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Relationship of vegetation to socioeconomic status in Austin, Texas Kimberly Nichter, Department of Geography and the Environment This study observes the.

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Presentation on theme: "Relationship of vegetation to socioeconomic status in Austin, Texas Kimberly Nichter, Department of Geography and the Environment This study observes the."— Presentation transcript:

1 Relationship of vegetation to socioeconomic status in Austin, Texas Kimberly Nichter, Department of Geography and the Environment This study observes the socioeconomic division that occurs on opposite sides of the city of Austin and aims to provide relationships between amount of vegetation in an area and its corresponding social status. It asks whether or not social parameters affect amount of vegetation, or vice versa, and if there are any trends evident between the two. The results of this study can be used to further improve the revegetation of the Austin metropolis as well as examine a correlation between environmental aspects and socioeconomic status when residing in certain portions of a diverse city. Introduction Methods To measure vegetation, an NDVI scale was created and values between 0.3 – 0.615 indicated the percentage of vegetation out of all land covers. NDVI represents photosynthetic activity and is used in this study to display amount of vegetation. Austin census data (income, race, consisting of total Latino and African American population, and crime rates) was joined with each census block group (CBG) A linear regression was created using the % vegetation for each CBG along with its specific socioeconomic variable value Many large cities are prone to socioeconomic division primarily regarding race, income, and other social factors, yet the relationship between vegetation and socioeconomics is a lesser- acknowledged factor. Austin, Texas, serves as a leading example of socioeconomic and vegetation inequality, as the city has a sharp social division between the west and the east. Through analysis of census data and vegetation indices in Austin and its surrounding neighborhoods, this study provides a correlation between vegetation and social status that acknowledges the natural geology of the area. This study compared census data, including income, race, and crime rate, to NDVI values in the greater Austin area to associate how social factors depend on vegetation, as well as how a region’s existing vegetation can influence who settles in specific neighborhoods. Statistical analysis on the data suggested a negative correlation between the social indictors of percentage of minorities and crime with vegetation rate, while income and vegetation show a positive correlation. This study provides a guide on how Austin can alter its vegetation structure to provide a higher quality of life in social and personal aspects, creating a greater sense of social justice in Austin. Abstract Results Conclusion & Discussion The positive relationship between vegetation and income signifies that wealthier people reside in areas with more vegetation The slight negative correlation between race and vegetation indicates that minority-rich neighborhoods have less vegetation than predominantly Caucasian neighborhoods. However, this data has extremely high variation with a low R 2 value and the correlation should be interpreted as a negligible relationship The negative relationship between vegetation and crime demonstrates that sparsely vegetated regions are prone to more crime than those with high vegetation Future Directions: Future research should classify NDVI values in more detail and use a smaller scale image More recent data will provide accurate and up to date results These results are intended to provide guidance to better landscape the city of Austin to promote higher vegetation levels in all socioeconomic classes Acknowledgments Thank you to Dr. Kenneth Young, Dr. Thoralf Meyer and associated graduate students, and EVS peers for their help and support during this research. References Grove, J.M. et al. 2006. Characterization of households and its implications for the vegetation of urban ecosystems. Ecosystems 9: 578-597. Grove, J. M. et al. 2006.Data and methods comparing social structure and vegetation structure of urban neighborhoods in Baltimore, Maryland. Society and Natural Resources 19:117-136. Locke, D. et al. 2010. Prioritizing preferable locations for increasing urban tree canopy in New York City. Cities and the Environment 3: 18 p. Schwarz, K. et al. 2015. Trees grow on money: Urban tree canopy cover and environmental justice. PLOS ONE. 10: 17p. Troy, A.; Grove, J. M.; O'Neill-Dunne, J. 2012. The relationship between tree canopy and crime rates across an urban-rural gradient in the greater Baltimore region. Landscape and Urban Planning 106: 262-270. Figure 2.C: Income and % vegetation have a positive, polynomial relationship with an R 2 value of 0.47376 Figure 2.B: % of minorities (out of total population) and % vegetation indicate a negative, exponential relationship with an R 2 value of 0.08005 Figure 2.A: Crime rates and % vegetation show a negative, exponential relationship with an R 2 value of 0.24108 7200 - 35,701 35,702 - 51,964 51,965 - 71,445 71,446 - 111,151 111,152 - 207,208 75 - 739 734 - 1550 1551 - 2752 2753 - 4816 4817 - 8768 Water Impervious Cover Residential Dense vegetation Rangeland Agriculture Figure 1.B: Population Density: Latinos and African Americans Figure 1.C: Income Figure 1.A: Land Cover Types Figure 1.A: NDVI (Normalized Difference Vegetation Index) scale portraying water, impervious cover, residential, dense vegetation, rangeland, and agricultural land. For this study, dense vegetation and rangeland were combined to find the total percentage of vegetation in each CBG. Agricultural land was omitted from this percentage because of its unclear relationship with surrounding neighborhoods. Figure 1.B: Census data indicating the amount of minorities in each CBG, represented by total population of both Latinos and African Americans. This study used the percentage of minorities within the entire population of Austin to compare to percent vegetation, so larger CBGs would not skew the data for having more total residents.. Figure 1.C: Census data mapping the median household income over the past year. Incomes are represented in US dollars and are the median of each CBG.


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