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A tree-planting decision support tool for urban heat mitigation

  • Zoey R. Werbin ,

    Contributed equally to this work with: Zoey R. Werbin, Leila Heidari, Sarabeth Buckley

    Roles Conceptualization, Data curation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Biology, Boston University, Boston, Massachusetts, United States of America

  • Leila Heidari ,

    Contributed equally to this work with: Zoey R. Werbin, Leila Heidari, Sarabeth Buckley

    Roles Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing

    Affiliation Department of Environmental Health, Boston University, Boston, Massachusetts, United States of America

  • Sarabeth Buckley ,

    Contributed equally to this work with: Zoey R. Werbin, Leila Heidari, Sarabeth Buckley

    Roles Writing – original draft, Writing – review & editing

    Affiliation Department of Earth and Environment, Boston University, Boston, Massachusetts, United States of America

  • Paige Brochu ,

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Environmental Health, Boston University, Boston, Massachusetts, United States of America

  • Lindsey J. Butler ,

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Environmental Health, Boston University, Boston, Massachusetts, United States of America

  • Catherine Connolly ,

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Environmental Health, Boston University, Boston, Massachusetts, United States of America

  • Lucila Houttuijn Bloemendaal ,

    Roles Data curation, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Earth and Environment, Boston University, Boston, Massachusetts, United States of America

  • Tempest D. McCabe ,

    Roles Methodology, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Earth and Environment, Boston University, Boston, Massachusetts, United States of America

  • Tara K. Miller ,

    Roles Data curation, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Biology, Boston University, Boston, Massachusetts, United States of America

  • Lucy R. Hutyra

    Roles Conceptualization, Project administration, Resources, Supervision, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Earth and Environment, Boston University, Boston, Massachusetts, United States of America


Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate heat by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called Right Place, Right Tree—Boston that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer daytime land surface temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting. This web application is designed to support decision-making at multiple spatial scales, to assist city officials as well as residents who are interested in expanding or maintaining Boston’s urban forest.


In a changing climate, urban areas are facing hotter, longer summers with more extreme heat events and increased heat-related mortality and morbidity [1, 2]. In Boston, for example, the heat-related mortality rate “may more than triple to 10.5 per 100,000 people under a moderate emissions reduction scenario or reach as high as 19.3 per 100,000 under the business-as-usual emissions scenario” by the 2080s [3]. Cities are often significantly warmer than surrounding rural areas, acting as “heat islands” due to heat-absorbing construction materials and low tree canopy cover [4, 5]. Urban tree canopy can be an effective and relatively low-cost tool for reducing summer temperatures and air-conditioning costs [69], yet tree canopy is often unequally distributed along class and race lines [1012]. This makes canopy expansion an important tool for building climate resilience during hot periods, as well as improving social equity in cities [5, 10].

The City of Boston has markedly lower canopy cover than other New England cities, with trees present on about 18% of land [13]. Boston leadership has attempted to expand its urban canopy, without much success [14]. In 2007, Boston set a now-abandoned goal to plant 100,000 trees, called “Grow Boston Greener,” yet urban canopy may actually have decreased since the goal was set [15, 16]. Although Boston officials are aware of tree-planting as a powerful climate mitigation and environmental health practice, high tree mortality and limited maintenance hinder efforts to expand Boston’s canopy [3, 17, 18]. The City of Boston–like many other cities without a current master plan for tree planting–would benefit from new tools to aid in urban canopy planning.

Consideration of regional heat vulnerability has not been implemented into Boston’s planting strategies. Tools such as a Heat Vulnerability Index (HVI) can help identify regions more susceptible to heat-related morbidity and mortality based on the demographic profiles of people who live there, for example older adults or younger children who are more susceptible to heat [19]. Another component of an HVI is the land cover in different regions, as impervious surfaces can exacerbate urban heat [20]. A 2015 Tufts University study suggested priority regions for tree planting in Boston based on a HVI which considers social vulnerability, adaptive capacity, and physical variables [56]. When used in the process of creating heat adaptation interventions, an HVI, when paired with expected heat exposure can support effective, targeted interventions [21].

