Aim

Street trees have been shown to provide many benefits to urban dwellers, including ecosystem services such as carbon sequestration, heat-island mitigation, and storm water retention (Roy, Byrne, and Pickering, 2012; Mullaney, Lucke, and Truman, 2015). Street trees have also been shown to provide health benefits, like a reduction in childhood asthma (Lovasi, Quinn, Neckerman, Perzanowski, and Rundle, 2008). Street trees are, therefore, an amenity for residents of urban areas. However, this resource is not always equitably distributed (Landry and Chakraborty, 2009; Pham et al. 2012). This study aims to examine the spatial distribution of street trees, including factors that may correlate with street tree distribution, such as income.

Question and problem

Are street trees equitably distributed in major cities?

Location and grain

This study will examine the distribution of street trees in Philadelphia, PA at the census tract level.

Methods

Data on street trees was obtained from the Philadelphia Department of Parks and Recreation (2016). Additional data was obtained from the iEcoLab (unpublished) and the Delaware Valley Regional Planning Comission (2018).

To determine which factors strongly correlate with street tree distribution, a multiple linear regression model was run with street tree density per census tract as the response variable.

In the full model, the following explanatory variables were included: percent impervious surface cover, percent green space, particulate matter concentration, ozone concentraton, rate of people with high blood pressure, rate of people with asthma, rate of people with bad mental health, corrected property values, youth population perccentile, older adult population percentile, female population percentile, racial minority population percentile,ethnic minority population percentile, foreign born percentile, limited english proficiency population percentile, disabled population percentile, and low income population percentile.

However, there was a high degree of collinearity in the full model. The final model included only the following explanatory variables: percent impervious cover, particulate matter concentration, ozone concentration, asthma rate, racial minority percentile, ethnic minority percentile, and property values. These variables were included in the final model because they were of interest and because they did not display collinearity.

Model formula: tree density ~ percent impervious surface + fine particulate matter + ozone concentration + asthma rate + corrected property values + racial minority percentile + ethnic minority percentile

Results

Multiple factors correlate with street tree density, including property value (a proxy for income).

Figure 1: Added variable plots of street tree density

Street trees are not equitably distributed in Philadelphia, PA.

Figure 2: Spatial distribution of (a) street tree density, (b) property values, (c) percent impervious surface, (d) particulate matter concentration, (e) ozone concentration, and (f) percentile of ethnic minority population

Conclusions

These results indicate that street trees are not equitably distributed across Philadelphia, and are correlated with factors such as property value and minority population proportion. While these relationships are clear, the mechanisms driving the distribution of street trees are not. There is a positive correlation between property values and street trees. It is possible that property values have been driven up by the presence of street trees, or that more street trees were planted in “nicer” neighborhoods where property values are higher.

This analysis also demonstrates that there is a relationship between air quality and street tree density. Although we did not see a significant relationship between asthma rates and density of street trees, it is likely that street trees provide a significant ecosystem service by filtering air. Because street trees provide services, equitable distribution of street trees should be city planning priority. The implications of these results could benefit city planners in planning future tree plantings in an equitable manner. Further analysis could determine priority areas for street tree plantings.

References

Delaware Valley Regional Planning Commission. 2016 Tract-level Indicators of Potential Disadvantage for the DVRPC Region [Dataset]. Retrieved from https://www.arcgis.com/home/item.html?id=ab586640e7ab40e58c0615f9355cb35a#overview.

iEcoLab (unpublished). tracts_benefits_overall (version 3) [Dataset].

Hemken, D. (2017). Writing a function to scale. Retrieved from https://www.ssc.wisc.edu/~hemken/Rworkshops/Writing%20Functions/scale.data.frame.r.

Landry, S. M., & Chakraborty, J. (2009). Street trees and equity: evaluating the spatial distribution of an urban amenity. Environment and Planning a, 41(11), 2651-2670.

Lovasi, G. S., Quinn, J. W., Neckerman, K. M., Perzanowski, M. S., & Rundle, A. (2008). Children living in areas with more street trees have lower prevalence of asthma. Journal of Epidemiology & Community Health, 62(7), 647-649.

Mullaney, J., Lucke, T., & Trueman, S. J. (2015). A review of benefits and challenges in growing street trees in paved urban environments. Landscape and Urban Planning, 134, 157-166.

Pham, T., Apparicio, P., Séguin, A., Landry, S., & Gagnon, M.. (2012). Spatial distribution of vegetation in Montreal: An uneven distribution or environmental inequity? Landscape and Urban Planning, 107(3), 214-224.

Philadelphia Department of Parks and Recreation (2016). Street tree inventory [Dataset]. Retrieved from https://www.opendataphilly.org/dataset/philadelphia-street-tree-inventory

Roy, S., Byrne, J., & Pickering, C. (2012). A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban Forestry & Urban Greening, 11(4), 351-363.