Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne
Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency an...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Australasian College of Road Safety
2020-11-01
|
| Series: | Journal of Road Safety |
| Online Access: | https://acrs.org.au/files/papers/arsc/2020/Use%20of%20spatial%20analysis%20techniques%20to%20identify%20statistically%20significant%20crash%20hot%20spots%20in%20metropolitan%20Melbourne.pdf |
| _version_ | 1818889981718953984 |
|---|---|
| author | Elizabeth Hovenden Gang-Jun Liu |
| author_facet | Elizabeth Hovenden Gang-Jun Liu |
| author_sort | Elizabeth Hovenden |
| collection | DOAJ |
| description | Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression. |
| first_indexed | 2024-12-19T17:17:39Z |
| format | Article |
| id | doaj.art-8212aa27ad184006a4472f4e8fb99598 |
| institution | Directory Open Access Journal |
| issn | 2652-4260 2652-4252 |
| language | English |
| last_indexed | 2024-12-19T17:17:39Z |
| publishDate | 2020-11-01 |
| publisher | Australasian College of Road Safety |
| record_format | Article |
| series | Journal of Road Safety |
| spelling | doaj.art-8212aa27ad184006a4472f4e8fb995982022-12-21T20:12:49ZengAustralasian College of Road SafetyJournal of Road Safety2652-42602652-42522020-11-01314365810.33492/JRS-D-19-00249Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan MelbourneElizabeth Hovenden0Gang-Jun Liu1https://orcid.org/0000-0002-4994-0971Department of Transport, Melbourne, AustraliaRoyal Melbourne Institute of Technology, Melbourne, AustraliaUnderstanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.https://acrs.org.au/files/papers/arsc/2020/Use%20of%20spatial%20analysis%20techniques%20to%20identify%20statistically%20significant%20crash%20hot%20spots%20in%20metropolitan%20Melbourne.pdf |
| spellingShingle | Elizabeth Hovenden Gang-Jun Liu Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne Journal of Road Safety |
| title | Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne |
| title_full | Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne |
| title_fullStr | Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne |
| title_full_unstemmed | Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne |
| title_short | Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne |
| title_sort | use of spatial analysis techniques to identify statistically significant crash hot spots in metropolitan melbourne |
| url | https://acrs.org.au/files/papers/arsc/2020/Use%20of%20spatial%20analysis%20techniques%20to%20identify%20statistically%20significant%20crash%20hot%20spots%20in%20metropolitan%20Melbourne.pdf |
| work_keys_str_mv | AT elizabethhovenden useofspatialanalysistechniquestoidentifystatisticallysignificantcrashhotspotsinmetropolitanmelbourne AT gangjunliu useofspatialanalysistechniquestoidentifystatisticallysignificantcrashhotspotsinmetropolitanmelbourne |