Eportfolio

Peer response one

Whilst agreeing partly with the research mentioned by Varmaz (2020) and outlined in your post, GDPR nonetheless increases costs for companies and fails to reach the regulator's ambition in fostering trustworthy innovation through harmonisation (Saeed et al., 2017). In addition, in a recent paper, Greene et al. (2019) mentioned how data scientist access to data has been limited due to companies being afraid to fall foul of GDPR. Lefrere et al., 2020 in an additional analysis, also gathered information on how GDPR has negatively impacted traffic on EU websites compared to the USA, potentially impacting revenue across the block.

Further research by Sørum and Presthus (2020) showed a variation in the level of feedback, response time, and quality of how businesses handle individual GDPR requests, which would be somewhat in contradiction with the aim of harmonisation, which was one of the ambitions of GDPR.

Undoubtedly, GDPR has positively impacted consumer data protection; nonetheless, the negative aspects that hinder growth or even negatively impact revenue for some businesses cannot be discounted. Chenet all. (2022) estimated the negative impact of GDPR on EU companies in the range of an 8% profit reduction and a 2% decrease in sales. Considering all the economic implications of GDPR, it is the author's view that whilst regulation of personal data is required, it should not affect businesses to the tune of an 8% profit reduction. In addition, GDPR's "one size fits all approach"needs further improvement, as Layton and Mclendon (2018) have suggested in a recent paper, to reducethe negative impact observed so far on the economy.

References

Chen, C., Benedikt, Ca. and Presidente, G. (2022). Privacy Regulation and Firm Performance: Estimating the GDPR Effect Globally. The Oxford Martin Working Paper Series on Technological and Economic Change. [online] Available at: https://www.oxfordmartin.ox.ac.uk/downloads/Privacy-Regulation-and-Firm-Performance-Giorgio-WP-Upload-2022-1.pdf [Accessed 4 Nov. 2022].

Greene, T., Shmueli, G., Ray, S. and Fell, J. (2019). Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers. Big Data. [online] doi:10.1089/big.2018.0176.

Lefrere, V., Warberg, C., Marotta, V. and Aqusti, A. (2020). The Impact of the GDPR on Content Providers. The 2020 Workshop on the Economics of Information Security.

Layton, R. and Mclendon, J., 2018. The GDPR: What It Really Does and How the US Can Chart a Better Course. FEDERALIST SOC’Y REV., 19, pp.234-236.

Saeed, A., Farkhondeh, H. and Farhad, F. (2017). Learning from Enforcement Cases to Manage GDPR Risks. MIS Quarterly Executive, Vol. 20(3) pp. p199-218.

Sørum, H. and Presthus, W. (2020). Dude, where’s my data? The GDPR in practice, from a consumer’s point of view. Information Technology & People, ahead-of-print(ahead-of-print). doi:10.1108/itp-08-2019-0433.

Varmaz, N. (2020). GDPR vs. Big Data & AI in FinTechs. In: Vierteljahrshefte zur Wirtschaftsforschung, Vol. 89. Available From: https://elibrary.duncker-humblot.com/article/60776/gdpr-vs-big-data-ai-in-fintechs

Peer response two

Lisiak-Felicka et al. (2021) findings are also reflected in a recent study by Bartolini et al. (2019) outlining how training can improve GDPR compliance.

Notwithstanding, Sinur et al. (2019), in a recent study, interviewed companies of different sizes, and it showed that whilst large enterprises were able to process compliance through training, SMBs struggled due lack of resources. Furthermore, SBMs could only adapt GDPR across some of the external contact points in their data network, leaving them at fault for the required compliance. Layton and Mclendon (2018) see the central issue in the GDPR’s “one size fits all” approach, which penalises SMBs the most.

Data protection is paramount today, but regulation should not be detrimental to SMBs’, due to their inability to muster enough resources. The reason for this approach can be found in what Schildberger (2016) defined as a concerted effort by large lobbies to shape GDPR during its inception and SMBs being underrepresented during the lobbying efforts.

Kutyłowski et al. (2020) further analysed the gap between the legal requirements of GDPR and what the technical state-of-the-art could effectively deliver. Even in this instance, it is reasonable to think that large enterprises have more financial power to make such significant investments in state of the art GDPR technologies. Whilst smaller enterprises are unable to muster similar resources for technological solutions.

