Urban development relies on many factors to remain viable, including federal assistance with infrastructure development. The 1982 US Coastal Barrier Resources Act (CoBRA) prohibits federal financial assistance for infrastructure, post-storm disaster relief, and flood insurance in designated sections (system units) of coastal barriers. How has CoBRA’s removal of these subsidies affected rates and types of urban development? Using building footprint and real estate data (n=1,385,552 parcels), we compare density of built structures, land use types, and land values within and outside of system units in eight southeastern US states. Here we show that CoBRA is associated with reduced development rates in coastal barrier zones, but also reveal how withdrawal of federal subsidies might be counteracted by local responses. As attention increases towards improving development policy in high hazard areas, this work contributes to understanding how limitations on infrastructure subsidies can affect outcomes under overlapping jurisdictions with competing goals.

We aim to find agglomerations of U.S. counties that are partitioned by commuting patterns by representing inter-county commuting patterns as a weighted network. We develop and use a community detection method based on the configuration model to identify significant clusters of nodes in a weighted network that prominently feature self-loops which represent same-county commuting. After we apply this method to county level commuting data from 2010, we find regions that are significantly different from existing delineations such as Metropolitian Statistical Areas and Megaregions. Our method identifies regions with varying sizes as well as highly overlapping regions. Some counties belong to as many as six different statistically significant clusters but some do not belong to any. Our results offer an alternative way of categorizing economic regions from existing methods and suggest that geographical delineations should be rethought.

The measurement and characterization of urbanization crucially depends upon defining what counts as urban. According to The Indian Planning Commission, less than a third of the Indian population lives in urban areas, and while Indian cities are increasingly important to the economy, India is perceived fundamentally as a rural country. In this paper, we show that this received wisdom is an artefact of the definition of urbanity and the official statistics vastly undercount the level of urbanization and its importance for development policies in India. We begin by creating temporally-consistent, high-resolution population maps from sub district level population data available from the Indian Census for 2001 and 2011. The modeling framework is a two-step process that applies a Random Forest-based model to generate a prediction weighting layer subsequently used to inform a gridded dasymetric redistribution of original census counts at 100 m resolution (Stevens et al. 2015). We then apply density thresholds, contiguity conditions, distance based clustering and minimum population sizes to construct urban agglomerations for the entire country. Compared to the official estimates, we find that this approach counts 8%-30% (depending on thresholds) more urban population in 2011. We find large urban agglomerations that span large portions of Kerala and the Gangetic plain. Thus, while official estimates count more cities in the country, we delineate fewer cities but large urban regions that span jurisdictional boundaries. This has implication for urban policies.

Categorizing places based on their network connections to other places in the regiom reveals not only population concentration but also economic dynamics that are missed in other typologies. The US Office of Management and Budget categorization of counties into metropolitan/micropolitan central/outlying is widely seen as insufficient for many analytic purposes. In this paper we use coreness index from network analysis to identify labor market centrality of a county. We use county-to-county commute flows, including internal commuting, to identify regional hierarchies. Indicators broken down by this typology reveal counterintuitive results in many cases. Population size and level of urbanisation is largely irrelevant to strong core counties. Employment in these strong core counties grew faster in the post-recession (2008-2015) than other types, that is missed by other typologies, suggesting that this categorization may be useful for regional analysis and policy.