Haklay et al.’s article exhibits how geospatial agents can replicate real-world environments – specifically, how pedestrians move throughout urban downtowns. Similarly to what we discussed last class with social networks, the researchers utilized the concept of nodes (“waypoints”) in a street network to methodize the individual agent’s “planned route” (12). Haklay et al.’s methodology for STREETS also considered impedance, which means they considered obstacles (e.g. buildings or large clusters of people) that would slow down the movement of a pedestrian from one “waypoint” to another.
After reading Sengupta and Sieber’s review article and comprehending the technical terms introduced, Haklay et al.’s STREETS methodology was easier to conceptualize. For instance, Haklay et al. described an agent-based model as one that is “autonomous and goal-directed,” which were two of the four factors described in Sengupta and Sieber’s article. Although Haklay et al. do not specifically describe STREETS as a geospatial agent that has all four properties described by Sengupta and Sieber, they state STREETS is unique because the agents understand where they are “spatially located” and are spatially “aware” (8).
What was interesting about this article, and what also parallels last week’s article, was that many parts of the methodology incorporated multiple attributes to determine how an agent/individual makes decisions. Like how Radil et al. considered both gang relations and territory in their spatial social network, Haklay et al. incorporated “behavior” and “socio-economic characteristics” in their street network (13-14). I think incorporating multiple variables is important because it replicates the real-world more accurately. Previous pedestrian movement models did not integrate an individual’s characteristics that would affect their choices. For these reasons, I am interested to see how STREETS will improve in the future, and how it will be able to incorporate even more modules/variables into the agent-based model.
-MTM