The Innovation Hubs

Recap: TPL's Second Annual Hackathon: Toronto Poverty Reduction Strategy

On Saturday, September 17 and Sunday, September 18 developers, data scientists, mappers and idea people were invited to learn about the City of Toronto Poverty Reduction Strategy at Toronto Public Library’s second annual Hackathon. The library in partnership with the Toronto Open Data Institute, City of Toronto and Social Planning Toronto hosted the event at the Toronto Reference Library with over 50 engaged citizens and mentors in attendance.

Participants were challenged to develop ideas that support the Toronto Poverty Reduction Strategy and poverty-related issues. Ten challenges were proposed and each of the ten challenges were tied to one of the six issue areas of the poverty reduction strategy, which are:

Housing Stability: The city needs more quality affordable housing so that individuals and families with low-incomes do not need to sacrifice basic needs to live in decent conditions.
Service Access: Not all residents find the services they need when they need them; the City can do more to make services available and effective.
Transit Equity: Public transit needs to be affordable and reliable; it needs to take residents to opportunities and bring opportunities to neighbourhoods.
Food Access: Torontonians, especially in many low-income communities, need better access to affordable, nutritious food.
Quality Jobs and Livable Wages: Toronto cannot achieve its vision of being an equitable and inclusive city while so many residents are unable to find quality jobs.
Systemic Change: Mobilizing an entire city to reduce and ultimately end poverty will take new ways of thinking and new ways of working.

Participants worked with the data sets provided by the Library and our partners including the City of Toronto's Open Data catalogue. 

The Hackathon's challenges were introduced weeks prior at the Toronto Civic Tech Meetups and some participants started to work on their ideas before the hack day on Saturday. The Civic Tech community were instrumental in providing valuable feedback to the challenge owners.

On the Sunday, participants showcased their work to their peers and also to members of the public. The day started with a panel discussion that consisted of representatives from the partnering organizations and hackathon participants. The discussion touched on how partnering organizations in the city are supporting the Toronto Poverty Reduction Strategy and how citizens can get more involved. The ideas from the presentations ranged from apps to improve access to city and library services to a deeper look into datasets that could provide insights into existing or future initiatives. The common thread among all the ideas was that everyone has a role to play in supporting the City's Poverty Reduction Strategy and poverty-related issues.

Big heartfelt thanks from the library to our participants, partners, mentors, our food sponsor Panago Pizza, and our beverage sponsor Starbucks, for feeding and hydrating our participants.

 

Project Presentations

Check out the different team presentations below and a description of their projects in their own words:

Project 1: Parks and Rec Program Demand
Challenge Owner: Social Planning Toronto

Geoff Martin, Hanifa Mamujee, Bill Yang, Jacob Zimmer

Project Overview:

1. Ensuring that services are being deployed effectively (eg. that Older Adult programs are available in areas with large elderly communities, etc) using data analytics
2. Increasing awareness of Parks and Recs programs, funding offers and access to facilities (eg. where somebody can play pick-up soccer, what financial assistance is available to a single mom, etc) using UX approach
3. Addressing challenges with regard to registration and enrolment (eg. ensuring all demographic segments have access to programs, improving registration experience, etc)

 

Project 2: Automating Digital Innovation Hub Bookings
Challenge Owner: Toronto Public Library

John Spragge

Project Overview:

In my participation in the hackathon, I aimed to address the automation of Digital Innovation Hub bookings. At this stage, I believe I can present at least an outline of a Tomcat servlet based application using AJAX technology and implemented primarily for mobile phones. With luck and a little energy, I may successfully demonstrate a basic working model of such an interaction.

Project 3: Automating Digital Innovation Hub Bookings
Challenge Owner: Toronto Public Library

James Teow, Joshua Gorner, Ryan Petroff, Alex Volkov

Project Overview:

We explored how to improve the process of both library employees and users of the Digital Innovation Hub. We believe that the Digital Innovation Hub is able help reduce poverty by providing access to technology and resources that would otherwise not be available to many people. We feel that improving the booking process would lower the barrier for people to access these services, and alleviate pain points experienced every day by library staff giving them more time for other activities. We also feel that it would make it easier to scale the Hub to other branches and would help provide more data that could be used to better understand the Hubs users.

 

Project 4: Automating Digital Innovation Hub Bookings
Challenge Owner: Toronto Public Library

Jonathan Mikhail

Project Overview:

This year, TPL's challenges boiled down to four major objectives: making it easier to find services, improving information gathering of those searching for these services, facilitating bookings, and better engaging the city's youth. In all cases, these needs can be addressed with a streamlined native mobile app, incorporating these and many other additional time-saving and usability features. The attached mockups illustrate what such an app might look like, including some of the service finding, booking and engagement functionality. Early feedback has been overwhelmingly positive; there is clearly much interest in such a development, and I certainly hope that TPL considers pursuing it!

 

Project 5: Neighbourhood Level Income Analysis
Challenge Owner: City of Toronto

Chux Ejieh, Dustin Gadal, Derek Howard, Leon Lukashevsky, Charmalee Sandanayke, Railye Shen, Sarah Shoker, Jane Zhang

Project Overview

Chux Ejieh - I worked on Keith's dataset that was about Poverty Levels based on Tax Returns. Specifically, I explored the relationship between Employee Insurance Benefits and Income levels in Toronto Neighbourhoods. These two charts show some of the findings made: chart 1 and chart 2.

