Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot

Abstract

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.

High-resolution gridded estimates of population sociodemographics from the 2020 census in California

“…Additional types of ancillary data could also be considered to further inform the apportionment of population to eventual target zones within census blocks, such as home address data [ 17 , 23 ] or mobile phone usage data [ 26 ]. The ancillary building footprint data that is utilized can also be potentially further analyzed to infer additional information about likely building types based on patterns and characteristics of building geometry and proximity to one another, analyses for which Jochem and Tatem (2021) [ 49 ] constructed the R package foot .…”

Assessing the Accuracy and Potential for Improvement of the National Land Cover Database’s Tree Canopy Cover Dataset in Urban Areas of the Conterminous United States

“…Datasets derived from satellites like Landsat 8 and the Sentinel program can provide the basis for such studies. Adding other data for population or housing density or building footprints (now available for an increasing number of countries, including Australia, Austria, Canada, Germany, Tanzania, Uganda, and the U.K. [46][47][48]) could improve estimates of tree cover. Opportunities may, thus, exist to improve tree canopy cover estimates in cities for other parts of the world using methods similar to ours, improving on global [49] or continental-scale [50] tree canopy cover datasets.…”

Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

“…We are not aware of any such global datasets at fine geographic scale that could be used for this purpose, yet; however, the IDEAMAPS Network is working toward developing such a layer [54], and several algorithms have been published and tested in small areas that could, in theory, be scaled across cites. Jochem and Tatem (2021), for example, published an R package that uses building footprints to classify settlement types, though it has only been tested in Europe and imperfectly distinguishes between urban settlements types [55]. The World Resources Institute released Python code to distinguish urban land use types, including "residential informal" land use, from Sentinel-II imagery and demonstrated its application in India and Mexico, though substantial bespoke training data were required [56,57].…”