Civic Data Education Series
  • Civic Data Education Series
    • About
    • License
    • Instructor Guide
    • Feedback
  • Segment 1: Introducing Civic Data
    • Module 1.1: Introduction to Open Civic Data
    • Module 1.2: The Lifecycle of Open Civic Data
    • Module 1.3: A Critical Approach to Open Civic Data
  • Segment 2: Understanding Civic Data Ecosystems
    • Module 2.1: Identifying Civic Data Intermediaries
    • Module 2.2: Introduction to Civic Data Ecosystems
      • Module 2.2 Activities
    • Module 2.3: Assessing the Civic Data Needs of Communities
  • Segment 3: Preparing Libraries for Sharing their Data
    • Module 3.1: Selecting and Sharing Open Data
      • Module 3.1 Activities
    • Module 3.2: Protecting Privacy
    • Module 3.3: Metadata for Open Civic Data
    • Module 3.4: Data Documentation for Open Civic Data
      • Modules 3.3 & 3.4 Activities
    • Module 3.5: File Formats for Open Civic Data
  • Segment 4: Community Engagement through Civic Data
    • Module 4.1: Defining Data Visualization
    • Module 4.2: Telling Stories with Data
    • Module 4.3: Outreach on Open Civic Data
  • Segment 5: Using Civic Data
    • Module 5.1: Asking Data Driven Questions
    • Module 5.2: Answering Data Driven Questions Computationally
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  • Introduction
  • Instructional Materials
  • Computational Notebook
  • Video
  • Activity
  1. Segment 5: Using Civic Data

Module 5.2: Answering Data Driven Questions Computationally

PreviousModule 5.1: Asking Data Driven Questions

Last updated 2 years ago

Introduction

In this module we will show some examples of using open civic data to answer a data driven question. This module will dive into several open civic datasets and use the Python programming language to manipulate and merge these datasets to gain new insights from the information they contain.

This module will also demonstrate how to download and manipulate open civic data using the Python programming language.

Guiding Questions

  • How can we use data and computation to learn about our communities?

  • How can we join datasets together to gain new insights?

  • What tools exist to engage and explore our data computationally?

Vignette

The librarian would like to investigate the usage of Public-wifi at various branches in their library system. Specifically, what neighborhood uses the most wifi? They use the Python programming language and Jupyter Notebooks to perform calculations and generate data visualizations. The librarian visits a local data portal, downloads the datasets of interested, and then write Python code to manipulate the data, join multiple datasets together, and produce a data visualization and answer a data driven question.

Instructional Materials

Computational Notebook

The instructional materials for this module have been written in the form of a Jupyter Notebook. The notebook is accessible in two different ways. There is a read-only mode and there is an interactive mode.

Video

Video Files

Activity

Overview:

After answering the computational questions in the instructional materials above. Open the activity notebook and work with the data.

Supplies: An Internet-connected device and web-browser.

Time: 20 mins.

Activity:

Open the computational notebook at the link below and follow the instructions in the notebook.

Click here to launch the INTERACTIVE activities
Click here to launch the INTERACTIVE instructional materials
Module 5.2 Answering Data Driven Questions Computationally Instructional Video
335KB
Module5.2_Using Civic Data.pdf
pdf
Module 5.2 Instructional Materials PDF
15MB
Module5.2_Instructions.mp4
Module 5.2 Instructions Video
47KB
Module5.2_Activities.pdf
pdf