Charmalee Sandanayke - I have a GIS/mapping background so I came into the open data hackathon hoping to map some data. I waded through the tax filer income data and found it hard to focus on one thing because the data set is very detailed. My team member Leon was very helpful and extracted child poverty data, which I mapped by neighbourhood per year.

Derek Howard - I've put together a map showing the economic dependency ratios for all persons averaged across 2010-2014. The EDRs are split into quintiles with the brightest being those with highest dependency ratio. I've attached an explanation of the EDR as well. Economic Dependency Ratio (EDR): Is the sum of transfer payment dollars received as benefits in a given area, compared to every $100 of employment income for that same area. For example, where a table shows an Employment Insurance (EI) dependency ratio of 4.69, it means that $4.69 in EI benefits were received for every $100 of employment income for the area.

Dustin Gadal - If you massage existing data a bit, you can convert it into Google's Dataset Publishing Language (DSPL) format.  A guide for doing this is available here. Once in DSPL format, you can upload it to Google's Public Data Explorer.  A guide for doing this is available here. Here is the T1FF_N2010-N2014.xslx table browsable online.

 

Project 6: Tool for Remote City Council Deputations
Challenge Owner: Social Planning Toronto

Patrick Connolly, Sharon Kennedy, Vincent Li, Devin Nathan-Turner, Anna Skrypnychenko

Project Overview:

1. To create a user friendly way for residents to submit video deputations to City Council’s subcommittees
2. To create a simple and integrated tool for City Clerks to review and approve video submissions for inclusion within agendas
3. To use automated tools to create rich data (i.e. issue-specific tagging, transcriptions, flagging abusive content)

 

Project 7: Digital Divide
Challenge Owner: Toronto Public Library  

Gordon Chan, Chris Graham, Edgar Valencia, Nora Vega

Project Overview:

We worked on "Bridging the Digital Divide," specifically looking at the Library's new Portable WiFi HotSpot Lending Program.  Specifically, we looked at how the Library could best ensure the program serves reduce poverty by targeting key communities.  At the same time, we don't want to get the library into the business of screening borrowers.

Our solution was to encourage the library to promote the program using external organizations that serve target communities (new immigrants, low income people, people with disabilities etc).  This should ensure a higher awareness of and demand for the program among those groups.  Additionally, these organizations would be able to survey these communities more broadly to allow the Library to better understand the demand for this program.

This approach also allows the library to administer the lending program using the same model and approach that it uses for any other lendable item in its collection.

Project 8: Ontario Works Wayfinding Improvement
Challenge Owner: City of Toronto

Jon Alexander, Raymond Ang, Adrian Haldenby, Bill Mann, Ryan McCormack, Muriel Schvartzman, Danny Tshitumbu

Project Overview:

Currently, the process for Ontario Works (OW) clients to access programming is almost completely dependent on the client's caseworker.  Our proposal empowers clients to take an active role in their case plan through the creation of an interactive application that will look at what programs may be suitable for the client based on the client's interests, skills, and personal attributes.  Creating a more engaging and client-centred process will hopefully result in improved success rates, better program development, and more time for caseworkers to work on other important tasks.

 

Project 9: Improving Tenant Info for Affordable Housing
Challenge Owner: Social Planning Toronto

Alex Belloni Alves, Leonel Oliveira, Greg Uchitel

Project Overview:

EZHouz:

1. It helps people to search for good affordable housing in a great neighbourhood, as well as assist these applicants to keep their places in waiting lists
2. A better housing choice can lead people to be proud of their home
3. Applicants should choose a great, comfortable, and hassle-free housing

 

Project 10: Improving Youth Access to Services
Challenge Owner: Toronto Public Library

Phillipa French, Jane Zhang

Project Overview:

In order to address the challenge of what services Toronto Public Libraries should be offering in their Youth and teen zones, we looked at 211 data of youth services and the library catchment area in order to see what services were offered, and what gaps may exist. We used a Value Proposition Canvas to understand our end users and looked at their needs and how the library services meet those needs. As a result, we created a simple prototype of how to categorize services and also summarized our key findings to guide our next steps.

Project 11: Improving Tenant Info for Affordable Housing
Challenge Owner: Social Planning Toronto

David Dou

Project Overview:

A dashboard for exploring municipal rental buildings inspection data.  Shows the problems associated with each property and the frequency of inspections made by the city.  

 

Project 12: Qualitative Data on Poverty Reduction Consultation
Challenge Owner: N/A

Howard Tam

Project Overview:

Deeper Human Experience Insights from Public Engagement Data
The City of Toronto (and many other municipalities) host a lot of public consultation exercises. Often, the comments collected may not pertain directly to the question being asked so it isn’t used in the final report. Yet, this participant feedback is based on real lived experience and as such is potentially a rich source of amazing insights waiting to be discovered. How can we capture these insights better? This project analyzed consultation comments collected during the development of the Toronto Poverty Reduction Strategy and looked at developing a basic taxonomy as well as testing natural language processing tools like sentiment analysis and topic extraction. Outcomes may include developing an aggregation and classification system for City-collected public consultation data to better connect this information across departments and allow us to learn deeper insights from this data.