The numerous advantages of urban street trees are too often overlooked. Just as trees play an integral part in the natural ecosystem, they play critical roles in the urban ecosystem. The benefits of street trees are many-fold and include the improvement of traffic and pedestrian safety on roadways, increased economic activity, lower temperatures, crime reduction, higher land values, improved overall health, longer pavement life, and absorption of water runoff, carbons, and pollutants (Burden, 2006).
As deforestation and urbanization continue hand-in-hand and global warming persistently threatens the wellbeing of future generations, the importance of incorporating vegetation cover into the built environment is becoming more important by the day. Since the vast majority of us will live in urban areas in the coming decades, it is important that we begin to coexist with nature rather than in isolation from it. Aside from parks, recreation lands, and nature preserves, one of the most effective ways to incorporate nature directly into the urban environment is through the planting of street trees.
Residents who lived on streets that are lined with healthy, mature trees have been reaping the benefits of their existence whether they realize it or not. We tend to notice street trees the most when they are overwhelmingly present or absent. In their absence, homes and buildings along the street often take on a bland, literal appearance, especially when the sun heats up dark pavement and brightly shines on uninterrupted surfaces of concrete, brick, siding, and rooftops. Here, the only shelter from the heat or rain is under manmade structures.
On a street lined with mature trees, natural outdoor pseudo-shelters are created and neighborhood aesthetics skyrocket. Shade keeps both outdoor and indoor environments cooler, rainwater runoff is mitigated by both leaves and roots, paved surfaces last longer due to lack of direct sunlight, and the urban streetscape suddenly escapes from the feeling of being segregated from nature. Therefore, both the seen and unseen effects of street trees provide a persuasive argument for incorporating nature’s largest plants into our built environment.
It should come by no surprise, then, that studies have found positive associations between urban tree canopy coverage and wealth. Although much research still needs to be done before making sweeping generalizations, the available evidence suggests that greater tree canopy coverage in a neighborhood equates with higher income of its residents, and also perhaps decreased presence of minority populations. In a study by Zhu & Zhang (2008), titled “Demand for Urban Forests in United States Cities,” a clear relationship between forest cover and per capita income was illustrated by analyzing tree canopy coverage in satellite imagery in 210 U.S. cities.
When comparing the tree coverage to economic data, one of the authors’ main findings was that forest cover increased by 1.76 percent for every 1 percent rise in per capita income. According to the researchers, the most probable reason for this relationship is the greater ability of higher income communities to afford trees as well as the presence of larger property sizes to accommodate them. Additionally, cities with more money in their coffers are often better able to plant and maintain a more robust forest canopy, even to the extent of having watchdog organizations such as shade tree commissions. In most areas, trees are seen as a luxury good as opposed to a needed part of urban infrastructure (De Chant, 2012; Zhu & Zhang, 2008).
In a study titled “Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity” by Landry & Chakraborty (2009), the relationships between public right-of-way street trees and racial and economic variables were investigated in Tampa, Florida using remote sensing procedures to quantify tree coverage. The results of their analyses indicated that higher quantities of low-income residents, renters, and African-Americans tended to reside in neighborhoods with lower amounts of tree cover. Their techniques also accounted for the heterogeneity of land use in urban areas as well as spatial dependence in the data (Landry & Chakraborty, 2009). Climate studies done by Dr. Will Wilson of Duke University in “Constructed Climates: A Primer on Urban Environments,” also suggest the same correlations between tree canopy coverage and wealth, housing tenure, and ethnicity (2011).
Why does this happen? There are a number of possible converging reasons. As suggested by Zhu & Zhang (2008), residents of higher income areas are more likely to demand trees, but primarily as luxury items to enhance aesthetics and boost land values. Cities in good financial straights are more likely to maintain existing street trees and to replenish tree-deprived streets. Even in larger cities in good financial states, poor, minority neighborhoods are more likely to be neglected by city services and programs. Just as poorer neighborhoods are often left in food deserts with crumbling infrastructure and inadequate access to public transportation and a decent education, poor residents are often left with little street tree coverage.
There are different ways to measure tree canopy coverage. The most intensive, yet most accurate method is called “bottom-up.” This involves a survey of each tree on the lands of interest, which includes measuring location, height, canopy width, and so on. For more detailed measurements, a Cajanus tube is used to measure canopy coverage throughout a grid or line of points along the forest (or street) floor. For larger-scale analyses, evaluation of aerial photographs or satellite images is conducted in what is appropriately called the “top-down” method. Aerial photographs can have high enough resolution for the clear identification of tree species by experts. Remote sensing techniques can be used to determine tree canopy coverage with an algorithm that calculates coverage using infrared satellite imagery.
A prime example of how land cover maps are used to determine tree canopy coverage is the Urban Tree Canopy (UTC) assessment (Alam, 2012). Dr. Tim De Chant’s “Income Inequality, as Seen From Space” (2012) posting on his Per Square Mile blog page illustrates how the public can easily see income inequality through satellite images on Google Earth. He displays two images for each selected city, the first representing a neighborhood deprived of adequate tree canopy coverage, and thus having lower income levels, and the second representing a neighborhood with ample tree coverage, thus indicating higher levels of wealth.
