Caitlin Hachmyer
May 9, 2012
Geographic Information Systems
Urban and Environmental Policy & Planning
EARLY CHILD CARE AND EDUCATION IN SONOMA COUNTY:
A LOOK AT SUBSIDIZED CARE
INTRODUCTION
Based on data available from the 2010 American Community Survey 5-year estimates,
there are over 56,000 children under nine years of age in Sonoma County, CA. There is
an estimated need of 20,000 to 40,000 licensed child care and education slots to serve
them. Currently there are 310 preschools and school-age programs, large family childcare homes, and children's centers in Sonoma County. [There are also a large number of
small family child care homes, but these programs are less likely to be licensed, to
employ permitted teachers, or to offer subsidized care. (LFA Group, 2010, 3,7)] This
represents a 30 percent shortage of licensed early child care and education slots. (Brion &
Associates, 2009, 7) The greatest shortfall is found in available subsidized care for
qualifying low-income families. Among the 310 programs in the county, 42 provide
state- or federally-subsidized care for qualifying families. 4,732 children are currently
enrolled in these centers, though 13,200 children qualified for subsidized care in 2009
and that number has grown. (Brion & Associates, 2009, 13). As the social and economic
benefits of high-quality early child care and education are better understood, it becomes
increasingly clear that these shortages represent a forfeiture of future stability and gains
for the community.
There are several organizations advocating for support for childcare and education in the
county. These include the Child Care Planning Coalition, First 5 Sonoma County, and the
Community Child Care Council and River to Coast Childcare Services. Several of these
organizations came together recently to put together a weeklong event called Week of the
Young Child. For this event I was asked to put together a map providing a visual
representation of all the childcare facilities in Sonoma County. This map was used on
tours groups took through the county each day to visit different facilities (See Figure 1.).
The advocacy groups were incredibly excited to have the map as a resource and asked if
it couldn’t be used for further analysis. I decided to use the map to pursue an analysis of
several factors related to childcare in Sonoma County.
Figure 1. Original Map for Week of the Young Child
QuickTime™ and a
decompressor
are needed to see this picture.
In the end, the maps I created were constructed to analyze the social and environmental
situation around subsidized care in Sonoma County. The maps will help the Sonoma
County community visualize the position of programs offering subsidized
care, understand where families in need and large populations of children are located, and
begin to think about other environmental factors impacting quality of care for all
programs. The main map shows all of the child care and education facilities separated in
to two categories – parent paid facilities and state or federally subsidized facilities. Two
maps were created showing the poverty rates for families with children under 18 in each
census tract in the county. These maps ach combined additional information – where the
subsidized care facilities are in one, and how many children 9 and younger (the age
qualifying as “Early Childhood Education”) live in each county. The information on
these maps was then combined to show more detailed, up-close relationships between
these three factors (poverty, subsidized care facilities and # of children) for the more
densely populated areas of the county.
Finally, a map was created to investigate the proximity of green space to both subsidized
and parent-paid childcare facilities. The importance of looking in to this is based on
scientific investigations in to the importance of green space on social, psychological and
physical health. Frances E. (Ming) Kuo, from the National Recreation and Park
Association explains that based on scientific evidence, “Access to nature, whether it is in
the form of bona fide natural areas or in bits or views of nature, impacts psychological, as
well as social functioning. Greater access to green views and green environments yields
better cognitive functioning; more proactive, more effective patterns of life functioning;
more self-discipline and more impulse control; greater mental health overall; and greater
resilience in response to stressful life events. Less access to nature is linked to
exacerbated attention deficit/hyperactivity disorder symptoms, more sadness and higher
rates of clinical depression. People with less access to nature are more prone to stress and
anxiety, as reflected not only individuals’ self-report but also measures of pulse rate,
blood pressure, and stress-related patterns of nervous system and endocrine system
anxiety, as well as physician-diagnosed anxiety disorders.” (Kuo, 2010)
These factors-- families living in poverty and children living without green space-contribute to the overall health and vitality of the present community, the children served,
and, subsequently, the future of our society.
DATA SOURCES and SHAPEFILES
Sonoma County GIS Data Portal
Vector Data
Major Roads
City Boundaries
Unincorporated Towns
Sonoma County Boundary
Scenic Landscape Units
Parkland
Habitat Connectivity Corridors
Green Community Seperators
Environmental Systems Resource Institute
Basemap
United States Census Bureau
Tables
Age and Sex
Poverty Status of Families
METHODS
Step 1: Mapping Sonoma County
First all of the shapefiles necessary for building the basic map of Sonoma County were
pulled from the Sonoma County Data Portal and ESRI. This includes the county
boundary, the city and town boundaries, the major roads, and the various kinds of green
space. These files were all clipped to the Sonoma County Boundary.
Step 2: Creating Geocoded data layers for child care facilities
I gathered data on childcare facilities from several sources in Sonoma County (I found
this to be a more comprehensive list that what I found in Reference USA). All of the
names and addresses of the sites were put in to two excel files: one for all centers (at the
time I made the original map last month) and one for just centers offering subsidized
care. These files were brought in to the map, and then geocoded.
Step 3: Census Tracts
I brought in and mapped the census tract data for the county. I then used the American
Fact Finder to gather census data on families with children under 18 living in poverty and
population demographics and created formatted excel charts (with just the specific
information I was looking to map like the total number of families with children living in
poverty from a larger chart of poverty data, for instance) to bring in to ArcMap and join
with the Census data. I used graduated colors to show the poverty data per census tract. I
used the statistics tool in the attribute table to find the overall estimate of families with
children living in poverty in the county – about 10%.
