Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High-Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state, excluding Alaska and Hawaii: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30 m cell size covering the 48 conterminous states. We also identify errors in the original building dataset. We evaluate precision and recall in the data for three large U.S. urban areas. Precision is high and comparable to results reported by Microsoft while recall is high for buildings with footprints larger than 200 m2 but lower for progressively smaller buildings.

Neighborhood characteristics influence natural urban energy fluxes and the choices made by urban actors. This article focuses on the impact of urban density as a neighborhood physical parameter on building energy consumption profiles for seven different metropolitan areas in the United States. Primarily, 30 × 30 m² cells were classified into five categories of settlement density using the US Geological Survey’s National Land Cover Dataset (NLCD), the US Census, and Census Block data. In the next step, linear hierarchical spatial and non-spatial models were developed and applied to building energy data in those seven metropolitan areas to explore the links between urban density (and other urban form parameters) and energy performance, using both frequentist and Bayesian statistics. Our results indicate that urban density is correlated with energy-use intensity (EUI), but its impact is not similar across different metropolitan areas. The outcomes of our analysis further show that the distance from buildings within which the influence of urban form parameters on EUI is most significant varies by city and negatively changes with urban density. Although the relationship between urban density parameters and EUI varies across cities, tree-cover area, impervious area, and neighborhood building-covered area have a more consistent impact compared to building and housing density.

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.

This research developed a method to explore density and growth rates in the 50 largest metropolitan areas in the United States. The outcomes of this method enable planners to understand the growth pattern of a region and compare it with other regions in the country. The main objectives of this research were: (1) to identify settlement types (high, medium and low density, suburbs, and urban fringes) in each metropolitan area; (2) to depict and compare general metropolitan structures across the nation; and (3) to evaluate the location of recently developed dwellings in relation to settlement types and structures and estimate growth trends. The paper combined the percentage of impervious surface raster dataset (30-m cell size) produced by the US Geological Survey (USGS) and block-level housing unit data produced by the Census Bureau to map housing density across the lower 48 states in 2000 and 2010. Neighbourhood density profiles were used to identify the settlement types (high, medium and low density, suburbs, and urban fringes). The paper then compared density-decay functions of 50 metropolitan areas to depict urban growth patterns and to quantify the number of housing units developed in each settlement type. This method effectively distinguishes regions with higher densification (infill development) rates versus regions with high-paced sprawl. The results showed that Los Angeles, New York and San Francisco metropolitan areas had high percentages of housing units developed in high-density areas between 2000 and 2010, while during the same period Birmingham, Nashville and Charlotte had high percentages of housing development in areas identified as suburbs.

In this paper, we examine two mixed-use redevelopment projects in the United States, including both regulatory and procedural contexts, to assess how decision-making processes affected microclimate outcomes. We investigate Belmar in Lakewood, CO and 29th Street Mall in Boulder, CO. Measurements and simulations of microclimate show that Belmar mitigates urban heat more effectively. Policies including building height, street patterns, and landscaping standards were key variables in this outcome. Through interviews and content analysis, we found five main factors shaping those policy choices: (1) urban vision, (2) land use and form controls, (3) design guidelines, (4) public financing, and (5) ownership/condemnation factors.