References:

Bartolini, C., Calabró, A., & Marchetti, E. (2019, January). GDPR and business processes: An effective solution. In Proceedings of the 2nd International Conference on Applications of Intelligent Systems (pp. 1-5).

Kutyłowski, M., Lauks-Dutka, A., & Yung, M. (2020, September). Gdpr–challenges for reconciling legal rules with technical reality. In European Symposium on Research in Computer Security (pp. 736-755). Springer, Cham.

Layton, R. and Mclendon, J., 2018. The GDPR: What It Really Does and How the US Can Chart a Better Course. FEDERALIST SOC’Y REV., 19, pp.234-236.

Lisiak-Felicka, D. and Szmit, M. (2021) “GDPR implementation in public administration in Poland – 1.5 year after: An empirical analysis,” Journal of Economics and Management, 43, pp. 1–21. Available at: https://doi.org/10.22367/jem.2021.43.01.

Schildberger, L. (2016). Lobbying and its influence on the draft of a General Data Protection Regulation of the European Union unveiled in 2012 (Doctoral dissertation, Wien).

Sirur, S., Nurse, J. R., & Webb, H. (2018, January). Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR). In Proceedings of the 2nd International Workshop on Multimedia Privacy and Security (pp. 88-95).

Summary Post

During the recent interaction with colleagues, it is evident that GDPR legislation has positively affected the privacy rights of citizens in the EU and further afield (Hintze and El Emam, 2018). It is important to note that events such as the Cambridge Analytica affair have been a significant example of the need for stringent regulations (Datoo, 2018). Notwithstanding, a critical view needs to be taken of the adverse side effects that the legislation has caused, which can be divided into two distinct sections. First, a one-fits-all approach has given SMBs similar burdens to large enterprises for being GDPR-ready. It is evident that the legislator did fully evaluate that most SMBs lack the technological bandwidth or legal expertise to put meaningful processes in place, as noted by several authors (McAllister, 2017; Schildberger, 2016; Sinur et al., 2019). Second, the legislator has yet to look forward to the advent of new technologies and the limitations that GDPR either puts or will incur in their evolution.

As rightly noted by colleagues, artificial intelligence and blockchain are typical examples of where GDPR might reach its limits, a point further discussed in a recent paper by Humbeeck (2019). In addition, data science might also conflict with GDPR due to the use of data sets that might lack approval from the original user (Layton and Mclendon (2018). In addition, the risk seen by a corporation in passing on data which puts them at fault with GDPR might further limit the scope of the data scientist. The effects of GDPR on corporations in the EU have been quantified in the range of 8% of sales and 2% of profits (Chen et al., 2022) and an increased complexity burden given to smaller companies that might not be able to be 100% GDPR compliant. A further study by Janßen et al. (2022) further documented the disappearance of more than a third of innovative applications due to the advent of GDPR, which further documents the negative impact of GDPR on growth in some sectors.

These documented challenges addressed by several authors mentioned in this post lay some of the fundaments that need revisiting by the legislator. The final result should be privacy regulations that, on one side, protect the individual from the illicit use of their private data, whilst ensuring that innovative technologies are supported in their growth and not penalising smaller businesses further in an already difficult market situation.

References:

Chen, C., Frey, C.B., and Presidente, G. (2022). Privacy Regulation and Firm Performance: Estimating the GDPR Effect Globally. The Oxford Martin Working Paper Series on Technological and Economic Change, [online] pp.1–42. Available at: https://www.oxfordmartin.ox.ac.uk/publications/privacy-regulation-and-firm-performance-estimating-the-gdpr-effect-globally/ [Accessed 6 Nov. 2022].

Datoo, A., 2018 "Data in the post-GDPR world." Computer Fraud & Security 2018.9 (2018): 17-18.

Hintze, M., & El Emam, K. (2018). Comparing the benefits of pseudonymisation and anonymisation under the GDPR. Journal of Data Protection & Privacy, 2(2), 145-158.

Humbeeck, A. V. (2019). The blockchain-GDPR paradox. Journal of Data Protection & Privacy.

Janßen, R., Kesler, R., Kummer, M. and Waldfogel, J. (2022). GDPR and the Lost Generation of Innovative Apps. SSRN Electronic Journal. doi:10.2139/ssrn.4104014.

