This paper is in the following e-collection/theme issue:

Public Health Data Visualization and Pattern Recognition (65)

Published on in Vol 12, No 1 (2020):

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Authors of this article:

Dr. Albert M. Lund ;   Dr. Ramkiran Gouripeddi ;   Dr. Julio C. Facelli1
The full text of this article is available as a PDF download by clicking here.
Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Vosoughkhosravi S, Jafari A, Zhu Y. Application of American time use survey (ATUS) in modelling energy-related occupant-building interactions: A comprehensive review. Energy and Buildings 2023;294:113245 View
  2. Hubal R, Cohen Hubal E. Simulating patterns of life: More representative time-activity patterns that account for context. Environment International 2023;172:107753 View
  3. Du H, Yuan Z, Wu Y, Yu K, Sun X. An LBS and agent-based simulator for Covid-19 research. Scientific Reports 2022;12(1) View
  4. Lund A, Gouripeddi R, Facelli J. STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains. Journal of Exposure Science & Environmental Epidemiology 2020;30(3):459 View

Books/Policy Documents

  1. Thakur D, Biswas S, Pal A. Internet of Things Based Smart Healthcare. View