The City of Boston and Boston University (BU) partnered in 2018 to address questions posed by municipal decision-makers around tree planting. An interdisciplinary team of PhD students at Boston University working at the nexus of environmental sciences and public health was challenged with determining how to maximize the benefits of future tree-planting efforts, while taking into consideration populations most vulnerable to the health effects of heat, feasibility of planting (due to limited areas with open space), and tree maintenance and survival. We took advantage of the recent advances in the availability of public data that can inform heat vulnerability, as well as developments in technology for creating interactive web tools to distribute statistical analyses, to create a decision support tool called Right Place, Right Tree | Boston. In this paper, we describe the analyses and recommendations that fed into a new decision support tool, as well as the tool itself. We also describe our engagement with stakeholders to improve the tool’s usability and practicality, as well as lessons learned for reproducing this tool for other cities.

Materials and methods


Through this collaborative effort, the team produced a decision support tool to encourage successful planting: Right Place, Right Tree | Boston. This tool implements the following decision-making framework: Step 1) Choose among regions for planting trees: identify priority census tracts based on heat exposure, HVI, and planting feasibility; Step 2) Learn about considerations for a chosen region: evaluate region-specific factors such as asthma prevalence, pest prevalence, existing canopy, land ownership, and community partners; Step 3) Choose the right tree species: reduce tree mortality and maintenance costs by choosing a tree that suits the hyper-local site conditions; and Step 4) Keep your tree healthy: explore resources for tree maintenance, education, and legal matters. In Steps 1 and 2, HVI assessment, planting feasibility analyses, and land temperature are reported at the census tract level for consistency with available health data and previous Boston HVI work; our census tract analyses are therefore complemented by a species selector (Step 3) that incorporates site-specific considerations.

Heat vulnerability index

The Heat Vulnerability Index takes an indicator-based approach to risk assessment, which calls for separate consideration of vulnerability, hazard, exposure, and capacity, allowing decision-makers to compare the resulting relative data by the administrative unit of choice [22]. While some HVIs include a measure of temperature exposure within a single vulnerability index [21], the present approach allows the viewer of the decision support tool to visualize vulnerability (HVI) and heat exposure as two distinct factors which city governance may weigh differently. The Boston HVI was calculated based on methods used by Nayak et al. [19] briefly summarized as follows: the 13 variables identified by Nayak et al. [19] to be related to heat vulnerability were extracted from the American Communities Survey (ACS) 2009–2013 5-year estimate [23] and the 2011 National Land Cover Database (NLCD) [24]. Variables included sociodemographics at a census tract level (i.e. percentage of the population that is Hispanic, black, foreign born, speak English less than “very well,” with income below poverty level, over 65 years of age, over 65 years of age and living alone, age 18–64 years with a disability, age 18–64 years and unemployed). Also included were percentage houses built before 1980, density of housing units per square mile, percentage land with high building intensity areas and of open undeveloped areas. We used SAS 9.4 [25] to perform rotated Principal Components Analysis (PCA) to assign weighting factors to each variable, retaining components that met the eigenvalue and interpretability criteria. Broadly the three components retained were related to the following dimensions: 1) sociodemographic, 2) urbanicity, and 3) population age and isolation [14]. We then multiplied each variable by PCA weight and standardized the weighted variables to have a mean of 0 and a standard deviation of 1, and further categorized them (scores 1–6) based on standard deviation for a neighborhood from the observed mean, with the least vulnerable category (more than 2 standard deviations below the mean) assigned a score of 1, and the most vulnerable category (more than 2 standard deviations above the mean) assigned a score of 6. These factor scores were then summed to an overall HVI score, where a high score indicated high heat vulnerability across one or more dimensions.

