DOI: 10.5061/dryad.jdfn2z3bb
title: Yards, block groups, and vegetation cover measures

description: Residential yards are a significant component of urban socio-ecological
 systems; residential land covers 11% of the United States and is often the
 dominant land use within urban areas. Residential yards also play an
 important role in the sustainability of urban socio-ecological systems,
 affecting biogeochemical cycles, water, and the climate via individual-
 and household-level behaviors. Vegetation, such as trees and
 grasses, are unevenly distributed across front and back yards, and we
 sought to understand how similar yards are to each other when compared to
 their neighboring yards and neighborhoods using aerial imagery. There are
 many ways to measure yard similarity, and we compared several measures to
 account for different definitions of ‘neighborness’. We examined the
 spatial autocorrelation of several yard vegetation characteristics in both
 front and backyards in Boston, MA, USA. Our study area included 1,027
 Census block groups (sub-neighborhood areas) and 175,576 parcels with
 matched front-backyard pairings (n = 351,152 yards in total) across
 Boston’s metropolitan area. This data package contains 1) 351,152 yard
 spatially-referenced yard polygons with five measures of vegetation
 summarized, 2) the containing block groups, and 3) and *.R script that
 replicates the analyses reported in Locke, D. H., Ossola, A., Minor, E.,
 & Lin, B. B. (2021). Spatial contagion structures urban vegetation
 from parcel to landscape. People and Nature, 00, 1–15.
 https://doi.org/10.1002/pan3.10254
description: <br> 1. <strong>Study Area</strong><br>
 This study focused on the Boston, MA, metropolitan region (42°21′29″N
 71°03′49″W), an area of approximately 703 km2. The region has a humid
 continental climate (mean annual temperature = 9.6 °C; mean annual
 precipitation = 1233 mm) (PRISM Climate Group 2015) and was historically
 covered with mesic forests. Forty-four percent of the land area is
 residential (Ossola et al., 2019a), which is consistent with other urban
 areas in western countries such as Baltimore, MD (Avolio et al., 2020),
 Chicago, IL (Lewis et al., 2019), Adelaide, (Australia)(Ossola et al.,
 2021), Edinburgh (Scotland), Belfast (Northern Ireland), Cardiff (Wales),
 and Leicester and Oxford (England) (Loram et al., 2007), and represents
 more than twice as much land area as parks and open spaces (18.43%)
 (Ossola et al., 2019b). Backyards compose 14% of all urban land area and
 contain ~21% of all tree canopy cover; front yards cover ~8% of the area
 and have ~8% of the study area’s tree canopy cover (Ossola et al.,
 2019b). 2. <strong>Open
 Data</strong><br> Classified LiDAR point cloud data (year
 2014) were obtained from the US Geological Survey (“MA Post-Sandy CMPG
 2013–14”, NPS = 0.7 m, vertical and horizontal accuracy = 0.05 m and 0.35
 m, respectively). High-resolution RBG-NIR imagery (1 m ground resolution,
 year 2014) were obtained from the National Agriculture Imagery Program
 (NAIP, USDA). Residential parcel polygons, building footprints, and road
 centerline data were downloaded from the open data portals of the
 Commonwealth of Massachusetts (2017) and the City of Boston
 (2017). 3. <strong>Geospatial
 analyses</strong><br> All front, corner, and backyards
 contained in all residential parcels with a house were located and
 classified in ArcGIS Desktop 10.5 (ESRI, Redlands, CA) by using the
 workflow described in Ossola and others (2019a, 2019b). Briefly, each
 house centroid was identified to fit an offset line perpendicular to the
 closest street centerline. Front and backyards were then located by
 splitting each parcel polygon with a dividing segment, perpendicular to
 the offset line, passing through the house centroid, and extending to the
 parcel’s border. Yards were classified by attributing the front yard as
 the closest unit to the respective road centerline. Corner yards, which
 lack clear front/back sides, were assigned to all parcels located within
 15 m from street intersections and were excluded from analyses. The
 workflow used to locate and classify yards exceeded 98% accuracy (Ossola
 et al., 2019a). Vegetation maps detailing tree height, canopy volume, and
 tree and grass covers were modelled and validated for their accuracy based
 on the LiDAR and RBG-NIR imagery as detailed in previous papers (Ossola et
 al., 2019a, 2019b). Briefly, tree canopy height was extracted from a
 canopy height model (1.5 m ground resolution) interpolated from the LiDAR
 data in ArcGIS Desktop 10.5 (ESRI, Redlands, CA). Tree and grass covers
 were modelled at 1.5 m resolution by using maximum likelihood supervised
 classification of ~100,000 pixels manually attributed to one of three land
 cover classes (i.e., tree, grass and non-vegetated cover), and based on
 the tree canopy height map and the RGB-NIR imagery (Singh et al., 2012).
