Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach

@inproceedings{Zhou2015PredictingAD,
  title={Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach},
  author={Zhengyi Zhou and David S. Matteson},
  booktitle={KDD},
  year={2015}
}
Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typically exhibit complex spatio-temporal dynamics and sparsity at high resolutions. We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it varies over hourly intervals… CONTINUE READING

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