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=SUM(all)’s Eco-Equality project was built on the hypothesis that the effective promotion of cycling as a viable means of transport could reduce a city’s fossil fuel emissions by 2030, a target set by many cities in the face of the current climate emergency. Widely available data on transport was interrogated then narrowed the scope to the project group’s own city, Edinburgh, to see whether it supported the hypothesis.
=SUM(all) examined data and literature from global to local sources. This led to rich opportunities for discussion enabling =SUM(all) to form the hypothesis and narrow the scope of the task. Coding, visualisation and data analytics skills were applied to interrogate the data more granularly with tools including Python, Tableau, PowerBI, Excel. Collaboration tools such as Miro, Slack, GitHub were used with the final design being done in Canva.
The data for this project was taken from multiple sources – Edinburgh bike hire data came from the Just Eat Bike hire scheme, while the Spaces For People Project data came from the City of Edinburgh Council. All data was opensource and, in the case of the Just Eat bike hire data, continuously updated.
Additionally, to examine how accessible are bike hire stations throughout the city, the data for Edinburgh from the Scottish Index of Multiple Deprivation was used to create an interactive map of the bike hire stations and their spread throughout all socioeconomic levels, also known as levels of deprivation.
Key findings can be seen in the following charts:
1) The map above shows that Just Eat bike stations are not located in areas affected by multiple deprivation, exacerbating existing transport inequalities linked to poverty and emphasising lack of access to green transport.
2) The chord charts focus on journeys that start and end in Portobello showing the bike scheme benefits tourists and students rather than residents.
3) An interactive map can be accessed here.
The graph on the left shows that the number of bike hire stations is lower in more deprived areas (1-5), and higher in less deprived areas (6-10).
Spaces For People Data
The City of Edinburgh has committed to significantly reducing its carbon emissions by 2030 and creating safe walking and cycling spaces for people. =SUM(all) interrogated some of its data relating to cycling and its recent “Spaces for People” project.
The graph on the right shows the distribution of access to private transport (no access, 1 car, 2 cars) across different areas in Edinburgh (Central, East, North, South).
Navy – not supportive of City of Edinburgh Spaces For People measures.
Orange – supportive of City of Edinburgh Spaces For People measures.
The residents of Edinburgh are largely in support of the new measures designed to improve travel for cyclists and pedestrians.
=SUM(all) analysed how males and females differ in their attitudes towards the Spaces for People project.
Similarly, the same analysis was done on different areas of Edinburgh to see how attitudes to various measures proposed by the Spaces for People project differ across Edinburgh.
Access these graphs here.
As the Spaces for People project is largely concerned with improving infrastructure for all road users, with the emphasis on cyclists and pedestrians, it was important to look at green transport availability in conjunction with the proposed measures and the residents’ attitudes towards them. While no particular area of Edinburgh or gender shows strong opposition to the measures, the data could have captured the socioeconomic status of the respondents better, as well as reflected how different genders use transport to get around the city and in what ways they would be impacted by the changes.
In conclusion, green transport options are not widely accessible to all residents of Edinburgh – this should be taken into account when planning cycle routes and major transport works. The findings suggest that higher importance should be placed on developing equal and inclusive, community-wide access to bikes, as this is currently severely lacking. More information on the findings can be found here.
“While doing this project, I implemented the data analysis skills that I learnt during the course. I enjoyed learning and using new tools like Miro, GitHub, Tableau. It was a great experience to work in a team and learn from each other. I enjoyed our discussions while working on the project. It was a good learning to listen to each other’s views and then to implement the ideas we discussed.”
“I have done bachelor’s degree in computer science. Initially I worked as a computer trainer and then software tester in India. After relocating to UK, because of my childcare responsibilities, I couldn’t work. After a gap of few years, I started working as a librarian.
I love programming and always wanted to work in Information Technology. However, the biggest challenge is the gap of few years where I was away from Information technology job.
After completing “PDA in Data Science” course, I feel that my dream can come true!”
“I have joined Kaggle to practise my data analysis skills and also to improve my knowledge.
I am really keen to work in this field. Hence looking forward to the right opportunity. Until then, I will practice and improve my skills in this field!”
“My early career background was in business (running an agency promoting fashion and design). Later I worked as an English Language Teacher with adults attending English language schools in Edinburgh and London then with Teacher Trainees on degree level courses in Universities in Poland.”
