The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Network and psychological effects in urban movement
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
Linking cognitive and computational saliences in route information
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
A probabilistic model for road selection in mobile maps
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
A graph-based model for the representation of land spaces
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Road segment selection with strokes and stability
Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction
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A city can be topologically represented as a connectivity graph, consisting of nodes representing individual spaces and links if the corresponding spaces are intersected. It turns out in the space syntax literature that some defined topological metrics can capture human movement rates in individual spaces. In other words, the topological metrics are significantly correlated to human movement rates, and individual spaces can be ranked by the metrics for predicting human movement. However, this correlation has never been well justified. In this paper, we study the same issue by applying the weighted PageRank algorithm to the connectivity graph or space-space topology for ranking the individual spaces, and find surprisingly that: (1) the PageRank scores are better correlated to human movement rates than the space syntax metrics, and (2) the underlying space-space topology demonstrates small world and scale free properties. The findings provide a novel justification as to why space syntax, or topological analysis in general, can be used to predict human movement. We further conjecture that this kind of analysis is no more than predicting a drunkard's walking on a small world and scale free network.