Jackpot! Why Winning the Lottery Won’t Change Our Data

With the three big winners in last night’s lottery splitting such a large pot, the income-level demographic data for where they live will be effected. However, depending on the way you look at this effect, there will either be slight changes if any, or there will be a large change. The reason for this difference is that demographic datasets supply both mean and median household incomes.LottreryBalls_Clipped

The lottery winners provide us with a great example of why we run median household income rather than average household income. This is because using a mean generally works well for data with normal distributions while medians are generally used on data with skewed distributions. And as you can probably guess, income data is quite the skewed dataset. Since a mean is so heavily influenced by outliers, we use a median. The median value will provide the value in the middle of the data (when sorted in ascending order).


To illustrate this, let’s pretend that one of the lottery winner’s home ZIP code has 10,000 people. Let’s also pretend that, by some kind of freak chance, every one of these people has an income of exactly $50,000. That would mean that that ZIP code has an average and median household income of $50,000. But, the lottery winner has now changed that. With their income changing from $50,000 to somewhere around $500,000,000, the new mean household income would be $99,995 and the median income would remain $50,000.

Read More

2015 Esri Health & Human Services GIS Conference

Last week on the blog we discussed the 2015 Esri User Conference. This week we are going to break down the second of our summer conferences the Esri Health and Human Services Conference that took place in Atlanta, Georgia earlier this month. Although it’s much smaller in size, it provides us with just as much useful information on the use of GIS in both the Public and Private healthcare spheres!


Read More

2015 Esri User Conference Report

It’s been a busy summer for the Stratasan GIS team. Luckily, we found the time to check out the two most important Esri conferences out there to make sure we continue to bring the best Health GIS solutions to our team and to all of our customers.

The first of the two conferences was the biggest of the year: The Esri User Conference or “UC” to us GIS insiders! This conference is where Esri unveils the newest and coolest upgrades to their software, reports, and data. It has a large international crowd and usually fills the San Diego Convention center with around 20,000 people for a week in late July every year.

Below we’ll break out some of our favorite things from the ESRI User Conference:


Read More

Healthcare Data Analytics Vendors: The Difference Between “Having Esri” & Actually Using GIS

Since its inception, Stratasan has been at the forefront of using mapping technology with the many forms of health data we use on a daily basis. Not surprisingly, our competitors have realized that consumers in the healthcare industry value maps as well. Many of the other data vendors in our industry have started using various Esri products since Stratasan arrived on the scene. These companies mention “having Esri” now, but if you look at their offerings and compare them to Stratasan’s you will quickly realize there is a BIG difference between “having Esri” and actually doing Healthcare GIS work using Esri.

Read More

Stratasan’s Demographic Data: [The Many Reasons] Why We Use Esri

One of Stratasan’s most powerful assets is our demographic/psychographic data [Especially now that their Tapestry Segmentation was updated this year and is better than ever!]. Using this data in tandem with our state, federal, and proprietary datasets we create actionable healthcare market intelligence from the block group level to the national level. What you might not know is that this annual demographic/psychographic data comes from Esri, the market leader in both GIS software and data. Most of our customers know that we use Esri for our demographic and psychographic data, but not everyone knows why.

Read More

Introducing: Hospital Market Share Dominance Maps

We are constantly searching for new, effective ways to present intelligence from data. In order to accomplish this, the Stratasan team uses tables, charts, graphs and maps instead of raw data tables alone. Pairing these visual tools with raw data increases the efficacy of our products and adds a necessary element of excitement to the black and white world of data for our customers. The most common requests we fulfill are those around market share. This report specifically benefits from converting the data into visuals. Our goal is to present market share data in a way that clearly displays a hospital’s (and their competitors’) presence in a market. We wanted it to be visually interesting while still providing a useful picture of a market area. So, Stratasan is pleased to introduce the Market Dominance Map.

Market Dominance Map

Map illustrating market share leader by ZIP; color gradient illustrates how strong their market share is within each ZIP.

