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DataNight: A Greener Future

On March 27, 2019, Data For Good held a DataNight at the offices of our host, Integrate.ai.
The goal was to explore data from A Greener Future and look at ways to analyze and visualize the impact that AGF has made through their amazing work.

Who is A Greener Future?

A Greener Future works hand-in-hand with local communities to promote environmental preservation through organized litter cleanups, educational programs, and events. Our expanding family of volunteers are committed to creating a clean, healthy environment that can be sustained for generations.

Rochelle Byrne founded A Greener Future in 2014. Since then, they have run cleanup programs all across Canada.

Some recent highlights:

  • In 2018, A Greener Future picked up their 1,000,000 pieces of litter!!
  • The 2018 Butt Blitz picked up 239,339 cigarette butts off the ground. This was accomplished by 171 volunteers in 20 locations across Canada.
  • Cleanups have been run in 105 unique cities at 276 different locations
  • There are 61 different sponsors

You can learn more about A Greener Future here: https://www.agreenerfuture.ca/

From Rochelle, “By picking up all that litter, I see what I’m picking up, things that people use on a daily basis that people don’t consider litter. Bandaids, Q-tips. It has changed me and how I live my life. I live almost a waste free lifestyle now. By changing one thing at a time I don’t feel like I’m missing out.”

What data are we talking about?? What is it’s lifecycle.

AGF volunteers collect data to document what kinds of litter are collected at each event.

“We still use a data sheets and binders. That works better than an iPad/Otterbox. They’re too much work. Volunteers that are regulars can use data sheets really quickly. After the events, the data sheets are transferred by hand to Google sheets. This allows realtime updates for whole team. Afterwards the Google sheets are transferred to a main database. It’s useful because so many events close are together. Nice to be able to look at data and see if we’re picking up more or less, same stuff as last year.”

Here’s an example of data collected from a 2018 event.

Date May 28, 2018
Latitude 43.816643
Longitude -79.03187
Location Rotary Park
City Ajax
Province Ontario
Country Canada
Event Love Your Lake
Coordinator Rochelle Byrne
Volunteers 17
Strange items Peppa Pig loot bag, tiny doll, hookah tweezers
Cigarette Butts 1056
Tobacco Packaging 16
Lighters 2
Plastic Grocery Bags 7
Other Plastic Bags 10
Paper Bags 3
Food Wrappers 339
Plastic Beverage Bottles 3
Plastic Bottle Caps 40
Plastic Lids 16
Paper Cups/Plates 4
Metal Bottle Caps 5
Glass Bottles 5
Straws 21
Utensils 13
Personal Hygiene 25
Clothing 14
Construction 16
Fishing 7
Plastic Pieces 158
Paper 92
Glass 62
Foam 703
Syringes 2

“…that’s what I want our data to show other people, that small changes in your daily life make big changes over time.”

AGF separates data about the litter they clean up into 30 different types.

Analyzing Litter Types Over Time

Looking at Volunteers

Audience Questions, Ideas and Discussion

Do smaller towns have different types of litter than larger towns/cities?
Prince Edward County has a lot garbage that travels on Lake Ontario currents. It is not litter from local litter bugs.

Does AGF have desire to get into policy changes?
We do work with municipalities in any area they work. They help promote events.
Volunteers leave feeling inspired. They know they can reduce their own waste and educate others, find local solutions, start petitions etc… We don’t need to wait for municipalities.

Are there ‘glory’ shots of all litter cleaned up at an event? Can that be used as a motivator for volunteers as well as a data viz of impact?Yes, However, litter is collected, sorted and recycled or reused so not actually that much garbage.
Still powerful to show total amount collected.

Comment: You can use data to prove a point or predict but you’ll need a lot more data. You can’t draw conclusions with current amount of data. Too many variables, geography, weather, seasonality, etc…

Is it possible to use app during a litter clean up to facilitate data collection?
We still use a data sheets and binders. That works better than an iPad/Otterbox. They’re too much work.

Idea: Imagine AGF has an app already. What kind of data could it collect?