Using the “World-Imagery” base map in ArcGIS ArcMap, along with census tract shapefiles and U.S. Census data from the 2010 American Community Survey 5-year population estimates, I sought to compare tree canopy coverage and wealth, along with other variables. Like Dr. De Chant’s post, my methods are simple and non-scientific, and involve the selection of four census tracts from a single region, each representing a different level of tree canopy coverage based on eyeballing the images. What I did differently, however, is choose groups of census tracts that have similar population densities (usually within 1,000 people per square mile of each other). In most cases, neighborhoods with lower population densities are able to house more trees due to larger parcels of land and the ability to incorporate more natural land areas. As mentioned by Zhu & Zhang (2008), wealthier communities often have more canopy coverage due to larger properties and the push of residents to have more trees as aesthetically pleasing luxury goods. Wealthier communities are also better able to maintain and replenish their street trees.
Thus, I sought to compare tree canopy coverage in census tracts of similar population densities to find out whether there are differences in wealth, race, and modes of commuting. Of course, similar population density does not go hand-in-hand with population distribution across the landscape, but it certainly increases the likelihood. For example, two census tracts could have the same land area but one tract is divided in half by a dense apartment complex and industrial warehouses. The other tract is fully covered by a single-family home development, yet both have the same population density. I tried, however, to limit this condition as much as possible and to limit visible land uses to residential in most cases. I was indiscriminate in choosing these census tracts and thus did not look at the census data before selecting them as to not bias the results. I only looked at population density and the visible tree coverage for selection.
Below are image series of census tracts from different areas of the country with complimentary Census data underneath each tract.
Key to the table abbreviations:
POPD = Population Density, POP = Total Population, MHHI = Median Household Income, MFI = Median Family Income, CPA = Percent on Cash Public Assistance, FSTP = Percent Receiving Food Stamps, IBPV = Percent Who’s Income is Below Poverty Level, UNPD = Percent Unemployed, WHT = Percent White, ASN = Percent Asian, PBTR = Percent Commuting by Public Transportation, WLK = Percent Commuting by Walking.
The results of this very small survey of tree canopy coverage by census tract in three areas of the United States indicate a varied relationship between income and tree coverage. The most solid median household income relationship to tree canopy coverage occurred in the southern Bergen County area in New Jersey. The census tracts in the Houston area also closely represented this relationship. In Charlotte and Newark, however, the relationship seemed to fall apart. This could be for a number of reasons. First of all, since I was looking for tracts with similar population densities, this restricted my search. I could have been biased and purposely selected a series of census tracts in the Charlotte area that represented the positive relationship between income and tree canopy coverage, but I did not do so.
There may be a series of census tracts with the same population density in the Charlotte area that do represent the street tree coverage-income hypothesis, but that relationship is not represented in my sample. The tract series from Newark is a prime example of how the relationship does not work. The census tract with the most tree coverage has the same, low median household income (about $39,000) as the tract with the least tree coverage. The two tracts in between shift from a median household income of about $59,000 to $26,000. If one looks at these two tracts alone, they are representative of the positive relationship between tree coverage and income. The entire series, however, paints a more complicated picture.
The added variables of poverty, race, and commuting seem to hold little to no relationship to tree canopy coverage. According to these samples, tree coverage seems to have little or no bearing on food stamps, cash public assistance, unemployment, income below poverty level, White persons, Asian persons, or the use of public transportation or walking to get to work. Of course, a much larger sample would be needed to make generalizations. The variable that most closely follows the amount of tree canopy coverage is median household income, even though the results in this sample are varied. In order to obtain a more accurate picture of how tree canopy coverage relates to wealth, studies need to control for variables such as population density and land use, to compare primarily residential neighborhoods of similar population distribution.
When comparing less dense, tree-abundant suburban areas to dense, tree-absent urban neighborhoods, the relationship is likely to hold true. Suburban areas tend to have more wealth and more land to accommodate trees. Their residents also tend to demand trees more than populations of other income levels (Zhu & Zhang, 2008). When controlling for variables such as density, however, I believe that the income-tree coverage relationship may be more complex. There is no doubt, however, that tree coverage alone is a significant indicator of wealth and could be effectively harnessed to conduct in-depth geographic studies of income distribution across the landscape.
Alam, R. (2012). Measuring Tree Canopy Cover in an Urban Environment. GoGeomatics. Available from < http://gogeomatics.ca/>.
Burden, D. (2006). 22 Benefits of Urban Street Trees. Glatting Jackson and Walkable Communities, Inc. Available from <northlandnemo.org/images/22BenefitsofUrbanStreetTrees.pdf.>
De Chant (2012). Income Inequality, As Seen from Space. Per Square Mile. Available from <http://persquaremile.com/>.
De Chant (2012). Urban Trees Reveal Income Inequality. Per Square Mile. Available from <http://persquaremile.com/>.
ESRI (2012). I-cubed Nationwide Prime 1m or better Resolution Imagery. “World Imagery” Base map in ArcGIS ArcMap
Lee, H. (2012). How Can You Measure Income Inequality? Count the Trees. Available from <http://colorlines.com/>.
Landry, S. and Chakraborty, J. (2009). Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity. Environment and Planning A, 41(11). 2651-2670.
U. S. Census Bureau. (2010). Selected Economic Characteristics and Detailed Race, Ethnicity. American Community Survey 5-year Summary Estimates.
Wilson, W. (2011). Constructed Climates: A Primer on Urban Environments. Chicago: University of Chicago Press. Available from <http://www.sciencetime.org/ConstructedClimates/>.
Zhu, P. and Zhang, Y. (2008). Demand for Urban Forests in United States Cities. Landscape and Urban Planning, 84(3-4). 293-300.