Edited by: Mary L. Cadenasso, University of California, Davis, United States
Reviewed by: Shawn Landry, University of South Florida, United States; Rüdiger Grote, Karlsruhe Institute of Technology (KIT), Germany
This article was submitted to Urban Ecology, a section of the journal Frontiers in Ecology and Evolution
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Municipal leaders around the world are demonstrating significant interest in urban greening to realize a range of socioecological benefits. The urban greening toolkit often includes street trees, an essential component of urban design informed by historic legacies of both human and environmental factors. To date, there has been little comparative analysis of street tree density and distribution across international and intercontinental settings, and associated research has not been situated within the broader discussion of historical legacies. This study focuses on five capital cities (Ottawa, Stockholm, Buenos Aires, Paris, and Washington, DC) situated in two climate zones and it addresses two research questions: (1) What are the density and distribution of street trees across a given city and its street hierarchy? and (2) How do these metrics compare within and between cities by climate zone? The analysis draws upon up-to-date datasets from local authorities and includes geospatial analysis of street trees across hierarchical street classes within the central zones of each city. The results show clear differences in street tree density in cities within and between climate zones as well as differences in street tree distribution in cities within the same climate zone. Substantial differences within climate zones further suggest that cultural factors—including but not limited to urban form, aesthetic norms, and governance regimes—may play a pivotal role in the distribution and density of street trees. This illustrates the importance of place-specific cultural and environmental legacies as determinants of street tree density and distribution and supports further comparative research on the topic.
Urban greening is a common practice around the world today that aims to realize a range of socioecological benefits. Urban greening activities involve organized or semi-organized efforts to introduce, conserve, and maintain outdoor vegetation in urban areas (
Today, street trees are a prominent type of flora in the urban greening toolkit and they are an essential component of urban design. They define the space of a street, delimit the pedestrian realm, calm traffic, filter sunlight, promote visual order, soften the streetscape, and introduce beauty in the form of nature (
Current patterns of urban tree distribution (or structure) and species composition are a legacy of both human and environmental (or biophysical) factors (
Street tree inventories are commonplace in urban forestry practice and research, and they typically address a range of factors including species composition, age, vigor, size, management costs, inventory methods, and estimates of ecosystem services such as carbon sequestration (
These studies illustrate the influence of cultural variables such as economic factors, development history, and street type on street tree density and distribution. This literature has, in turn, assessed street tree density and distribution in individual cities, or cities within the same region of a country. However, there has been little comparative analysis of street tree density and distribution across international and intercontinental settings and these metrics have not been included in broader discussions of legacy effects. This is noteworthy because there is substantial evidence that both national and continental contexts inform urban tree discourse, practice, and preferences (
In response to the aforementioned gaps, this article addresses two research questions: (1) what are the density and distribution of street trees across a given city and its street hierarchy? and (2) how do these metrics compare within and between cities by climate zone? The analysis of these metrics introduces broader questions of how environmental and human legacies inform the spatial structure of urban trees. The study includes five capital cities—Ottawa, Stockholm, Buenos Aires, Paris, and Washington, DC—which have shared characteristics including sites of display and monumentality, places of tourist pilgrimage, and hosts to diplomatic quarters (
As noted above, this study involves an analysis of the density and distribution of street trees in five capital cities that are located in two Köppen–Geiger climate zones. Publicly accessible street tree inventories and street hierarchy classification systems were used to conduct a geospatial analysis of street tree density and distribution.
Each of the selected cities is situated in a larger metropolitan region. Central zones were identified in each city and are characterized as medium- to high-density with high frequency of street use and diverse land use (
Maps of the central zones and street hierarchies for the five cities in the study.
Urban tree inventory research has emerged over the past four decades and has grown to encompass a variety of methods, including satellite-assisted data collection, airplane-assisted data collection, and both ground-level photography and manual visual inspection methods (
Sources of spatial data for the five cities.
City | Trees | Streets | Jurisdictional area | Bodies of water |
City of Ottawa | City of Ottawa | City of Ottawa | City of Ottawa | |
City of Stockholm | The Swedish Transport Administration | City of Stockholm | City of Stockholm | |
Jefatura de Gabinete de Ministros | Ministerio de Educación e Innovación | Instituto Geográfico Nacional | Instituto Geográfico Nacional | |
Ville de Paris | OpenStreetMap | Ville de Paris | Institut National de l’Information Géographique et Forestière | |
Washington, DC | District Department of Transportation | District Department of Transportation | United States Census Bureau | United States Census Bureau |
The datasets for each of these cities are routinely updated and the most recent version of each inventory is from 2019. There is variability in the methods of data collection by each locality; this constitutes one of the inherent challenges of doing comparative research across cities and countries (
Data were processed in ArcMap to categorize each city’s street network and to facilitate quantitative analysis of street tree density and distribution in Excel and MATLAB. There was variation in the definition of the urban core and the street classification system for each city. For example, the urban core of Ottawa was derived from the twelve urban wards (constituting a land area of 323 km2), while the urban core of Stockholm was derived from four inner city districts (constituting a land area of 36 km2). Ottawa and Stockholm are the largest and smallest urban cores in the study with Buenos Aires (204 km2), Paris (103 km2) and Washington, DC (158 km2) having intermediate spatial extents. With respect to street classification, the number of functional classes ranged from 4 in Buenos Aires to 32 in Paris.
