MAPPING A “HEAD START” INTO
THE FUTURE:
Community-Focused Strategic Planning using GIS Mapping
Ruth
Wetta-Hall,
Julie Oler-Manske, Edward Young, Craig Molgaard, Doren Fredrickson
Introduction:
Swift and unparalleled changes in contemporary society have produced unstable environments for nonprofit human service organizations (Menefee, 1997; Edwards, Cooke & Reid, 1996). These changes threaten to alter the provision of child welfare services in ways that that are difficult to predict. However, strategic planning offers a solution to reduce uncertainty, ambiguity and risk, while maintaining a focus on organizational outcomes. Excellent organizations are adaptable and responsive to a constantly changing environment (Allison & Kaye, 1997). Strategic planning, the hallmark of corporate America, has become increasingly evident in the not-for-profit sector. The strategic planning process helps to develop organizational priorities and produces the foundation for resource allocation and control (Strasen, 1987).
Strategic planning is labor intensive, analytical, and relies on multiple data streams and sources of information. Although strategic planning made its corporate debut in the early 1970s, it has been slow to catch on in the not-for-profit arena, largely due to its expense (Kotler, 1991). Planning, especially good planning may be one of the most challenging activities for non-profit, community-based organizations. Frequently, administrators of these organizations must base their decisions on limited, varied and unstructured information. At times they may need to rely upon second-hand or dated information to formulate their strategies for customer service. The challenge is to produce the best possible information for the least cost, to create “value-laden” analysis.
How can community-based,
not-for-profit managers make the most of limited information and scant
budgets? One solution may be the
application of management planning tools to existing data sources. Using a combination of secondary
information, administrative data, mapping techniques and advanced planning
tools, the Department of Preventive Medicine at the University of Kansas
Medical School-Wichita was able to produce an easy to understand, usable,
cogent analysis for the Head Start strategic planning process in Wichita,
Kansas. By using readily accessible,
community-based information, costs associated with this analysis were
approximately $5,000. A portion of the
analysis is presented in this paper, which includes 1) a demographic profile of
the community and Head Start clients, 2) Geographic Information Systems (GIS)
maps of selected community indicators and Child Care Association (CCA) Head
Start client base, 3) projections of future growth using CACI Marketing
Systems, The Source Book Zip Code
Demographics (14th Edition), and 4) the application of a
prioritization matrix to selected demographic indicators.
GIS maps have been used in previous child welfare
studies (Ernst 2000; Robertson & Wier, 1998; Queralt & Witte,
1998). The graphical display helps
people to understand information quickly and easily. The prioritization matrix assists in prioritizing tasks, issues,
or possible actions based on recognized, weighted criteria (Brassard,
1989). Using a combination of ranked
criteria this tool serves to narrow options and identify alternatives that are
most advantageous. When large amounts
of competing information are presented, the prioritization matrix may help make
sense of overwhelming amounts of data (Thome & Wetta-Hall, 1997). Moreover, the prioritization matrix may be
used to help build consensus and support for the outcomes of the planning
process.
Head Start is a
program for three and four year old children from low-income families. Using a holistic approach, Head Start
attempts to meet the intellectual, emotional, physical and social needs of at
risk children as well as their families. As a portion of the overall Sedgwick County Child Care Association (CCA)
planning process, the program director and staff of Head Start outlined three
questions to be answered as a portion of their strategic planning process.
1.
Where
do low-income families with young children live in Wichita/Sedgwick
County?
2.
Are
these families locating to new areas of Wichita/Sedgwick County?
3.
Do
we need to relocate Head Start Centers?
To support strategic planning of the Head Start program, The Department of Preventive Medicine at the University of Kansas Medical School-Wichita recommended an assessment that incorporated the Head Start administrative database, census demographic statistics, and Geographic Information System (GIS) mapping. The Head Start database provided an understanding of those clients currently served in the community, while additional demographic statistics supplied a snapshot of shifting demographics of Wichita/Sedgwick County. GIS mapping permitted the visual display of locations of the client families in relationship to the current location of Head Start Centers, and allow spatial analysis in relationship to where future need may exist.
The project used the following data sources: 1990 United States census data, 1996 Wichita/Sedgwick County Metropolitan Area Planning department updates, 1997 Sedgwick Community Health Assessment Project (CHAP) GIS maps based on 1990 census, 1999 Child Care Association-Head Start database, general demographic patterns of Wichita/Sedgwick County—current versus projected, and the City of Wichita-GIS data dictionary. SPSS 9.0 was used for descriptive analysis and graphs, while Esri Arc View was used to build GIS maps.
