Nature & Child-Wellbeing in Massachusetts Examining Academic Achievement and Socio-economic Indicators Relative to Proximity to Open Space in Massachusetts Public Schools
ARIAM FORD MAY 6, 2014 UA 654 FINAL PROJECT
INTRODUCTION As American communities continue to navigate the changing educational and skill needs of tomorrow’s future leaders, it is important for them to take into account all of the areas where improvements may be made to allow our children to excel academically as well in their personal lives. This particular study focuses on how nature can be used to achieve this goal. In an effort to localize academic theory surrounding the benefits of open space and nature connectedness on cognitive functioning and general well-being, my research begins to examine the relationship between proximity to open space and academic achievement as well as well-being of children in Massachusetts public schools. I hypothesize that as distance from a protected open space increases, child-wellbeing decreases. To test my hypothesis, I ask three major questions through my study for which spatial analysis is particularly useful:
Is there a relationship between proximity to open space and academic achievement in Massachusetts? Does proximity to open space vary for Massachusetts school age children based on socio-economic level? Does proximity to open space vary by household type, specifically focusing on the relationship between single mother households and open space?
I have used both academic achievement and two relevant socio economic indicators in order to achieve a more balanced and well-rounded understanding of the potential benefits of proximity to open space in Massachusetts. I chose the metric single mother households in particular because this household type is growing quickly, both state wide and nationally. Approximately 53% of Boston families are single parent households who raise 54% of Bostonian children. This is of particular interest because single parents face different challenges than two parent families, all of which have effects on the children in the household. In her article, Anderson uses an analysis of a collection of sociology studies focusing on the challenges of single parents relative to two parent families to identify a set of common themes. Some challenges that appeared across the research included the difficulty of doing things alone, coping with the loss of a relationship (applicable to those who become single parents after a divorce), enduring financial hardship, working longer hours, facing more stressful life changes with more frequency, and less emotional support. She also points out that female households have higher significantly higher rates of poverty than two parent families, leading to higher levels of depressive symptoms as well as higher dependence on government assistance. Anderson points out that many single parents become so as teenagers, meaning that from the beginning, they have less education, fewer resources, and weaker social networks (Anderson, 2003). Given this information, it would be wise to begin to understand all the levels of disparity relative to this household type, as they share a considerable part of the burden of raising America’s children.
CONCEPTUAL FRAMEWORK To situate my study in existing scholarship, I undertook a literary review of the concepts of biophilia and nature connectedness, as well as the effects of proximity to nature. The biophilia hypothesis contends that humans have co-evolved with the rest of the natural world in such a way that we have developed an innate tendency to seek connections with nature and other forms of life, and that this connection is essential to the maintenance of physical and psychological well-being (Rogers, 2010). Studies show that the impact of exposure to nature on well-being include boosted levels of enjoyment and increased endorsement of intrinsic goals. Nature connectedness has also been associated with feelings of autonomy, personal growth, and purpose in life (Howell, Dopko, & Buro, 2011). Howell, Dopko and Buro examine the relationship between nature connectedness and levels of well-being and mindfulness using a study of Canadian university students. To assess nature connectedness, the researchers used a 14 point scale to assess respondent’s sense of oneness with the natural world. To assess well-being, the authors used a 40 item measure including factors of overall life satisfaction, psychological well-being and social well-being. To assess mindfulness, they used a Mindful Attention Awareness Scale that uses 15 items to assess the extent to which individuals are aware and attendant to current experiences. The statistical analysis of the correlation of these variables showed that there are significant positive associations between nature connectedness and wellbeing as well as mindfulness. This finding is relevant to my study because it shows that there are positive benefits to achieving nature connectedness. My study builds on this theory by attempting to assess the effects of having the opportunity to build nature connectedness through the proximity to open space, using metrics of child wellbeing to determine if there may be a connection. (Howell, Dopko, & Buro, 2011) 1
Wells’ study examines the effects of nature on the cognitive functioning of children. The sample studied children of low-income urban families, using a before and after scenario, where the cognitive function of children was measured while they lived in “poor” quality housing with few natural resources, and then again when they moved to higher quality housing with more natural resources. The naturalness of housing environment was measured by using a 10 point scale that took into account the amount of nature in the window views from the living room, kitchen and bedrooms. To measure cognitive functioning, the researchers depended on the Attention Deficit Disorders Evaluation Scale developed by McCarney. A statistical regression analysis of the pre-move and post-move data showed that natural elements within the home environment are profoundly positively correlated with cognitive functioning. This study is considerably relevant to my study, as it presents the benefits of nature connectedness in the home, while my study looks