114 Cowett: Methodology for Spatial Analysis of Municipal Street Tree Benefits erence city data is, as McPherson (2010) admits, a poor substitute for modeling based on local data, and extrapolating modeling results from Queens to Buffalo, New York, or from Minneapolis to Fort Wayne, Indiana, can reduce estimate accuracy and validity. Nevertheless, while recognizing these limi- tations, Streets accounts for differences in street tree benefits accrued from differences in leaf surface area while metrics based on a street tree count do not. However, Streets is not a GIS program and does not provide spatial analysis of street tree benefits. Additionally, Streets does not generate benefit esti- mates for each individual street tree, but aggre- gates estimates for all street trees located within a user-defined zone, such as a municipality, neigh- borhood, or management unit. Therefore, to use Streets for the spatial analysis of street tree ben- efits, methods must be utilized in which Streets benefit estimates are referenced geographically so that user-defined zones in Streets correspond to the areas of the variables with which they are being correlated. For example, if street tree benefits are being correlated with race or owner-occupied housing, which are variables associated with United States Census Bureau blocks, user-defined zones in Streets must correspond to block boundaries. Simi- larly, if street tree benefits are being correlated with median household income or educational attain- ment, which are variables associated with Census Bureau block groups, user-defined zones in Streets must correspond to block-group METHODS The following steps illustrate a case in which street tree benefit estimates generated by i-Tree Streets are correlated with median household income data associated with block groups in a municipality. As stated, street trees and the ben- efits they provide must be correctly assigned to block groups, not a small task, when thousands or tens of thousands of street trees are involved and can be accomplished using a GIS program. Street tree inventory data from 2007 was obtained for Providence, Rhode Island, U.S. The data set consisted of more than 27,000 trees. Data for each inventoried tree included genus, species, DBH, and longitude and latitude coordinates. Next, a 2010 TIGERLine (Topologically Integrated Geographic Encoding and Referencing) block-group boundary ©2014 International Society of Arboriculture boundaries. shapefile for the 2000 Census was obtained from the U.S. Census Bureau (U.S. Census 2011a; U.S. Census 2011b). Since the implementation of the Census Bureau’s MAF/TIGER Accuracy Improve- ment Project (MTAIP) in 2002, and the release of MTAIP shapefiles in 2009, census geographic area boundaries and “local” street centerlines generally, but not always, conflate (i.e., line up), an impor- tant consideration since street tree locations are typically geo-referenced to local street centerlines. Aſter verifying that block-group boundaries lined up accurately with street centerlines, street tree data were assigned to block groups in GIS. Different options were available depending on the GIS program. For example, in ArcGIS, this can be done via a Spatial Join. In Manifold GIS, this can be done using an SQL statement. Whichever program or operation the user chooses, species and DBH data for each tree in the dataset must be associated with their respective block group because Streets requires species and DBH data at a minimum to generate benefit estimates for each inventoried tree,. Additionally, each block group must be uniquely identified in such a way that benefits generated by Streets for each user-defined zone (in this example the block group) can later be joined with block- group data variables, such as median household income. Unique identifiers can include an STFID or GEOID code (a concatenation of codes for the state, county, tract, and block group). The STFID code was used in this case. Benefit estimates were gener- ated in Streets for each block group. Benefit density was calculated by dividing benefits per block group by total street length associated with each block group. This was done because block groups vary by size and the amount of street length associated with each block group tends to vary proportion- ately. It is also consistent with methods employed by Lovasi et al. (2008). Summed street length for each block group was calculated using GIS. Ben- efit density values were then joined to a table con- taining median household income using the block group STFID code as the target column to match. To facilitate comparison with the benefit density variable, street tree counts and street tree density val- ues were calculated for each block group using meth- ods similar to those just described. Street tree density values were joined to the table already containing median household income values and benefit density
March 2014
| Title Name |
Pages |
Delete |
Url |
| Empty |
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success. You will be contacted by Washington Gas with follow-up information regarding your request.
This process might take longer please wait