Layton, R. and Mclendon, J., 2018. The GDPR: What It Really Does and How the US Can Chart a Better Course. FEDERALIST SOC’Y REV., 19, pp.234-236.

McAllister, C., 2017. What about small businesses: the GDPR and its consequences for small, US-based companies. Brook. J. Corp. Fin. & Com. L., 12, p.187.

Schildberger, L. (2016). Lobbying and its influence on the draft of a General Data Protection Regulation of the European Union unveiled in 2012 (Doctoral dissertation, Wien).

Sirur, S., Nurse, J. R., & Webb, H., 2018. Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR). In Proceedings of the 2nd International Workshop on Multimedia Privacy and Security (pp. 88-95).

Data Analysis Report And Final Essay

Giuseppe Raneli

Student Number: 12691423

Word count: 1459

Data Analytics Report

Purpose

This paper aims to create a Data Analytics Report (DAR), which will provide a modus operandi for analysing and evaluating the National Survey for Wales, 2013-14 – Transport survey (NSWT). To support the analysis further, a recent strategy paper (Welsh Government, 2021) that addressed the goals of the Transport Wales department will be used to develop some KPIs for measurement. The goals set in the Welsh strategy paper address three distinct areas. First, bringing service to people by aiming for 30% of the workforce to work from home by creating new travel routes and moving oversized haul freight onto smaller vehicles. The second goal is centred around facilitating the movement of people and goods, focusing on effectiveness and efficiency. Here the strategy is centred on reliability, safety, reach, understandable fares, ease of use, congestion reduction and improved working infrastructure. Third, achieve sustainability through shared services, reduced public transport costs, increased alternative transport and encouraging people to use public transport. These strategies would also align with the overarching goals of the UK transport department around improvement, sustainability and innovation (GOV.UK, 2021).

As rightly noted by colleagues, artificial intelligence and blockchain are typical examples of where GDPR might reach its limits, a point further discussed in a recent paper by Humbeeck (2019). In addition, data science might also conflict with GDPR due to the use of data sets that might lack approval from the original user (Layton and Mclendon (2018). In addition, the risk seen by a corporation in passing on data which puts them at fault with GDPR might further limit the scope of the data scientist. The effects of GDPR on corporations in the EU have been quantified in the range of 8% of sales and 2% of profits (Chen et al., 2022) and an increased complexity burden given to smaller companies that might not be able to be 100% GDPR compliant. A further study by Janßen et al. (2022) further documented the disappearance of more than a third of innovative applications due to the advent of GDPR, which further documents the negative impact of GDPR on growth in some sectors.

In 2021 transport of Wales published a more recent survey, and the aim will be to benchmark the 2021 results against the 2013/14 results to see if the previous aims have been achieved and where still more work needs to be delivered.

Design Approach

As the NSWT is quantitative, the design methodology used for evaluation should have two defined characteristics. First, the ability to be replicated by others and the possibility to measure improvements or deteriorations. Bowerman et al. (2002) suggested that government initiatives should encourage benchmarking in most spheres of public activity. Considering the ongoing use of benchmarking in UK public initiatives (Hinton et al., 2000), the author suggested a similar approach for the DAR.

Benchmarking is a well-known tool used by different governments to measure the performance of infrastructure or do comparative analyses (Henning et al., 2011), which would be the aim of the DAR. The benchmarking process will follow Spendolini’s (1992) five-step approach, as seen in exhibit 1:

Exhibit 1

In this instance, we will focus on steps one, four and five by first defining the KPIs, collecting and analysing the data and providing a report on critical areas of concern.

As mentioned previously, the KPI's selection has been linked to the goal set by the Welsh government in their 2021 paper. Whilst the selection is not exhaustive, it does address some of the core ambitions outlined in the paper and is provided in Table 1.

Table 1:

One of the significant challenges in establishing the benchmarking process is selecting the correct data as defined in stage four of Spendolini’s model.

Understandably, using a public opinion survey system is integral for governments to implement decisions (Ji, 2011), as the NSWT suggests. The NSWT survey is divided into 42 different tables and can be divided into four distinct classes as defined in Exhibit 2:

Exhibit 2:

Stand-alone survey data can easily lead to confusion if the overall structure is ignored and the inherent goals or KPIs are not clearly defined and understood (Guo, Wang and Fan, 2009).

