Markovian Reeb Graphs turn Reeb graphs into generative models for simulating individual and population-level spatiotemporal human mobility patterns by embedding Markovian probabilistic transitions.
Urban Energy Flux: Human Mobility as a Predictor for Spatial Changes
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abstract
As a key energy challenge, we urgently require a better understanding of how growing urban populations interact with municipal energy systems and the resulting impact on energy demand across city neighborhoods, which are dense hubs of both consumer population and CO2 emissions. Currently, the physical characteristics of urban infrastructure are the main determinants in predictive modeling of the demand side of energy in our rapidly growing urban areas; overlooking influence related to fluctuating human activities. Here, we show how applying intra-urban human mobility as an indicator for interactions of the population with local energy systems can be translated into spatial imprints to predict the spatial distribution of energy use in urban settings. Our findings establish human mobility as an important element in explaining the spatial structure underlying urban energy flux and demonstrate the utility of a human mobility driven approach for predicting future urban energy demand with implications for CO2 emission strategies.
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2025 1verdicts
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Markovian Reeb Graphs for Simulating Spatiotemporal Patterns of Life
Markovian Reeb Graphs turn Reeb graphs into generative models for simulating individual and population-level spatiotemporal human mobility patterns by embedding Markovian probabilistic transitions.