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In experienced hands, external DCR has good postoperative success with a low complication rate. Early DCR does not have a substantial advantage over late surgery with regard to surgical outcome.

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The 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.

This article presents a method for investigating the spatial distribution of vehicle and pedestrian traffic crashes relative to the volume of vehicle and pedestrian movement in urban areas. This method consists of two phases. First, vehicle and pedestrian traffic volumes on the street network are modeled using a space syntax configurational analysis of the network, land use data, and observed traffic data. Second, crash prediction models are fitted to the traffic crash data, using negative binomial regression models and based on traffic volume estimates and street segment lengths. The method was applied in two areas in Tel Aviv, Israel, which differ in their morphological and traffic characteristics. The case‐studies illustrated the method's capability in identifying hazardous locations on the network and examining relative crash risks. The analysis shows that an increase in vehicle or pedestrian traffic volume tends to be associated with a decrease in relative crash risk. Moreover, the spatial patterns of relative crash risks are associated with the design characteristics of urban space: areas characterized by dense street networks encourage more walking, and are generally safer for pedestrians, while those with longer street segments encourage more driving, are less safe for pedestrians, but safer for vehicles.