Planting feasibility and heat exposure

Our planting feasibility metrics rely on a dataset produced by the University of Vermont Spatial Analysis Lab (UVM SAL). The UVM SAL created a geospatial land cover dataset using 2013–2016 imagery of Boston [26]. This dataset included mutually-exclusive classifications such as buildings, impervious surfaces, vegetation (grass/shrub), and tree canopy. Our analysis focused on the opportunities for immediate tree planting, thus impervious surfaces were not considered to be “potential” canopy. Potential canopy was defined as the vegetation (grass/shrub) land cover class [26]. Data were reported in square footage at the block group spatial scale, which was aggregated to the census tract level. Aggregating to the census tract level allowed this feasibility metric to determine whether tree-planting was a particularly good approach for heat mitigation within a census-tract, or whether other heat-mitigation approaches should be prioritized. Existing and potential canopy is reported as both total square footage and percentage of total area; both metrics are informative due to variation in census tract size. Processing of geospatial data was done in ArcGIS Pro, ArcMap 10.6, and SAS 9.4.

Census tracts for intervention were prioritized using our HVI metric as well as a proxy for heat exposure, mean of summertime mid-morning land surface temperatures (LST) derived from 2000–2013 Landsat remote-sensing imagery [27]. While LST generally overestimates air temperature, this data product was compared to a 25-station urban sensor network and displayed a very strong correlation with air temperature measurements at 2-m height. Canopy expansion was the recommended intervention for census tracts with high planting feasibility; alternative solutions were recommended for census tracts with low feasibility.

Species selector

Once census tracts with high planting feasibility and high temperature or heat vulnerability were identified, we built a dataset of tree species, based on the 34 tree species currently approved for urban planting by the City of Boston. We compiled tree characteristic data that could be used to filter tree species, including canopy spread, light requirement, resistance to breakage, site type (i.e. street or park/yard), and pollen allergenicity levels. Data on canopy spread, light requirements, and resistance to breakage were extracted from species fact sheets produced by the U.S. Forest Service [28]. Species recommendations for streets or parks/yards were produced by the Arnold Arboretum [29]. Allergens were included [30] because they can trigger asthma symptoms, creating an environmental health concern; planting high-allergen trees can therefore counteract some of the equity improvements that trees otherwise represent. Tree heat-reduction potential was derived using estimates of species transpiration rates and species leaf area, and was produced by i-Tree, a peer-reviewed software suite developed by the U.S. Department of Agriculture (USDA) Forest Service [31]. For the trees recommended based on the above conditions, we report additional information from the Forest Service fact sheets that can inform decision making, such as tree height, growth rate, pest and disease vulnerability, soil condition requirements, drought tolerance, and pruning requirements. Within this list of recommended trees, species with high potential to reduce land surface temperatures (according to i-Tree classification) are highlighted with an icon.

Right tree, right place Boston decision tool development

The decision-making tool was developed using the Shiny web framework for R 3.5. [32, 33]. App framework was created using the shinydashboard R package [34]. Maps were created using census data downloaded through the tigris R package [35] and visualized using the leaflet R package [36]. The species selector was created using simple filtering from the curated tree database using the DT R package [37]. Code and data required to generate the application are hosted at (DOI: 10.5281/zenodo.3515227).

Results and discussion

Recommendations for heat reduction through urban canopy expansion

Trees bring a number of significant benefits to a region, including heat-reduction, pollution mitigation, and stormwater runoff absorption [3840]. Because tree planting initiatives often underserve low-income communities and communities of color [41, 42], canopy expansion efforts should aim to reduce inequities by planting trees with social and environmental health considerations in mind. Therefore, we developed a Heat Vulnerability Index (HVI) that incorporates information about the socio-demographic and land cover characteristics of a region, compared spatial patterns of HVI with summertime land surface temperature, a proxy for heat exposure. Unlike other cities, however, we found that the regions of Boston that are most vulnerable to heat-related mortality and morbidity are not the same regions that experience the most intense summer heat [1012]. This is likely due to the presence of multiple urban parks, such as the Arnold Arboretum and the Emerald Necklace, which tend to be located in lower-income regions of the city. An HVI that includes a direct measure of heat exposure may therefore obscure these competing trends; we instead present our HVI results alongside LST to allow decision-makers to weigh both considerations. Results for each census tract are reported in S3 Table.

