Submitted in Consideration for the Howard T. Fisher Prize in Geographic Information Science Academic Year May 06, 2010

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1 Submitted in Consideration for the Howard T. Fisher Prize in Geographic Information Science Academic Year May 06, 2010 Quantifying Greening Opportunities in Urban Areas Yuin Mae Ng Candidate for Masters in Design Studies 2011 GSD6322 Fundamentals in Geographic Information Systems Final Project INTRODUCTION Inner Boston can be considered visually green in terms of the many public open spaces and street trees. However, there is a simultaneous reality that our fragile ecosystem and heritage is gradually being lost through insensitive urban developments. Conversely, greenery conservation and re-building need not be solely an expensive affair, especially in the light of many cities' worsening urban policy budgets today. Biodiversity and nature now have the opportunity to be treated in the light of the "green economy" - as an integral, economic part of a city's economic flows and exchanges. Introducing greenery and re-creating biodiversity within urban areas may also have an invigorating impact on a city's economy, real estate, tourism and also employment. OBJECTIVE The objective is to develop a biodiversity-sensing GIS model that seeks out greening potential within Brookline based on a multiplicity of planning parameters such as urban population and building densities, terrain and proximity to transport infrastructure as opposed to current simplified methods of appraising vacant and available spaces. BACKGROUND Towns, cities, and even some counties have been setting tree canopy 1 goals in order to achieve various environmental, social and economic benefits. For example, Boston s Urban Forest Coalition has set a goal to plant 100,000 new trees by 2020 and New York s Mayor Bloomberg has announced a million-tree goal to attain 30% canopy cover by On the other hand, some of these publicly announced goals are broad-brushed targets aiming to increase greenery wherever space is easily or currently available and does not go on to link these proposed increases in tree canopy cover to benefit populated local communities directly. Ecologically, biodiversity is highly complex. However, forests, as carriers of high biodiversity, can be broken down into a hierarchy of trees, shrubs and groundcovers. In an urban context, an area of planted tree species is a humble beginning for biodiversity. Even in such urban conditions, diversity of Figure 1. Inner Boston and the Town Of Brookline Figure 2. Inner Boston s Percentage Tree Cover 1 The tree canopy is the area of land covered by a tree s leaves, branches and trunk when viewed from above YUIN MAE NG P1

2 bird-life can exist and it is on this basis that a biodiversity network can be proposed and created in Boston. Thus, a first step towards the building of such urban biodiversity necessitates the exploration of potential sites augmented by various parameters and conditions and forms the basis of this paper. STUDY AREA The town of Brookline (Figure 1) was chosen for its most complete datasets available, its proximity to downtown Boston (and hence as a study of an urban fragment of Greater Boston), and also because it displayed a unique structure of having highly urbanized corridors with a mix of large green spaces. DATASETS The 7-primary datasets are obtained at 3-levels: national, state and city/ town. The national datasets used for this study are U.S. Geological Survey s (USGS) National Land Cover Dataset s (NLCD) 2001 Land Cover, Tree Canopy and Impervious Surface 30-meter resolution datalayers, the U.S. Census Bureau s Topologically Integrated Geographic Encoding and Referencing System (TIGER) 2000 Census Block population datalayer and the National Elevation Dataset s (NED) 1arcsecond (approximately 30-meter) resolution elevation datalayer. The state datasets are the Massachusetts Geographic Information Systems (MassGIS) Community Boundaries datalayer and the Executive Office of Transportation Office of Transportation Planning Roads (EOT-OTP) roads datalayer. Thirdly, the required city/ town s datasets are the Town of Brookline s 2009 parcels and buildings datalayers. Figure 3. Tree Canopy & Impervious Surface Land Cover METHOD Firstly, two study area context maps were created: a thematic map (Figure 2) showing tree cover percentages within inner Boston s cities and towns using ArcGIS zonal statistics tool and a map (Figure 3) showing the intensity of tree cover alongside impervious surfaces (which approximates the level of urban development) around the study area. In Figure 3, each cell in the NLCD tree canopy 30-meters resolution grid has values ranging from 10 to 100 and represent tree canopy percentage area distribution. Hence, each cell s has an area of 900m 2 (30m x 30m) and a cell with a value of 10 represents 10% tree cover in a single 900m 2 cell, which is calculated as 90m 2 in tree canopy coverage within that cell. Similarly for a cell value of 50, the tree canopy coverage area would be 450m 2 and for 100 it would be 900m 2. The cell values were converted to area and summed to get the total tree coverage area within the town. The use of NLCD s tree canopy here is a starting point for establishing the amount of greenery in Brookline and can be taken as a first look at the biodiversity structure of the town. For the core analysis, selected datalayers are mapped at the town level in 6-sections that cumulate into a combined statistical cell map, where each of the 6 sections represent proposed parameters that come together to visualize the desired biodiversity potential in Brookline - allowing the user to spatially identify greening opportunities within parcels and streets. Table 1 describes the incremental steps taken, the datasets required for each section and the output generated. Figure 4 shows the new values assigned to the selected classes within the 6-sections, Figure 5a and 5b the corresponding thematic maps generated for each section within the study area and Figure 6 the summary mean cell statistical map of 5 of the 6-sections. In Figure 7, the generated map is a composite visualization of the greenery potential for all town parcels and is intended as a first snap-shot at looking at the biodiversity opportunities within YUIN MAE NG P2