to see if those benefits can be expanded into the classroom. (Wells, 2000) Evans’s work provides a summary of academic literature concerning the relationship between the physical environment and child development. In regards to the relationship between natural setting and child development, popular scholarship concludes that children prefer outdoor settings predominated by nature. They also find natural settings to be restorative in that they play a role in reducing cognitive fatigue, and that the longer the outdoor experience, the greater the benefits. Most relevant to my study, scholarship supports the arguments that access to nearby nature is beneficial as well, with proximity of residence to open space being positively related to attentional and emotional self-regulation in children. This overview of literature is particularly relevant for my study, as it supports my assertion that proximity to open space can have measurable positive effects on the wellbeing of children. (Evans, 2006) Following is a summary of similar studies that make use of GIS for their analysis. These studies are useful because they are undertaken in a similar style and have a focus on spatial analysis, similar to my own study. The study undertaken by Dunton is responding to evidence showing that physical inactivity increases the risk of serious health conditions. Coupled with this evidence is research that shows that the levels of physical activity in children is linked to built environmental characteristics. The study focused on children in grades 4-8 living in low-medium density suburbs in San Bernardino County, CA in households with less than $210,000 annual household income. Data was collected using an accelerator and GPS device that the children wore for 7 days, as well as by qualitative interviewing. To assess park availability, the authors created 500 meter buffers around participant’s houses to identify ‘walkable’ parks in the area. GPS and accelerometers were used to track time spent in parks by participants, as well as the duration and type of activity taking place. Using this information the researchers created a logistic regression analysis to see if distance to the nearest park, level of neighborhood park greenness, total neighborhood park area, and the number of parks available predicted the likelihood of park use by children. Their results showed that while the use of neighborhood parks by children is generally low, the closer a park to a child’s home and the greater the vegetation density of that park, the higher the use of the park by children. This study is different from mine in that it is concerned with actual use of open spaces for physical activity with public health nuance, while my study is looking at the benefits of merely being regularly near an open space. However, their finding about vegetative density is particularly interesting, as it could point to a difference between rural and urban open spaces. There are many schools, both urban and rural, within a conceivably reasonable distance to an open space. However, in more urban compact places, the open spaces are smaller and more compact. While this train of thought is currently outside of my scope of study, it would be interesting to see a study done on the relationship between vegetative density and childhood wellbeing. The study conducted by Weiss seeks to understand how negative characteristics of neighborhood environments influence access to parks, with the hopes of understanding the disparity in health between socio-economic and racial groups relative to their access to open space. The authors used GIS to make adjustments to park proximity information for special variation in negative characteristics, including crime, traffic and noxious land uses. For a study area, the researchers use census tracts in New York City examining the number of parks accessible from a tract, the number of acres of parkland accessible from a tract, the total number of facilities in the parks accessible from tracts, and the number of unique facility types accessible from each tract. This information was compared to racial and economic breakdowns of each census tract. To measure negative characteristics, the authors used metrics such as homicide rates and automobile accidents. The results of this study show that adjusting for differences in social access to parks help to explain disparities in spatial access within communities. Their findings show that while urban disadvantaged social groups had higher spatial access to parks, their advantage is diminished due to the poor neighborhood conditions precluding people from making use of the outdoor space. This study is 2
useful to my project because it shows how merely being close to an open space does not mean it is always possible to reap the benefits of proximity. This paper helps to show limitations in my own research, as I will not be analyzing the relationship between social proximity to open space and child wellbeing.
DATA & METHODOLOGY My hypotheses for my 3 research questions can be stated as such:
As distance from open space decreases, academic achievement will increase. As distance from open space decreases, socio-economic level increases. As distance from open space decreases, the number of children living in single mother households will decrease.
To address my research questions, I began my study by operationalizing the concepts I wished to observe into specific metrics. Below is a table of this conversion: Concept
Operationalized Variable
Proximity to Open Space
Distance of school to a state designated protected open space that is open to the public and designated for recreational or conservational use
Academic Achievement by School for 2010
%Graduates attending higher education %Drop Out Rates MCAS Scores SAT Scores (out of 2400) %Student Discipline Data
Socio-Economic Level
Percent of population enrolled in K-12 under poverty line by Census Tract (Census 2010)
Household Type
Percent of students living in single mother households by Census Tract (Census 2010)
Below is a list of the data sets from which I gathered my information. I made the attempt to standardize as much of the data around year 2010, as this was the earliest year for which the most recent census 100% data was available.
