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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.