Instruments, materials, and tools used in this thesis would essentially be that of having access to A.R.C. G.I.S., Google Earth Pro, eCognition, a laptop with external hard drive and sufficient processing capabilities, Excel, Statistical Package for the Social Sciences (S.P.S.S.), transportation, and internet access. If additional software is deemed necessary the student shall approach the Department of Rehabilitation to acquire funding for such software. eCognition helps fix some of the issues presented with pixel-based classification and allows for better analysis of remote sensing data (Flanders, Hall-Beyer, & Pereverzoff, 2003). S.P.S.S. is a software program that assists to statistical data analysis and would be helpful for linear regression and multivariate analysis, if these are deemed appropriate to be used (Kent State University, n.d.) C. Data Collection
Participants of this study are all individuals who have been accounted for in the U.S. census and individuals in any of the data that has been collected by various
studies in organizations such as but not limited to the Environmental Protection Agency.
The data collected would be at a regional focus looking for scale. Overall, this would be measuring the health issue of asthma and green space at the tract scale and data about the population of those tracts will have to be taken into account to control for social-environmental factors that impact rates of asthma. All of the remote sensing data would
be acquired from L.A.R.-I.A.C., the Los Angeles Region Imagery Acquisition
Consortium. The Los Angeles Region Imagery Acquisition Consortium is a collaboration of 30+ counties, 40+ municipalities, and additional public agencies; their goal is working together to acquire digital aerial imagery data (Los Angeles County G.I.S. Data Portal, n.d.). Although the main issue of this thesis focuses on dispersed green space, areas of large green space and no green space need to be documented for comparison. Areas with no green space will serve as a control.
Whereas the study by Hu collected health data from Florida C.H.A.R.T.S., health data would instead be obtained from C.D.C.500 cities and CalEnvironScreen. This is to acquire data on rates of asthma, air pollution, race, income and any other factors deemed necessary. As in the study performed by Hu et al., air emission data could also be taken from U.S. E.P.A. (Environmental Protection Agency) for toxic release inventory sites data, A.A.D.T. (annual average daily traffic for average traffic) and the California Department of Environmental Protection (CalE.P.A.) (Hu et al., 2008). Point polluters in this study without emission data were calculated with point density surfaces (Hu et al., 2008). For comparing air pollution in this study, CalEnvironScreens will be used to acquire data.
In Li's work she used high-resolution remote sensing imagery to help identify vegetation cover (Li, 2008 ). This would be replicated in this study. Akin to Li's work individual level (human scale) vegetation cover would be measure via remote sensing (Li, 2008).
D. Deciding Areas to Sample
Determining areas to sample will strongly rely on if there is found to be equal variance in the data collected. Variance refers to the distribution of data, and equal variance is when variances are always equal to 1 (The University of New Mexico, n.d.).
Random sampling should be used if there is regular and equal variance found during data collection. Random sampling (more specifically simple random sampling, is defined as "of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected"
(Moore & McCabe, 2006, p. 219).
Multivariate cluster and multi-factor clustering could help explain the relationship between vegetation and asthma if a random sample cannot be used. Both of these have been used previously when looking at crime and vegetation, thus making them viable options (Li, 2008). Multivariate analysis is defined as "the statistical study of data where multiple measurements are made on each experimental unit and where the
relationships among multivariate measurements and their structure are important."
(Olkin & Sampson, 2001, p. 10240). Multivariate clusters is a tool in G.I.S. that helps establish clusters in data that are natural by looking at attributes specified in the
analysis fields parameter and the clusters are made with a K-Means algorithm (ArcGIS Desktop, n.d.)
Also in Hu et al., work they divided green space up by accessible (to the public) and inaccessible green space (Hu et al., 2008). This would be replaced by categories of the following: large areas of green space, no green space, and dispersed green space.
Their is a limited amount of research where green space has been quantified in this way. However Cooper et al., identifies dispersed green space as space that is not a
park (public, state, national, private, preservation, ext.) (Cooper et al., 2010). This would be used as the standard of measurement.
E. Determine Types of Street Trees
In addition to using the methods of Hu et al., and Li there would be analysis on the types of tree from using screen truthing and so that kinds of trees can be noted and their relation to amount of allergens they produce. This will help establish possible impacts of pollen. Within the green spaces, random sampling could be conducted as to see the kind of trees that are in different areas, if equal variance is found. Alternatively, multivariate analysis can be used, similar to Li's work (Li, 2008).
After this, allergen levels can be taken into considerations. In order to attain this data, screen truthing will be used for identification from Google street view to obtain data on the kinds of trees in the area. Types of trees that would be looked for include:
catalpa, elm, hickory, olive, pecan, sycamore, walnut, ash, box elder, cottonwood, date palm, maple (red), maple (silver), Phoenix palm, poplar, and willow (National Institute of Environmental Health Science, n.d. b.). As it was identified earlier, screen truthing has found to be reliable in comparison to ground truthing (Li et al., 2012).
