Geographic information systems (GIS) play a critical role in emergency response planning and risk assessment. One of the key challenges in this field is determining whether a specific location falls within an area of interest. This capability is especially valuable for property and casualty (P&C) insurers, who need to assess their insured property exposure when severe weather events such as tornadoes occur.

A tornado warning is typically defined as a polygon outlining the affected region. Insurers and emergency responders must determine whether specific homes, represented by latitude and longitude coordinates, fall within this polygon. SQL Server provides spatial functions that allow us to perform this kind of geographic analysis efficiently. This article explores how SQL Server’s spatial data types can be used to store and manipulate geographic information, how tornado warning areas can be defined as polygons, and how homes can be checked for their presence within the warning zone using the STIntersects() function. Finally, we will validate this method by executing a query and examining the results.

SQL Server supports two primary spatial data types: GEOMETRY and GEOGRAPHY. The GEOMETRY data type is used for planar coordinate systems, while the GEOGRAPHY data type is specifically designed for real-world applications that use latitude and longitude. Since we are dealing with actual geographic locations, we use the GEOGRAPHY type, which follows the WGS84 coordinate system (EPSG:4326).

Setting Up the Database

To analyze tornado impact on homes, we first create a database schema that includes two tables: one for storing insured home locations and another for storing tornado warning polygons.

The Homes table contains essential details such as address, city, state, zip code, latitude, and longitude. Additionally, a GEOGRAPHY column is added to store each home’s location in a spatial format. The table is created using the following SQL statement:

CREATE TABLE Homes (
    HomeID INT IDENTITY(1,1) PRIMARY KEY,
    Address NVARCHAR(255),
    City NVARCHAR(100),
    State NVARCHAR(50),
    ZipCode NVARCHAR(20),
    Latitude FLOAT,
    Longitude FLOAT,
    GeoLocation GEOGRAPHY
);

Once the table structure is in place, we insert sample data to represent insured properties in different locations.

INSERT INTO Homes (Address, City, State, ZipCode, Latitude, Longitude)
VALUES 
('123 Main St', 'Kansas City', 'MO', '64106', 39.0997, -94.5786),
('456 Elm St', 'Topeka', 'KS', '66603', 39.0489, -95.6780),
('789 Oak St', 'Springfield', 'MO', '65806', 37.2089, -93.2923);

At this stage, the Latitude and Longitude columns store numeric values, but the GeoLocation column is still empty. To populate it, we update the table by converting each home’s latitude and longitude into a GEOGRAPHY object.

UPDATE Homes
SET GeoLocation = GEOGRAPHY::Point(Latitude, Longitude, 4326);

This conversion allows SQL Server to recognize home locations as spatial data, enabling intersection calculations with tornado polygons.

Next, we define the TornadoWarnings table, which stores tornado warning areas as polygons.

CREATE TABLE TornadoWarnings (
    WarningID INT IDENTITY(1,1) PRIMARY KEY,
    WarningArea GEOGRAPHY
);

Tornado warnings are typically represented as polygons composed of multiple latitude and longitude coordinates. The following SQL statement inserts a sample tornado warning area covering multiple locations:

INSERT INTO TornadoWarnings (WarningArea)
VALUES (GEOGRAPHY::STGeomFromText(
    'POLYGON((-95.50 37.80, -95.00 38.10, -94.50 37.90, 
              -94.60 37.50, -95.10 37.30, -95.40 37.50, 
              -95.50 37.80))', 4326));

The STGeomFromText() function converts the polygon into a GEOGRAPHY object. The polygon is closed, meaning the first and last points are identical. The coordinates are listed in counterclockwise order, which is necessary to ensure SQL Server correctly interprets the polygon.

Checking if a Home is Inside the Tornado Polygon

With the spatial data prepared, we can now determine which homes fall inside the tornado warning polygon. This is achieved by joining the Homes and TornadoWarnings tables using the STIntersects() function.

SELECT h.HomeID, h.Address, h.City, h.State, h.ZipCode
FROM Homes h
JOIN TornadoWarnings t
ON t.WarningArea.STIntersects(h.GeoLocation) = 1;

This query checks whether the GeoLocation of each home intersects with the tornado polygon. If a home is inside the polygon, it appears in the result set.

Results

After executing the query, we obtain the following sample results:

This output confirms that these homes fall within the defined tornado warning polygon and are potentially at risk. These results can be used by insurance companies to notify policyholders, prepare claims processing teams, and conduct pre-emptive risk analysis.

Improving Performance with Spatial Indexing

For insurers managing thousands or even millions of policies, optimizing performance is essential. SQL Server provides spatial indexing, which significantly speeds up the process of checking for intersections between homes and tornado polygons. The following SQL statements create spatial indexes for both tables:

CREATE SPATIAL INDEX idx_Homes_GeoLocation ON Homes(GeoLocation);
CREATE SPATIAL INDEX idx_TornadoWarnings_WarningArea ON TornadoWarnings(WarningArea);

Spatial indexing allows SQL Server to efficiently locate homes within the tornado path without scanning the entire dataset, making large-scale risk assessments more feasible.

Applications for Property & Casualty Insurers

This approach provides significant value to P&C insurers by allowing them to assess tornado-related risk exposure in real time. Claims teams can use this analysis to identify insured homes that are at risk before policyholders file claims. Underwriters can leverage this information to refine risk models, ensuring that premiums reflect the actual exposure to severe weather events. Additionally, insurers can use this data to send proactive alerts to policyholders, helping them take precautionary measures before a tornado strikes.

By integrating SQL Server’s spatial capabilities into their operational workflows, insurers can enhance catastrophe risk modeling, improve customer engagement, and optimize claims processing. This method not only streamlines risk assessment but also provides real-time insights that support informed decision-making in the insurance industry.

Conclusion

Using SQL Server’s spatial functions, insurers and emergency response teams can accurately determine which homes fall within a tornado warning area. This allows for better preparedness, more efficient claims processing, and improved communication with policyholders. By leveraging spatial indexing, organizations can scale this approach to process large datasets efficiently. As weather-related risks continue to impact insurers, integrating geospatial analysis into risk management workflows will be a crucial step toward building a more resilient and data-driven insurance ecosystem.