We used land cover data to determine potential areas for tree planting, which we defined as land in the vegetation (grass/shrub) land cover class. For census tracts with low potential canopy (due to high amounts of impervious surfaces or existing canopy), we recommend against prioritizing tree-planting efforts; rather, we suggest alternative solutions for heat mitigation, which include retrofitting existing buildings with cool roofing materials, or depaving, which refers to removing impervious surfaces to open up space for potential vegetation. The city could follow New York City’s lead in beginning a depaving campaign on public lands [43], and there are local grassroots efforts to depave various private properties [44] which could be subsidized or encouraged by the city. Green roofs and green walls are additional ways that plants can be integrated with the existing cityscape and buildings. Green roof gardens are particularly known for their ability to cool urban interior spaces [45], and can help address stormwater issues by capturing rainwater and removing it through evapotranspiration [46]. Green spaces, like green roofs and walls, have community benefits including increasing the aesthetic appeal of urban spaces, creating new spaces for community events, and opportunities for engaging with the process of growing plants [47]. Incorporating public-benefit features could be required of all new buildings or buildings undergoing major renovations. Green roof mandates have been incorporated into the policies of eight cities worldwide, with other forms of tax incentives, grants, or loans available in dozens of additional cities [48].

Land-cover assessments at the census-tract resolution may shape city priorities, but finer resolution is necessary to fill gaps in tree canopy and identify planting sites for individual trees. Future studies could make use of an in-development canopy inventory program, BranchingOut, through which volunteers use a smartphone app to catalog Boston’s individual trees and planting sites [49].

Tree-planting is not a panacea, especially if the tree species planted have undesirable qualities or high mortality rates. Within Boston’s list of 34 approved trees, we identified species with potential invasive qualities (e.g. Acer campestre), high volatile organic compounds (VOCs) that can contribute to ozone pollution (e.g. Nyssa sylvatica, Liquidambar styraciflua), and especially low benefits for biodiversity (e.g. Ginkgo biloba) [50]. We therefore recommend that this approved tree species list be carefully reviewed. As hardiness zones are expected to change in the coming years [51], we also recommend that this list be periodically updated to ensure that listed species can tolerate projected increases in temperature.

Mortality can further be reduced through a comprehensive re-assessment of maintenance approaches, approved trees, and public-private partnerships. The current maintenance approach includes professional maintenance guarantees for 2 years, after which tree mortality is highest [18]. Expanding the duration of planting maintenance contracts or enlisting more community support for watering may reduce the deaths of young trees. A high priority should also be placed on protecting older, larger trees, which contribute a disproportionate amount of environmental benefits [18]. Opportunities exist for public-private partnerships for tree planting and maintenance, such as Cambridge’s “Back of Sidewalk” program, in which the city plants trees on private land up to 20 ft from public sidewalks; this allows trees to be planted in conditions where they are more likely to thrive [52]. Tree-planting practices that involve community engagement may also be more likely to succeed [42].

Decision-support tool

Priority area analyses and other relevant information have been compiled in a web application called Right Place, Right Tree | Boston. The app is organized into four tabs for the following decision-making framework: 1) Identify priority areas based on heat or HVI (Fig 1). On a map of Boston, census tracts with high canopy expansion feasibility and high heat or high HVI values are highlighted. Below this map, users can view the potential for canopy expansion in each census tract. 2) Once a census tract of interest is chosen, census-tract-specific factors can be evaluated (Fig 2). A clickable map of Boston returns higher-resolution data on asthma prevalence, community partners, and land ownership. 3) Choose a tree species that fits the hyper-local site conditions well (Fig 3). “Site” here refers to the ultra-fine spatial resolution within a patch of land, at which trees experience differences such as light availability and proximity to streets. An interactive species selector allows users to filter trees at this finer resolution by preferences, such as light availability, available area, site type, allergenicity, and maintenance requirements. 4) Keep your tree healthy, using a collection of resources for city officials and residents, including city-produced guides, pest sighting updates, and links for requesting maintenance via Boston’s 311 service (Fig 4).

Fig 1. “Step 1: Choose a region for planting trees”.

