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.