Avoid Healthcare Map Missteps by Properly Using Legends, Titles, Scale Bars, and Sources

By Jason Haley

 

Most people who see healthcare maps every day don’t give much thought to their content and cartographic execution. Titles display the purpose of a healthcare map, scale bars display distance, sources explain where the data originated, and legends identify the attributes on a map. All of these cartographic elements combined embody the important final touches that make healthcare maps readable and useful. Cartographic elements help those looking at the map understand the various layers and data without having to concentrate, ask questions, or decipher. This is why the choices made on the final touches of healthcare maps are the most essential part of the map-making process.

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

by Jason Haley

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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.

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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!

ESRI_HHS2015_Banner

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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:

UC2015_Banner

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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.

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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.

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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.

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