This situation has led to the development of more sophisticated models to understand the relationships between data (Ji, 2011) in survey management. Ji further evolves the model by introducing the concept of the system into survey management to evaluate and understand the complexity of interactions between the survey using UML as an analysis tool. In this instance, a similar process will be applied for the data modelling of this study.

In the present case, the connections between the tables, the KPIs and the actors involved will be represented with a UML use case diagram by mapping the structure of the systems using actors and use cases as suggested by Gemino and Parker (2009). UML use case diagrams often describe a set of activities and services the system needs to perform whilst actors work in defined roles within the described system.

Exhibit 3, in this instance, describes the system to deliver on the KPI called satisfaction, the survey tables required to address the aims, the Welsh government, and the citizen representing the actors involved.

Exhibit 3:

Similarly, Exhibit 4 describes the systems linked to travel modus operandi, safety and sustainability whilst identifying the purpose of the survey, KPIs and actors involved:

Exhibit 4:

The expected results linked to the citizen as final actors have been derived from a study by Beirão and Cabral (2007). In the study, the authors suggest that an increase in public transportation usage can be achieved by addressing the level of service required by customers.

Key findings indicate that to increase public transport usage by the public; the service should be designed to accommodate the levels of service required by customers and, by doing so, attract potential users.

In addition, satisfaction towards public transport has a strong influence on transport modus operandi. Nonetheless, it has been noted that most users will maintain the same level of private car ownership but that an increase in public transport users can be expected (Jensen, 1999) if attractiveness increases. This attitude towards transport needs significant focus as it is the most crucial determinant for mode choice, as further analised by Beirão and Cabral (2007). This increased intention to switch by improving the attraction of public transport has been used as the final aim for the citizen being the final stakeholder, as shown in the above UML diagrams.

Whilst our aim should be to address all survey data available, the complexity would be outside the scope of this study, and we will focus on five tables per class to address the above KPIs.

Results

The author will present the results through histogram graphs in which 2013/14 data is compared to 2021 data alongside a critical analysis to asses improvements or deteriorations. Whilst the NSWT is limited in terms of data points, and Microsoft Excel as a tool would suffice for the analysis, Python will be preferred due to the ability to use packages such as Matplotlib, Pandas and Seaborn for more detailed visualisation and data interpretation as suggested by the Corporate Finance Institute (2022)

Summary

Using benchmarking in transport evaluations is quite common, as recent studies suggest by McKinnon (2009) and Henning et al. (2011). In this instance, we will use Spendolini’s (1992) approach, focusing on steps one, four and five of the process. The KPIs to measure the benchmark have been taken from the recent discussion paper from the Welsh Government (2021) and identified as Satisfaction, Travel Modus Operandi, Safety and Sustainability. As JI (2021) suggested, a UML representation has been used to identify KPIs, actors, connections and interactions within the survey system. The final result will be a critical analysis of the results in line with histograms for graphical visualisations that will outline possible improvements and deterioration using Python as a data analysis tool.

Research limitations/implications

Despite the benefits of benchmarking, results are often only effective if goals are well-defined (McGuire, 2021). This lack of definition might be an issue in this paper due to its comparative nature and the impossibility of suggesting improvements. In addition, this paper will lack follow-up studies to see if ongoing progression is being achieved and which should be an integral part of the benchmarking process (McKinnon, 2009). Last but not least, the study focused on four KPIs and might be reductive considering the number of data points available, limiting the scope of the benchmarking process and not focussing on other significant inter-relationships that could have provided a more complex analysis. In addition, the NSWT has provided data that might have included errors, such as not representing the whole population or having misinterpretations linked to the survey collection and delivery method. It must be noted that the survey consisted of 42 different tables. It would need a minimum of 5 respondents’ pro questions to provide essential data, which could not be verified in this instance (Memon et al. (2020).

