![\"Trees](\"https:\/\/explore.globalcreations.com\/theblog\/wp-content\/uploads\/2015\/07\/TreesandRoad550.jpg\")
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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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
![\"variables](\"https:\/\/explore.globalcreations.com\/theblog\/wp-content\/uploads\/2015\/07\/variableschart.jpg\")
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.<\/p><\/div>\n
Using a\u00a0couple types of\u00a0statistical 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.<\/p>\n
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. \u00a0The regression analysis performed for this study found that factors that\u00a0affected health perception in a positive way were:<\/p>\n
![\"De](\"https:\/\/explore.globalcreations.com\/theblog\/wp-content\/uploads\/2015\/07\/De_Grassi_St_In_Autumn225.jpg\")
De Grassi St. in Toronto
Photo By GTD Aquitaine (Public domain)<\/p><\/div>\n
street tree density (adding\u00a010 more trees per city block\u00a0improved\u00a0health perception the same as\u00a0a $10,200 increase in annual household income)<\/li>\n
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\u00a0in play here.<\/p>\n
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\u00a019% of the variance. \u00a0They were unable to derive meaningful information for ‘Mental Disorders’ and ‘Other Disorders.’ \u00a0The latter situation may be a result of the fact that health conditions were self reported. \u00a0People have a tendency to under-report disorders in general and mental disorders in particular.<\/p>\n
A second analysis used canonical-correlation. \u00a0this is a\u00a0general procedure for investigating the relationships between two sets of variables. \u00a0In this case the ‘Demographics and Tree Density’\u00a0variables vs the ‘Health Factors.’ \u00a0This analysis explained\u00a015% of the variance in all original variables and gave generally the same results as the regression analysis.<\/p>\n
![\"Lawrence](\"https:\/\/explore.globalcreations.com\/theblog\/wp-content\/uploads\/2015\/07\/Lawrence_Avenue225.jpg\")
Lawrence Avenue in Toronto
Photo By\u00a0 GTD Aquitaine (Public domain)<\/p><\/div>\n
The authors concluded that it was primarily trees along roads that benefitted health factors more than trees in parks or on private property. \u00a0Thus 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.<\/p>\n
The study did not examine the reasons for the beneficial effects of trees, but the\u00a0authors\u00a0suggest\u00a0improved air quality, reduced\u00a0stress, or promoting physical activity as\u00a0possibilities.<\/p>\n
The final conclusion:<\/p>\n