop solar FiT program would maximize benefits to
low-income households and workers, I used data from Layouts 1 – 5 to create a Solar Equity
Index for both multi-family and industrial/commercial opportunities.
As Table 1 (next page) shows, I divided the data for each element of the index—number of
multi-family or industrial/commercial opportunities, median household income, unemployment
rate, and distance to closest training facility—into four categories, and assigned each category a
score between zero and three.
For number of opportunities per block group, I divided the data into four categories with an
even number of block groups within each category. I assigned higher weights to block groups
with more opportunities, and lower weights to block groups with fewer opportunities.
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For median household income, I assigned higher weights to block groups with lower incomes,
and vice versa. I divided the data into categories based on the following information:
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$21,954 = poverty level for a family of four in LA County
$24,850 = extremely low income for a family of four in LA County
$41,400 = very low income for a family of four in LA County
$66,250 = low income for a family of four in LA County
For unemployment rate, I assigned higher weights to block groups with higher unemployment
rates, and lower weights to block groups with lower unemployment rates. I divided the data so
that block groups with unemployment rates closer to the U.S. rate (8.9%) received a lower
score than block groups with rates closer to California’s rate (12.4%).
Lastly, for distance to closest training facility, I assigned higher weights to block groups with
shorter distances to the closest training center, and vice versa. I assigned the highest weight to
those block groups within a walkable distance (< 0.05 mile) of a training center, and the next
highest weight to those block groups within a bikeable distance (< 3 miles) of a training center.
Table 1 below details the weights and categories of each element in the Solar Equity Index.
Table 1 – Elements of Social Equity Index
Layout 6 (next page) displays the spatial distribution of optimal locations containing multifamily solar sites where a rooftop solar FiT program would maximize benefits to low-income
households and workers. Layout 7 (next page) shows a similar spatial distribution of optimal
locations containing industrial/commercial solar sites. Locations that maximize benefits have
the highest “solar equity potential.”
Layouts 6 and 7 (next page) show similar patterns. Most of the block groups with maximum
solar equity potential are within the City of Los Angeles, specifically in the central part of the
city. Interestingly, for both multi-family and industrial/commercial sites, areas at the southern
tip of the city also maximize solar equity potential. Layout 7 displays a few pockets of solar
equity potential scattered throughout the southeastern part of LA County.
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Layout 6
Layout 7
To create Layouts 8 and 9, I used data from Layouts 1 – 5. I classified the data using the Natural
Breaks (Jenks) method, divided the data into five classes, and assigned labels ranging from
lowest to highest solar equity.
Layouts 8 – 9: Hotspot Analysis of Potential Multi-Family and Industrial/Commercial Solar
Sites
To test a different method of identifying the optimal locations where a rooftop solar FiT
program would maximize benefits to low-income households and workers, I conducted a
Hotspot Analysis. Using the same index elements as above—number of multi-family or
industrial/commercial opportunities, median household income, unemployment rate, and
distance to closest training facility—Layouts 8 and 9 display the spatial distribution of locations
that would maximize solar equity potential.
Layouts 8 and 9 show similar clustering patterns similar to Layouts 6 and 7 above. Not
surprisingly, the highest solar equity potential falls within the City of Los Angeles. However,
Layouts 8 and 9 show more clustering in the northern part of the city than the Solar Equity
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Index in Layouts 6 and 7. Overall, Layouts 8 and 9 show a higher concentration of solar equity
potential in the southern part of LA County than in the northern part.
Layout 8
Layout 9
To create Layouts 8 and 9, I used data from Layouts 1 – 5 and created the following models:
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I then used the Raster Calculator to add the index element raster layers to create Hotspot
Analyses of both multi-family and industrial/commercial solar sites:
Lastly, I classified the data using the Equal Interval method, divided the data into four classes,
and assigned categorical labels ranging from lowest to highest solar equity.
Layouts 10 – 11: Multi-Family and Industrial/Commercial Solar Hotspots – Political Analyses
In order to pass a rooftop solar feed-in tariff program for the City of Los Angeles, the policy
needs political support. Layouts 10 and 11 display the Los Angeles City Council Districts
overlapping the spatial distribution of solar equity (the hotspot analyses from Layouts 8 and 9).
Layout 10
Layout 11
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By overlaying solar equity potential and PV solar installation training center locations with
boundaries of political jurisdictions, we can identify which council districts would benefit the
most from a rooftop solar FiT program. Layouts 10 and 11 also contain tables identifying how
many multi-family and industrial/commercial opportunities are within each council district.
Specifically, Layout 10 shows a concentration of multi-family solar hotspots in Council District
(CD) 4 (718 opportunities), CD 8 (687 opportunities), and CD 2 (666 opportunities). Likewise,
Layout 11 shows a concentration of industrial/commercial hotspots in CD 5 (207 opportunities),
CD 2 and 11 (both with 172 opportunities), and CD 4 (144 opportunities). Since CD 4
(Councilmember Tom LaBonge) and CD 2 (Councilmember Paul Krekorian) contain the largest
amounts of multi-family and industrial/commercial opportunities combined, its residents and
workers stand to gain much of the benefits from a rooftop solar FiT program in Los Angeles.
(Since PERE is doing a case study of Bonnie Brae Village in Westlake—an affordable housing
development for formerly homeless seniors with 100% solar capacity already installed—
Layouts 10 and 11 display an outline of the Westlake Community Plan Area as well.)
To create Layouts 10 and 11, I used data from Layouts 1 – 5, as well as some additional
shapefiles from ESRI. I then spatially joined City Council Districts with both multi-family and
industrial/commercial parcels with system sizes of over 50 kW, and summed the number of
opportunities within each council district. I then overlaid other cities within LA County to
identify surrounding areas containing training centers. The classification method and number of
categories are the same as in Layouts 8 and 9.
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Conclusion
By identifying clusters of multi-family and industrial/commercial opportunities for rooftop solar
installation, median household income and unemployment rate by block group, and distances
between block groups and closest PV solar installation training programs in LA County, this
study identifies the geographic areas in which a rooftop solar feed-in tariff program would
maximize benefits to low-income households and workers. Within LA County, the City of Los
Angeles contains the highest concentrations of solar equity hotspots. Specifically, Council
Districts 2 and 4 stand to gain the most from a rooftop solar FiT program for Los Angeles.
Since this analysis measures the spatial relationships between demographic and metric
characteristics of block groups, GIS is an essential piece of this study. Without GIS, I would not
have been able to identify the number of rooftop solar opportunities within geographic areas,
find the distances between block groups and the closest PV solar training programs, or create
the solar equity indices and hotspots. Clearly, this study relies heavily on spatial analysis.
Ultimately, due to the clustering of solar equity hotspots in the City of Los Angeles, many
residents and workers are poised to gain economic and environmental benefits from the
adoption of a rooftop solar feed-in tariff program. This study clearly supports the adoption of
such a policy.
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References
J.R. DeShazo and Ryan Matulka. (2010). Bring solar energy to Los Angeles: An assessment of the
feasibility and impacts of an in-basin solar feed-in tariff program. UCLA Luskin Center for
Innovation, School of Public Affairs.