Where should you plant trees to optimize reduction in stormwater runoff?
Which neighborhoods meet your biodiversity goals and which do not?
Which neighborhoods should you prioritize for future plantings?
Edmonton’s trees provide more than $30M in annual benefits. Ecosystem benefits for each tree entered into yegTreeMap, a map maintained by the City of Edmonton in Alberta, Canada, get calculated using regional and species-specific standards set by the USDA Forest Service (iTree). Calculating ecosystem benefits can help justify funding for maintenance and investment in urban trees. However, there are not many tools to model the impact of management decisions in scientific terms or figure out what actions to prioritize.
In this post, we’ll look at observations that can be deduced from data on tree location, diameter and species, and more specifically how trees are distributed across the city. For our analysis, we used tree data from yegTreeMap, which contains information on over 260,000 trees across 375 neighborhoods, and Edmonton’s Open Data Portal, a free repository of over 500 city-level data sets. In some cases, analysis of urban forestry data can provide opportunities to explore topics like political advocacy and environmental justice in quantifiable terms.
How are trees distributed throughout the City of Edmonton?
One way to answer this question is to break down Edmonton into its constituent neighborhoods and sort them, first by total number of trees and then by density to control for geographic area.[2] As expected, neighborhoods with large public parks dominate the lists for raw number of trees and tree density per square mile. Industrial parks fall out toward the bottom of the list.
In addition to measuring tree density, simple queries of tree inventory data can also reveal more sophisticated insights. We used a similar sorting technique to identify the largest tree of each species in each neighborhood, and plotted the results in an interactive map here (Figure 1). This visualization can indirectly illustrate biodiversity (or lack thereof) by comparing it with the tree density ranking we created earlier. If you have two or more neighborhoods with a similar density, they should theoretically look similar on the map in Figure 1 if biodiversity is uniform across the neighborhoods. But, if one of those neighborhoods only has a few species – even if they have many trees – the map will look comparatively sparse.
For example, the neighborhoods of Ottewell and Clareview Town Centre have almost identical tree densities with 225 and 225 per square kilometer, respectively. Ottewell’s map is speckled with dozens of dots, each representing a unique species. In contrast, Clareview Town Centre shows just nine distinct species (equating to roughly 28% of Ottewell’s biodiversity).
How do you explain the discrepancy? Hundreds of American Elm trees were planted in Clareview, almost to the exclusion of other species creating a concentrated monoculture. This limited biodiversity puts Clareview at a greater risk of losing trees to pests and diseases like Dutch Elm disease. Identifying neighborhoods with lower biodiversity and prioritizing plantings in those areas can avert disaster decades later when a disease or invasive pest attacks one species aggressively.
What variables are related to the distribution of trees throughout Edmonton?
When using analytical methods to better understand tree composition and distribution, the natural impulse is to look for complementary data sets. Establishing a casual connection between tree counts and economic or social variables is nearly impossible. However, correlational data can help explain some of the variation in tree count in geographically similar neighborhoods. In Edmonton, we looked at neighborhood-level statistics for variables ranging from employment to real estate to school enrollment to identify those bore the closest relationship to the number of trees in each neighborhood.
The first level of analysis looks at which variables correlate strongly with higher tree count (i.e. positively correlation) and which correlate with lower tree count (i.e. negatively correlation). Distinctly positive variables included the number of duplex homes, single detached homes and employed people in a given neighborhood, while distinctly negative variables included the number of row homes and whether or not the neighborhood was designated a manufacturing zone.
Those relationships are relatively intuitive, but a second level of analysis, looking at the statistical significance of those relationships, provides more insight. For instance, the number of homemakers in a neighborhood is only 50% significant for predicting the number of trees—a neighborhood with a high number of homemakers is just as likely to have a low number of trees as a high number of trees. Similarly, knowing the number of retirees and unemployed residents is not useful for predicting the amount of trees in a given neighborhood.
Most variables are poor indicators on their own, but we can combine them to get more descriptive models. In Edmonton’s case 40% of the variance in tree count across neighborhoods can be explained by just two variables: the number of employed people under the age of 30 and the number of row homes. The former is positively correlated with tree count, while the latter is negatively correlated.
Looking at more variables provides diminishing returns—our models that used three variables could account for 45% of the variation, four variables for 47%, five variables for 48%, and so on. The complex systems interacting with a large urban forest like Edmonton’s can’t be satisfactorily explained by the relationship among a few variables.
Regression analysis can identify auxiliary data sets that are most closely related to the overall health of the urban forest. In Edmonton’s case, employment numbers and real estate characteristics were inextricably tied to the number of trees in each neighborhood while school enrollment data and residents not in the labor force (homemakers, retirees, etc.) bore little in common with tree count.
Conclusion
Simply knowing the location and species of trees within an inventory can yield worthwhile insights into the strengths and weaknesses of your urban forest. Understanding more about the composition and distribution of trees can inform resource allocation, project prioritization and risk assessments. By combining tree inventory data with other open data sets, analysis can yield richer insights into the impact an urban forest has on its community.
For additional detail on performing your own tree inventory analysis see Loading Spatial Data into PostGIS with QGIS and Analyzing OpenTreeMap Data with PostGIS.
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[1] Image Source: http://bit.ly/29tUXET
[2] Edmonton’s neighborhoods are actually standardized geographical territories recognized by the city and are used as census tracts.
Read these related posts:
- How to calculate the environmental and economic benefits of your bioswales and rain gardens
- How to Incorporate Natural Disaster Preparedness into your Management Plan
- How to Geocode Address-Based Tree Inventory Data
- Practical Methods for Reducing Urban Tree Mortality
- Building the Best Technology for the Longterm Monitoring of Urban Trees