F. Preliminary Pattern Recognition on Green Space-Asthma relation
There are many triggers that impact asthma that are uncontrollable at this level, such as race, socioeconomic status, and access to healthcare (A.A.F.A., n.d.). As many of these variables should be measured when possible to control for them, especially socioeconomic. All of this data is already easily accessible.
In 2012, Li & Radke used multiple sources of data but these were not always compatible (Li & Radke, 2012). To replicate this all data would be converted into raw data and then integrated into a geospatial database (Li & Radke, 2012). This would include geo-coding so that physical addresses could be matched up (Li & Radke, 2012).
Li & Radke were able to get 100% and 95% match during their data integration process using this method (Li & Radke, 2012). A hotspot analysis grid would also be used, in order that the distribution patterns could be easily identified (Li & Radke, 2012).
Additional cues taken from Li and Radke for modeling to look at patterns would be using data management and modeling tools including, but not limited to: conversion, spatial join, overlay, attribute calculation, projection, and geo-referencing (Li & Radke, 2012).
From here a general comparison of trends might be seen via linear regression mapping in A.R.C. G.I.S., but further analysis will be crucial.
G. Limitations
There would be limitations in this study, especially due to the fact that qualitative and individual data will not likely be taken. At the scale this would be performed in, it would be unrealistic to provide such data. There are many factors that impact asthma that will not be accounted for in this study. This includes elements such as but not limited to mental health issues, individual emotions, Vitamin-D intake, atopy and other genetic factors, diet, weather, chronic illness, respiratory illnesses, and personal hygiene (A.A.F.A., n.d. b.; Brigham et al., 2015; Davison, Emberlin, Cook, Venables, 1996; Mei Cheng et al., 2014; Mathur & Viswanathan, 2014; Pearce, Pekkanen, &
Beasley, 1999; Polunina, 2013). It is also very important to mention that this study would look at correlation, not causation.
Additionally, this research would be limited in that it would set up a path to go into further research and would look at this as a regional pattern. Therefore, it would not give
any concrete prescriptions at this time. Bias should be noted in that the success of finding positive results is more desirable than finding negative ones. Despite all of this, this study is still worth doing. The limitations mainly arise from this being a regional pattern study. It lays the way for further research that would eliminate those issues.
V. Analysis
A. Previous Analysis
The study performed by Hu et al. used a Bayesian hierarchical statistical model and Dasymetric mapping (Hu et al, 2008). The Bayesian hierarchical modeling was used to look at the relation of income, health issues, and environmental exposure (Hu et al, 2008). Remotely sensed data would be used to calculate green space at the human scale unlike the study conducted by Hu et al. (Hu et al, 2008). Dasymetric mapping is defined as "Dasymetric mapping depicts quantitative areal data using boundaries that divide the area into zones of relative homogeneity with the purpose of better portraying the population distribution." (United States Geological Survey, n.d.). The United States Geological Survey has developed an automated G.I.S. tool to help automate this
mapping process (USGS, n.d.). Hierarchical modeling is described as "a statistically rigorous way to make scientific inferences about a population (or specific object based on many individuals (or observations)"; and bayesian means "uncertainty of inference"
(Wolfgang, n.d.).
This might be used or it might be based on other work. Instead this step could be based off of Li's work, using linear regression (Li, 2008 ). Linear regression is essentially a way to show a relationship between two variables and the dependent variable is being predicted for (Yale University Department of Statistics and Data Science, 1997-1998).
Further into the process it can be decided which is the most appropriate method for analysis.
B. Proposed Study Area
The proposed location would be Los Angeles county. This location is optimal due to the strong diversity of neighborhoods, the amount of air pollution, and opportunity for development in dispersed green space (Goldhagen, 2017). In Los Angeles County, 1,221,000 people suffer from asthma (California Department of Public Health, 2017). Of those individuals 75.8% are adults (C.D.P.H., 2017). The neighborhood of Antelope Valley has the highest number of sufferers of asthma and the neighborhood of Metro has the lowest (Think Healthy LA, n.d.).
Los Angeles, as a city, has less than 10% (specifically 6.7%) of its land cover as green space which is considered the minimum amount for urban spaces, and is not promising for the county (Goldhagen, 2017). In the county of Los Angeles, only a third of all residents live in what is considered to be a walkable distance (quarter mile) to any green space (Li, n.d.).
C. Perform Statistical Analysis
The dependent variable of this study would be cases of asthma and the
independent variable would be tree coverage. These results would be analyzed based on if the findings are deemed to be normally distributed and of equal variance. It would be preferable to do a linear regression test if these conditions are found to be present which would then help establish levels of association (Yale University Department of Statistics and Data Science, 1997-1998). The statistical analysis all could be calculated
using excel. If necessary the graduate student will access the graduate resource center for additional help with statistical analysis. This has been used before, expect with looking at crime and green space with successful outcomes and should be replicable when substituting asthma for crime (Li, 2008). This will have to be decided as to which is the best method, after data has been collected and examined for overall patterns.
