| 000 | 02511nab a2200253 4500 | ||
|---|---|---|---|
| 999 |
_c11649 _d11649 |
||
| 003 | OSt | ||
| 005 | 20210415114559.0 | ||
| 007 | cr aa aaaaa | ||
| 008 | 210415b ||||| |||| 00| 0 eng d | ||
| 100 |
_aOmer, Itzhak _935469 |
||
| 245 | _aStructural properties of the angular and metric street network's centralities and their implications for movement flows | ||
| 260 |
_bSage, _c2019. |
||
| 300 | _aVol 46, Issue 6, 2019,( 1182-1200 p.) | ||
| 520 | _aThe street network's angular centralities have been found more suitable than metric centralities for explaining the observed pedestrian and vehicle movement flows in various urban areas. Some studies relate this state to ‘network effects’ – outcomes of the underlying street network structure. However, we have yet to be ascertained how ‘network effects’ work and why angular centralities are superior to metric centralities for modeling movement in the network. The aim of this article is to clarify this issue. The investigation entailed analysis of the street network centralities and movement flows obtained through agent-based simulations conducted for two cities that differ in the pattern and size of their street networks. The findings indicate that the correlations between street network centralities and simulated movement flows, and the superiority of angular centralities in this respect, can be affected by two network's interrelated structural properties: (i) agents who calculate the shortest paths by means of metric distance pass through street segments with relatively high angular Betweenness more often than do agents who calculate the shortest paths by means of angular distance pass through street segments with a relatively high metric Betweenness; and (ii) the angular foreground sub-network (street segments in which Betweenness and Closeness values increase significantly across spatial scale) is relatively more prominent and fits the simulated movement flows better than do the metric foreground sub-networks. These structural properties are found to be nearly identical in both study cities. | ||
| 650 |
_aUrban movement, _945922 |
||
| 650 |
_astreet network structure, _945923 |
||
| 650 |
_a distance type, _944842 |
||
| 650 |
_aspace syntax, _945924 |
||
| 650 |
_aagent-based model _939569 |
||
| 700 |
_aKaplan, Nir _945925 |
||
| 773 | 0 |
_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science |
|
| 856 | _uhttps://doi.org/10.1177/2399808318760571 | ||
| 942 |
_2ddc _cART |
||