POWER-BI-CHALLENGE

Challenge 17- Environmental Data Reporting

Data Challenge Participants,

OK, we’ve got a really interesting one for you this month. This is the first time we’ve done a challenge based on environmental data, and the information provided is real, unmasked data that is currently being studied to address real-world environmental problems. Thus, in addition to honing your Power BI skills, you actually have the ability in this challenge to potentially influence the manner in which this data is visualized and reported.

For those of you who’ve been looking for a way to build a portfolio to advance your career or elevate your profile, there’s no better way to do so than within this challenge, where you can get feedback from the community, eDNA experts and Sam McKay, and in the end produce a complete reporting solution that addresses real-world, important issues.

THE BRIEF

You are a data analyst and visualization expert at a large environmental organization in the United States Mid-Atlantic region representing Delaware, District of Columbia/Washington DC, Maryland, Pennsylvania, New York, New Jersey, Virginia, and West Virginia. You are tasked with developing a report to communicate relevant environmental data to help visualize growing patterns. The indicators have been selected and data have been gathered. It’s your job to determine the best way to visualize the data and develop a reporting solution to communicate the key information.

What are the indicators?

An indicator is a numerical value derived from actual measurements of a stressor, state, exposure, or human health or ecological condition over a specified geographic area, whose trends over time represent or draw attention to underlying trends in the condition of the environment. The indicators are independent and for the purposes of visualization, no correlation or causation is to be connected or alluded to.

The report should include information on all four environmental indicators that are important to the region.

  • – Heavy Precipitation: yearly summary of daily rainfall from 23 sites (22 airport, 1 non-airport location) throughout the region.
  • – Ambient Air Toxics: concentration of air toxics that are harmful to human and ecological health at sites in the region. Note that concentrations are expressed in µg/m3 (micrograms per cubic meter of air).
  • – Asthma Prevalence: prevalence of asthma in adults (through modeling) by census tract level and prevalence in children by state percentage throughout the region. Note that in the children’s asthma data, “suppressed” means that the number is too low to show without potentially exposing someone’s identity and “unstable” (usually overlapping with “suppressed”) means that the numbers used to calculate the rate are so low that they’re statistically unreliable.
  • – Human Exposure to Contamination: contaminated sites within the region that have human exposure concerns. There are only three categories of sites: Potential for Concern, No Concern for Exposure and Insufficient Data.

Who are the users?

This report will be used by three types of end users with different goals:

  • – Regional Data Scientists who will use the report as a starting point for more comprehensive assessment of environmental trends
  • – Upper-Level Managers who are not familiar with the details of the datasets, but will use the report to track major environmental trends and will use the visualizations generated to communicate this information in external presentations
  • – Policy Makers who need to understand the data at a glance to support decisions about how to allocate funds to the aspects of their region that need the most attention

Is there anything else to consider?

  • – Entries are not limited by number of pages or technique. Feel free to use all the techniques at your disposal – tooltips, drill throughs, page navigation, etc.
  • – However, the front page of the report should be a summary dashboard, capturing what you think is the most important information for each of the four indicators. Having a plain language summary of findings as a component of this would definitely be viewed as a plus.
  • – The biggest challenges here will likely be identifying key trends in each of the indicators, and figuring out how best to clearly present a large volume of data spanning multiple locations and decades.
  • – Be mindful of accessibility issues (e.g., color schemes should be readable by color blind users)
  • – You may want to play with different population scales (census tract, county, state, etc.) that best fits the data available.
  • – Feel free to add in demographic data to support the visualization. You can find demographic data from the United States Census Bureau 30.

Submission of entries

To be considered within the competition, entries are due no later than 11:59pm PST Sunday, December 12.

If you are not already following Enterprise DNA on LinkedIn please do so.

How to submit:

  • – Email the completed PBIX file to powerbichallenge@enterprisedna.co
  • – Take an image and the Publish to Web URL of your report and post it to the Enterprise DNA forum.
  • – Take the image and URL and post it on LinkedIn tagging Enterprise DNA saying “I accepted the Enterprise DNA challenge.”
  • – We always encourage all participants to do a writeup and share their experience of participating in the challenge and sharing it on the forum and on social media.
  • – If you need any help with publishing, please reach out to one of the team for assistance (post in the forum or email to brian.julius@enterprisedna.co).

Judging Categories

Overall Winner – all entrants are eligible

First Time Participant winner – open to any Enterprise DNA member who is taking part in the challenge for the first time.

Winning Non-member – open to entrants not currently eDNA members.

There are some excellent prizes on offer from free membership (for all category winners) and more, to having your work showcased across the Enterprise platform. So please do get involved and share this opportunity with others who might be interested.

As always, best of luck!

Download dataset here