3 Table 1. GIS Steps, Datasets Required and Output Generated GIS Sections and Steps GIS Dataset GIS Output A. TREE CANOPY 1. Select City/ Town for analysis City/ Town (Polygon) 2. Re-classify tree canopy cell values from percentages to areas 3. Assign new weight values to cells 4. Parameter : Find tree canopy gaps using focal statistics B. POPULATION 5. Analyze spatial distribution of population density using kernel density calculation 6. Assign new weight values to population density 7. Parameter : Find greening opportunities as weighted against population density (ie. where more people live) using cell statistics C. BUILDINGS 8. Analyze spatial distribution of buildings 9. Assign new weight values to building neighborhoods 10. Parameter : Find greening opportunities as weighted against building neighborhoods (ie. where highly urbanized areas are) using cell statistics D. ROADS 11. Re-classify roads according to their classes 12. Assign new weight values to road classes 13. Parameter : Find greening opportunities as weighted against road classes (ie. where heavy road corridors are) using cell statistics E. SLOPE 14. Re-classify slopes according to their steepness 15. Assign new weight values to slopes 16. Parameter : Find greening opportunities as weighted against slope steepness using cell statistics (ie. determining where steep areas are, which are not suited for planting) F. PARCELS 17. Re-classify parcels into property type classes 18. Assign new weight values to parcels 19. Parameter : Find greening opportunities as weighted against parcel categories using cell statistics (ie. determining building uses which are also representative of urbanization levels - eg. public service parcels are weighted more for planting) G. GREENING OPPORTUNITIES 20. Summarize greening opportunities as weighted against all categories using cell statistics - Composite Deliverable : a graduated mapping of greenery potential NLCD 2001 Tree Canopy (30-metre Raster) City/ Town Population Census Block (Points) City/ Town Buildings (Polygon) EOT-OTP Roads (Arcs) NED (30-meter Raster) City/ Town Parcels (Polygon) i. City/ Town Summary Statistics Name of Town Total Population Total Area (Hectares) ii Tree Canopy Summary Statistics Total Tree Canopy (Hectares) Tree Canopy Cover Percentage iii.tree Canopy Clumps and Gaps i. Kernel Population Density ii. Tree Canopy Population Density Statistics i. Building Neighborhoods ii. Tree Canopy Building Neighborhoods Statistics i. Road Re-classification ii. Tree Canopy Road Classes Statistics i. Slope Re-classification ii. Tree Canopy Slope Statistics i. Parcel Re-classification ii. Tree Canopy Parcel Statistics i. Summary Statistics YUIN MAE NG P3

