Autoridad social en la Argentina: historia de un fracaso

Human related factors. Similar to the approach applied in the subsections 4.3.2 and 5.4.2 above, two separate tables were created to support the understanding of the data distribution. Each table contains the estimated scores of the non-captured data obtained from the analysis of all the data fields in the Human related factors.

However, the set of scores assembled in Table 29, demonstrate a huge difference between the highest and lowest scores across the four data fields. In the table, an extreme score of 126 non-captured data is considered as the highest score, while a lowest score of 7 non-captured data is found. The huge difference between the two scores indicate that some data elements are completed easier and more often in the ARF than many other data fields.

The approach used to compute the class intervals of the two histograms obtained in this section is based on the mathematical approach explained in the subsection 3.3.2.5.2. The distribution of the estimated scores assembled in the table below demonstrates a wide range of non- captured data in three data fields, namely the Ages of drivers/cyclists, Gender of

drivers/cyclists, and Race of drivers/cyclists. These are the four factors with lowest average

scores in Figure 45. From the same table, a complete and accurate completion of Vehicle

manoeuvre is observed in April indicating a zero-data loss. A graphical illustration of the

estimated scores is presented in Figure 46, in order to reveal the rate at which data is lost.

In the chart, a distribution gap is observed within the range of 35-51 non-captured data, with no frequency score within the specified range. Ultimately, the clustered part of the distribution produced the highest peak score of 13 counts within the range of 94-110 non-captured data. The peak score illustrates that large estimates of data are regularly uncaptured in the Human related factors between the range of 94 to 110, which is considered extremely high compared

to the results achieved in both the Accident related factors and Road related factors in the analysis of the non-captured data with large scores. The peak score further demonstrates the amount of times a particular range of non-captured data is observed in the Human related factors. In addition, the clustered part of the distribution falls within the ranges with higher class interval of non-captured data, indicating a predominant high number of missing data points in the three data fields.

Table 29: Estimates of non-captured data for data fields with least missing data in Human related factors

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

Months Ages of drivers/cyclists Gender of drivers/cyclists Race of drivers/cyclists Vehicle manoeuvre Jan 80 71 69 7 Feb 105 111 112 13 Mar 96 104 126 22 Apr 64 82 64 May 64 97 96 12 Jun 78 74 80 11 Jul 74 73 79 7 Aug 109 101 105 13 Sep 91 92 87 17 Oct 100 100 106 14 Nov 105 110 123 12 Dec 61 74 78 8

However, to the left side of the gap, a frequency estimate of one count was observed within the range of 18-34 non-captured. This range illustrates the lowest frequency observation of the amount of errors discovered within the rate of 18 to 34 non-captured data in each field monthly. Further observation shows a high frequency score of 10 counts within the range of 1-17 non- captured data per field, illustrating a few counts of errors committed in each field per month. For the range 1 to 17 the non-captured data consisted entirely in the field ‘Vehicle manoeuvre’. On the other hand, Table 30 contains the six data fields with the highest scores from Figure 45. The distribution of the estimated scores presents wide scores between 79 [lowest score] and 244 [highest score]. This set of scores partially dictates the size of the class interval. Aside from the statement given above, the size of the scores also determines the degree at which data are being mishandled in these data fields.

Figure 46: Distribution of non-captured data for data fields with least missing data in Human related factors

The six data fields are Nationality of drivers/cyclists, Driving/learner licence type, Seatbelt fitted/helmet present, Seatbelt/helmet definitely used, Liquor/drug use [suspected] and Liquor/drug use [evidentiary tested]. The chart presented below, demonstrates a distribution shape with the highest peak score of 18 counts within the range of 113-133 non-captured data.

Table 30: Estimates of non-captured data for data fields with most missing data in Human related factors

Total estimates of non-captured data for most missing data in Human related factors

Months Nationality of drivers/cyclists Driving/ learner licence type Seatbelt fitted/helmet present Seatbelt/helmet definitely used Liquor/drug use [suspected] Liquor/drug use [evidentiary tested] Jan 88 152 95 112 114 128 Feb 133 217 125 144 167 169 Mar 143 233 161 184 212 220 Apr 85 177 108 123 137 143 May 118 221 115 139 159 177 Jun 105 169 104 113 139 145 Jul 102 166 87 99 117 120 Aug 129 244 113 121 159 162 Sep 112 131 125 136 150 149 Oct 150 206 118 130 155 157 Nov 135 219 121 97 156 162 Dec 79 134 89 93 101 106

The shape of the distribution produced is positively skewed to the right side of the chart. This distribution demonstrates a fewer frequency scores farther to the right side of the chart, which

comprises the high class-interval ranges of non-captured data. In addition, the distribution represents the occurrence rate of a score classified within a specific range of non-captured data. From 175 and higher, frequency scores less than 5 counts are observed.

Further practical observations were performed to determine other useful results in the chart. As a result of this, from a clear indication, on the left side of the chart, a low frequency score of 5 counts is observed within the range of 71-91 non-captured data. This illustrates that fewer amount of errors is discovered at the rate of 71 to 91 non-captured data occasionally in each section during the data collection activities.

Ultimately, due to the range difference between the lowest and highest scores, it is clear that some users struggle to complete these data fields. Evidently, among other related factors discussed earlier in this study, it can be assumed that related data fields grouped in the Human related factors are largely misrepresented during any data collection activities. According to the investigation performed, complains are levelled against the refusal of the road users such as drivers, cyclists, and pedestrians in disclosing their personal details, such relevant information as age, residential/home address, nationality, and ID type/ID number. This effect contributed to the huge extent of non-captured data in the Human related factors.

Figure 47: Distribution of non-captured data for data fields with most missing data in Human related factors

The ages of some road users, who are South Africans, were managed to be captured through the information arranged on their personal driving licence, but major concerns were raised against the inability of obtaining the ages of the foreign drivers/cyclists. More so, the issue of data mishandling could also be ascribed to the unexpected number of road accidents reported each day, which could influence the reliability of the reporting process due to factors like

unreliable deployment, poor training, ineffective response, and incompetency. Besides, the irregularities affecting the collection of the right data, or the inability to avoid errors habitually contributes to the huge amount of uncaptured data in each field.

In that case, the reporting officers should be conversant with the necessary steps required to accumulate the right data pertaining to all the relevant data fields. Additionally, the reporting officers are advised to have a firm approach towards the accident victims, to facilitate valuable cooperation from them in order to acquire sufficient data for road accident analysis.