The first page of the interactive decision-support tool recommends identifying a region (census tract) within Boston for planting trees. This page displays maps of mid-morning land surface temperature (LST) as well as Heat Vulnerability Index (HVI), and clicking on the map displays the neighborhood name. Below these maps, an expandable section addresses the potential for canopy expansion for each census tract, and provides a list of resources for alternatives to tree-planting that may also counter urban heat. Map tiles were republished under a CC BY license, with permission from CARTO, original copyright 2020. Data courtesy of OpenStreetMap (; OpenStreetMap contributors).

Fig 2. “Step 2: Learn about regional considerations”.

The second page of the tool displays a map of Boston with additional information that may influence decision-making within a census-tract, such as pest sightings, property ownership, and names of community organizations that may assist with planting or maintenance. Map tiles were republished under a CC BY license, with permission from CARTO, original copyright 2020. Data courtesy of OpenStreetMap (; OpenStreetMap contributors).

Fig 3. “Step 3: Choose the right tree species”.

The third page of the tool allows for the user to use preferences and hyper-local site characteristics to filter a list of tree species approved by the City of Boston. Choices can be selected on the left and the trees that meet all specifications appear to the right. Each listing includes a small image, basic size and growth information, a link to a full fact sheet, and an icon that indicates if a tree has particularly high potential for heat-reduction (based on transpiration rates and leaf area).

Fig 4. “Step 4: Keep your tree healthy”.

The fourth page of the tool provides users with resources that can be consulted for maintenance of the tree they have selected using steps 1 through 3. This includes links for contacting Boston’s tree maintenance teams, up-to-date information about pests, and tips for maintenance from the website.

This tool was created to address the obstacles to successful canopy expansion in Boston. For instance, HVI metrics are rarely ever used for decision-making, possibly due to difficulty in interpretation [53]. Additionally, HVIs derived from larger geographies and applied to smaller areas like an individual city have produced mixed results [19, 5456], leading to calls for HVIs tailored for specific regions [21]. One study applied a national HVI to Boston, but concluded that a Boston-specific HVI and geospatial analysis of surface temperatures was necessary [57]. We used similar methods to construct our HVI, but used data and methods to weight the factors specifically to Boston’s geography and demographic makeup. Compared to the previous national HVI [57], we also included additional variables in our HVI, capturing linguistic isolation, disability and employment status, highly developed land, and age and density of housing. Another obstacle to canopy expansion has been tree maintenance and upkeep. We therefore highlight potential community partners that may be contacted for maintenance help, and a filter on the species selector can be chosen to show only trees that are resistant to breakage, which may have lower maintenance requirements.

Stakeholder engagement

Development of this decision-making tool began in response to a request from a City of Boston official, who outlined general questions of interest relating to Boston’s tree canopy. We found that many questions had been thoroughly answered in the scientific literature, but had not reached Boston’s tree planting efforts or prioritization. A Boston-specific HVI analysis, coupled with a new species selector tool, could help translate scientific knowledge into practice. After an initial presentation of the tool to city officials, we received feedback that the tool could assist more with the practical aspects of decision-making, so we redesigned the app to include HVI and feasibility analysis results, maintenance-related information, and a clear step-by-step decision-making framework. We found that engaging meaningfully and repeatedly with the city officials helped ensure that our work would have a focused, direct effect on the practical decisions regarding mitigation of urban heat with tree canopy cover enhancement.

Previous research shows that canopy expansion programs can fail due to poor public opinion, caused by (among other things) exclusion of the city residents from decision-making about tree species and maintenance [42]. The decision-making tool was therefore designed for use by city residents as well as city officials. We include information on how residents, once they have selected a tree species, can request a city-funded tree planting on public land. We also include information on obtaining and caring for healthy trees if residents plant on their own property.

Broader applications

Boston is just one of a handful of cities that have struggled to expand urban tree canopy cover (see discussions of Detroit and San Francisco, respectively, in [42] and [58]). We therefore designed our decision-making framework and Shiny application to be extendable for almost any U.S. city. The Supplementary Materials include methods for calculation of an HVI using census tract data in SAS. The code for the Shiny application is hosted in Github, with commentary for adapting the text and data to other cities. The species-selector tool requires aggregating tree characteristic data for regional species, which we did manually due to our variables of interest, but for a limited number of variables the i-Tree Species Utility [31] makes this task straightforward: users can download a CSV file containing data on all trees for a region. While the regional species, demographic makeup, and urban layout will differ among U.S. cities, our proposed decision-making framework and tool provides a systematic way to balance many variables.