Idea: Can you partner with Uber to detect street level trash. Train their models to perceive more than just cars and people?

Discussion: Could access to infrastructure (trash cans) be an issue rather than just a behavioural issue of throwing garbage on the ground?

Do you know how do your volunteers hear about you?
Lots of cleanups aren’t public. Rochelle works directly with schools.
Twenty people is a good number for teaching a cleanup group. Highschools with kids in Ecoclub or Environmental Program in the school.

What portion of waste is recyclable/reusable?
We try to recycle as much as possible. The tricky part is plastics. If its too dirty then it can’t be put into the recycling stream. AGF has overloaded Terracycle with more plastics than they can use. Part of the issue is to find places that will use collected plastics.

Suggestions of organizations to work with:
Cycling clubs, rock climbing clubs, hiking organizations, sailing clubs, etc….

 

A big thank you to our sponsor for this DataNight!

We want to send a big thank you to Integrate.ai for their space and hospitality. They have been very generous with the use of their space.

DataNight: A Greener Future 2019-06-08T13:58:13+00:00

CASE STUDY: Youth Without Shelter

CASE STUDY: Youth Without Shelter

“We have never stopped with our vision. We want to end youth homelessness one youth at a time and that is our focus. The people that come through our doors are individuals and we treat them as such and we want to help them overcome the struggles they are experiencing.”
– Steve Doherty, YWS Executive Director

Who is Youth Without Shelter?

Youth Without Shelter’s Mission statement is, ”Empowering youth facing homelessness to reach individual potential.”

Youth without Shelter (YWS) was founded in 1986 by a group of teachers and guidance counsellors who were frustrated with the lack of safe, emergency housing and support programs for their students. YWS has now helped more than 15,000 homeless youth to build their confidence and life-skills and to find long-term stable housing and jobs.

An impressive 87% of every dollar donated is directed to youth programs and related support (remaining 13% for admin and fundraising).

YWS has been nationally recognized for their work and most recently as Charity Intelligence Top Charity Pick and as a Money Sense 2019 Top 100 Charity.

Problems we were asked to solve.

There were four primary questions YWS asked us to tackle during the datathon.

  1. Generate a map of segmented donor profiles of current YWS donors using geographical data from StatsCan and existing donor data.
  2. Validate the hypothesis of whether volunteering is a gateway to donating by matching volunteer data to donor data and if there are any insights about these volunteers who do become donors. E.g. Do they donate more?
  3. Identify the most loyal and consistent givers in the past 5-10 years such as those who have given at least 7 times in the past 5 years; and those who have cumulative giving of $1,000 over the past 5 years to aid in major gifts program launch.
  4. Identifying the profiles of lapsed and new donors. For lapsed donors find a pattern of why they have lapsed and who would likely return. For new  donors find a pattern of what channel they first became a donor and any patterns for repeat donations.

What did the dataset look like?

YWS provided an extensive set of donation and volunteer data spanning 20 dimension and close to 100,000 rows of data. The earliest records date back to 1989!

After cleaning and preparing the data from YWS, the resulting tables including the following data types:

  • Unique Constituent ID
  • Key Indicator
  • Gender
  • First Gift Amount
  • First Gift Date
  • Last Gift Amount
  • Last Gift Date
  • Largest Gift Amount
  • Largest Gift Date
  • Total Gift Period (days)
  • Donor FSA

  • Donation_approach
  • donation_designation
  • gf_type
  • volunteer_hours
  • volunteer_events
  • is_volunteer
  • is_constituent
  • Is_donor
  • Days_between_donations
  • was_Volunteer
  • Volunteer_TotalHours
  • Volunteer_MostRecentDate

Next, StatsCan Forward Sorting Area tables were used to associate postal code information with their geographic areas in Canada. This allowed us to readily add socio-economic and demographic information for each FSA. The data included details such as total population, number of people in each income bracket, age breakdowns, etc…

To assist Data For Good volunteers, several data dictionaries were created to provide metadata about each dimension of the data provided by YWS for analysis.

