Table 1.
Currently available measurements and summary statistics available in the foot package.
Fig 1.
Defining building footprints within a geographic area using the zonalIndex function.
Users can provide shapes to define a zone. Shaded regions of polygon footprint are included in the morphology calculations and summary measures for the red “zone.” The building footprint shapes can be included in the zone if their centroids intersect the zone (A), if any part of the footprint intersects the zone (B), or the footprint shapes can be clipped to the zone bounds (C). Building footprint data shown in this figure are made available by Microsoft under the Open Data Commons Open Database License (ODbL v1.0).
Fig 2.
Morphology metrics summarised in different representations.
The foot package calculations can include the building level (A), the area level (B), or on regular grids without (C) or with (D) overlapping local windows to create a smoothed summary calculation. Building footprint polygons are overlaid on Fig 2B, 2C, and 2D. Building footprint data shown in this figure are made available by Microsoft under the Open Data Commons Open Database License (ODbL v1.0).
Fig 3.
Varying resolution and focal radius in gridded summaries of building counts.
The output spatial resolution can be varied (A and B) and this can be used in conjunction with a circular window with a user-defined focal radius (C and D) to produce gridded summaries. Data shown are the authors’ calculations using building footprints are made available by Microsoft under the Open Data Commons Open Database License (ODbL v1.0).
Fig 4.
Overview of gridded count of buildings calculated within a 250 m focal window using the R package foot.
Results shown are 100 m x 100 m spatial resolution gridded data. Examples of the results are shown for areas around Edinburgh (A), London (B), and Liverpool (C). Full datasets are provided in the supplemental materials. Data shown are the authors’ calculations using building footprints and boundaries released by Ordnance Survey under the Open Government Licence (OGL) v3.0 (Contains OS data © Crown copyright and database right 2018, 2020).
Fig 5.
Example of gridded settlement patterns.
The settlement types were created using unsupervised clustering methods to identify potential typologies based on morphology measurements of building footprint polygons. Full dataset provided in the supplemental materials. Data shown are the authors’ calculations using building footprints and boundaries released by Ordnance Survey under the Open Government Licence (OGL) v3.0 (Contains OS data © Crown copyright and database right 2018, 2020).
Table 2.
Comparison of footprint pattern classes and the 2011 census rural-urban classification for output areas in England and Wales.
Table 3.
Comparison of footprint pattern classes and MODUM clusters for output areas in England Wales.