Consideraciones finales

In this section, typically, two separate tables containing the estimated scores of the non- captured data discovered in the Road related factors are presented, with the purpose of computing a suitable choice of the class interval/bin for a reliable result. Ultimately, an appropriate distribution shape, offering practical insight into the structure of the estimated scores assembled was achieved through the selection of a suitable class interval. The scores organised in Table 22 and Table 23 illustrate monthly accumulation of the non-captured data observed in the related data fields.

Table 22: Estimates of non-captured data for data fields with least missing data in Road related factors

Total estimates of non-captured data for least missing data in Road related factors

Months Road surface type Quality of road surface Road surface Road marking visibility

Obstructions Road signs clearly visible Condition of road signs Jan 6 2 2 5 5 10 9 Feb 11 1 5 41 9 8 Mar 6 2 7 25 18 18 Apr 5 9 6 10 16 5 9 May 4 9 5 19 10 14 Jun 5 5 3 11 20 16 17 Jul 5 5 2 3 11 11 10 Aug 3 6 4 7 26 11 11 Sep 3 5 5 12 26 13 16 Oct 12 7 4 9 36 10 23 Nov 3 3 3 8 27 13 16 Dec 7 3 3 5 19 14 14

From the Table 22, within the 12-month period, only road surface among all the thirteen related data fields is perfectly and absolutely completed in February, March and May during the data collection proceedings. The outcome of the accuracy is denoted as empty cells [no errors are

detected] in the Table 22. The magnitude of the non-captured data grouped in the Road surface related data fields, such data fields as road surface type, quality of road surface and road surface reflects minimal omissions/errors.

The table above presents set of estimated scores generated for the frequency analysis of non- captured data per field, within the frequency range of 1 to 41 non-captured data. The estimates offer a simple formation of the class interval [bin] towards a practical understanding of the frequency distributions of the non-captured data.

However, the distribution shape obtained in this section is similar to the distribution shape presented in Figure 24, but the slight difference in the shape shows a gap in the distribution actualised. Observations from Figure 35, reveals that the distribution shape further exhibited a peak score of 30 counts within the lowest range of non-captured data. This produces a J- shaped distribution chart, and positively skewed to the right side of the chart, wherein each bar represents the amount of times a particular range of non-captured data is counted.

Figure 35: Distribution of non-captured data for data fields with least missing data in Road related factors

The result displayed in the chart demonstrates that high counts per non-captured data clustered within the ranges of 1 to 20 non-captured data per month. However, fewer counts per non-captured data are observed within the ranges of 21 to 45 non-captured data, which appeared farther to the right tail of the histogram. Out of the seven data fields presented in the Table 22, only three data fields contributed massively to the ranges with large scores of non- captured data per month, such data fields as Obstruction, Road signs clearly visible, and

Condition of road sign.

Conversely, other six related data fields characterised with large scores of non-captured data in the Road related factors are displayed in the Table 23. The lowest and highest scores observed in this table are 20 and 156 non-captured data. The distribution shape presents a clustered distribution of high scores without a gap unlike the previous chart.

The frequency distributions with high counts fall mostly within the high ranges of non-captured data. From a graphical illustration, highest peak of 19 counts demonstrate a large estimate of data mismanaged within the range of 61-80 non-captured data. Observably, a frequency estimate of 14 counts is observed within the range of 41-60 non-captured data, followed by

frequency estimate of 12 counts within the ranges of 21-40 and 101-120 non-captured data respectively. A frequency estimate of 10 counts is observed within the range of 81-100 non- captured data. The high frequency estimates presented in Figure 36, reveals that high amount of data is mismanaged in the Road related factors.

Table 23: Estimates of non-captured data for data fields with most missing data in Road related factors

Total estimates of non-captured data for most missing data in Road related factors Months Speed limit Built-up area Road type Junction type Overtaking

control Traffic control type Jan 49 75 68 103 24 61 Feb 78 110 104 156 39 67 Mar 51 92 78 124 34 113 Apr 22 47 59 79 20 82 May 71 107 114 130 31 84 Jun 58 77 70 97 30 81 Jul 72 103 81 110 20 51 Aug 86 104 81 117 34 82 Sep 53 75 73 106 52 69 Oct 41 61 46 75 50 94 Nov 35 64 45 104 44 69 Dec 25 25 25 54 21 67

Figure 36: Distribution of non-captured data for data fields with most missing data in Road related factors

On the contrary, some ranges produced fewer counts with a frequency estimates below 4 counts. From the illustration given in the chart above, fewer frequency estimate of non-captured

data are observed within the range of 1-20, 121-140, and 141-160 respectively. The results obtained demonstrate the incompetence of the reporting officer towards completing of the six data fields, with high counts of non-captured data in Road related factors because many data elements are misinterpreted, and respectively omitted in the process of data collection.

Among the six data fields presented in the table above, built-up area, road type, junction type and traffic control type contributed extremely high scores of non-captured data [refer to subsection 5.4.1 above]. The consequence of omitting or excluding a high estimates of data elements, rendered much of the data gathered ineffectual. This limits the possibility of correlating the findings with the actual circumstances of the road accidents.