Hotspot analysis of single-vehicle lane departure crashes in North Dakota

Khan, Ihsan Ullah; Vachal, Kimberly; Ebrahimi, Sajad; Wadhwa, Satpal Singh

doi:10.1016/j.iatssr.2022.12.003

Cited by 11 publications

(10 citation statements)

References 30 publications

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“…The Emerging Hot Spot Analysis tool begins by using the results of a successfully executed space time cube (STC) as an input to conduct a hot spot analysis using the Getis-Ord Gi * statistic for each bin already created in the space time cube (Khan et al 2023). The Neighborhood Distance and Neighborhood Time Step parameters define how many surrounding bins, in both space and time, will be considered when calculating the statistics for a specific bin.…”

Section: Emerging Hotspot Analysismentioning

confidence: 99%

“…The Neighborhood Distance and Neighborhood Time Step parameters define how many surrounding bins, in both space and time, will be considered when calculating the statistics for a specific bin. Then, the hot and cold spot trends detected by the Getis-Ord Gi * hot spot analysis are evaluated with the Mann-Kendall test to determine whether trends are persistent, increasing, or decreasing over time (Khan et al 2023). The results are symbolized by seven different categories describing the statistical significance of hot or cold spots and the location's trend over time.…”

Section: Emerging Hotspot Analysismentioning

confidence: 99%

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Where will the next oil spill incident in the Niger Delta region of Nigeria occur?

Wekpe,

Whitworth,

Baily

2024

Environ. Res. Commun.

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Oil spill incidents are almost a daily occurrence within the Niger Delta region of Nigeria with far reaching environmental, economic and social consequences. This study aimed at understanding the spatial and temporal context of the problem as a panacea for forecasting likely locations of oil spill incidents within the region. About 76.77% of crude oil spilt in the Niger Delta is lost to the environment with only about 23% of the crude oil recovered from the environment, this represents a very worrying statistic in terms of the known and unknown negative impacts of oil spills. Space Time Pattern Mining (STPM) tools were adapted to explore and interrogate historical spill data. Time series forecasting was then used for forecasting possible locations of future oil spills within the region. Results show that there is a pattern to oil spill occurrences in the Niger Delta with statistically significant hotspots identified in Rivers State, Bayelsa State and Delta State. Forecast root mean square error (RMSE) and forecast validation RMSE are -1.016328 and 1.035992 respectively. This suggests an ability of the model to fairly predict likely locations of future oil spills. This was further verified by counting the number of spills that occur within any area based on the predicted likelihood of spill occurrence. This study has shown that STPM tools can be deployed in understanding the occurrence and prediction of oil spill incidents. This will ultimately aid in the deployment of scarce management resources to where they are most needed.

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Section: Emerging Hotspot Analysismentioning

confidence: 99%

Section: Emerging Hotspot Analysismentioning

confidence: 99%

Where will the next oil spill incident in the Niger Delta region of Nigeria occur?

Wekpe,

Whitworth,

Baily

2024

Environ. Res. Commun.

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“…Several studies have been conducted on Road Traffic Accident Black Region (RTABR) identification methods. [12][13][14] Identifying and determining RTABR for each city is one of the important steps in urban planning for RTA countermeasures and control.…”

Section: Introductionmentioning

confidence: 99%

Road Safety as an Essential Need of Aged Population: A Case of Tabriz City, Iran

Fathi,

Ahmadinia,

Pourhassan

et al. 2023

Int J Aging

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Objectives: To investigate and determine the hotspot points of traffic accidents in the Tabriz metropolis during 2020-2021. Design: Cross-sectional descriptive study. Setting(s): Tabriz, Capital of East Azarbaijan province in northwest Iran. Participants: The patients were injured in traffic accidents and transferred to hospitals with the prehospital system in Tabriz in 2020 and 2021. Interventions: Prehospital ambulances. Outcome measures: The data included the number of traffic accidents, demographic variables of patients, vital signs, the severity of the accident, the type of vehicle involved in the accident, and the location of each accident based on the division of the municipal area. Results: In this study, the ratio of male to female was 3:1, and the mean age of patients was 35±15 years. In 2020, there were 5423 traffic accidents, of which 17.6% occurred in the 7th municipal region, 16.9% in the 4th municipal region, and 12.2% in the 6th municipal region of Tabriz. In 2021, 6611 missions were carried out, 16.6% of which occurred in the 4th and 7th municipal regions, while 12.6% occurred in the 6th municipal region of Tabriz city. Conclusions: Based on the results of this study, regions 7, 4, and 6 are among the Road Traffic Accident Black Regions (RTABRs) in the Tabriz metropolis; therefore, it is necessary to review and investigate the causes of traffic accidents and carry out interventions to increase road safety.

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“…In particular, due to the massive nature of TN data, existing methods face challenges in dealing with this issue. Spatial autocorrelation methods are divided into global spatial autocorrelation and local spatial autocorrelation, and local spatial autocorrelation analysis can determine whether the datum is an outlier or not based on the similarity between observations and neighbouring observations [12]. Among them, local indicators of spatial association (LISA) are a commonly used method in spatial anomaly detection [13].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Zheng,

Lan,

Zhangzhong

et al. 2023

Agronomy

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Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

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Hotspot analysis of single-vehicle lane departure crashes in North Dakota

Cited by 11 publications

References 30 publications

Where will the next oil spill incident in the Niger Delta region of Nigeria occur?

Where will the next oil spill incident in the Niger Delta region of Nigeria occur?

Road Safety as an Essential Need of Aged Population: A Case of Tabriz City, Iran

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

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