However, Bayesian hierarchical model and dasymetric mapping will be used if linear regression is determined to be an inappropriate fit, considering trends in the data.
Overall, analysis would to be performed to also take into account to control for other factors that impact rates of asthma, as listed earlier.
VI. Discussion
The results of this study would aim to look at and try to determine what value dispersed green space has in terms of any regional patterns in Los Angeles County, especially with disadvantaged communities. These findings could encourage future studies of dispersed green space and its benefits in Los Angeles, dispersed green space and asthma, and/or green space when taking pollen into consideration and it's impacts on asthma.
VIII. Timeline
WEEK # PREPARATION DURING SUMMER (Assuming start is July 16th) 1-5 Collection of Data
6-10 Determine areas to sample 11-15 Determine Types of Trees 16-20 Perform Ground Truthing WEEK # FALL SEMESTER 1-3 Compare Patterns
4-8 Perform Statistical Analysis 9-10 Determine Pattern Trends 11-15 Finalize draft of all work
WEEK # FINAL STAGE OF THESIS (Spring Semester) (Milburn, 2019)
1 Submit completed thesis to thesis advisor/chair for review and comments (Milburn, 2019) 3 Revise based on thesis advisor/chair comments (Milburn, 2019)
4 Submit thesis to thesis committee and thesis advisor/chair for review and comments (Milburn, 2019)
6 Revise based on thesis committee and thesis advisor/chair comments (Milburn, 2019) 7 Submit revised thesis to thesis committee and thesis advisor/chair for approval to defend
(Milburn, 2019)
9 Advertise defense to department (Milburn, 2019) 10 Thesis defense (Milburn, 2019)
11 Submit revised thesis to thesis advisor/chair for review (Milburn, 2019) 13 Revise based on chair comments (Milburn, 2019)
14 Submit revised thesis to thesis committee and thesis advisor/chair for approval (Milburn, 2019)
16 Submit thesis to University/Library (Milburn, 2019)
16 Submit culminating experience form to the graduate coordinator to get approval for graduation. (Milburn, 2019)
X. Conclusion
A. Research Value
The question this thesis would ask is how do dispersed green spaces and adult asthma relate in terms of regional patterns in Los Angeles County? The values of this thesis would be helping to lay groundwork for other researchers to look at what actually is the role of urban landscapes and their impact on asthma and if green space holds potential to improve human health in even more ways than is currently documented. In order to help provide better care through the built environment to those in
disadvantaged communities, being able to pinpoint the benefits of green space is of paramount importance, especially dispersed green space. This is important because for the small sizes of land available for greening in many U.S. cities such as Los Angeles and thus dispersed green space is important (Sherer, 2006). This could include project such as alley greening and pocket parks. Additionally, creating a stronger link between public health and landscape architecture would empower landscape architects to be capable of engaging in professional dialogue dealing with public health and the
influences of the built environment on public health. Finally, using remotely sensed data measured at the human scale would help address a current gap in research that has not been found to be addressed as of yet.
B. Value for the Field
Landscape architecture is a discipline depending on many different backgrounds.
In the past, public health has played a small, if an even noticeable roll in the field. In order to help expand opportunities for landscape architects and better equip the profession to be able to serve disadvantaged communities, public health needs to be further studied in the context of landscape architecture. Neither Le Notre, the American Society of Landscape Architects (A.S.L.A.), the Landscape Architecture Foundation (L.A.F.), Landscape Journal (L.J.), and the Council of Educators in Landscape
Architecture (C.E.L.A.) consider the interactions between landscape architecture and public health to be a core domain of knowledge in landscape architecture (Deming &
Swaffield, 2011).
At best L.J. and C.E.L.A. consider human and environmental relationships to be a category, but nothing so directly tied to health (Deming & Swaffield, 2011). In
considering research agendas in North America, A.S.L.A., C.E.L.A., and L.A.F.
respectively mention livable communities, healthy communities, and health and well-being as research categories, but never distinctly engage in public health (Deming &
Swaffield, 2011). Yet it has been established that landscapes have an impact on public health (Goldhagen, 2017). In order to give the profession a stronger ability to positively impact the health of disadvantaged communities, more research about landscape architecture and public health needs to be conducted so that more informed decisions can be made when designing landscapes.
Landscape architecture in America has strong ties in origin to public health;
Frederick Law Olmsted worked as head of the U.S. Sanitary Commision in 1861-1865, the time period of the civil war before he began to found the field (Fisher, 2010). This occurred after he had begun to design public spaces. Olmsted made the argument, soon after John Snow found cholera was linked to bad water (rather than bad vapors) in London, that public parks could assist in helping individuals of low socio-economic standing with providing better environmental conditions (including bad air) (Green, 2010). Olmstead was asked to become the U.S. Sanitary Commissioner after his work in Central Park (Fisher, 2010). Landscape architecture in America had strong roots in public health, but now has drifted far from it. By looking at topics such as these, the field can start to put a foot in the door to get back to where it all started.
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