Abstract
We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memory, but must also enable rapid searches to be made. We have developed such an efficient implementation in C with an R interface written as an extension to the R package spatstat. The algorithms handle realistic large networks, as we demonstrate using a database of all road accidents recorded in Western Australia.
| Original language | English |
|---|---|
| Pages (from-to) | 1-37 |
| Number of pages | 37 |
| Journal | Journal of Statistical Software |
| Volume | 90 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jul 2019 |
Cite this
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Efficient Code for Second Order Analysis of Events on a Linear Network. / Rakshit, Suman; Baddeley, Adrian; Nair, Gopalan.
In: Journal of Statistical Software, Vol. 90, No. 1, 07.2019, p. 1-37.Research output: Contribution to journal › Article
TY - JOUR
T1 - Efficient Code for Second Order Analysis of Events on a Linear Network
AU - Rakshit, Suman
AU - Baddeley, Adrian
AU - Nair, Gopalan
PY - 2019/7
Y1 - 2019/7
N2 - We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memory, but must also enable rapid searches to be made. We have developed such an efficient implementation in C with an R interface written as an extension to the R package spatstat. The algorithms handle realistic large networks, as we demonstrate using a database of all road accidents recorded in Western Australia.
AB - We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memory, but must also enable rapid searches to be made. We have developed such an efficient implementation in C with an R interface written as an extension to the R package spatstat. The algorithms handle realistic large networks, as we demonstrate using a database of all road accidents recorded in Western Australia.
KW - geometric correction
KW - K-function
KW - pair correlation function
KW - point process
KW - R
KW - shortest-path distance
KW - spatstat
U2 - 10.18637/jss.v090.i01
DO - 10.18637/jss.v090.i01
M3 - Article
VL - 90
SP - 1
EP - 37
JO - Journal of Statistical Software
JF - Journal of Statistical Software
SN - 1548-7660
IS - 1
ER -