In order to find the number of children per census tract under 9 years of age, I had to take
a couple of steps. First, I had to create a new field and use the field calculator to combine
the data on % of children under 5 and % of children 5-9 to find the % of the total
population under 9. I was then able to take that % and get a real number for children
under 9 in each census tract and map that once joined with census track data. (See Figure
2.) I was also able to look at the statistics to find that there are 56,219 children under 9 in
the county. I used graduated symbols to show the number of kids in each census tract so
that I could show it along with the census poverty data. (See Figure 3.) I then had to
convert the polygons created for the number of kids in to points so that when I showed
the information along with the care facilities, the care facilities would show up on top.
Figure 2. Table with added fields for the Total % under 9 and the Total number of kids
under 9 in ArcMap.
Figure 3. Creating graduated symbols to represent the number of children in each tract.
I was able to create maps that showed just the locations of different kinds of childcare
facilities, a map that showed the number of children compared to poverty rates of
families with kids in each tract, a map of the poverty rates and the locations of subsidized
care facilities that those families would ostensibly need access to and then a map
compiling all of the information to help deduce where gaps in needed care may be.
Step 4: Green Space and Proximity
After pulling in the preserved green space layers and the subsidized care facilities, I
decided it was useful to more clearly represent that the urban childcare facilities in
Sonoma County have little access to green space. I used the ‘Near’ tool to perform a
proximity analysis and determine the distance between both subsidized care facilities and
green space, and parent-paid facilities and green space. (I first had to project the childcare
facility data, which until then was only given in coordinates.)
I then reclassified the distances from care facilities and applied graduated colors to show
facilities that are <100 meters, between 100 and 500 meters, or further than 500 meters
from green space. (See Figure 4.) Looking at the statistics, I discovered that the average
distance to green space for subsidized care centers is 1848 meters, and for parent-paid
programs it is 1549 meters.
Figure 4. Reclassifying in the proximity analysis.
CHALLENGES
Sonoma County is tricky because it is an agricultural county. This means that much of the
rural areas would be considered “green space.” Therefore, deciding how to represent
green space was challenging. I decided to only look at preserved green space because I
noticed that most of the subsidized care facilities were in urban areas, and therefore likely
not in close proximity to agricultural land.
I struggled deciding which poverty data to use. Originally I just looked at overall poverty
data. When I found the poverty data on families with children, it mapped very differently.
This made me really consider what it was that I wanted to show, because depending
which data I used, I was telling a slightly different story. In some cases tracts that in the
overall poverty data was bright orange (highest poverty rates) was a dull peach color once
I was looking specifically at families. In the end I decided using the data specific to
families with kids made more sense.
I realize, now that my poster is done and ready for the exposition on the 10th, that there
may be an inaccuracy with the proximity analysis. Originally, I geocoded all care
facilities. Then, I made a second dataset containing just the subsidized care facilities. I
never took the subsidized care facilities out of the original dataset, however. When
mapped together using different colors, the subsidized care centers just replaced/covered
up the icon representing the same facility in the original data set. That worked fine for
mapping, but now I realize that the subsidized centers were included in the proximity
statistics and therefore skew the data on the average distance between what I mistakenly
indicated was just parent-paid facilities, but was in fact all facilities.
Overall, I am quite happy with the analysis I have begun for the childcare advocates in
Sonoma County. I have printed an additional poster for them, and I am hoping they will
provide feedback and ideas for future analysis that would be useful to them, that I may be
able to continue over the summer. I did feel it was a rather linear analysis, and that
perhaps with more thought I can create some more intricate methods for analysis.
Resources
Brion & Associates and Nilsson Consulting. 2009. “Final Report – Child Care Needs
Assessment – 2009, Sonoma County.” Child Care Planning Council of Sonoma
County.
This document provided data on child care centers and needs in the county.
Kuo, Frances E. (Ming). 2010. “Parks and Other Green Environmental: Essential
Components of a Healthy Human Habitat.” National Recreation and Park
Association
Kuo, Frances E. (Ming) and Sullivan, William C. 2001. “Aggression and Violence in
the Inner City – Effect of Environment via Mental Fatigue.” Environment and
Behavior, Vol. 33 No 4. July 2001.
Fraces E. (Ming) Kuo does work studying the effect of green space on people. The
second of these articles included in depth statistical analysis in looking at The two articles
that I looked at by Kuo led to my interest in looking at the proximity of childcare
facilities to green space, in particular for centers serving low income populations.
LFA Group. 2010. “Sonoma County Early Childhood Education Professional
Workforce Survey Summary of Results: Full Report.” First 5 Sonoma County and
The Childcare Planning Council of Sonoma County
This document provided “soft” data on early childhood education in Sonoma County.
Malachowski, George. 2011.“Sonoma County Communities – Cumulative Risk and
Educational Outcomes.” Sonoma County Human Services Department Information
Integration Division
This Report looked at different communities and school districts in Sonoma County,
assessed their ‘cumulative risks’ and the educational outcomes. They used GIS to map
the school districts and cumulative risks in different communities. I have included one of
their maps below (Figure 5.) and one of my maps (Figure 6.) which both look more
closely at the same area of Santa Rosa. You can see that some of the neighborhoods they
have mapped as high risk are the same areas that are mapped as neighborhoods with high
% of families with kids in families, and perhaps not enough subsidized care facilities.
Figure 5. Cumulative Risk Map from Malachowski’s Report
QuickTime™ and a
decompressor
are needed to see this picture.
Figure 6. My close-up map of urban centers mapping families with kids in poverty,
number of kids and subsidized care facilties.