“Latterly I have worked as a Disability Adviser in Scottish universities advising staff about legislation, policy and procedures when working with students who disclose disabilities. I also assessed and reported the needs of disabled students to lecturers and to Student Award Agency Scotland (SAAS) so students were able to access funding for any specialist support and technology they needed. “
“Just before the first lock-down I decided to pursue more creative endeavours leaving full time employment to work in a freelance capacity.”
“Participating in Code Division’s data science course gave me new insights into how data can be used to enhance understanding of complex issues. My interest is specifically in the context of data journalism and content production. I am confident that, as a result of completing the course, given a large data set I will now be able to clean and interrogate it (using tools in Python or Excel) to discover, write up and present interesting stories that might otherwise be missed.”
“I am a biotechnologist specialised in Molecular Biology and Biomedicine, I’ve been working in a variety of places including the private industry (medical devices and bioanalysis -pharmacy) in Scotland, where I lived for three years.”
“I’ve been interested in improving my skills in data analysis for a couple of years now. When the opportunity to take this course and properly re-skill arose, I decided to take this course and it really surprised me. On top of the hard skills, the PDA in Data Science had a lot of emphasis in the soft skills: I got to know a wide variety of tools, both for analysis and related tasks (resourcefulness, time management, productivity…) and for team collaboration (specially for the latter, which no doubt is an essential for any career direction). This course gave me the opportunity to get to know and practice quite a few different tools, solidifying my knowledge in the already familiar ones, and learning the basics of the previously unknown to me. And that was really useful.”
“While finishing the course I had the honour of joining an organisation that combines my two passions, molecular biology and data manipulation (open science and open data). This course was my last piece of work leading me to it.”
“I come from a psychology background, having done an undergraduate in psychology and a masters in mental health. When Covid hit, I wanted a change in my career and sought to retrain in tech.”
“Having had previous experience in research, I thoroughly enjoyed taking a fresh perspective on the importance of data and how it can be used to uncover patterns to better understand the world around us.
It was great to work in a group of people who all brought something different to the project – skills, experiences, their own background – learn on the go about new tools (Miro, Streamlit, Slack) and step outside my comfort zone.
I had the opportunity to better my programming skills when creating interactive data visualizations using maps, and while challenging at times, it was immensely rewarding to see the results.”
“I am really glad that Code Division has made it all possible and gave me an invaluable boost in switching to tech.”
“I am currently working in web development, and I really enjoy learning new things every day. I plan to keep furthering my skills in tech, delve deeper into coding, and stay knowledge-hungry.”
“I have been working in accounting and finance for many years. I have had a few long stints of being out of work and felt strongly that I was falling behind on the data skills front and missing out on opportunities which required stronger digital and analytical skills.”
“This course has provided the fundamental skills needed to analyse and visualise data. The course has essentially made me feel more empowered on a personal and professional level. I thoroughly enjoyed the collaboration with my fellow teammates, this took me away from my comfort zone as I am more used to working on solo projects. So this has been a real confidence booster. It was really rewarding to see my new found skills put to practice on the Project and I was really proud to be able to tell a story using data visualisations such as Maps and Chord Diagrams using Python.”
“I am still currently working in accounting and finance however I am looking to take on more data driven tasks and can now do so with confidence.”
How we did this
=SUM(all) used a wide variety of tools in this project.
The project was started off by brainstorming:
- Pick a topic
- Target an audience
- Create a plan of action
- Establish a question to be answered or a hypothesis
Miro, Slack and Github were very useful at this stage. Slack was used as a primary mode of communication, while Miro helped to collate ideas and visualise them immediately. GitHub acted as a place to store resources such as links to articles, images, while keeping in line with version control.
Weekly meetings were had, during which progress was reviewed, next week’s tasks and goals were set. Each week team members swapped roles for team lead, meeting chair, recording minutes, managing resources, and setting the agenda. Towards the end of the project, the team split up into smaller groups in order to work together on separate tasks, such as analysing data, creating visualisations and write-up.
During the analysis and visualisation stage the project team members used a combination of tools – Tableau was used to create dashboards for the Spaces For People data.
Folium was the library of choice when creating interactive maps, while Pandas in Jupyter Notebooks was used extensively to analyse data for the Just Eat bike hire scheme.
The chord diagrams earlier, which show the relationship between start and end bike stations, were created using the Holoviews library, while an interactive map app was created using Streamlit.
=SUM(all) utilised a vast array of tools – some of which were already known to the team, while some required on-the-go learning. The team faced challenges that came with dealing with real-world datasets and remote collaboration, but effectively mobilised resources, varied backgrounds and skillsets of each member to tackle the project.