Modeling Spatiotemporal Trajectories

Author(s):  
Berkay Aydin ◽  
Rafal A. Angryk
Author(s):  
Rick Grush

This article outlines a unified information processing framework whose goal is to explain how the nervous system represents space, time, and objects. It explains the concept of the emulation theory of representation and describes an extension of the emulation framework for temporal representation. It discusses Alexandre Pouget's basis function model of spatial representation and describes how to combine the basis function model of spatial representation with the trajectory emulation model of temporal representation to yield an information processing framework that genuinely represents behavioral spatiotemporal trajectories of behavioral objects.


2020 ◽  
Vol 9 (11) ◽  
pp. 646
Author(s):  
Antoni Domènech ◽  
Inmaculada Mohino ◽  
Borja Moya-Gómez

World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such as the overcrowding of central spaces, are arousing the need to develop and protect heritage assets. Hence, the analysis of tourist spatial behaviour is critical for tackling the needs of touristified cities correctly. In this article, individual visitor spatiotemporal trajectories are reconstructed along with the urban network using thousands of geotagged Flickr photos taken by visitors in the historic centre of the World Heritage City of Toledo (Spain). A process of trajectory reconstruction using advanced GIS techniques has been implemented. The spatial behaviour has been used to classify the tourist sites offered on the city’s official tourist map, as well as to identify the association with the land uses. Results bring new knowledge to understand visitor spatial behaviour and new visions about the influence of the urban environment and its uses on the visitor spatial behaviour. Our findings illustrate how tourist attractions and the location of mixed commercial and recreational uses shape the visitor spatial behaviour. Overflowed streets and shadow areas underexplored by visitors are pinpointed.


2014 ◽  
Vol 7 ◽  
pp. c1-c17
Author(s):  
Catherine Carré ◽  
Jean-Paul Haghe

The small urban rivers of the Paris conurbation are subject to local land use and segmentation processes at the threshold between urban politics and environmental policy. At present, the obligation to restore these streams pursuant to the Water Framework Directive is challenging stakeholders to proceed as collectively as possibly in this undertaking. This article attempts to identify the points of agreement and disagreement within a shared representation of local decision-makers’ relations with waterways through several spatiotemporal trajectories that are specific to each small river. We will show that the shared management of a river involves the management of not only the resource but also of a shared space. Choosing to model the relation between local societies and their river in time and space around a land-based diagram provides local and regional authorities with an explanation of their interactions with the river and its environments and can foster their capacity to act cohesively.


2020 ◽  
Vol 4 ◽  
pp. 239821282097287
Author(s):  
Andrew S. Alexander ◽  
Jennifer C. Robinson ◽  
Holger Dannenberg ◽  
Nathaniel R. Kinsky ◽  
Samuel J. Levy ◽  
...  

Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories.


2007 ◽  
pp. 151-175 ◽  
Author(s):  
Pedro M. Jorge ◽  
Arnaldo J. Abrantes ◽  
João M. Lemos ◽  
Jorge S. Marques

This chapter describes an algorithm for tracking groups of pedestrians in video sequences. The main difficulties addressed in this work concern total occlusions of the objects to be tracked, as well as group merging and splitting. Because there is ambiguity, the algorithm should be able to provide the most probable interpretation of the data. A two layer solution is proposed. The first layer produces a set of spatiotemporal trajectories based on low level operations which manage to track the pedestrians most of the time. The second layer performs a consistent labeling of the detected segments using a statistical model based on Bayesian networks. The Bayesian network is recursively computed during the tracking operation and allows the update of the tracker results every time new information is available. Interpretation/recognition errors can thus be detected after receiving enough information about the group of interacting objects. Experimental tests are included to show the performance of the algorithm in complex situations. This work was supported by FEDER and FCT under project LT (POSI 37844/01).


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