A hot topic this season is hurricane risk and impact. Due to the massive damage caused by multiple hurricanes in the 2017 season, businesses and organizations are scrambling to get data on how it affects them.
This is one of the classic cases where geographical analysis goes far beyond typical business intelligence.
The ability to do advanced analysis such as a distance buffer capture of data along the path of the storm is invaluable.
Grids and charts simply cannot give enough detail and cannot organize the data geographically. In this blog entry, we will explore how our customers analyze storms using map tools such as Visual Crossing.
Types of Analysis
There are 3 types of analysis categories where maps can help with this situation: Results Analysis, What-if Scenarios, Action Support. We will also discuss other visualizations of risk areas. We will take each scenario and describe how this can help with your analysis.
The most obvious analysis and perhaps the most powerful is the ability to take a hurricane path after it has passed and analyze the area for damage and risk. Insurance companies practice this as a standard part of their operations but all businesses can benefit from knowing how their customers are affected. To start our analysis, we will simply import a shapefile from NOAA that represents the path of the storm as seen here overlaid on top of business layers of locations and customers as points.
Notice that the path we are using here contains segments that are color-coded by wind speed. This can help add value to the analysis by understanding a damage ratio related to windspeed. For the basic scenario, we will select the main segment of the ISABEL path and ask the system to capture all customers within a 25-mile radius buffer along the path.
Now we can look at the selection statistics which aggregates your metric values found in the capture area. In this example, we have used a very basic $Risk metric to show policy exposure or other potential cost to the business. However, since we are utilizing the user's metrics, you can create their own complex calculations that you find the most effective. The geo-analysis here simply aggregates these values.
We can now see the number of affected customers, the total and mean risk values.
If your map system allows your analysis to be flexible, you can do more advanced analysis such as dragging the line buffer more to the right to give more weight to the 'dirty side' of the storm. Here we have dragged the previous capture 5 miles to the right to account for heavier damage on this side.
By making this manual shift note that the number of affected households and the $Risk metric has increased.
Most businesses will try to get ahead of the storm to prepare for what is about to happen. Up-to-the-minute forecasts are an invaluable tool and your maps can offer the same type of solution for you. In this scenario, we will manually use our line selection tool to draw a path of our own to understand what our risk would be should the storm follow our anticipated path.
From this point, we can again use the ability to drag the selection path east or west to determine what our risk would be if the path would follow a different path. Another option is to draw a second path (or more) and compare the risk of different paths.
Above we use the Selection Comparison tool to show both possible tracks at the same time and compare their risk metrics. (Please note that the selection tool intelligently includes overlapping selected points into both track selections so that they are independent.)
Other than analysis and aggregation of metrics, map tools can assist in other ways. The first we will show you is quite simple but very effective: Export. The ability to export the selection can help businesses and organizations reach out to those in the storm's path. They can provide safety information or tips to reducing damage or even saving lives. This simple action be of great value to businesses. Here we show the export of our first what-if selection.
Another action support feature would be identifying service locations that have a relationship with those affected or supply locations that these customers may be reaching out to before the storm. This can help businesses prepare for staffing or supply chain shifts. Using customer affinity layers (AKA spider or network mappings) can help decisionmakers visualize where help is needed.
Above we can see that business locations that are not in the expected path may still be affected in that they are supplying product or services to those who are in the path. Conversely, we can also see that some business locations that are in the path may not be able to provide to customers who are not in the path after the storm comes through. Understanding business-to-customer relationships both before and after the storm are an invaluable piece of knowledge.
As an extra bit of analysis, it may be helpful to understand exactly where your aggregations of risk are before you even start your what-if analysis. To do this we can simply view our customers using different point visualization options. Here are Heatmaps, Hotspots and Point Clusters:
Each visualization can bring value to this analysis and having multiple tools is always helpful. Additional metrics can also add value, this exercise we have only touched on a risk aggregation metric and counts of customers affected. Feel free to explore these visualizations and selections using other metrics that relate to your business.
Hopefully you can see a few simple tools here that can help any business to gather valuable information about storms and their effects. This is by no means a complete list but rather a good, general starting point. Whether you are working on Excel, MicroStrategy or SAP BusinessObjects Lumira, the Visual Crossing Maps solution on these platforms is an easy way to provide some advanced analysis without the cost or complexity of traditional GIS systems.