Real-time reachable space based on space-time prism:Conceptual models and measurement
The real-time interaction between human behavior and geographic context is becoming varied and dynamic.Understanding the interaction between human activity and environment has become a frontier in geographical research.Space-time prism provides a spatiotemporal integration method to understand the interaction,but its classical model is still a static model.In this study,.we utilize the space-time prism model to investigate how individual decision-making relates to their potential activity space and real-time accessibility.We enhance the classical space-time prism model by introducing the concept of decision points,resulting in an improved conceptual model of real-time reachable space that accounts for both the real-time changes of environment and the dynamics of individual decisions.We apply time slices and isochronous circles to measure real-time reachable space quantitatively.Additionally,we propose a methodology for comparing real-time accessibility across different decision points to facilitate a detailed analysis of the real-time dynamics and accessibility of human behavior within geographic space.Furthermore,to expand the model's applicability under different real-world scenarios,we introduce a real-time reachable space model that considers specific path selection and time budget as input constraints.Dynamic measurement of an individual's real-time reachable space holds great potential for understanding how geographic space influences human behavior.By quantifying variations in real-time reachable space,we can gain valuable insights into the ways geographic factors shape individual behavior.These insights will greatly contribute to evaluating and optimizing facility and service resource allocation,thereby enhancing the fitness,support,and guidance of the geographic environment for individual behavior in the modern life.
space-time prismdynamic decision makingreal-time reachable spacetime geographypotential path space
NETL
NSTL
万方数据
维普