Data Analytics Implementation

Introduction

This paper aims to create a data analysis and critical evaluation of the Wales, 2013-14 – Transport survey (NSWT). In addition, data provided by a recent strategy paper (Welsh Government, 2021) that addressed the goals of the Transport Wales department (TW) and updated surveys for 2019 and 2021 were used to support the analysis further. The objectives addressed by the 2021 strategy paper cover three distinct areas. First, bringing service to people by aiming for 30% of the workforce to work from home, create new travel routes, and move oversized haul freight onto smaller vehicles. Second, it seeks to facilitate the movement of people and goods by focusing on effectiveness and efficiency. Here the strategy is centred on reliability, safety, reach, understandable fares, easiness of use, congestion reduction and improved working infrastructure. Third, achieve sustainability through shared services, reducing public transport costs, increasing alternative transport and encouraging people to use public transport (Raneli, 2022). It is noteworthy that the strategies mentioned above also align with the overarching goals of the UK transport department around improvement, sustainability and innovation (GOV.UK, 2021). TW has delivered two further surveys in 2019 and 2021 (Welsh Government, 2022), which restated similar aims. These updated results have been part of the benchmark process and compared to the 2013/14 results and help in critically highlighting divergences and proposing a possible course of action.

Data Pre-Processing And Methodology

In the recent data analytics report (Raneli, 2022), the author proposed a benchmark for comparing the 2013/14 and 2021 results. The original scope has been further enlarged by adding a table around satisfaction from the 2019 survey. As a methodology, Spendolini's (1992) five-step benchmarking approach was used, as seen in exhibit 1:

Exhibit 1

Spendolini's process has been chosen due to its traditional use in government projects (Hinton et al., 2000) and monitoring progress in mass transport initiatives (Guo, Wang and Fan, 2009). Furthermore, benchmarking has a long tradition for analysing improvements or deteriorations in UK local authorities (Bowerman et al., 2002), a core aim for this paper. In this instance, we will focus on steps I, IV, and V of Spendolini’s benchmarking process, as outlined below.

Step I: Identifying what to benchmark.

TW identified in its 2021 paper four core KPIs to improve its mass transport system. As a first KPI, satisfaction is interpreted as a core indicator of how the public perceives the mass transport system. This indicator would support Jensen's (1999) thesis that despite the public not abandoning car usage, overall satisfaction with public transport tends to be a leading indicator of increased usage. Tables 1 to 14 and 23 to 38 of the NSWT tend to address this KPI by measuring satisfaction with the service and easiness of reaching healthcare institutions. Second Travel modus operandi to understand car usage and ownership. This KPI aims to reduce usage through increased public transport usage and would further aid in increased sustainability. Tables 15 to 22 of the NSWT address this by gauging car usage and ownership. Third perception of safety on public transport. Several authors (Ait Bihi Ouali et al., 2019; Currie et al., 2013; Delbosc & Currie, 2012) have indicated how perceived unsafety is a leading cause of reduced public transport usage. The perception of safety as a KPI is addressed in tables 39 to 42 by inquiring perceived sense of safety across different indicators. Sustainability has been identified as the fourth KPI as it is linked to an increase in public transport usage with the ultimate aim of reducing personal car use in line with countrywide and Welsh initiatives. Tables 15 to 22 as mentioned before, outline ownership and usage, which address the KPI.

Due to a large amount of data available, this paper has a narrower focus on two core areas: First, a quantitative evaluation of sustainability and safety as key indicators. Second, a qualitative analysis to critically evaluate the leading causes of dissatisfaction with the mass transport system. The aim has been achieved by comparing tables from the 2019 and 2021 surveys versus the 2013 survey. This approach is dictated by the fact that the 2013 and 2021 surveys differ in their questions and scope, making a quantitative like-for-like comparison difficult or prone to misinterpretations. A satisfaction table around bus and train usage from 2019 has been chosen to aid this process.

Step IV: Collect and analyse benchmarking information

2013, 2019, and 2021 TW surveys provided the data for this paper. Hence the main focus of Step IV has been on the process used for data preparation and critical analysis. Unfortunately, TW provided the data in different formats. Therefore, a manual import into a python environment in CSV formats for data preparation and modelling has been required to make the data accessible.