To establish a consistent hierarchy of streets, the street network in each city was normalized to three classes commonly used in traffic engineering: local, collector, and arterial (
Official street tree definitions were identified in three of the five cities from a review of publicly available online information and through inquiries to municipal representatives. The definitions from the three cities are fairly consistent and designate street trees as those trees located in the public rights-of-way on city streets (see
Local authority definitions of street trees in each city.
City | Street tree definition |
“Any tree located completely on the right-of-way or jointly owned between the city and the property owner (where any part of the tree trunk is growing across properties)” |
|
Groups of trees or solitary trees within the public place boundary on streets, allés, and in squares |
|
Those located on urban streets and sidewalks within the jurisdictional bounds of the city |
|
[No definition identified] | |
[No definition identified] |
To reduce the heterogeneity of data derived from our cities, two strategies were employed. The first of these concerns street datasets, which were cleaned by removing irrelevant street segments representing highways, tunnels, and bridges (which rarely include street trees). Bridges were assumed to be portions of street segments that spanned bodies of water, and were removed by means of geographic overlay, allowing for a more granular quantification of street network length. The second method to reduce heterogeneity across the data concerns street tree data obtained from local authorities, which was cleaned by first removing datapoints categorized as non-street trees, subsequently removing datapoints flagged as dead, non-existent, or duplicate, and finally by applying a buffer to ensure that all trees were located within 30 m of roadway centerlines (see
Street tree data filtration process.
In each city, geospatial analysis was used to calculate the street tree density for each street segment as well as to identify the coordinate points of each segment. The coordinate points corresponding to typical segments were then used in Google Street View to obtain visual representations with minimal seasonal variation for each hierarchical class across all cities. The visual representations were used to illustrate the mean street tree density for each class in each city as experienced by the “peripatetic subject” (
Data were graphed for comparative analysis using Excel and MATLAB, and subsequently tabulated with imagery from Google Street View. Street tree point data were spatially joined to roadway segment polyline data in ArcMap to produce a segment-based density metric. The length of a given segment was used to produce a weighted mean density for each street class within a city as well as a city-wide weighted mean across all street classes. To assess the distribution of street trees across a given city, these weighted mean density values were plotted in an Excel bar chart and tabulated with its typical street image to provide an
The street tree density for the three street classes in each city as well as a weighted mean for the entire city are summarized in
Weighted mean street tree density by street class, city, and climate zone.
In the cooler climate zone, the city-wide density of Ottawa (3.5 trees/100 m) was over three times greater than that of Stockholm (1.0 trees/100 m). Much of the difference in the city-wide density of the cities can be attributed to the local street class in the two cities (Ottawa averaged 4.3 trees/100 m and Stockholm averaged 0.8 trees/100 m), in contrast to the similar densities of the arterial street class in each city (1.1 and 1.0 trees/100 m, respectively). In the warmer climate zone, Buenos Aires, Paris, and Washington, DC exhibited marked differences in street tree density on local streets (10.6, 3.6, and 7.5 trees/100 m, respectively) but similar densities on collector streets (8.7, 9.2, and 7.9 trees/100 m, respectively) and arterial streets (7.3, 7.5, and 6.6 trees/100 m, respectively).
Statistical distribution of street tree density by street segment.
Google Street View images representing the street tree density for each street class are presented in
Typical street view depiction for street tree density (trees per 100m) by street class.
The data reveal distinct differences in street tree density across the street classes, though no conclusive patterns can be defined for a given street class. Collector streets exhibited the highest street tree density in three out of five cities. The arterial street class in Paris exhibited the highest street tree density (7.5 trees/100 m) of all cities, and this is illustrated in the typical westward-looking view of Boulevard Lefebvre. Likewise, the collector class streets in Paris exhibited a higher street tree density (9.2 trees/100 m) than the collector street class of the other cities, as shown in the southwest-looking image of Boulevard Henri IV. In contrast, the local street class of Buenos Aires had a higher street tree density (10.6 trees/100 m) than the local street class of the other cities, as shown in the northeast-looking image of Camarones.
Arterial streets in Ottawa had a lower street tree density than the collector and local streets. The local streets comprise 66% of the total street network and the average of 4.3 trees/100 m is almost four times greater than the arterial streets (1.1 trees/100 m). Stockholm’s collector streets (2.1 trees/100 m) had a higher street tree density than local and arterial streets (0.8 and 1.0 trees/100 m, respectively). Reflecting the city’s large number of treeless street segments, the
This study illustrates clear differences in urban street tree density (the number of trees per 100 m of street) between the two climate zones, as well as within the same climate zone. The street tree density in the three warmer climate zone cities (Cfa/Cfb) ranged from 4.9 to 9.9 trees/100 m, while in the two cooler climate zone cities (Dfb) it ranged from 1.0 to 3.5 trees/100 m—a notable difference between and within climate zones. Within the cooler climate zone, the streets of Ottawa were on average 3.5 times as tree-dense as the streets of Stockholm, and in the warmer climate zone, the streets of Buenos Aires were on average two times as tree-dense as Paris.