“There are 2000 youth experiencing homeless every night in the city of Toronto and less than 400 shelter beds and transitional housing beds. It is a challenge we continue to experience and it is going to take imagination and people like yourselves to help make a difference so thank you for making a difference.”
– Steve Doherty, YWS Executive Director

The tools and analysis we used to help

Multiple tools were used, including Tableau, R, Python / Jupyter Notebooks, PowerBI and Excel.

Several different approaches were used to analyze the data. Each group of Data For Good volunteers chose their own approaches. Some of the models put into use are listed here:

  1. Logistic Regression
  2. Lasso & Ridge Regression
  3. Random Forest
  4. Support Vector Machines
  5. Naïve Bayes

Key Findings and Recommendations

Some of the most interesting findings came from analyzing donor profiles and creating segments to better understand who YWS’ most loyal donors are.

Donor Distribution by Gender

Donor vs Volunteer Location

Total Donations by FSA Code

Recommendations included focusing fundraising efforts around donors with the following attributes:

  • Female groups in urban, middle-income areas who are recent joiners
  • Potential campaign days: Mother’s Day, International Children’s Day, Donor’s Birthday

Other significant findings looked at volunteering and how it correlates with donations.

  1. 5% of Constituents are Volunteers and 4% of them are Donors
  2. Volunteers donated 28% of the overall amount
  3. Organizations tend to volunteer and donate more than individuals
  4. Volunteers and donors come from the GTA

Volunteers Donate more, and Donors also Volunteer more

 

 

Acknowledgements

These events are made possible by the support of amazing sponsors. Data For Good and YWS are very grateful to Capco who generously volunteered the use of their space, resources and most importantly food!!

Case Study questions? Ask stephen.boyd@dataforgood.ca

Interested in working with Data For Good? Get in touch with us! contact@dataforgood.ca

 

CASE STUDY: Youth Without Shelter 2019-06-04T22:34:01+00:00

Workshop Series: How to use Canadian Census Data for Good

Data for Good hosted a workshop in collaboration with Statistics Canada and the City of Toronto. Paul Laffin from Statistics Canada outlined the different methods that are used to collect and store the Census information. Whereas Heath Priston from the City of Toronto spoke to the value to the community of having this data readily available. In attendance were representatives from various local NFPs and data enthusiasts.

Statistics Canada gathers data through the census every five years, directly from Canadians. This data provides information on the demographic and the socio economic conditions of the population. The “geographic hierarchy” of the census data is divided into various administrative and statistical geographic areas which adds value by allowing better use of the data. In December 2017, Toronto city council adopted a policy framework to guide how it would work with community based NFPs and made a commitment to pursue a more consistent and a clear policy. This gave the city more access to data and data management capacity that could assist with strategic initiatives within communities by analyzing vulnerabilities.

Statistics Canada Resources

Paul shared details on how to access the open census data via the website and an extraction tool (Beyond 20/20) Note, Beyond 20/20 only works on PCs. Data extracted from the website can be downloaded in CSV, TAB, IVT, XML format as data tables or full extracts.  Statistics Canada also has a monthly newsletter, list of webinars and information sessions, individuals and organizations are encouraged to join the distribution list for updates to census data and other tools that may assist in using the data in a meaningful manner.

Workshop Discussion

During the workshop, NFPs in attendance voiced some of the data gaps they had and how Statistics Canada or City of Toronto could help in filling these gaps.

In particular, City of Toronto provided some details on some successful partnerships between the two entities such as:

  • Data, research and maps portals
  • Neighbourhood level profiles that represent local trends from Census data, at a smaller level than the Dissemination Areas in Census data
  • Statistics about community well being across Toronto neighbourhoods
  • Community data programs and participations

Participants agreed that increased access to this data assists for effective policy making as it helps identify trends and address gaps. However there are limitations to this – the data can only be used as a secondary source of information that will help validate hypothesis/ideas.

Written by Anam Mazhar & Eunice Lo

Workshop Series: How to use Canadian Census Data for Good 2018-07-04T01:33:12+00:00