 The average vertical accuracy of the tree height data, as recorded by the
 LiDAR point cloud, is 5.3 cm. The accuracy of the grass and tree canopy
 cover classification is 91.7% and 98.9%, respectively (Ossola &
 Hopton, 2018a). Canopy volume was calculated as the product of tree canopy
 cover and height within each pixel, assuming this volume to be completely
 occupied by vegetation (Ossola & Hopton, 2018a, 2018b), which
 overestimates total volume. Because these remotely sensed data view the
 earth from above, and tree canopy overhangs turf, the turf estimates are
 plausibly underestimates (Akbari et al., 2003). 
 <strong>References</strong>
 Akbari, H., Rose, L. S., & Taha, H. (2003). Analyzing the
 land cover of an urban environment using high-resolution orthophotos.
 Landscape and Urban Planning, 63(1), 1–14.
 https://doi.org/10.1016/S0169-2046(02)00165-2<br> Avolio, M. L.,
 Blanchette, A., Sonti, N. F., & Locke, D. H. (2020). Time Is Not
 Money: Income Is More Important Than Lifestage for Explaining Patterns of
 Residential Yard Plant Community Structure and Diversity in Baltimore.
 Frontiers in Ecology and Evolution, 8(April), 1–14.
 https://doi.org/10.3389/fevo.2020.00085<br> Lewis, A. D., Bouman, M.
 J., Winter, A. M., Hasle, E. A., Stotz, D. F., Johnston, M. K., Klinger,
 K. R., Rosenthal, A., & Czarnecki, C. A. (2019). Does nature need
 cities? Pollinators reveal a role for cities in wildlife conservation.
 Frontiers in Ecology and Evolution, 7(JUN), 1–8.
 https://doi.org/10.3389/fevo.2019.00220<br> Loram, A., Tratalos, J.,
 Warren, P. H., & Gaston, K. J. (2007). Urban domestic gardens (X):
 The extent & structure of the resource in five major cities.
 Landscape Ecology, 22(4), 601–615.
 https://doi.org/10.1007/s10980-006-9051-9<br> Ossola, A., &
 Hopton, M. E. (2018a). Climate differentiates forest structure across a
 residential macrosystem. Science of the Total Environment, 639, 1164–1174.
 https://doi.org/10.1016/j.scitotenv.2018.05.237<br> Ossola, A.,
 & Hopton, M. E. (2018b). Measuring urban tree loss dynamics across
 residential landscapes. Science of The Total Environment, 612, 940–949.
 https://doi.org/10.1016/j.scitotenv.2017.08.103<br> Ossola, A.,
 Jenerette, G. D., McGrath, A., Chow, W., Hughes, L., & Leishman,
 M. R. (2021). Small vegetated patches greatly reduce urban surface
 temperature during a summer heatwave in Adelaide, Australia. Landscape and
 Urban Planning, 209.
 https://doi.org/10.1016/j.landurbplan.2021.104046<br> Ossola, A.,
 Locke, D. H., Lin, B., & Minor, E. (2019a). Greening in style:
 Urban form, architecture and the structure of front and backyard
 vegetation. Landscape and Urban Planning, 185(November 2018), 141–157.
 https://doi.org/10.1016/j.landurbplan.2019.02.014<br> Ossola, A.,
 Locke, D. H., Lin, B., & Minor, E. S. (2019b). Yards increase
 forest connectivity in urban landscapes. Landscape Ecology, 7(12).