4 Figure 4. New Values Assigned to the 6-Sections Figure 5a. Thematic Maps for Section A to C YUIN MAE NG P4

5 Figure 5b. Thematic Maps for Section D to F Figure 6. Summary Mean Cell Statistics Map YUIN MAE NG P5

6 Figure 7. Overlaying and Cell Analysis YUIN MAE NG P6

7 RESULTS The final statistical results generated for the town of Brookline are tabulated in Table 2. Table 2. Summary Statistical Results Town of Brookline Greening Opportunities Population (2001) persons Priority areas No. of cells (900m 2 ) Area (Ha) (%) High % Town Area (Ha) 1767 hectares % % Total Tree Canopy 561 hectares % Cover Low % No Data % Canopy Coverage (%) 32% (561/1767) Total % DISCUSSION As outlined earlier, the 6-sections represent a first effort at looking at greenery/ biodiversity potential through multiple parameters. These parameters, with their re-assigned values and weights, help planners and managers to prioritize and introduce greenery where it is deemed crucial - suitable sloped areas in more built-up, populated, publicly-used places and heavy transportation corridors in an attempt to soften one of our greatest physical manifestations of urbanization. These 6-sections, when taken together, underscore a more targeted approach to the re-creation of urban biodiversity; in contrast to standard approaches like planting trees wherever space is available. Urban biodiversity can start by planting clusters of selected trees in high priority areas as a backbone for such greenery to build upon. For example, looking at the map results in Figure 7, the composite pattern of red (high priority) cell clusters fall along Beacon Street (one of two major roads in Brookline), indicating a higher populated and built-up area with a high-intensity transport corridor. This linear pattern forms the focus of potential greenery areas for Brookline. The different compositions of these priority cell areas (linear, points, etc.) can be analysed further and can provide strong justifications for enhancing urban biodiversity. This can lead to the formation of greenery plans and policies and where: Policy follows Form. In Table 2, a large number of the high priority areas (red cells total some 32 hectares or 2% of the town area) rest along Beacon Street. A possible first-step for a biodiversity policy would be the implementation of linear planting along the entire stretch of Beacon Street. Today, some may contend that parts of Beacon Street are well planted. However, as a thorough biodiversity corridor, the tree cover along Beacon Street can be improved by planting at closer intervals various tree species and inter-planting between many of the centre median carpark lots that exist today. With proper soil works, it may also be possible to plant smaller trees on the gravel areas next to the existing tramlines along this street. These layering of trees can also help reduce the visual scale of Beacon Street as a road/transport corridor. Consequently, the interpretation of this map result has demonstrated two important example initiatives - creating an urban biodiversity corridor and proposing green mitigation measures for transport infrastructure. Another example can be taken off the map results in Figure 7. Adjacent to Beacon Street on both sides are orange (lower priority) cell cluster areas that fall into regions as compared with the linear red cell pattern along Beacon Street. Such area-based situations allow for planting in collections, ie. trees planted in groups according to species/ genera and/ or assimilating endemic planting clusters (and where actual site conditions allow). Studying an area in detail, the junction of Centre Street and Beacon Street has a large open space car-park without any greenery. This area has the potential to test the area-based collections of trees (eg. the planting of native Elms [Ulmus species from the Ulmaceae family]). Here another element of transport infrastructure has the potential to double up as a botanical exhibit YUIN MAE NG P7

8 In addition, the study s generated results are controlled by USGS tree canopy 30-meter cell size datalayer and are adequate for a city/ town scale analyse. Furthering the study, imputing finer grained data such as 10-meter cell size from city/ town street and park trees datalayers can generate neighborhood-scale results. NEXT STEPS The analysis of the composite graduated map for Brookline highlights the potential for future biodiversity. More importantly, it could be used to assist in some form of intervention required for further policy action. To refine the current model, the next steps will be to look at the biodiversity structure in terms of its network such as greenery fragmentation and patch sizes. At the tree species level, the species composition and richness (eg. number of species within selected corridors) and the percentage and proportion of native, endemic tree species to overall tree species should also be considered. Presently, the necessary data for this next step needs to be created through field surveys as not many cities/ towns have such data. Even if they do, the data is not publicly accessible. Starting with tree flora and species, a finer-grained structure can be built into Brookline (where the cell sizes in the model are reduced) and the town may even be a first example of a town-scale analysis to have a significant level of biodiversity. While there is no concise formula, the heightened biodiversity and increased tree species would essentially lead to subsequent attraction of bird-life and small mammals. For further extrapolation, this model can look into a few complex enhancements. Firstly, time-based projections such as growth of tree canopies, development of new urban parcels, growth of population and changes in traffic volume, etc.; and secondly, imputing economic value of greenery and the relationships to adjacent real estate. For the former, it will be most useful to model 5 to 10-year planning scenarios for longer-term programs and policies; while the latter can give rise to a costbenefit type of rationale for such a model, which offers a better rationale for any biodiversity initiatives in politicians eyes. CONCLUSION For politicians and planners, this entire exercise is also the creation of a model that may be further refined to address carbon sequestration patterns, GHG remediation, eco-tourism, etc. (that are tied to tree canopy data) in the larger ambit of ecological policies; and which are slowly but surely taking more prominence now. Such tools offer ecological perspectives over current planning and zoning tools - a critical path that seems unavoidable for the future of sustainable towns and cities. While this paper exclusively demonstrates the greenery/ biodiversity potential on a town scale, it would be expected that larger regions examined together using this method and tool could better draw on synergistic relationships between each city/ town in attempting to create a full-scaled biodiversity network. ACKNOWLEDGEMENTS The author would like to thank Paul Cote, lecturer for the GSD6322 Fundamentals in Geographic Information Systems course; for his patience, instruction and guidance. REFERENCES Massachusetts Geographic Information Systems. (n.d.). Download Free Data. Retrieved 2010, Feb 10, from Town of Brookline GIS Dataset. (2009). From ~_content&view=article&id=93&itemid=256. Retrieved 2010, Apr 20, indirectly through Harvard University Graduate School of Design Network Drive Geo Folder. U.S. Geological Survey GIS Datasets. (n.d.). National Land Cover Datasets (2001) & National Elevation Dataset (n.d.). Retrieved 2010, Apr 20, from ~seamless/viewer.htm YUIN MAE NG P8