Process Behind Data Samples

public_day.jpg

 

Civic Innovation Lab will focus on the following data sets for the first session. We plan on revisiting these choices using feedback and observations from participants.

We narrowed down the sample data sets based on a few parameters, which include:

  • Data must be granular to the neighborhood level (i.e. zip code);
  • Data sets must include more than a cumulative summary or snapshot (e.g., the Number of Commuter Buses was interesting, but didn’t give us very much);
  • Was available to audit (e.g., one seemingly particularly useful data set could not be audited outside of LA County, whereas some of the GIS packages did not list data fields;
  • Is updated with some regularity—similarly, outdated data sets were not considered.

Once we established the data sets, we focused on communicating the data sets to a large audience who has varying levels of experience working with data sets.

Each data set will be summarized on a 5×7 index card. These are color-coded by theme (see above).

The front of each card has:

  • Bucket Theme (e.g. small business)
  • Title of data set
  • Short summary
  • Icons/text depicting type of data set
  • Icons/text depicting how this data impacts the city/community

On the back of the cards you will find: 

  • Important column titles from the data set
  • Example of how this data could be used for a product or service
  • “I want to make a [blank]…”
  • “that can [blank]…”
  • “so I need [blank]…”

Data_Card_Instruction

The examples introducing the concepts of Data, Information, and Insight. “I want to make” is a goal, usually a product or service. “That can” demonstrates the mechanics or information that the goal has. The last section, “So I need” connects these more abstract ideas to tangible pieces of data. The framework is designed to teach people who don’t usually work with data how to tie their social good ideas to columns in a spreadsheet.

We also had a similar intention with the iconography on the front of the cards—to implicitly teach participants how to think about the concept of data. The first row of icons indicate to the reader what kind of data set they are working with. The categories are (from left to right):

  • What type of data is primarily represented
  • People (e.g., demographics)
  • Locations (e.g., GIS data)
  • Objects (e.g., Counts of assets or resources)
  • Money (e.g., budgets or revenue streams)
  • How often is the data set updated? (e.g., monthly, weekly)
  • Is the data primarily quantitative or qualitative?
  • What is the size of the data set?
    • Small (under a hundred rows)
    • Mid-sized (between a hundred and a thousand rows)
    • Large (over a thousand rows)
  • How accessible is this data set to work with?
  • General knowledge data sets can be manipulated by most professionals
  • Specialized knowledge data sets require either a specific understanding of the data itself or an advanced knowledge of how to manipulate spreadsheets or GIS databases

The second row is a series of markers that guide participants in thinking how this data, and subsequent products, might affect the community at large. These categories were adapted from work done by Richard Layard, program director of the Centre for Economic Performance at the London School of Economics, who argues that there are seven factors central to happiness. To define these factors, he drew on research from the US General Social Survey. We’ve adapted these factors, moving from personal happiness to the happiness of people within the context of t!heir communities.

1) Family Relationships - Data sets tagged with this factor can have an impact on family resources and priorities.

2) Finances - This factor looks at both a larger macro scale of how citizens interact with money, including budgetary and financial revenue data sets, as well as the financial situation of individuals.

3) Jobs - These data sets may offer insight into how people feel about their work, as well as what types of jobs are available and how many.

4) Social Bonds - Another way of saying ‘human connection’, these data types have to do with the way people relate to one another, often seen in conjunction with education or public safety.

5) Health - Health and well-being related data are tagged with this factor.

6) Democracy - Data tagged as “Affects Democracy” acts to elucidate government practices, or could be used to help citizens identify and defend their political positions.

7) Priorities - This factor refers to data sets that may help citizens to prioritize resources or needs within a community.