Dietrich et al. (2015) define data preparation as the process in which we explore and condition data prior to modelling and analysis. The authors further state the importance of familiarising with the data and transforming it to be useful for modelling. Brownlee (2020) further suggests that the preparation process should include additions, deletions or transformations. This study's primary preparation process will focus on deletions and transformations. As the 2013/14 data has a different format from the 2021 data, quartile data has been deleted whilst focusing on the total values to make the data comparable. This outcome was achieved using Python's drop functions to reduce both data to similar formats. Furthermore, as the data is presented with different indexes, a rename and transpose function was applied to generate similarity. Brownlee (2020) further suggests transforming data to scale or distribute data as required. This process is not required in this context due to the similarity of the tables used. The final step is to join the separate tables with a concat function and use the transformed data for visualisation.

In order to highlight differences between 2013/14 and 2021 data, bar chart visualisations. Taher et al. (2017) advocate the usage of bar charts as an effective tool for "preparing views to communicate insights about data, whilst Kolodziej et al. (2016) propose bar charts for setting priorities in visualisation. In this instance, bar charting has been chosen due to the possibility of priority setting, highlighting differences between years and their communicative power. Seaborn and Matplotlib have been used to create visualisations due to the possibility of making statistical graphics in Python and automating most of the process (Waskom, 2021).

Data Analysis and evaluation (Step V)

Exhibit 2:

TW created a new survey in 2021, which in contrast to 2013/14, inquired about why public transport is not routinely used. Lack of direct routes, overall cost, frequency and the convenience of cars have been cited as the main reason for limited usage, as seen in exhibit 2 and comparable with similar observations from other authors (Beirão & Sarsfield Cabral, 2007; Şimşekoğlu et al., 2015). The 2013/14 survey nonetheless inquired about overall satisfaction with the transport system, as seen in exhibit 3, which showed a positive median of 6. Nonetheless, a critical evaluation of the 2021 survey item "I am generally satisfied with all public transport" showed a sharp dissatisfaction, indicating a deterioration in overall perception since 2013/14.

Exhibit 3:

A further survey done by transport of Wales in 2019, shown in exhibit 3a, highlights that 42% of surveyed people are either "Very" or "Fairly satisfied" with buses and trains. Notwithstanding, a significant amount of the surveyed did not express an opinion, and most dissatisfaction was centred around buses.

Exhibit 3a:

This result would further confirm a similar perception across the whole of the United Kingdom, as observed by a recent study by Monsuur et al. (2021)

A recent study by Cullinane (2002) showed that affordability and increased satisfaction with public transport decreases overall car ownership. Contrary, a moderate increase can be noted if we compare car ownership between 2013/14 and 2021 in Exhibit 4.

Exhibit 4:

This result would support the previous analysis that satisfaction has decreased and is a possible cause for increased car ownership. Furthermore, this result also negatively affects most KPIs such as sustainability, increased service and public transport use. Redman et al. (2013) noted that a trend reversal is possible if accessibility and quality are targeted, which were negatively evaluated in the 2021 survey.

Esad Ergin (2022) further highlighted the perception of a lack of safety as a critical indicator of reduced public transport usage. A comparative analysis between 2013/2014 and 2021 perception of safety data, as shown in exhibits 5 and 6, reveals a changing attitude and deterioration in some categories, which would provide an additional reason for dissatisfaction with the Wales transport system and an increased drive towards car ownership. This link between increased car ownership and safety is also highlighted in research from Delbosc and Currie (2012).

Exhibit 5 shows the comparison of safety perception across ages. Interestingly, most categories that had a perception of "Very safe" or "Very unsafe" in 2013 have further strengthened their position, which could be interpreted as an effect of polarisation. Nonetheless, a deeper analysis of the “Fairly safe” and Fairly unsafe” categories provide a slightly deteriorating picture, with only people above 75 years feeling less unsafe. Despite overall deterioration, most surveyed perceive the mass transport system as either "Very" or "Fairly safe".

Exhibit 5:

Further analysis of the perception of safety across gender shows a similar picture. Most surveyed have a feeling of either very or fairly safe of the transport system, despite an overall deterioration observed since the 2013/14 survey, with females bearing most of the opposing view as seen in Exhibit 6.

Exhibit 6:

Despite the above results, the 2013 survey around car ownership per gender observes that most owners were male. Unfortunately, the 2021 survey did not further analyse car ownership per gender, which would help to gauge if the perceived sense of deteriorating safety impacted increased female car ownership, which is a limitation of this study.