MDESE-2010-2011 Graduates Attending Institutions of Higher Education (School). All Colleges and Universities- All Students MDESE-2010 Graduation Rate Report (School) for All Students. 4-Year Graduation Rate MDESE-2010-2011 SAT Performance Report (School) for All Students MDESE-2010 MCAS Report (School) for All Students) MDESE-2012-2013 Student Discipline Data Report (School) All Offenses-All Students MAssGIS Protected and Recreational Open Space (1/14/2014) MassGIS Data-Schools (PK-High School) (October 2012) MassGIS Data-Massachusetts Department of Transportation (MassDOT) Roads (April 2012) Census 2010 SF1 (Use LOGSF1 in geography and LOGRECNO in SF1 to join) ACS 2010 5Yr (Use LOGACS061 in geography and LOGRENCO in ACS to join)
Next, I realized I would need to narrow down what I meant in referring to protected and recreational open space in Massachusetts. The way I chose to do this was by including only designated open spaces that are open to public
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access and are designated for recreation and conservation as their primary purpose. To do so, I used the select by attribute function twice, first to narrow from the full dataset of protected open space to those which were open to public access(Pub_Access=Y). Then, I exported the selected data into a new data layer called Protected_open_space_public. I repeated the same process within the protected_open_space_public layer to select those that were designated for recreational and conservation use, and created a layer called protected_open_space_public_rec_conserv.
Data Source Census.gov Census Tract Layer year 2010
Data Source Census.gov Census Tract Layer year 2010
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Data Source Census.gov Census Tract Layer year 2010
I then decided to narrow down the scope to public high schools in Massachusetts. In order to do so, I used the select by attribute feature to select schools by “Grade�, selecting by unique value only those with 12 th grade included. I did this individually for each unique value that included 12th grade, as the grades were not recorder or reported in a standardized manner. I then saved the selected data as a separate file.
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Data Source Census.gov Census Tract Layer year 2010
Data Source Census.gov Census Tract Layer year 2010
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Below is a map of the narrowed down data set I worked with for the first research question.
Data Source Census.gov Census Tract Layer year 2010
To get a more accurate measurement of the distances between schools and open spaces, I used the network analyst tool. Using the EOTROADS_ARC layer from MassGIS, I created service areas multiple times using the public high schools layer for .5mi, 1 mi, 1.5mi, and 2 mi. Each time I created a service area, I would search by location for the polygons created by the service areas distance parameters that intersected with the public open space layer. I would then export the selected polygons as a separate data file representing the schools within a certain distance of a protected open space. Next, I would select by location again, but this time selecting schools from the Public High School Achievement layer that intersected with the service area polygon layer. The resulting schools selected would allow me to gather achievement statistics for schools within a certain distance from protected open space. I did this for each distance.
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The statistics gathered at each distance using the network analyst tool are shown below: # of Schools in Category
Average % Graduates Attending Higher Ed.
Average Dropout Rate
Average % Proficient or Higher on MCAS English
Average % Warning or Fail on MCAS English
Average % Proficient or Higher on MCAS Math
Average % Warning or Fail on MCAS Math
Average SAT Scores (Combined)
Average % Students Disciplined
.5mi
3
45.8%
6.6%
44.3%
4.6%
39.6%
10.3%
970
13.5%
1mi
118
64.2%
9.7%
66.6%
5.3%
63.8%
9.0%
1269
9.7%
1.5mi
193
64.5%
9.3%
65.4%
4.7%
62.2%
8.8%
1256
10%
2mi
256
63.7%
9.7%
65.7%
5.2%
61.6%
9.8%
1243
10.8%
First, we must note that data from the half-mile distance is not to be trusted in any analysis, as sample size is too small to be useful. The other results, although varying and slight in significance, show that 6 out of 8 of the metrics support my hypothesis that there is a negative relationship between proximity to open space and academic achievement in Massachusetts, including graduates attending higher education, scores of proficient or higher on MCAS English, scores of proficient or higher on MCAS math, scores of warning or fail on MCAS math, SAT scores, and % of students disciplined. Despite the results, the changes in the values are too slight to be taken with any significance. Also, the numbers have not been tested for statistical significance. The most important use of this data is as a crude pretest for a larger study. To answer my second and third research question, I used the Spatial Join tool in GIS. I chose to quantify my socioeconomic indicators by measuring how many acres of public open space are in census tracts that meet certain parameters in terms of % of students enrolled in k-12 under the poverty line and % of students enrolled in k-12 living in single mother households. To accomplish this task, I used the layer containing the census data by tract on enrollment poverty and households which has been joined to the Census tract TIGER files for Massachusetts. Using that layer, I used the select by attribute tool to create layers containing census data based on specific parameters. For example, to create a data layer containing the census data for tracts with between 5% and 10% students enrolled in k-12 education under the poverty line, I entered those parameters in the select by attribute calculator, and exported the selected tracts as a separate layer. I did this
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for both students in poverty and single mother households, 4 parameters each. The next step was figuring out the average amount of acres of public open space that existed in each type of situation. To do so, I joined each new layer by spatial location to the public open space layer, and summarized the open space data by sum. There is an attribute in the public open space layer called GIS Acres, which is the GIS calculated acreage of the open space parcel. After I created a new layer from the spatial join, I used the statistics view of the selected cases to get the average amount of acres for all of the tracts in that parameter. I repeated this step for each different situation.
