Analysing point patterns on networks — A review
We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological er...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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Elsevier
2021
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91581 |
| _version_ | 1848765554483003392 |
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| author | Baddeley, Adrian Nair, Gopalan Rakshit, Suman McSwiggan, Greg Davies, Tilman |
| author_facet | Baddeley, Adrian Nair, Gopalan Rakshit, Suman McSwiggan, Greg Davies, Tilman |
| author_sort | Baddeley, Adrian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial statistics based on stationary processes. Topics include kernel density estimation, relative risk estimation, parametric and non-parametric modelling of intensity, second-order analysis using the K-function and pair correlation function, and point process model construction. An important message is that the choice of distance metric on the network is pivotal in the theoretical development and in the analysis of real data. Challenges for statistical computation are discussed and open-source software is provided. |
| first_indexed | 2025-11-14T11:37:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-91581 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:37:06Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-915812024-04-11T04:38:17Z Analysing point patterns on networks — A review Baddeley, Adrian Nair, Gopalan Rakshit, Suman McSwiggan, Greg Davies, Tilman Science & Technology Physical Sciences Technology Geosciences, Multidisciplinary Mathematics, Interdisciplinary Applications Remote Sensing Statistics & Probability Geology Mathematics Distance metric Kernel density estimation K-function Nonparametric estimation Pair correlation function Stationary process KERNEL DENSITY-ESTIMATION 2ND-ORDER ANALYSIS K-FUNCTION COMPOSITE LIKELIHOOD COMPUTATIONAL METHOD PARAMETER-ESTIMATION INTENSITY ESTIMATION STATISTICAL-ANALYSIS REGRESSION-MODELS LOCAL INDICATORS We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial statistics based on stationary processes. Topics include kernel density estimation, relative risk estimation, parametric and non-parametric modelling of intensity, second-order analysis using the K-function and pair correlation function, and point process model construction. An important message is that the choice of distance metric on the network is pivotal in the theoretical development and in the analysis of real data. Challenges for statistical computation are discussed and open-source software is provided. 2021 Journal Article http://hdl.handle.net/20.500.11937/91581 10.1016/j.spasta.2020.100435 English http://purl.org/au-research/grants/arc/DP130102322 Elsevier fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Geosciences, Multidisciplinary Mathematics, Interdisciplinary Applications Remote Sensing Statistics & Probability Geology Mathematics Distance metric Kernel density estimation K-function Nonparametric estimation Pair correlation function Stationary process KERNEL DENSITY-ESTIMATION 2ND-ORDER ANALYSIS K-FUNCTION COMPOSITE LIKELIHOOD COMPUTATIONAL METHOD PARAMETER-ESTIMATION INTENSITY ESTIMATION STATISTICAL-ANALYSIS REGRESSION-MODELS LOCAL INDICATORS Baddeley, Adrian Nair, Gopalan Rakshit, Suman McSwiggan, Greg Davies, Tilman Analysing point patterns on networks — A review |
| title | Analysing point patterns on networks — A review |
| title_full | Analysing point patterns on networks — A review |
| title_fullStr | Analysing point patterns on networks — A review |
| title_full_unstemmed | Analysing point patterns on networks — A review |
| title_short | Analysing point patterns on networks — A review |
| title_sort | analysing point patterns on networks — a review |
| topic | Science & Technology Physical Sciences Technology Geosciences, Multidisciplinary Mathematics, Interdisciplinary Applications Remote Sensing Statistics & Probability Geology Mathematics Distance metric Kernel density estimation K-function Nonparametric estimation Pair correlation function Stationary process KERNEL DENSITY-ESTIMATION 2ND-ORDER ANALYSIS K-FUNCTION COMPOSITE LIKELIHOOD COMPUTATIONAL METHOD PARAMETER-ESTIMATION INTENSITY ESTIMATION STATISTICAL-ANALYSIS REGRESSION-MODELS LOCAL INDICATORS |
| url | http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91581 |