DynamicsCon 2025 - Chicago

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I am honored to be presenting at DynamicsCon 2025 in Chicago - May 13 - 16.  https://dynamicscon.com/ I have two sessions: On Wednesday, May 14th at 3:45pm - I am co-presenting with Kent Nielsen.  Our topic is Canvas vs. Model-Driven Power Apps. You can find our slides here:  On Thursday, May 15th at 9:45am - I am presenting one of my most popular sessions - Getting started with Power Automate for D365 F&SCM.  Even if you have seen this session before, there is some new info on environment variables, building solutions and storing approval information in D365. You can find my slides here : 
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COVID 19 Dashboard – Article 4 – Reporting based on population

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I will continue to build on my earlier posts about my COVID19 Dashboard.  In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process.
The current list of planned topics include:
PART 4 - Reporting based on population

In looking at the data, I quickly realized simply looking at total numbers or even daily changes was only giving me a limited view of what was happening.   Were the large number of cases in New York simply because they have more people than most other states, or were they growing at a different rate.  Are New York citizens at a higher risk of acquiring the virus than California citizens.  It became evident that I needed to look at the data based on the population. 



Step 1 – Make sure I had the population data

I needed to make sure I had the population data at the levels I needed it.  The data I began with had the population detail for each US County, but I did not have Global population numbers.  I did some quick research to find the latest country population numbers at https://www.worldometers.info/world-population/population-by-country/.  Since this data does not change very often, I did not link Power BI to the source data.  Instead, I simply used Copy and Paste to create a new table in my data model. 

I did run into a few issues when I brought in this information.  Since I only had Country Name and Population, I was going to build the relationship to my country table on country name.  However, some of the countries were spelled differently or they were showing as a province of another country.  I used the transformation tools of Power Query, to clean the data up as much as possible.  Once the data was clean, I brought may population table into my data model and related it to my country table.


Step 2 – Create the need measures

This was actually a pretty straight forward step.  I identified that I needed four different measures related to population.  I was interested in looking at both total cases and total deaths related to the population, and I wanted to view it two different ways.
  • As a % of the population
  • Per 100k people

The four measures I created were simply division
  • Total Cases / Population Total Case % of Population = DIVIDE([Total Cases],sum('US State Province'[Population]),0)
  • Total Deaths / Population Total Death % of Population = DIVIDE([Total Deaths],sum('US State Province'[Population]),0)
  • (Total Cases / Population) * 100,000 Total Cases per 100k = DIVIDE([Total Cases],sum('US State Province'[Population]),0)*100000
  • (Total Deaths / Population) * 100,000 Total Deaths per 100k = DIVIDE([Total Deaths],sum('US State Province'[Population]),0)*100000

Step 3 – Create my desired visualizations

A simple matrix with conditional formatting on the measures
A scatter chart looking at the relationship between cases and deaths

With these measure and visualizations, I was able to quickly confirm that most states have very similar numbers.  However, there are a few states (New York, New Jersey and Louisiana) that appear to be outliers.

Step 4 – Compare total cases with total cases per 100k

In looking at what I had discovered so far, I decided it would be good to compare the total case numbers with total cases per 100k population.  To do this I decided to rank the states based on total number of cases and total number of cases per 100k.  This required using the RANKX function.
  • Rank total cases = Rankx(ALL('US State Province'[State]),[Total Cases]) 
  • Rank total cases per 100k = Rankx(ALL('US State Province'[State]),[Total Cases per 100k])

Since I am using RANK in the table, I need to make sure it ranks each state against all of the other states, so instead of simply entering the table, I included ALL(‘table’[rank column])

This quickly showed me that not only is New York #1 in total cases, the are #1 in total cases per 100k.  However, California is #6 in total cases, but #32 in cases per 100k


This certainly puts a different perspective on the numbers.

In my next article, I will look at some of the mapping abilities.

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