The expansion of urban canopy can provide many environmental and social benefits to a city, but planting and maintenance of trees requires thoughtful decision-making and proactive maintenance. Because the City of Boston has faced obstacles to creating lasting expansion of urban canopy, researchers from Boston University reviewed the city’s approach to canopy expansion. We identified obstacles in incorporating scientific knowledge into tree-planting decisions, and worked with the city to design a tool to facilitate decision-making. The tool itself can be replicated or adapted for other cities, and the development process serves as an example of a successful academic-public partnership. We anticipate strengthening this partnership as an active part of building climate resilience in Boston.

Supporting information

S1 Methods. Heat Vulnerability Index data sources and calculations.


S1 Table. Data sources and variables used in Heat Vulnerability Index calculation.


S2 Table. Principal component factor weights used to calculate Heat Vulnerability Index.


S3 Table. Heat Vulnerability Index, summer land surface temperature, and potential tree canopy for all Boston census tracts.

Land surface temperature was calculated using remotely-sensed midmorning temperatures during the months of July-August. Heat Vulnerability Index (HVI) was derived using demographic data from the American Communities Survey (2009–2013 estimate) and the 2011 National Land Cover Database. Current and potential tree canopy cover were estimated using the 2014 Boston Land Cover dataset from the University of Vermont Spatial Analysis Lab.


S1 Appendix. Copyright permissions (CC-BY) for map tiles included in Figs 1 and 2.



We thank Dr. Alison Brizius from the City of Boston for guiding our work. We also thank the BU Biogeoscience and Environmental Health programs for facilitating meeting times and spaces. We thank Dr. Patricia Fabian and Dr. Pat Kinney for guiding the HVI work and for assistance from the Biostatistics and Epidemiology Data Analytics Center (BEDAC).