The Market Dominance Map (above) shows hospital reach by color and the significance of that reach by color saturation. If a hospital has a majority market share in a ZIP code, that ZIP is filled with the hospital’s color according to the map legend. Depending on how high the hospital’s leading market share is, the color is shaded from dark (high market share) to light (lower market share). We refer to this visual representation of reach as “dominance” of a ZIP code.  The percentage number in each ZIP represents the leading hospital’s market share. As you can see in the example above, only the leading hospital per ZIP is represented. This is a quick and clear picture of the market’s major providers and what portions of the market these providers have cornered. “But what about our competition within ZIPs?”

Market Dominance, Highly Contested ZIPs

ZIPs with less than 10% market share separating the two leaders are cross-hatched

To increase the value of this project, including the top competitor is the obvious move. To maintain the clarity of the map, we put a limit on what constitutes “competition.” In each ZIP code, if the dominant hospital has market share less than ten percentage points higher than the next hospital; the second leading hospital’s color will cross-hatch through the ZIP.

Highly Contested ZIPs

Limiting map to only those ZIPs that are “highly contested”, defined as the top two market share leaders being within 10% of each other.

The final view for this project is to give the hospital strategic planning team a view of the ZIP codes in their service area where the market leader is in a “highly contested” market share race with another facility. The percentage listed in each ZIP code is the market share percentage of the second facility. Market share is an undeniable part of hospital strategy. For a clear, concise portrait of dominance in your market, contact us today.

The Art (and Science) of Defining Service Areas – Part 2

This post is a follow-up case study to illustrate the detail outlined in Part 1 found here.

Case Study – Memorial Hospital, Anytown, USA. 

In the last blog post, we covered how to define a service area.  In this post, we will cover a case study demonstrating the pros and cons of different service area definitions focusing on competitor identification.  Below is a case study based on a real hospital and real service areas blinded to protect the innocent.

Anytown is a city of 33,000 located in Mine County population 159,000, 20 miles north of a metropolitan city with population of 752,000.  Below is a table of market share by hospital by the different service area definitions discussed in-depth in the previous blog post.  Eighteen ZIP Codes represent 90 percent patient origin, 7 ZIP Codes represent 75 percent patient origin (all contiguous ZIP Codes meeting Stark service area definition) and 2 ZIP Codes represent 50 percent patient origin.   The 7 ZIP Code area is essentially the primary service area (PSA) and secondary service area (SSA) defined by PSA being 50 percent patient origin (the 2 ZIP Code area referenced above) and the SSA the next 25 percent of patient origin combined.


The table above demonstrates how the competitors change based on the definition of service area.  Using the two larger service area definitions, the primary competitor is Neighbor Hospital.  However, when only looking at the two ZIP Codes that make up 50 percent of patient origin, Large Medical Center becomes the primary competitor.  If market share by service line is available, look at outmigration by service line to Large Medical Center.  The only services out-migrating may be tertiary services Memorial Hospital doesn’t provide.  In that case, our focus would shift to Nearby Town Medical Center as our primary competition if they are seeing patients in service lines where we offer the services.  In the 7 ZIP service area, Neighbor Hospital is our primary competitor, followed by Nearby Town Hospital.

Also, notice the how the market share percentages change as the service area changes.  Using the 18 ZIP Code definition, our hospital has a market share of 13.1%.  When using the 7 ZIP Code service area our market share is 27.7%.  Using the 2 ZIP Code definition, our market share is 56.9%.  I am arguing the primary service area of our hospital is the 2 ZIP Code definition.  Those are the people we primarily serve.

This exercise demonstrates the importance of a thoughtful, deliberate service area definition.  A well-defined service area assists with targeted marketing and physician business development.  It also assists in understanding who your true competitors are by service line.  The service area definition is both science and art involving the use of analytics and judgment.


1. http://www.mwe.com/info/news/hlu0404.pdf

Page 1 of 3123