Wichita, the largest city in the state, is located in Sedgwick County. According to the U.S. Bureau of Census, the Wichita MSA had 544,343 residents in 1998. Population projections for 2004 are expected to exceed a half-million residents (573,769). Estimates for 1999 indicate there are 212,195 households in the Wichita MSA with a median household income of $41,216. However, nearly 40% of community households had an annual income under $25,000 per year and 15.5% of households less than $15,000 per year. Estimated per capita income for 1999 was $20,912. The predominant ethnicity in the Wichita MSA is white (85.6%), followed by African-American (7.9%), Hispanic (6.0%), Asian or Pacific Islander (2.4%) and Other Races (4%).
A 1997 Community Health Assessment Project (CHAP) (Dismuke et al, 1997) identified a pattern that falls along the central corridor of the city. In addition to lower income, less education, and higher numbers of minority inhabitants, residents of these neighborhoods reported being in worse health than the general population.
Results
Of the 834 Head Start participants in 1999, there was a nearly equal gender representation with 50.6% females and 49.4% males. The client base was nearly 75% minority; African-Americans had the greatest representation (44.8%), followed by White (25.8%), Hispanic (24.6%), Asian/Pacific Islanders (3.5%) and Native American (1.1%). The average age was 3.5 (SD 0.53) years with a maximum age of 5 years and minimum age of 2 years.
The median number of family
members was four persons. Family size
ranged from one to eleven people with the majority ranging from three to five
family members. Inspection of family
size by ethnic group indicated minimal variability; African-American, Hispanic
and White families had similar characteristics. Black, White and Hispanic families had a median of four family
members (Black 3.88,CI 4.03, 4.72), (Hispanic 4.19, CI 4.00, 4.37), (Whites
3.74, CI 3.54, 3.94). Native Americans
had slightly smaller families on average (3.63, CI 2.63, 4.62). Asian/Pacific Islanders have the largest
number of family members (4.93, CI 4.33, 5.54).
Families reported a mean
annual income of $10,240 (CI $9,832, $10,648).
Of the five minority groups, Native Americans have the lowest annual
income, ($9,246 CI $4,313, $14,179), followed by African-Americans ($8,923 CI
$8,357, $9498) and Asian/Pacific Islanders ($9,893 CI $8,141, $11,645). Hispanic and Caucasian families have
comparable annual incomes of $12,242
(CI $11,391, $12,140) and $10,934 (CI $10,129, $11,838), respectively.
In 1999, the median number of years each child has participated in Head Start was one year (1.27 CI 1.23, 1.30). Inspection of years participated varied somewhat by ethnic group. Blacks and Asian-Pacific Islander children had participated in Head Start for one to three years (Black mean 1.3 years, CI 1.2, 1.3), and (Asian/Pacific Islander mean 1.4 years, CI 1.2, 1.6), respectively. Hispanic children had participated from one to two years (1.26 years, CI 1.20, 1.32). Whites and Native Americans had the least variability in years of participation (White 1.26 years, CI 1.19, 1.32)(Native American 1.13 years, CI 0.83, 1.42).
The goal of the first analyses was to document where and how many children were enrolled in the program. Head Start GIS maps were created from a database maintained by Head Start, which included 429 records. Due to lack of agreement between the Head Start address database and City of Wichita street address database, approximately 24% (102) addresses could not matched, and were excluded from the analyses. Figure 1 mapped Head Start sites in relationship to residences of participating children, using a graduated color scheme and zip codes to visually distinguish areas of high versus low representation. Dark brown areas represented the highest number of children (61-80), followed by light brown (41-60 children), red (21-40 children) and pink (0-20 children). Black flags represent the location of Head Start sites.
