
Spatial regression techniques usually applied to area data are here adapted to work with network data - in this case road sections. An example is given exloring spatial patterns in shooting incidents in a neighbourhood of New York City, making use of open data. Risks of a shooting occuring are modeled using both Poisson and negative binomial distributions, and compared using an Aikake Information Criterion (AIC) approach - which suggests that the negative binomial model (in which events tend to cluster) is more plausible than the Poisson.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