  1. 1. Fitzpatrick MC, Dunn RR. Contemporary climatic analogs for 540 North American urban areas in the late 21st century. Nat Commun. 2019;10: 1–7. pmid:30602773
  2. 2. Hondula DM, Davis RE, Saha MV, Wegner CR, Veazey LM. Geographic dimensions of heat-related mortality in seven U.S. cities. Environ Res. 2015;138: 439–452. pmid:25791867
  3. 3. Walsh MMJ. Climate Ready Boston Executive Summary. 2016. Available:
  4. 4. Oke TR, Mills G, Christen A, Voogt JA. Urban Climates. In: Cambridge Core [Internet]. Sep 2017 [cited 17 Apr 2020].
  5. 5. Loughner CP, Allen DJ, Zhang DL, Pickering KE, Dickerson RR, Landry L. Roles of urban tree canopy and buildings in urban heat island effects: Parameterization and preliminary results. J Appl Meteorol Climatol. 2012;51: 1775–1793.
  6. 6. Norton BA, Coutts AM, Livesley SJ, Harris RJ, Hunter AM, Williams NSG. Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landsc Urban Plan. 2015;134: 127–138.
  7. 7. Solecki WD, Rosenzweig C, Parshall L, Pope G, Clark M, Cox J, et al. Mitigation of the heat island effect in urban New Jersey. Environ Hazards. 2005;6: 39–49.
  8. 8. Moss JL, Doick KJ, Smith S, Shahrestani M. Influence of evaporative cooling by urban forests on cooling demand in cities. Urban For Urban Green. 2019;37: 65–73.
  9. 9. McDonald R, Kroeger T, Boucher T, LongZhu W, Salem R. Planting healthy air: a global analysis of the role of urban trees in addressing particulate matter pollution and extreme heat. Plant Healthy Air Glob Anal Role Urban Trees Addressing Part Matter Pollut Extreme Heat. 2016 [cited 26 Apr 2020]. Available:
  10. 10. Schwarz K, Fragkias M, Boone CG, Zhou W, McHale M, Grove JM, et al. Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice. PLOS ONE. 2015;10: e0122051. pmid:25830303
  11. 11. Landry SM, Chakraborty J. Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity. Environ Plan Econ Space. 2009;41: 2651–2670.
  12. 12. Jennings V, Larson L, Yun J. Advancing Sustainability through Urban Green Space: Cultural Ecosystem Services, Equity, and Social Determinants of Health. Int J Environ Res Public Health. 2016;13: 196. pmid:26861365
  13. 13. I-Tree Software Suite. i-Tree Canopy. 2018. Available:
  14. 14. Abel D. Boston fails in promise to plant 100,000 trees. Boston Globe. 2018. Available:
  15. 15. Abel D. Boston struggles with goal to plant 100,000 trees. Boston Globe. 2012. Available:
  16. 16. Nowak DJ, Greenfield EJ, Hoehn RE, Lapoint E. Carbon storage and sequestration by trees in urban and community areas of the United States. Environ Pollut. 2013;178: 229–236. pmid:23583943
  17. 17. Pazzanese C. Empty sidewalk pits worry pedestrians. Boston Globe. 2011.
  18. 18. Smith IA, Dearborn VK, Hutyra LR. Live fast, die young: Accelerated growth, mortality, and turnover in street trees. PLoS ONE. 2019;14: 1–17. pmid:31067257
  19. 19. Nayak SG, Shrestha S, Kinney PL, Ross Z, Sheridan SC, Pantea CI, et al. Development of a heat vulnerability index for New York State. Public Health. 2018;161: 127–137. pmid:29195682
  20. 20. Trlica A, Hutyra LR, Schaaf CL, Erb A, Wang JA. Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape. Earths Future. 2017;5: 1084–1101.
  21. 21. Bao J, Li X, Yu C. The Construction and Validation of the Heat Vulnerability Index, a Review. Int J Environ Res Public Health. 2015;12: 7220–7234. pmid:26132476
  22. 22. van Westen, C.J. Carribean Handbook on Risk Information Management. 5.5 Methods for risk assessment. 2014. Available:
  23. 23. American Communities Survey. U.S. Census Bureau. 2018.
  24. 24. Homer C, Fry J. The National Land Cover Database. US Geol Surv Fact Sheet. 2012; 1–4.
  25. 25. SAS Version 9.4. Cary, NC, USA: The SAS Institute Inc.;
  26. 26. University of Vermont Spatial Analysis Lab. Boston Land Cover. 2014. Available:–boston
  27. 27. Wang JA, Hutyra LR, Li D, Friedl MA. Gradients of Atmospheric Temperature and Humidity Controlled by Local Urban Land-Use Intensity in Boston. J Appl Meteorol Climatol. 2017;56: 817–831.
  28. 28. Gilman EF, Watson DG. Fact Sheet ST-560. Environ Hortic Dep Univ Fla. 1994.
  29. 29. Del Tredici P. Hardy Urban Trees for the Northeast: Recommended and Otherwise. 1 May 2014.
