Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users
Abstract
:1. Introduction
- Developed a framework of an alternate method to identify the clustering strength and threshold distance for network-constrained hot spot analysis.
- Demonstrated the use of the cross-K- and cross-G-functions to select the best crash hot spots among different outcomes.
2. Literature Review
3. Methodology
3.1. Study Area and Data
3.2. Methods
3.3. Network-Constrained Point Pattern Analysis: K-Function and G-Function
3.4. Incremental Spatial Autocorrelation: Global Moran’s Index Analysis
3.5. Advantages, Disadvantages, and Limitations
3.6. Network Constraint Local Indicator of Clusters: Getis-Ord Gi* Statistics
3.7. Definition of Hot Spots and Cold Spots
4. Results and Discussion
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Articles | Pros | Cons | |
---|---|---|---|---|
1. | Kernel density estimation | [26,33,35,38,39,45,46,47,48] | KDE can be employed to visualize crash densities across various geographical regions. The smoothness of the density surface is conditional upon the careful selection of the appropriate bandwidth. Network-Constrained KDE offers an enhanced level of accuracy by providing route-specific density information. | KDE results are overly sensitive to the choice of bandwidth. The resulting density estimates can exhibit variations when different kernel shapes are employed, for example, Gaussian, Epanechnikov. Furthermore, the computational process might become demanding, especially when dealing with larger geographical areas and more extensive datasets and network constrained estimation. |
2. | Getis-Ord Gi* | [11,19,21,22,26,34,35,36,42,48] | Getis-Ord Gi* effectively identifies clustering patterns of high-high or low-low values. High-high clusters, known as hot spots, are characterized by a highz-score and a small p-value. Conversely, low-low clusters, referred to as cold spots, involve a low negativez-score and a small p-value. | The Gi* statistics are sensitive to the choice of threshold distance. Moreover, employing various distance methods (such as Manhattan, Euclidean, or actual network distance) can yield divergent outcomes. Hence, it is crucial to opt for an appropriate distance method to establish accurate spatial relationships. |
3. | Moran’s Index | [11,19,21,22,36,47] | Moran’s Index is used to identify the clustering or dispersion in data based on spatial autocorrelation supported byz-score and p-value. The null hypothesis used in Moran’s Index is that “the values of the features are spatially uncorrelated” [49]. Using the Incremental Spatial Autocorrelation tool, a threshold distance to be used in Local Moran’s I and Getis-Ord Gi* analysis is calculated. | Similar to Getis-Ord Gi*, Local Moran’s I is also influenced by scale and sensitive to a threshold distance, which can be identified in advance through the Incremental Spatial Autocorrelation method or the method we are proposing in this research, i.e., K-function and G-function method. Furthermore, inappropriate distance method selection may lead to an inaccurate Moran’s I result. |
4. | Ripley’s K-function | [11,26,38,42,50] | Ripley’s K-function is used to identify spatial patterns of data (clustering, dispersion, or randomness). K-function can be used on both local and global scales. A network restraint K-function provides more accurate results in case of crash data. Additionally, the K-function enables the comparison of spatial patterns between two distinct datasets. | Unlike Moran’s I, K-function lacks consideration of attribute values, thus not providing insights into the underlying causes of spatial patterns. Similar to Getis-Ord Gi* and Moran’s I, Ripley’s K-function results are also dependent on the type of distance method used. The calculation process may be computationally extensive in cases of network restraint Ripley’s K-function. |
5. | Other methods | [42,43,44,45,46,47,51] | - | - |
Severity Level | KABCO Scale [54] | 2017–2019 | 2020–2021 | ||||
---|---|---|---|---|---|---|---|
Bikes | Pedestrian | Total (%) | Bikes | Pedestrian | Total (%) | ||
Fatal Crash | K | 5 | 46 | 51 (1.30%) | 2 | 18 | 20 (1.20%) |
Injury (Severe) | A | 106 | 299 | 405 (10.6%) | 54 | 160 | 214 (12.6%) |
Injury (Other Visible) | B | 585 | 736 | 1321 (34.6%) | 269 | 329 | 598 (35.3%) |
Injury (Complaint of Pain) | C | 698 | 1341 | 2039 (53.4%) | 309 | 552 | 861 (50.9%) |
Total | 1394 | 2422 | 3816 (69.3%) | 634 | 1059 | 1693 (30.7%) |
Ripley’s K- and G-Function | Moran’s Index: |
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Advantages:
| Advantages:
|
Ripley’s K- and G-Function | Moran’s Index: |
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Disadvantages and Limitations:
| Disadvantages and Limitations:
|
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Habib, M.F.; Bridgelall, R.; Motuba, D.; Rahman, B. Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users. Safety 2023, 9, 57. https://doi.org/10.3390/safety9030057
Habib MF, Bridgelall R, Motuba D, Rahman B. Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users. Safety. 2023; 9(3):57. https://doi.org/10.3390/safety9030057
Chicago/Turabian StyleHabib, Muhammad Faisal, Raj Bridgelall, Diomo Motuba, and Baishali Rahman. 2023. "Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users" Safety 9, no. 3: 57. https://doi.org/10.3390/safety9030057
APA StyleHabib, M. F., Bridgelall, R., Motuba, D., & Rahman, B. (2023). Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users. Safety, 9(3), 57. https://doi.org/10.3390/safety9030057