Analytics: Bringing Geospatial Data to Life

Sep. 29, 2016

Geospatial data is leveraged as a layer in so many applications that its value is finally being realized by governments and private businesses. But it’s the analytics within those applications that really brings the data to life. In this article, we are going to discuss what analytics are and list several applications of analytics in real world situations.Larry_Sept._29.jpg

What is an analytic? For our purposes, we will use the following definition: Analytics are the automated interpretation of multiple datasets to obtain insight, an answer or analysis.

With this definition, here are some examples of analytics in action:

Peril Models & Underwriting: Assessing the risk of flood and other perils is a messy business. Elevations around a particular location help determine where the water will flow, but other factors like drainage, the type of ground surface, and the amount of rain over a given period of time also play a key role as well. The real-world variables that determine where flood waters will ultimately go are impossible to predict, but this volatility is what makes analytics necessary. All flood models are wrong, but that doesn’t mean some aren’t useful. Due to this unpredictability, underwriters should expect errors and inaccuracies, and should base their business on this fact. By using better data in multiple independent datasets, the impact of imprecise data is greatly lessened on a portfolio.

Retail Site Selection: Retail businesses often spend hundreds of thousands and even millions of dollars on selecting new store locations. Even then, many new locations fail. Analytics can help retail chains identify potential locations duplicating their most successful stores. Site selection analytics begins with an understanding of common factors in the most successful locations. Is it a large population of a particular demographic or neighborhood makeup? Is it proximity to another business or industry? Does it require a certain traffic flow? Once those factors are identified, they can be incorporated into the analytic. Retail site selection is a combination of art and science. Analytics narrow the location choices so the “art” of retail site selection is more effective.

Infectious Disease Prevention & Control: In order to maintain the health of a nation, the medical community utilizes analytics to effectively fight infectious diseases. Geospatial data is one of the foundational datasets. There are many factors in determining where and how quickly infectious diseases spread including how the disease is spread (i.e. parasite, bacteria, virus, or fungi, etc.); the location and prevalence of the host organism; the incubation period (how long before the infected person exhibits symptoms), and the length of the contagious period among other factors. Leveraging a well thought out analytic along with the right data sets allows the medical community to rally resources to the optimum locations to inoculate at risk members and provide medicine to treat those infected by the disease.

Once an analytic is created, it’s not the stopping point, it’s just the beginning. Analytics are designed to be continuously tweaked and adjusted to improve results. Analytics thrive in complex and volatile environments. As such, there might be variables that you didn’t initially consider that can bring better results once added to the analytic. One of the biggest mistakes I see in leveraging analytics is complacency with an analytic. As with most things in business, the goal is the constant improvement.

The better the data used in analytics, the better the result. No data can be accurate enough to remove all uncertainty, but results will improve with more accurate data. Accurate elevation data along with other datasets is the beginning of a strong and useful analytic.

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