  30. 30. Cariñanos P, Adinolfi C, Díaz de la Guardia C, De Linares C, Casares-Porcel M. Characterization of Allergen Emission Sources in Urban Areas. J Environ Qual. 2016;45: 244. pmid:26828180
  31. 31. i-Tree Software Suite. i-Tree Species Utility. Available:
  32. 32. Chang W, Cheng J, Allaire J, Xie Y, McPherson J. shiny: Web Application Framework for R. 2019;R package.
  33. 33. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2017.
  34. 34. Chang W, Borges Ribeiro B. shinydashboard: Create Dashboards with “Shiny.” CRAN. 2018;R package. Available:
  35. 35. Walker K. tigris: Load Census TIGER/Line Shapefiles. CRAN. 2018;R package.
  36. 36. Cheng J, Karambelkar B, Xie Y. leaflet: Create Interactive Web Maps with the JavaScript “Leaflet” Library. CRAN. 2019;R package.
  37. 37. Xie Y. DT: A Wrapper of the JavaScript Library “DataTables.” 2019;R package.
  38. 38. Wang J. Land surface temperature and urban heat island effects on air temperature and vapor pressure deficit in Boston, MA. 2019.
  39. 39. Livesley SJ, McPherson GM, Calfapietra C. The Urban Forest and Ecosystem Services: Impacts on Urban Water, Heat, and Pollution Cycles at the Tree, Street, and City Scale. J Environ Qual. 2016;45: 119. pmid:26828167
  40. 40. Roy S, Byrne J, Pickering C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For Urban Green. 2012;11: 351–363.
  41. 41. Pincetl S, Gillespie T, Pataki DE, Saatchi S, Saphores JD. Urban tree planting programs, function or fashion? Los Angeles and urban tree planting campaigns. GeoJournal. 2013;78: 475–493.
  42. 42. Carmichael CE, McDonough MH. Community Stories: Explaining Resistance to Street Tree-Planting Programs in Detroit, Michigan, USA. Soc Nat Resour. 2019;32: 588–605.
  43. 43. Schwartz A. Trying to Create a More Permeable New York. Gotham Gaz. 2011. Available:
  44. 44. Davis M. Striking a blow for a softer, greener world. Boston Globe. 2010.
  45. 45. Kumar R, Kaushik SC. Performance evaluation of green roof and shading for thermal protection of buildings. Build Environ. 2005;40: 1505–1511.
  46. 46. Denardo JC, Jarrett AR, Manbeck HB, Beattie DJ, Berghage RD. Stormwater mitigation and surface temperature reduction by green roofs. 2005;48: 1491–1496.
  47. 47. Orsini F, Gasperi D, Marchetti L, Piovene C, Draghetti S, Ramazzotti S, et al. Exploring the production capacity of rooftop gardens (RTGs) in urban agriculture: the potential impact on food and nutrition security, biodiversity and other ecosystem services in the city of Bologna. Food Secur. 2014;6: 781–792.
  48. 48. Savarani S. A Review of Green Roof Laws & Policies Domestic and International Examples. 2019.
  49. 49. BranchingOut Inventory. Speak for the Trees Boston. [cited 10 Aug 2020]. Available from:
  50. 50. Narango DL, Tallamy DW, Marra PP. Nonnative plants reduce population growth of an insectivorous bird. Proc Natl Acad Sci. 2018;115: 11549–11554. pmid:30348792
  51. 51. Parker LE, Abatzoglou JT. Projected changes in cold hardiness zones and suitable overwinter ranges of perennial crops over the United States. Environ Res Lett. 2016;11.
  52. 52. City of Cambridge. About Cambridge Urban Forestry: Tree Planting Programs. Available:
  53. 53. Wolf T, Chuang W-C, McGregor G. On the Science-Policy Bridge: Do Spatial Heat Vulnerability Assessment Studies Influence Policy? Int J Environ Res Public Health. 2015;12: 13321–13349. pmid:26512681
  54. 54. Harlan SL, Declet-Barreto JH, Stefanov WL, Petitti DB. Neighborhood Effects on Heat Deaths: Social and Environmental Predictors of Vulnerability in Maricopa County, Arizona. Environ Health Perspect. 2013;121: 197–204. pmid:23164621
  55. 55. Maier G, Grundstein A, Jang W, Li C, Naeher LP, Shepherd M, et al. Assessing the Performance of a Vulnerability Index during Oppressive Heat across Georgia, United States. Weather Clim Soc. 2014;6: 253–263.
  56. 56. Chuang W-C, Gober P. Predicting Hospitalization for Heat-Related Illness at the Census-Tract Level: Accuracy of a Generic Heat Vulnerability Index in Phoenix, Arizona (USA). Environ Health Perspect. 2015;123: 606–612. pmid:25635952
  57. 57. Coutts Erin, Ito K, Nardi C, Vuong T. Planning Urban Heat Island Mitigation In Boston. Tufts Univ Urban Environ Policy Plan. 2015. pmid:25567543
  58. 58. San Francisco Urban Forestry Council. Annual Urban Forest Report. 2018. Available: