Geospatial Analysis of Severe Road Traffic Accidents in ... · Geospatial Analysis of Severe Road...

Geospatial Analysis of Severe Road Traffic Accidents in

Singapore

Dr Wee Choon Peng Jeremy

• Injury is a significant cause of mortality and morbidity. In 2012, Road Traffic Accidents (RTAs) are the 9th leading cause of death worldwide and is projected to be the 7th leading cause of death by the year 2030 (1)

• Our goal is to investigate which areas in Singapore have a statistical significantly higher incidence of RTAs resulting in severe Tier 1 injuries, which is defined as an Injury Severity Score (ISS) of more than 15 (2), and develop a spatio-temporal model describing the patterns of RTAs in Singapore in order to identify potential hotspots

• Through this we hope to decrease the incidence of RTAs and provide better care and treatment for the victims.

• Singapore is one of the smallest but most densely populated countries in the world. (3)

Introduction

Materials and Methods

• Data from 1st January 2013 till 31st December 2013 was obtained from the National Trauma Registry (NTR) (4)

• The coordinates where severe RTAs occurred weregeomapped onto the Singapore base map via ArcGIS (5)

• Spatial statistical techniques were used to identify hotspotsvia the Getis-Ord-Gi* algorithm (6)

• The incident data was aggregated using the Integrate function within the Collect Events tool. This generated an icount field which was a collection of weighted points rather than individual incidence points.

• The Spatial Autocorrelation tool was used to create a graph of z scores at each distance and the peak was used as the distance band that reflected maximum spatial correlation for the hotspot analysis. Each of these hotspots were studied to identify if any particular road had 2 or more RTAs.

Materials and Methods

Hotspot

GiZscore

GiZscore P value

X-Coord

Y-Coord

ICOUNT

Type

1

3.551755

0.000383

103.846185

1.319478

21

Motor vehicle driver

1

3.551755

0.000383

103.857135

1.322414

21

Motorbike rider

1

3.551755

0.000383

103.849087

1.332516

21

Pedestrian

1

3.551755

0.000383

103.836995

1.335397

21

Cyclist vs vehicle

1

3.551755

0.000383

103.852287

1.339127

21

Cyclist not vs vehicle

1

3.551755

0.000383

103.862183

1.317196

21

Pedestrian

1

3.551755

0.000383

103.850284

1.336231

21

Pedestrian

1

3.551755

0.000383

103.855445

1.318128

21

Motorbike rider

1

3.551755

0.000383

103.855432

1.313609

21

Cyclist vs vehicle

Hotspot

GiZscore

GiZscore P value

X-Coord

Y-Coord

ICOUNT

Type

1

3.551755

0.000383

103.862260

1.335376

21

Motorbike rider

1

3.551755

0.000383

103.865455

1.324500

21

Pedestrian

1

3.551755

0.000383

103.860629

1.318330

21

Motorbike rider

1

3.551755

0.000383

103.840249

1.329616

21

Motorbike rider

1

3.551755

0.000383

103.843256

1.319650

21


1

3.551755

0.000383

103.852465

1.338096

21

Others

1

3.551755

0.000383

103.862421

1.331386

21

Motor vehicle back passenger

1

3.551755

0.000383

103.867039

1.327954

21


1

3.551755

0.000383

103.852951

1.329453

21

Motorbike rider

1

3.551755

0.000383

103.849369

1.341727

21

Pedestrian

1

3.551755

0.000383

103.863435

1.322233

21


2

3.206358

0.001344

103.873563

1.362289

5

Pedestrian

2

3.206358

0.001344

103.874529

1.354622

5

Pedestrian

2

3.206358

0.001344

103.864295

1.353857

5

Motorbike rider

2

3.206358

0.001344

103.858053

1.339891

5

Cyclist vs vehicle

2

3.206358

0.001344

103.870716

1.339496

5

Pedestrian

2

3.206358

0.001344

103.867822

1.355351

5

Motorbike rider

3

3.059226

0.002219

103.870812

1.313187

13

Pedestrian

3

3.059226

0.002219

103.881872

1.315021

13

Cyclist vs vehicle

3

3.059226

0.002219

103.882038

1.315906

13

Motorbike rider

3

3.059226

0.002219

103.876767

1.313682

13

Motorbike rider

3

3.059226

0.002219

103.873650

1.310907

13

Pedestrian

3

3.059226

0.002219

103.878822

1.304350

13

Pedestrian

3

3.059226

0.002219

103.878856

1.318340

13


3

3.059226

0.002219

103.875782

1.320256

13


3

3.059226

0.002219

103.879852

1.312667

13

Pedestrian

3

3.059226

0.002219

103.872719

1.314650

13

Pedestrian

3

3.059226

0.002219

103.882115

1.313020

13

Pedestrian

3

3.059226

0.002219

103.885303

1.2979850

13

Motorbike rider

3

3.059226

0.002219

103.883025

1.3027450

13

Pedestrian

4

2.616189

0.008892

103.905751

1.317317

7

Motorbike rider

4

2.616189

0.008892

103.906748

1.314030

7

Pedestrian

4

2.616189

0.008892

103.893508

1.301954

7

Motorbike rider

4

2.616189

0.008892

103.903667

1.305080

7


4

2.616189

0.008892

103.894158

1.314231

7

Motorbike rider

Hotspot

GiZscore

GiZscore P value

X-Coord

Y-Coord

ICOUNT

Type

4

2.616189

0.008892

103.898141

1.315381

7

Pedestrian

4

2.616189

0.008892

103.893769

1.317078

7

Pedestrian

5

2.440506

0.014667

103.851067

1.305219

12

Pedestrian

5

2.440506

0.014667

103.850777

1.292737

12

Pedestrian

5

2.440506

0.014667

103.844085

1.287272

12

Motorbike rider

5

2.440506

0.014667

103.854162

1.280845

12

Pedestrian

5

2.440506

0.014667

103.851077

1.282050

12

Pedestrian

5

2.440506

0.014667

103.850686

1.294459

12

Cyclist vs vehicle

5

2.440506

0.014667

103.849609

1.283834

12

Pedestrian

5

2.440506

0.014667

103.856932

1.301969

12

Cyclist vs vehicle

5

2.440506

0.014667

103.861996

1.290278

12

Motorbike rider

5

2.440506

0.014667

103.850074

1.303508

12

Pedestrian

5

2.440506

0.014667

103.860304

1.283993

12

Pedestrian

6

2.242448

0.024932

103.891862

1.323215

10


6

2.242448

0.024932

103.891250

1.330655

10

Motorbike rider

6

2.242448

0.024932

103.905668

1.334417

10

Pedestrian

6

2.242448

0.024932

103.910005

1.331132

10

Motorbike rider

6

2.242448

0.024932

103.886592

1.329888

10


6

2.242448

0.024932

103.895955

1.338068

10

Motorbike rider

6

2.242448

0.024932

103.890079

1.326887

10

Cyclist vs vehicle

6

2.242448

0.024932

103.910159

1.327722

10

Motorbike rider

6

2.242448

0.024932

103.895469

1.323842

10

Motorbike rider

6

2.242448

0.024932

103.898881

1.343146

10

Motorbike rider

• RTAs injured a total of 18 047 people. Out of which there were 467 victims whose ISS were greater than 15 (Tier 1).

• There were 6 statistically significant hotspots

• The individual points were studied within each of the hotspots and the roads that had a relatively high incidence of events were identified (see attached table)

ResultsResults

Discussion• Tier 1 injured RTA victims tend to be young males and most

were motor cyclists and pedestrians

• Police enforcements of traffic laws at the hotspots can help

• Analysis of these hotspots can guide efforts to decrease the response time of the ambulances and transport times

• Despatch centres should be located near these hotspots and staffed adequately

• From the location of the hospitals in Singapore and their proximity to the hotspots, Tan Tock Seng Hospital and Changi General Hospital receive many of the Tier 1 RTA victims and they should receive adequate resource allocation

• Limitations: 1) Only Tier 1 were studied 2) hotspots are determined via distance and do not take into account of how the roads run 3) Data was about 88% complete

Discussion

• Coordinated efforts from multiple agencies are needed for

• Road safety• Prevention of RTAs, • Treatment and eventual rehabilitation of RTA

victims • Important agencies include

• Law Enforcement• Land/Road safety professionals• Prehospital EAS• Trauma Centres and Rehabilitation facilities

• Thus through the study of hotspots of incidences of traumatic events especially those resulting in severe injuries; multiple agencies can use this information to direct their efforts and resources.

ConclusionsConclusions1. Injuries and violence: the facts 2014. Geneva:

World Health Organization, 2014 9789241508018.

2. Baker SP, OʼNeill B, Haddon W, Long WB. The Injury Severity Score. The Journal of Trauma: Injury, Infection, and Critical Care. 1974;14(3):187-96.

3. The World Factbook 2016-17 Washington, DC: Central Intelligence Agency; 2016 [Available from: https://www.cia.gov/library/publications/the-world-factbook/geos/sn.html.

4. National Trauma Registry Annual Registry Report 2013. Health Promotion Board, Singapore, Office NRoD; 2014.

5. ESRI. ArcGIS Desktop. 10.4 ed: Environmental Systems Research Institute.

6. Getis A, Ord JK. The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis. 2010;24(3):189-206.

https://www.cia.gov/library/publications/the-world-factbook/geos/sn.html

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