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The tables below show the results of the spatial join analysis and accompanying maps showing the geospatial dispersion of the metrics:
Average Amount of Public Open Space Relative to % of K-12 Students Under Poverty Line by Census Tract 600 400
574
200 70
3
0 0-5%
5-10%
10-20%
Average Amount of Public Open Space (Acres)
10
0.1 Above 20%
Average Amount of Public Open Space Relative to % of K-12 Students in Single Mother Households by Census Tract 800 600 400
685
567
200
73
0 0-5%
5-10%
10-20%
0.4 Above 20%
Average Amount of Public Open Space (Acres)
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The data shows that as the % of students in poverty increased, the amount of open space in the census tract decreased. This observation supports my hypothesis that as distance from open space decreases, socio-economic level decreases. Also, the results show that as the % of students living in single mother households increased, the amount of open space in the census tract decreased. This finding supports my hypothesis that there is a negative relationship between open space and single mother households. One thing to note is how higher percentages and poverty and higher percentages of single mother families seem to cluster in the same locations. One of these cluster sites is the Boston metro area. I believe that the results from the spatial join are much more useful than the network analyst results, as the changes in values across various distances are large enough to warrant further study.
LIMITATIONS There were many limitations of my study that should be considered when overlooking the results of this analysis. First, the size, type and quality of open spaces used in the sample were not accounted for in this analysis. This is relevant because you could have an urban school that is said to be near an open space, but that open space may not be of relative high quality or size compared to a rural school. Also, the fact that the difference in school size affects how heavily one students test score is weighted is of particular importance to this study, given the dependence on academic achievement data by school. Another flaw is that I only chose 4 different distances for my analysis. An extension of this study would certainly include a wider range of distances as to have a more statistically sound model. Next, I would like to mention that I am in no way arguing that proximity to open space determines use or appreciation of that space. This study was merely concerned with existence of open space and proximity, and does not attempt to give any insight into how people interact with open space. Finally, I would like to reiterate that no statistical tests were conducted for this study, and therefore the strengths of the relationships shown here are anecdotal at best.
CONCLUSIONS The greatest takeaway of this study is that it seems that socio-economic level and household type arguably have more to do with the proximity of children to open space than academic achievement does. While this goes against the bulk of my original hypothesis, the findings are useful to future study, as they highlight the important role that socio-economics and household make up play in a child’s relationship with nature, as well as the potential benefits that may be gleaned from such a relationship. I think my approach worked well for being my first geospatial analysis. However, I wish I had more time to conduct a statistical analysis to back up my conclusions. A concern for next time will be to think ahead, and plan out what data and how much of it I would need to construct a statistical model. Also, I believe that the study could be expanded to include other potential traits that affect a child’s proximity to open space, such as living in an urban or rural environment, age of the child, ethnicity, and even immigration status. This would be useful to increase the statistical strength of the study, as well as to identify any potential overlapping variables in the study. Overall, I thought this study concerning the relationship between open space and child-wellbeing was a success.
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REFERENCES Anderson, C. (2003). The diversity, strengths, and challenges of single-parent households. Normal Faimly Processess: Growing Diversity and Complexity, 301-336. Dunton, Phd, MPH, G. F., Almanza, MPH, E., Jerrett, PhD, M., Wolch, PhD, J., & Pentz, PhD, M. (2014). Neighborhood Park Use by Children: Use of Accelerometry and Global Positioning Systems. American Journal of Preventative Medicine, 46(2), 136-142. Evans, G. W. (2006). Child Development and the Physical Environment. Annual Review Psychology, 423-451. Howell, A. J., Dopko, R. L., & Buro, K. (2011). Nature connectedness: Associations with well-being and mindfulness. Personality and Individual DIfferences, 51, 166-171. Retrieved from http://www.sciencedirect.com.ezproxy.bu.edu/science/article/pii/S0191886911001711 Lima, A., & Melnik, M. (2012). Boston By The Numbers: Families. Boston: Boston Redevelopment Authority. Retrieved from http://www.bostonredevelopmentauthority.org/research-maps/research-publications/boston-by-thenumbers?viewall=1 Rogers, K. (2010, October 4). Biophilia Hypothesis. Retrieved from Encyclopedia Britannica : http://www.britannica.com/EBchecked/topic/1714435/biophilia-hypothesis Weiss, C. C., Purciel, M., Bader, M., Quinn, J. W., Lovasi, G., Neckerman, K. M., & Rundle, A. G. (2011). Reconsidering Access: Park Facilities and Neighborhood Disamentities in New York City. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 88(2), 297-310. Wells, N. M. (2000). At Home With Nature: Effects of "Greeness" on Children's Cognitive Functioning. Environment and Behavior, 32(6), 775-795.
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