The largest number of children enrolled in Head Start resided in zip code 67214, nearly twice the number of enrollees as the next most frequent zip code, 67216. Several zip codes had 21 to 40 children, and the remaining zip codes throughout Sedgwick County had 20 or less children enrolled in Head Start. There were two Head Start sites in 67214, and one in each of the remaining zip codes in the central corridor of Wichita. More than 40 GIS maps documenting demographics and health indicators were created as a result of the 1997 CHAP. These maps provided a visual description of the relationship between social, demographic and health status in Wichita, and served as a point of comparison for Head Start maps (see web site for full list of maps http://www.sedgwick.ks.us/chap/_private/maps.pdf)
To project estimates of future growth from 1999 to 2004, The Source Book Zip Code Demographics, 14th Edition (CACI Marketing Systems) was used. Current versus future growth was forecasted using indicators commonly applied in urban planning and had applicability to Head Start planning. These indicators included population size, estimated population growth, ethnicity, age, income, number of families and average household size. Synthetic population growth estimates for children five and younger were derived using zip code demographics and the Center for Economic Development and Business Research at the Wichita State University (CEDBR, 1999). Several tables were built that incorporated statistics on population size, growth and ethnicity as well as average household size and number of families residing in the zip code (not shown).
Ethnicity
Several
zip codes (67218, 67216, 67213, 67211, 67214) had more than 20,000 residents
each, and fell within the central corridor of Wichita. Although the ethnicity of Sedgwick County
was predominantly White (85%), the older central area of Wichita had the
largest greatest representation of minority population. African-Americans
represented the largest percentage of the population within zip code 67214
(64.7%), followed by 67219 (35.8%) and 67202 (30.2%). The Hispanic community was spread across several zip codes with
the greatest concentration located in 67202 (15.5%), 67214 (11.0%), 67213
(9.5%), 67211 (8.2%) and 67210 (7.5%).
There were three zip codes where Asian residents reside including 67210
(15%), 67212 (5.8%) and 67216 (5.1%).
Financial
Economic indicators cannot be interpreted in separately. Annual household income provides an indication of the wealth of the household, while per capita income translates into per person income. It is a measure of income, not wealth and represents the “spending power” of the individual, so a higher per capita rate represents a more financially sound household. More than 70% of Wichita/Sedgwick County households had higher household incomes than those neighborhoods. A high percentage of households in eight of nine zip codes had household incomes of less than $25,000 annually. Zip code 67202 has the highest percent of households with an annual income of less than $25,000 (84.9%), followed by 67214 (59.4%), 67213 (42.2%) and 67211 (42.0%). The remaining zip codes had less than 40% of households with $25,000 annual income or less (see Table 2). Per capita rates in the nine zip codes presented a slightly different picture. All the selected zip codes had per capita incomes of $21,000 or less, and five zip codes had per capita income of less than $15,000. Zip codes 67214, 67219 and 67210 had the lowest per capita income of the nine selected zip codes at $10,103, $11,861 and $12,910, respectively.
Family Size
Three zip codes have more than 6,000 families
living within its boundaries including 67216 (6,862 families), 67218 (6,410
families), and 67213 (6,090 families). Zip
codes with 3,000 to 6,000 families include 67211 (5,680 families), 67214 (4,664
families), and 67210 (3,048 families).
Zip code 67220 will experience a two percent annual percentage growth in
number of families. All other areas are
projected at less than two percent growth.
Zip code 67202 is expected to have negative growth (e.g. families will
move out of 67202). The average
household size ranges from 2.0 to 3.0 members.
Growth in Numbers of
Children
The number
of children five years of age or younger residing in the inner city zip codes
was approximately 12,500 and is projected to grow to 13,300
by 2004. The ethnic composition of
children in these neighborhoods is: Caucasian 70%, African American 18 %, Hispanic
8% and Asian 4%. Table 1 describes projected
population growth of children age five years or less, and household income
characteristics in selected zip codes in Wichita/Sedgwick County for 1999 and
2004. Zip codes 67220 are projected to
have the largest percent population growth at 10%, followed by 67210 (7.4%),
and 67216 (7.3%). Four additional zip
codes, (67211, 67213, 67218, and 67219) will have slower growth between 6.0%
and 7.0%.
Integration of Information
Sources
One of the challenges in planning
today is making competent and timely decisions based upon limited
information. Decision making at its
simplest consists of identifying, evaluating and choosing alternatives. One of the planning tools described in the
Memory Jogger Plus is the prioritization matrix. This tool assists in prioritizing tasks, issues, or possible
actions based on recognized, weighted criteria. It uses a combination of ranked criteria to narrow options and
identify alternatives that are most advantageous. When large amounts of competing information are presented, the
prioritization matrix may help make sense of overwhelming amounts of data.
