Contesting environmental injustice in contexts of criminalization

There are traffic samples which meet all prerequisites from the previous section and some others that do not. All collections used in the thesis satisfy the necessary conditions. Historical measurements which do not meet all criteria are not necessarily to be omitted from studies in general, but it is essential that this investigation is based on reliable evidence in order to make successful predictions when these will be compared with the actual data once they are released. Performing studies on not so consistent information might result in error percentages from 10% to 20% or more. Even if the range of 10-20% seems acceptable – and as a matter of fact is already the case for many related investigations – it is not preferred for the present studies. The contribution of this thesis must be achieved to the maximum and this suggests forecast errors less than 10% on average and ideally below 5%.

Unsuitable historical traces can produce dispersed figures at the fitting stage and this can lead to magnified forecasting errors. The example of table 10 is the incoming and outbound aggregate traffic that has crossed the Amsterdam IXP in 2014. We can observe the traffic generally increases from the beginning to the end of the year for both incoming and outgoing (and consequently for their totals), but in between certain months there are significant drop offs. Modeling any data set that displays seasonal variations is susceptible to significant deviations at later study stages and should maybe assigned to more dynamic techniques. Therefore this sample may not be suitable to form a reliable basis for predictions and cannot be used for either fitting, relation

forming or projection purposes. However, if all numbers of the data set are added together to represent the totality of volume for 2014, then all relevant aggregate volumes from past years of the same IXP can be merged into one table in chronological order. The new table will have the total volumes of the traffic from several consecutive years of the past, e.g. from 2009 to 2014, to predict figures for years 2015 to 2018. The latter is indeed a core project in this thesis and is presented later.

Table 10: Amsterdam IXP: variations on monthly historical volumes [Ams2014]

Table 11: Estimated backbone traffic in USA [UoM1], [UoM5]

Further examples that can possibly lead to huge prediction errors are measurements given in broad ranges or traffic activity compiled into a single graph without precise

figures, as shown in table 11 and figure 24 respectively. Table 11 may be a good indication of what is to be expected for 2012 and later, but no constructive investigation can be conducted to detect patterns or produce fittings. It is quite obvious that those data do not display a specific traffic pattern in terms of accuracy for either prediction or modelling, mainly because of the wide range of the numbers they are given. Although traffic uncertainty is relatively high, the data in the table could be a good start in revealing patterns but these would be only based on the lower and upper limits of the values of each year as the latter increases to 2011. Projections using this method would again produce some specific ranges and estimates would not be accurate, even if this method is safer due to the increased probability defined by the wide range of the numbers. The same applies to extrapolating figures by producing curves or lines that fit into the area specified by the lower and the higher levels of the numbers. In contrast, detecting patterns in the progression properties of standard numbers rather than in the properties of dispersed ranges is expected to lead to better estimations on future values.

The convenience of forecasting traffic volumes within excessive ranges cannot be regarded as producing “safe results” because networking and investment companies need more precision when it comes to their future plans: they need projections that come with fixed numbers or at least with some very narrow ranges.

Figure 24: Traffic exchanged at the DE-CIX in Frankfurt [Dix2015]

The graph in figure 24 comes from the “Deutscher Commercial Internet Exchange”

(DE-CIX) in Frankfurt [Dix2015] and is certainly useful, especially the average traffic (yellow) as opposed to the peak activity (red) which exhibits medium-level fluctuations in 2014 and 2015. The former meets all specified criteria but obviously there is absence of figures. At close inspection, one might be able to extract all numbers as they progress from 2012 to 2015 but these would be within certain ranges – even narrow – or they would come with a standard level of tolerance. Any lack of precision may not be an important issue for the general public’s information but for high-level studies accurate figures collection is strongly advised. assign the new value to the volume of year ε. Any number on the right hand side of the table depends on its previous value, i.e. the volume information is always related to that of previous year and can be expressed with a mathematical equation including variable ε as a separate parameter. The numbers of the table have been observed to produce a hidden model to characterize the traffic and the fitting attributes, which is able to further indicate the future values in advance with a low associated error. In broad format, the equation is expressed as:

V(Ԑ) = [V(Ԑ−1)]^P (12)

V(Ԑ) refers to the total traffic volume we want to predict for year Ԑ, which clearly depends on the available historical volume V(Ԑ−1) from previous year. Exponent P is to be formed according to the investigation case in each chapter and is a more complex parameter that includes variables and constants defined at the fitting and pattern detection process. The same idea is as well to be applied to all history traffic data used in this documentation, including further tables with several years of recorded traffic from large and consecutive data samples.