  Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, and social processes across the rural-urban
continuum. Most existing classifications are, however, only available at
relatively aggregated spatial scales, such as at the county scale in the United
States. The absence of rurality or urbanness measures at high spatial
resolution poses significant problems when the process of interest is highly
localized, as with the incorporation of rural towns and villages into
encroaching metropolitan areas. Moreover, existing rural-urban classifications
are often inconsistent over time, or require complex, multi-source input data
(e.g., remote sensing observations or road network data), thus, prohibiting the
longitudinal analysis of rural-urban dynamics. Here, we develop a set of
distance- and spatial-network-based methods for consistently estimating the
remoteness and rurality of places at fine spatial resolution, over long periods
of time. We demonstrate the utility of our approach by constructing indices of
urbanness for 30,000 places in the United States from 1930 to 2018 and further
test the plausibility of our results against a variety of evaluation datasets.
We call these indices the place-level urban-rural index (PLURAL) and make the
resulting datasets publicly available (https://doi.org/10.3886/E162941) so that
other researchers can conduct long-term, fine-grained analyses of urban and
rural change. In addition, due to the simplistic nature of the input data,
these methods can be generalized to other time periods or regions of the world,
particularly to data-scarce environments.
