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

The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five U.S. metropolitan areas

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Settlement relationships and their morphological homogeneity across time and scale

Homogeneous settlement morphologies negatively impact urban vibrancy, the environment, and emotions. Mainly resulting from the separation of functions such as work and living, homogeneous settlements have often been found around large cities. However, it remains unknown whether this phenomenon occurs in settlements of any size and persisted over time. In this study, we investigated the relationship between the internal structures of settlements and their location within a settlement network at a large spatial scale and a fine resolution, over seven time steps covering 120 years of settlement development. Using building footprints and road geometries from historical maps of the Swiss Plateau in combination with historical travel speeds, we analyzed networks at both the local- (building networks) and the regional-scale (settlement networks). Our findings show that particularly small settlements located near larger settlements exhibit a high degree of morphological homogeneity, and that this pattern persisted since the early twentieth century despite strong changes in mobility. These results suggest that the position of a settlement within a settlement network can have an impact on its morphological homogeneity, which in turn can have consequences for the functionality and livability of the settlement and provides useful insight to the development of settlements.

Geographical characterisation of British urban form and function using the spatial signatures framework

The spatial arrangement of the building blocks that make up cities matters to understand the rules directing their dynamics. Our study outlines the development of the national open-source classification of space according to its form and function into a single typology. We create a bespoke granular spatial unit, the enclosed tessellation, and measure characters capturing its form and function within a relevant spatial context. Using K-Means clustering of individual enclosed tessellation cells, we generate a classification of space for the whole of Great Britain. Contiguous enclosed tessellation cells belonging to the same class are merged forming spatial signature geometries and their typology. We identify 16 distinct types of spatial signatures stretching from wild countryside, through various kinds of suburbia to types denoting urban centres according to their regional importance. The open data product presented here has the potential to serve as boundary delineation for other researchers interested in urban environments and policymakers looking for a unique perspective on cities and their structure.

Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19.

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

This paper introduces a series of high resolution (100-meter) population grids for eight different sociodemographic variables across the state of California using data from the 2020 census. These layers constitute the ‘CA-POP’ dataset, and were produced using dasymetric mapping methods to downscale census block populations using fine-scale residential tax parcel boundaries and Microsoft’s remotely-sensed building footprint layer as ancillary datasets. In comparison to a number of existing gridded population products, CA-POP shows good concordance and offers a number of benefits, including more recent data vintage, higher resolution, more accurate building footprint data, and in some cases more sophisticated but parsimonious and transparent dasymetric mapping methodologies. A general accuracy assessment of the CA-POP dasymetric mapping methodology was conducted by producing a population grid that was constrained by population observations within block groups instead of blocks, enabling a comparison of this grid’s population apportionment to block-level census values, yielding a median absolute relative error of approximately 30% for block group-to-block apportionment. However, the final CA-POP grids are constrained by higher-resolution census block-level observations, likely making them even more accurate than these block group-constrained grids over a given region, but for which error assessments of population disaggregation is not possible due to the absence of observational data at the sub-block scale. The CA-POP grids are freely available as GeoTIFF rasters online at github.com/njdepsky/CA-POP, for total population, Hispanic/Latinx population of any race, and non-Hispanic populations for the following groups: American Indian/Alaska Native, Asian, Black/African-American, Native Hawaiian and other Pacific Islander, White, other race or multiracial (two or more races) and residents under 18 years old (i.e. minors).

Classification of Building Types in Germany: A Data-Driven Modeling Approach

Details on building levels play an essential part in a number of real-world application models. Energy systems, telecommunications, disaster management, the internet-of-things, health care, and marketing are a few of the many applications that require building information. The essential variables that most of these models require are building type, house type, area of living space, and number of residents. In order to acquire some of this information, this paper introduces a methodology and generates corresponding data. The study was conducted for specific applications in energy system modeling. Nonetheless, these data can also be used in other applications. Building locations and some of their details are openly available in the form of map data from OpenStreetMap (OSM). However, data regarding building types (i.e., residential, industrial, office, single-family house, multi-family house, etc.) are only partially available in the OSM dataset. Therefore, a machine learning classification algorithm for predicting the building types on the basis of the OSM buildings’ data was introduced. Although the OSM dataset is the fundamental and most crucial one used for modeling, the machine learning algorithm’s training was performed on a dataset that was prepared by combining several features from three other datasets. The generated dataset consists of approximately 29 million buildings, of which about 19 million are residential, with 72% being single-family houses and the rest multi-family ones that include two-family houses and apartment buildings. Furthermore, the results were validated through a comparison with publicly available statistical data. The comparison of the resulting data with official statistics reveals that there is a percentage error of 3.64% for residential buildings, 13.14% for single-family houses, and −15.38% for multi-family houses classification. Nevertheless, by incorporating the building types, this dataset is able to complement existing building information in studies in which building type information is crucial.

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

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.