 https://doi.org/10.1007/s10980-019-00923-7<br> Singh, K. K., Vogler,
 J. B., Shoemaker, D. A., & Meentemeyer, R. K. (2012).
 LiDAR-Landsat data fusion for large-area assessment of urban land cover:
 Balancing spatial resolution, data volume and mapping accuracy. ISPRS
 Journal of Photogrammetry and Remote Sensing, 74(November), 110–121.
 https://doi.org/10.1016/j.isprsjprs.2012.09.009
description: This package includes 1) a shapefile of yard polygons, and 2)
 an R markdown: bf_chm.shp is a polygon
 layer (n=360,846) containing yards' morphological characteristics.
 Each of the fields are described below.
 Boston_residential_autocorrelation.Rmd the R Markdown file used
 to perform all of the analyses. Additional exploratory work, and analyses
 not included in the paper can also be performed with that file and all of
 the paper contents' can be replicated.  
    YARD_ID is a unique identifier for each yard<br>    YARD
 describes whether a yard is in the "front" of "back"
 of a house in each parcel<br>    YARD_AREA is the yard area (m2) in
 each parcel [part (ie within each YARD_ID)]  
  BACK_AREA is the back-yard area (m2)<br>    FRONT_AREA is the
 front-yard area (m2)    canBACK is the percent canopy
 cover in the back-yard<br>    canFRONT is the percent canopy cover
 in the front-yard<br>    volBACK it the vegetation volume on a per
 area basis (m3/m2) in the back-yard<br>    volFRONT it the
 vegetation volume on a per area basis (m3/m2) in the back-yard<br>  
  AhgtBACK is the Average woody vegetation height in the
 back-yard<br>    AhgtFRONT is the Average woody vegetation height in
 the front-yard<br>    MhgtBACK is the Maximum woody vegetation
 height in the back-yard<br>    MhgtFRONT is the Maximum woody
 vegetation height in the front-yard<br>    turfBACK is the percent
 turf cover in the back-yard<br>    turfFRONT is the percent turf
 cover in the front-yard <br>    PARCEL_ID is a
 unique identifier for each parcel<br>    PARCELAREA is the total
 parcel area (m2)<br>    T_YARDAREA is the yard area (m2) in each
 parcel<br>    BUILD_AREA is the area of all building footprints
 within a parcel (m2)    TYPEHH is the type of
 residential household (1, 2 or 3 families) from parcel data
    Offset is the perpendicular distance of the house centroid
 from the closest road centerline<br>    Downtown is the distance
 from the house centroid to the City of Boston Town Hall Building
 (downtown)<br>    POINT_X is the horizontal coordinate
 (NAD_1983_2011_UTM_Zone_19N, WKID: 6348 Authority: EPSG)<br>  
  POINY_Y is the vertical coordinate (NAD_1983_2011_UTM_Zone_19N, WKID:
 6348 Authority: EPSG) <br>    ID_CBG is a unique
 identifier for Census Block Groups<br>    NAME is the name of Census
 Block Groups<br>    THHBASE is the base number of households used
 in ESRI's Tapestry classification system of block groups (https://www.esri.com/en-us/arcgis/products/data/data-portfolio/tapestry-segmentation)<br>    TADULTBASE is the number of adults used in ESRI's Tapestry data<br>    Shape_Leng polygon perimeter length<br>    Shape_Le_1 polygon perimeter length, redundant<br>    Shape_Area polygon area<br>    TSEGNUM Tapestry Segment Number (https://www.esri.com/en-us/arcgis/products/data/data-portfolio/tapestry-segmentation)<br>    TSEGCODE Tapestry Segment Code<br>    TSEGNAME Tapestry Segment Name<br>    TLIFECODE Tapestry LifeMode Code<br>    TLIFENAME Tapestry LifeMode Name<br>    TURBZCODE Tapestry Urbanization Code<br>    TURBZNAME Tapestry Urbanization Name<br>    MEDHINC_FY Median Household Income from American Community Survey 2014 5-year estimate<br>    sample used for randomly subsetting and cartography<br>    STREETNAME <br>    RIGHTOFWAY <br>    street_nam