Physics – Data Analysis – Statistics and Probability
Scientific paper
2009-10-16
Physics
Data Analysis, Statistics and Probability
some figures taken away, and Appendix B added Accepted by International Journal of Geographical Information Science, minor cha
Scientific paper
Relying on random and purposive moving agents, we simulated human movement in large street networks. We found that aggregate flow, assigned to individual streets, is mainly shaped by the underlying street structure, and that human moving behavior (either random or purposive) has little effect on the aggregate flow. This finding implies that given a street network, the movement patterns generated by purposive walkers (mostly human beings) and by random walkers are the same. Based on the simulation and correlation analysis, we further found that the closeness centrality is not a good indicator for human movement, in contrast to a long standing view held by space syntax researchers. Instead we suggest that Google's PageRank, and its modified version - weighted PageRank, betweenness and degree centralities are all better indicators for predicting aggregate flow.
Jia Tao
Jiang Bin
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