Solutions

Whilst the ambitions of TW are to bring increased service to people, improve sustainability and achieve an increase in mass transport usage, from the results, we can observe a different picture of decreased satisfaction, a slight deterioration in perceived safety and an ulterior increase in car ownership since 2013. In addition, the 2021 satisfaction survey showed the ease of car usage, lack of transport routes and cost as contributing factors to reduced usage. In view of the multitude of negative factors, proposing a unique solution would be complex.

Considering that the UK is one of the safest places in the world (Macrotrends, 2022), Shiwakoti et al. (2019) proposed a solution for the Australian Transport Authority, which could serve as a blueprint. The author proposed that authorities increase safety awareness through constant communication, as perceived safety on transport systems aids towards increased usage. In addition, the same author proposes extending connections to augment existing routes to bring service to areas not covered, increasing usage further. An increase in routes and safety would ultimately improve the perception of quality and further reduce car ownership and public transport usage, as argued by Cullinane (2002). Increasing overall usage would provide further funds to decrease usage costs, increase productivity, and help TW reach their 2021 goals. A similar solution has been implemented in Germany with visible success and increased sustainability and could serve as a blueprint for TW (Buehler & Pucher, 2011).

Limitations

Several limitations do affect the study. First, most of the data have some structural differences across the years, and TW has also changed some of the survey questions, making a like-for-like comparison difficult and incomplete across most data available, which is due to several survey questions being omitted altogether over the years. Overall satisfaction with the mass transport system is one of the most salient absences. Furthermore, both sets, 2013/14 and 2021, have shown data collection imperfections, such as totals not adding up or being over or under 100%. Last but not least, the modality of result quantification has changed over the years, moving from medians to percentages. This change complicates a like-for-like quantitative comparison for specific tables as comprehensive survey data is unavailable, making only a qualitative interpretation possible.

References:

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Beirão, G. and Sarsfield Cabral, J. A. (2007) ‘Understanding attitudes towards public transport and private car: A qualitative study.’ Transport Policy, 14(6) pp. 478–489.

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Cullinane, S. (2002) ‘The relationship between car ownership and public transport provision: a case study of Hong Kong.’ Transport Policy, 9(1) pp. 29–39.

Currie, G., Delbosc, A. and Mahmoud, S. (2013) ‘Factors Influencing Young Peoples’ Perceptions of Personal Safety on Public Transport.’ Journal of Public Transportation, 16(1) pp. 1–19.

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Shiwakoti, N., Stasinopoulos, P., Vincec, P., Qian, W. and Hafsar, R. (2019) ‘Exploring how perceptive differences impact the current public transport usage and support for future public transport extension and usage: A case study of Melbourne’s tramline extension.’ Transport Policy, 84, December, pp. 12–23.

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Reflective Piece

Becoming a data professional has been quite a difficult journey coming from a pure business background, a situation often encountered in similar situations (Ellaway, 2014).

Firstly, creating an e-portfolio from nothing, with no experience in either GitHub or HTML, has been a steep learning curve, and the final result has been a personal achievement. In addition, it helped me to learn HTML. Codio has been of zero use, and most of my exercises have been done in Jupiter notebook or external platforms.

Nonetheless, the learning has been quite significant, as preparing for the discussion boards has shown. Colleague's feedback around issues such as the future of the profession and the evolution that the role has to complete to become fully productive has been quite enlightening. Critically it also poses personal doubts, as overall, the potential of the MSc is minimal for my development as a quant trader.

The discussion forum has also been quite insightful for the view on data management and data protection, where challenges for SMBs have been totally discounted. Sometimes, the benefits are limited in scope despite the best intention to guarantee rights to EU citizens.

Lastly, the work on data analysis on the Welsh Transport system has been an insightful experience and good practice on what we have learned so far. The possibility of comparing data across several years and creating a benchmark using Spendoli's (1992) process has been quite enlightening. In addition, it solidified my understanding of seaborn and data transformation.

References:

Ellaway, R. H., Pusic, M. V., Galbraith, R. M., & Cameron, T. (2014). Developing the role of big data and analytics in health professional education. Medical teacher, 36(3), 216-222.

Spendolini, M. J. (1992). The benchmarking process. Compensation & Benefits Review, 24(5), 21-29.