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Memory-endowed US cities and their demographic interactions.

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    US cities exhibit demographic correlations over 25 years and 200 km, showing more coherent evolution than Spain. Cities demonstrate a demographic amnesia following historical events like wars and economic crises.

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    Area of Science:

    • Urban dynamics
    • Demography
    • Spatial analysis

    Background:

    • Understanding urban demographic dynamics is crucial for sustainable city planning.
    • Historical city evolution and interactions with neighboring urban areas require comprehensive theoretical frameworks.
    • Quantitative analysis can reveal patterns in population changes over time and space.

    Purpose of the Study:

    • To empirically investigate time and space correlations in US county demographic dynamics.
    • To quantify the characteristic memory time and distances of interaction in urban population changes.
    • To assess the resilience of US cities to significant historical events.

    Main Methods:

    • Utilized an exhaustive 170-year database from the US Census Bureau.
    • Analyzed demographic data to identify temporal and spatial correlations.
    • Measured the demographic response of cities to historical events such as wars and economic crises.

    Main Results:

    • Identified significant time and space correlations in US county demographics with a 25-year memory time and 200 km interaction distance.
    • Observed stronger demographic correlations in US cities compared to a European country (Spain).
    • Found evidence of demographic 'post-traumatic amnesia' in US cities following major historical events.

    Conclusions:

    • US cities exhibit a more coherent demographic evolution than observed in Spain, characterized by long-range spatial and temporal correlations.
    • Historical events leave a measurable, albeit temporary, imprint on urban demographic trajectories.
    • The findings provide a quantitative basis for understanding urban growth and resilience, informing sustainable urban planning.