Table 3 is prioritization
matrix created from selected criteria in tables created for the project. A simple rank ordering methodology was
applied to selected zip codes, which were ranked from highest to lowest for
each of the following criteria: percent minority representation, number of households with annual
income less than $25,000, per capita income, projected percent growth in
population, projected number of
children five years or less in 2004, existing Head Start site present,
projected annual family growth rate, and number of
families residing in zip code in 1999. For example, the zip code with the lowest per
capita income would receive a ranking of nine, whereas the zip code with the
highest per capita income would receive a rank of one. The rankings for each zip code were summed
across all criteria. The totals column
provided a prioritization score. The
zip code with the highest score indicates greatest priority in planning
activities. Figure 2 displays the results of
the prioritization matrix in a GIS map to enhance understanding and promote
communication to staff.
According to ethnicity, income, population,
and family growth criteria presented in this analysis, zip code 67216 received
the highest ranking, and had highest priority for increased development. Zip codes 67214, 67210 67211, 67213, 67219
and 67218 follow closely behind. The
close proximity of the zip codes suggests resource sharing between programs
more feasible.
Conclusions and Recommendations
The Child Care Association had established a strong presence in areas of documented need in Wichita/Sedgwick County. GIS maps indicate that locations of Head Start were serving high need areas, however, community benefit may be realized through expansion and/or relocation of sites to areas of anticipated growth and documented deficit. The majority of at-risk children in Sedgwick County reside within the central corridor.
Using a prioritization matrix methodology,
Head Start descriptive statistics, Head Start database maps and selected
income, population, and family growth indicators specific to Wichita/Sedgwick
County, several zip codes were rank ordered in terms of magnitude of need and
growth. Zip code 67216 received the
highest ranking score, and has the highest priority across programs for
increased development. Zip codes 67214,
67210 67211, 67213, 67219 and 67218 follow closely behind. The close proximity of these zip codes may
make resource sharing among programs a feasible strategy.
For the Head Start program,
increased participation in 67203, 67202 and 67219 would be optimal as areas of
Sedgwick County with higher than average documented need. It was recommended that Head Start leadership
consider increasing their presence in zip codes 67214 and 67219, as areas of
great need and lower enrollment numbers.
One suggested strategy was to redistribute existing sites or to create
or recruit of new sites.
Head Start resources are readily evident in these neighborhoods, but greater penetration may be necessary to fully serve the needs of these families. It may be challenging to provide services for children of non-English speaking families, particularly Spanish-speaking, as these families live in a geographically wide area. Analysis of ethnic representation suggests that the Head Start program may want to focus resources on zip codes 67214 or 67202.
In the Wichita/Sedgwick County area, the real challenge of serving low-income families may not be so much in relocation of facilities, but identifying strategies to engage these families in Head Start, Early Head Start and other CCA programs. Language barriers present an additional challenge. The Spanish-speaking client base will continue to grow, creating a demand for more interpreters and employees with Spanish as a second language.
As stated earlier, the majority of non-profit organizations are service oriented with minimal budgets for strategic planning. Using a combination of secondary information, organization-based data, mapping techniques and advanced planning tools, our department produced a functional product for approximately $5,000. The process has streamlined the decision-making process and promoted community-based involvement in child welfare program planning. GIS maps have enhanced the communication of the strategic plan to front line staff. The true benefit of this process has yet to be realized. The director of Head Start is currently implementing his strategic plan. Not only has the analysis supported planning efforts, but the information has also been incorporated into grant proposals and funding requests.
|
Zip code |
Number of children five years or younger in 1999 |
Percent growth |
Projected number of children five years or younger in 2004 |
Households with children five years or younger and annual income < $25,000 in 1999 |
Per capita income |
|
|
|
|
|
|
% |
Frequency |
|
|
67202 |
95 |
1.9% |
97 |
84.9% |
82 |
$21,010 |
|
67210 |
1,021 |
7.4% |
1,097 |
34.1% |
374 |
$12,910 |
|
67211 |
1,840 |
6.7% |
1,964 |
42.0% |
825 |
$16,187 |
|
67213 |
1,981 |
6.0% |
2,100 |
42.2% |
886 |
$13,631 |
|
67214 |
1,667 |
4.9% |
1,749 |
59.4% |
1,039 |
$10,103 |
|
67216 |
2,037 |
7.3% |
2,186 |
30.6% |
669 |
$14,943 |
|
67218 |
2,024 |
6.0% |
2,146 |
34.8% |
747 |
$20,594 |
|
67219 |
956 |
6.2% |
1,016 |
38.8% |
394 |
$11,861 |
|
67220 |
902 |
10.1% |
993 |
22.6% |
224 |
$20,546 |
|
Total |
12,524 |
|
13,346 |
|
5,240 |
|
|
Avg.