The findings also show clear differences in the distribution (the number of trees per 100 m across hierarchical street classes) of street trees in cities located in the same climate zone. In Ottawa, street tree density decreased moving up the hierarchy of street classes (from 4.3 trees/100 m on local streets to 1.1 trees/100 m on arterial streets), while Stockholm exhibited a higher street tree density in its collector streets (2.1 trees/100 m) compared to its local (0.8 trees/100 m) and arterial (1.0 trees/100 m) streets. Street tree density in Buenos Aires decreased for larger streets (from 10.6 trees/100 m along local streets to 7.3 trees/100 m along arterial streets) in the same way as Ottawa, while the collector street class of Paris had a higher tree density (9.2 trees/100 m) than its local (3.6 trees/100 m) and arterial (7.5 trees/100 m) streets, similar to Stockholm. Washington, DC, on the other hand, exhibited comparable street tree densities across its street hierarchy (7.5, 7.9, and 6.6 trees/100 m).
While this study comprises a small sample size and caution should be exercised in generalizing findings beyond the cases covered, the differences in street tree density and distribution in these five cities is noteworthy. This points to the strong influence of place-specific legacies. Legacy effects can include a broad range of environmental and cultural drivers (
As noted in
Underlying urban form may also be a factor. In Buenos Aires, street trees are abundant and hierarchically distributed, with local streets displaying the highest tree density. Laid out in 1580 on an orthogonal street grid typical of the “new world” in North and South America (
In Paris, the study findings show that collector and arterial streets are between two and three times as tree-dense as local streets (see
In Washington, DC, by contrast, street tree distribution is fairly even across all street types and the city has on average 2.4 more trees per 100 m street segment than Paris (see
This study is limited to five cites, and for this reason caution should be exercised in generalizing findings beyond the cases covered. An additional limitation of this study is the variation in local data collection methods. This is a fundamental challenge of doing international comparative research, and to reduce the degree to which this variability influenced the findings, protocols were applied as described in the section on “Materials and Methods.”
By showing clear differences in the density and distribution of street trees in cities within and between climate zones, this study illustrates the importance of local legacy effects. As noted by
The heterogeneous findings of this study illuminate the need for more comparative analysis of urban greening research and practice across national and cultural settings (
Because this study relied upon a small sample of cities and only addressed two climate zones, it would be useful to expand this research to a wider range of climates and cultural contexts. This study also highlights opportunities to advance new geospatial research methods. For example, the original approach to the study involved the use of Google Earth satellite imagery to manually count trees on a 100 m segment of city street closest to the centroids of 100 cells of a grid overlaying the municipal boundary of each study city. But through the course of research, up-to-date geospatial street tree and street network datasets were identified and acquired for each city. This type of data may not be available in many cities, in which case the aforementioned method may be appropriate.
This comparative assessment of street tree density and distribution reveals substantial variation across five capital cities spanning two climate zones, and these differences can be attributed to place-specific legacy effects. The environmental legacy of a city was observed to inform differences in street tree density: Ottawa and Stockholm, located in the cooler climate zone, generally exhibited lower street tree density than Buenos Aires, Paris, and Washington, DC, which are located in warmer climate zones. However, the findings also suggest that street tree density and distribution cannot be explained by environmental factors alone. The tree density on local streets in Buenos Aires and Washington, DC was more than double that of Paris, while tree density on local streets in Ottawa was more than four times that of Stockholm. Moreover, the distribution of trees across a three-tiered street classification showed no consistent pattern. These findings reinforce the importance of place-specific legacies as determinants of citywide street tree density and distribution. Substantial differences within climate zones further suggest that cultural factors including but not limited to urban form, aesthetic norms, and governance regimes may play a pivotal role in the distribution and density of street trees, and these dimensions should be foregrounded in urban greening scholarship.
Publicly available datasets were analyzed in this study. This data can be found here: Ottawa tree inventory: Open Ottawa
Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
TSE conceptualized the study. NS developed the methodology, acquired and curated the data, and led data analysis. AK and TSE supervised the analysis and writing process. NS created the original draft. All authors edited and revised subsequent drafts of the manuscript, approved the final version, and agree to be held accountable for the work.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank the School of Architecture and the Built Environment at the KTH Royal Institute of Technology for funding this study and hosting Theodore Eisenman as a visiting scholar in the fall of 2019.
Genevieve Raymond (Section Manager at the City of Ottawa), email to Nicholas Smart, 24 August 2020.
Jennifer Gustavsson (Engineer at the City of Stockholm), email to Nicholas Smart, 1 September 2020.
City of Buenos Aires, email to Nicholas Smart, 18 February 2020.
Rederic Toussaint (Chef de la Cellule Méthode et Patrimoine), email to Nicholas Smart, 5 October 2020.
Earl Eutsler (Associate Director at DDOT Urban Forestry Division), email to Nicholas Smart, 31 August 2020.