Trees and Health

Trees and Road

Omid Kardan, Department of Psychology, The University of Chicago, and his research team took two data sets and analyzed them to find that the more greenspace available, in the form of tree canopy along roads, the better it is for health.

Study Background

The first dataset combined satellite data with a database of all trees in Toronto, Ontario, Canada. This allowed the researchers to get detailed greenspace, in this case tree canopy cover, information on any location in the city. They focused on trees in public areas, assuming that the general population had more access to them than trees located on private property.

The second data set was created from questionnaires about general health perception, cardio-metabolic conditions and mental illnesses that fed the Ontario Health Study. This data associated the health of individuals with known addresses in the city.

Since everyone in Canada has equal access to health care, that variable was removed from the study. Socio-economic data was used because in Canada those with higher socio-economic status tend to use health care services more, thus, presumably, leading to better health.

variables  chart

Figure 4 from the study: The canonical correspondence model that was used in our canonical correlation analyses to assess the relationship of the predictors (socio-economic, demographic and tree density variables) with health factors.

Using a couple types of statistical analyses, the researchers were able to quantify the value of the positive effects provided by trees in the urban area while reducing the statistical ‘noise’ from other factors that can affect health.

Regression Analysis

A regression analysis is a statistical approach that attempts to describe the relationships among several variables (predictors) and their affect upon another variable (the dependent variable), in this case, health perception.  The regression analysis performed for this study found that factors that affected health perception in a positive way were:

  • De Grassi St. in Toronto

    De Grassi St. in Toronto
    Photo By GTD Aquitaine (Public domain)

    street tree density (adding 10 more trees per city block improved health perception the same as a $10,200 increase in annual household income)

  • eating more servings of vegetables and fruits in one’s diet (1 more serving per day predicts 1.2% better health perception
  • being younger (10 years less age predicts 1.5% better health perception),
  • being male (males have on average almost 1% better health perception than females),
  • having higher education (belonging to one higher educational group predicts 1.6% better health perception,
  • living in more affluent neighborhoods (belonging to one higher area median income group predicts 0.7% better health perception), and
  • having higher household income (belonging to one higher income group predicts 1.6% better health perception ).

While these results are suggestive, all predictors together explained only 9% of the variance in health perception. That’s certainly not an unusual result with data of this type, but it does tell us that there are many other factors in play here.

When using the other health factors as dependent variables, the authors found similar results for cardio-metabolic conditions although the variables that explained the positive health benefits accounted for 19% of the variance.  They were unable to derive meaningful information for ‘Mental Disorders’ and ‘Other Disorders.’  The latter situation may be a result of the fact that health conditions were self reported.  People have a tendency to under-report disorders in general and mental disorders in particular.

Canonical-Correlation Analysis

A second analysis used canonical-correlation.  this is a general procedure for investigating the relationships between two sets of variables.  In this case the ‘Demographics and Tree Density’ variables vs the ‘Health Factors.’  This analysis explained 15% of the variance in all original variables and gave generally the same results as the regression analysis.

Conclusions

Lawrence Avenue

Lawrence Avenue in Toronto
Photo By  GTD Aquitaine (Public domain)

The authors concluded that it was primarily trees along roads that benefitted health factors more than trees in parks or on private property.  Thus they suggest that planting more trees along roadways would be much cheaper than raising the income of every person that benefitted by over $10,000 each.

The study did not examine the reasons for the beneficial effects of trees, but the authors suggest improved air quality, reduced stress, or promoting physical activity as possibilities.

The final conclusion:

“In summary, our results show that street trees are associated with a significant, independent and reliable increase in health benefits in urban populations and that small increases in the number of trees along the street could improve health markedly and in cost-effective ways.”

More Information

If you’d like to look into this study in more detail, here is the citation:
Kardan, Omid; Peter Gozdyra; Bratislav Misic; Faisal Moola; Lyle J. Palmer; Tomáš Paus; Marc G. Berman. Neighborhood greenspace and health in a large urban center. Sci. Rep. 2015/07/09/online. Macmillan Publishers Limited

Header Photo Credit: By Averette [CC BY 3.0], via Wikimedia Commons

 

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