Disposable Income |
|
|||||
|
Zip Code Area |
Age Under
20 |
Age 20
to 44 |
Median Age |
<
35 years |
35-44
years |
Percent Households
<
$25,000 |
Per Capita Income |
|
67202 |
36.0 |
11,352 |
10,844 |
84.9% |
|||
|
67210 |
40.2% |
43.1% |
25.1 |
24,460 |
33,000 |
34.1% |
$12,910 |
|
67211 |
27.0% |
37.3% |
35.3 |
23,916 |
30,698 |
42.0% |
$16,187 |
|
67213 |
29.3% |
38.0% |
33.5 |
24,925 |
28,369 |
42.2% |
$13,631 |
|
67214 |
36.0% |
35.2% |
30.2 |
18,180 |
23,938 |
59.4% |
$10,103 |
|
67216 |
33.2% |
38.2% |
31.3 |
27,263 |
32,648 |
30.6% |
$14,943 |
|
67218 |
25.5% |
35.8% |
37.4 |
26,304 |
36,325 |
34.8% |
$20,594 |
|
67219 |
39.7% |
35.4% |
27.4 |
23,529 |
32,655 |
38.8% |
$11,861 |
|
67220 |
30.1% |
41.3% |
32.5 |
30,700 |
41,716 |
22.6% |
$20,546 |
|
Kansas |
29.6% |
35.9% |
21.2% |
35.6 |
35.6 |
31.1% |
$18,873 |
Source: CACI Marketing Systems. The
Source Book Zip Code Demographics.
14th Edition.
Arlington, VA. 1999. Shaded zip codes indicate inclusion in
“Inverted T”
|
Zip code |
% minority |
Number of households with annual income < $25,000 |
Per capita Income |
Percent
|
Existing
Head Start Site Present 1=yes,
2=no |
Existing
Early Head Start |
%
Annual Family Growth 1990-1999 |
Number of families in 1999 |
Total Score |
Ranking |
|
|
67202 |
8 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
16 |
7 |
|
67210 |
5 |
2 |
7 |
8 |
4 |
1 |
2 |
8 |
4 |
41 |
3 |
|
67211 |
2 |
7 |
4 |
6 |
6 |
1 |
2 |
5 |
6 |
39 |
4 |
|
67213 |
1 |
8 |
6 |
3 |
7 |
2 |
1 |
4 |
7 |
39 |
4 |
|
67214 |
9 |
9 |
9 |
2 |
5 |
1 |
2 |
2 |
5 |
44 |
2 |
|
67216 |
4 |
5 |
5 |
7 |
9 |
1 |
2 |
7 |
9 |
49 |
1 |
|
67218 |
3 |
6 |
2 |
4 |
8 |
1 |
2 |
3 |
8 |
37 |
5 |
|
67219 |
7 |
4 |
8 |
5 |
3 |
1 |
2 |
6 |
3 |
39 |
4 |
|
67220 |
6 |
2 |
3 |
9 |
2 |
2 |
1 |
9 |
2 |
36 |
6 |
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Ruth Wetta-Hall, RN, MPH, MSN
Teaching Associate and
Community Health Improvement Project (CHIP) Coordinator
Department of Preventive Medicine
University of Kansas School of Medicine--Wichita
1010 N. Kansas
Wichita, Ks. 67214-3199
phone: 316.293.2627 fax: 316.293.2695
email: rwettaha@kumc.edu
Julie
Oler-Manske, BA
Research
Associate, Department of Preventive
Medicine
University of Kansas School of Medicine--Wichita
Edward
Young,
Program
Director, Head Start
Wichita/Sedgwick
County Child Care Association
Craig
Molgaard, PhD, MPH
Professor
and Vice Chair, Department of Preventive
Medicine
University of Kansas School of Medicine--Wichita
Doren
Fredrickson, MD, PhD
Associate
Professor, Department of Preventive
Medicine
University of Kansas School of Medicine—Wichita
