How to Perform Radius-Based Search using Spark and Haversine Formula for Large-Scale Geospatial Data

Geospatial data analysis has become increasingly important in many fields such as transportation, urban planning, and social sciences. One of the fundamental operations in geospatial analysis is radius-based search, which allows users to find all points within a certain distance from a given location. In this blog, we will discuss how to perform radius-based search using Spark and Haversine formula for large-scale geospatial data analysis.

What is Haversine Formula?

Haversine formula is a mathematical formula used to calculate the distance between two points on a sphere, such as the Earth. The formula takes into account the curvature of the Earth's surface, making it more accurate than simple Euclidean distance calculations. The formula is as follows:

where lat1, long1, lat2, long2 are the latitude and longitude of the two points, Δlat and Δlong are the differences between the latitudes and longitudes, and R is the radius of the Earth (6,371 km).

How to use Spark and Haversine formula to perform radius search?

Step 1: Define imports

Step 2 : Create Spark Session

Step 3: We shall use Sample dataset which has 0.2M Records Uber Fares Dataset

Radius Based Search

Suppose I want to analyze the number of rides taken near Continental Army Plaza. In order to achieve this, we will perform a radius search to retrieve all the rides within a 1-mile radius of the plaza

Add columns for the distance to the search center and whether the ride is within the radius

Output of dataframe

Filter the Data within the search radius

Output DF

Plot on Map

Result

Advantages of using Spark for geospatial data analysis

Using Spark for geospatial data analysis has several advantages:

1. Scalability: Spark can handle large-scale geospatial data that cannot fit into memory of a single machine. Spark can distribute the data across multiple machines and process them in parallel, which enables faster and more efficient analysis.
2. Speed: Spark is designed for in-memory processing, which can significantly speed up the geospatial analysis. Spark can also leverage the computation power of GPUs and other accelerators to further speed up the analysis

Entire Code

https://soumilshah1995.blogspot.com/2023/04/how-to-perform-radius-based-search.html

Conclusion

In conclusion, performing radius-based searches on large-scale geospatial data can be a challenging task, but using Spark and the Haversine formula can greatly simplify the process. By leveraging Spark's distributed computing capabilities, we can efficiently process and analyze large volumes of data. The Haversine formula is a well-established and accurate method for calculating distances between two points on a sphere, making it a reliable choice for geospatial analysis.

While Spark and the Haversine formula are excellent tools for performing radius-based searches, there are other alternatives worth considering as well. For example, Geo Hash and Uber H3 library are two popular alternatives for geospatial indexing and querying. Ultimately, the choice of tool will depend on the specific needs of the project and the characteristics of the data being analyzed.

In summary, performing radius-based searches on large-scale geospatial data requires careful consideration of the available tools and methods. By using Spark and the Haversine formula, we can efficiently and accurately analyze large volumes of data to gain valuable insights and make data-driven decisions.