Benchmark: LatLongDistance - MeasureThat.net

Script Preparation code:

function getDistanceFromLatLonInKm2(lat, lng, lat0, lng0) {
  //https://jonisalonen.com/2014/computing-distance-between-coordinates-can-be-simple-and-fast/
  
  let deglen = 110.25
  let x = lat - lat0
  let y = (lng - lng0)*Math.cos(lat0)
  return deglen*Math.sqrt(x*x + y*y)
}

function getDistanceFromLatLonInKm(lat1, lon1, lat2, lon2) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  var dLon = deg2rad(lon2-lon1); 
  var a = 
    Math.sin(dLat/2) * Math.sin(dLat/2) +
    Math.cos(deg2rad(lat1)) * Math.cos(deg2rad(lat2)) * 
    Math.sin(dLon/2) * Math.sin(dLon/2)
    ; 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 
  var d = R * c; // Distance in km
  return d;
}

function deg2rad(deg) {
  return deg * (Math.PI/180)
}

Tests:

full
getDistanceFromLatLonInKm(51.496, -0.143, 51.601, -0.118);
quick
getDistanceFromLatLonInKm2(51.496, -0.143, 51.601, -0.118);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

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Test case name	Result
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quick

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Slowest: N/A

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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Let's break down the benchmark and its various components.

**Benchmark Definition**

The provided JSON defines two benchmark functions: `getDistanceFromLatLonInKm` and `getDistanceFromLatLonInKm2`. Both functions calculate the distance between two points on the Earth's surface using the Haversine formula. The main difference between the two functions is that `getDistanceFromLatLonInKm2` uses a simpler, more straightforward approach to calculate the distance.

**Options Compared**

The benchmark compares two options:

1. `getDistanceFromLatLonInKm`: This function uses the Haversine formula, which takes into account the Earth's radius and the latitude and longitude differences between the two points.
2. `getDistanceFromLatLonInKm2`: This function uses a simplified version of the Haversine formula, which is faster but may not be as accurate.

**Pros and Cons**

* `getDistanceFromLatLonInKm`:
	+ Pros: More accurate results
	+ Cons: Slower performance due to the more complex calculation
* `getDistanceFromLatLonInKm2`:
	+ Pros: Faster performance, potentially suitable for low-latency applications
	+ Cons: May produce less accurate results

**Library and Purpose**

There is no explicit library mentioned in the benchmark definition. However, it's likely that the functions use built-in JavaScript functionality, such as trigonometric functions (e.g., `Math.sin`, `Math.cos`) and mathematical constants (e.g., π).

**Special JS Feature or Syntax**

There are no special features or syntaxes used in this benchmark.

**Other Considerations**

* The benchmark assumes that the input coordinates are in degrees and the Earth's radius is 6371 km.
* The `deg2rad` function converts degrees to radians, which is used in the Haversine formula.
* The benchmark uses Safari as the target browser, with a specific version and platform.

**Alternative Approaches**

Other approaches to calculate the distance between two points on the Earth's surface include:

1. Using a geodesic calculation method, such as the Vincenty formula, which is more accurate but also more complex.
2. Using a third-party library or service that provides pre-calculated distances for common latitude and longitude pairs.
3. Using a different algorithm or data structure to represent the Earth's surface, such as a spherical harmonics representation.

In summary, the benchmark compares two options for calculating the distance between two points on the Earth's surface: `getDistanceFromLatLonInKm` (more accurate) vs. `getDistanceFromLatLonInKm2` (faster). The choice of approach depends on the specific requirements of the application, such as accuracy, performance, and complexity.

Related benchmarks:

LatLongDistance (version: 0)

Comparing performance of: full vs quick

Created: 5 years ago by: Guest

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full

quick

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):