# When To Use Spherical Vs. Ellipsoidal Modeling For Coordinate Transformations

## Deciding Between Spherical and Ellipsoidal Models

Choosing the appropriate coordinate system model is a critical decision when working with geographic data. The two main options are spherical and ellipsoidal models. Spherical models represent the Earth as a perfect sphere, which simplifies calculations but decreases accuracy. Ellipsoidal models account for the oblate spheroid shape of Earth, providing greater realism and accuracy but with added complexity.

Key factors to consider when deciding on a model include the geographic extent of the area of interest, the required positional accuracy, and computational efficiency. Additionally, compatibility with systems like GPS is ensured only with an ellipsoidal model. Weighing these elements will dictate the ideal approach for a given use case.

## Understanding the Key Differences

Spherical and ellipsoidal models vary in their depiction of the Earth’s form as well as in computational properties. On a fundamental level, spherical models assume a perfectly round shape for the planet. However, Earth exhibits an oblate spheroid form, with slight bulging around the equator due to centrifugal forces from Earth’s rotation.

Ellipsoidal models account for this oblateness by defining an ellipsoid of rotation with different equatorial and polar radii. This better reflects reality and enables higher accuracy in positioning and measurement. The ellipsoid fits local topography at any point on Earth better than a simple sphere.

Using a sphere significantly simplifies coordinate calculations, especially for global-scale analysis. Conformal flat globe projections are also easier to generate from a sphere. But the cost is geometric distortion and lower positional precision for large areas.

In contrast, ellipsoidal models introduce mathematical intricacy but position points on the Earth’s surface with greater accuracy. Compatibility with satellite-based global navigation systems depends intrinsically on adopting an ellipsoid. Overall, increased complexity trades for enhanced realism and precision.

## When to Use Spherical Models

### Simplicity and Computational Efficiency

The spherical model shines when computational speed and simplicity are prioritized. Mathematically, a sphere uses one fixed radius value to represent the Earth, greatly easing calculations. Established spherical trigonometric equations then enable efficient coordinate conversions and area or distance measurements.

This single parameter also facilitates rapid iteration and programming. Minimal data storage is another advantage – only a mean Earth radius need be stored and accessed. Polygon representations of continents, countries, and other features can be generated quickly on a sphere as well.

Use cases such as real-time visualization or querying of low-resolution global datasets run faster with spherical models. The uncomplicated geometry and formulas keep computation times low even for extensive numerical analysis or simulation tasks.

### Acceptable Accuracy Over Small Areas

Over more confined geographic areas under 1,000 kilometers across, accuracy from spherical models may suffice for some applications. At these regional scales, the spherical assumption holds reasonably well, with distortion rising gradually as area grows. Precision of around 1-3 kilometers can be achieved for sub-national level mapping when using finely tuned sphere parameters.

For example, utility network predictive modeling across a city or county could rely on spherical math given focused scope. As long as results suggest general trends rather than pinpoint decisions, approximation errors will have limited impact. Visual interpretability of the model output takes precedence over high precision here as well.

Other use cases where regional sphere calculations generate adequate accuracy include flood risk mapping, transportation accessibility metrics, site suitability analysis, and general urban planning tasks. Just be aware accuracy declines substantially past 1,000 kilometer extents.

## When to Use Ellipsoidal Models

### Highest Positional Accuracy

Requirement for utmost accuracy in measurement and navigation necessitates adopting an ellipsoidal framework. Ellipsoidal representations align closely with the true irregular shape of Earth, accounting for oblateness and flattening based on intensive geodetic surveying.

This enables geopositioning of features across large areas with errors as low as 1-2 centimeters relative to control points. Precision holds at any scale, from local to global, unlike the sphere. Rigorously constructed reference ellipsoids like WGS84 offer optimal accuracy for a majority of applications.

Scientific analysis and computational simulations relying on geospatial data depend on the highest attainable accuracy to produce valid outputs. Ellipsoidal models satisfy this need. Common examples include plate tectonic motion tracking, earthquake risk modeling, satellite orbit propagation algorithms, and other fields demanding precision.

### Compatibility with GPS and Satellite Systems

Interfacing with satellite-derived measurements and navigation dictates use of an aligned ellipsoidal system. The foundational workings of Global Positioning Systems like GPS intrinsically assume an ellipsoid, with orbital broadcasts containing timing data tied to chosen system parameters.

Using a sphere for computations will lead to compounding conversion and measurement errors when ingesting GPS surveys or other satellite-sourced coordinates. Ellipsoidal methods are crafted specifically to interface with these space-based platforms. So adopting the native reference ellipsoid used by a given GPS variant ensures optimal positional consistency.

This consideration applies to any application directly utilizing signals from satellite constellations, whether for navigation, remote sensing, meteorology, or related use cases. Maintaining technical compatibility and accuracy necessitates matching geospatial foundations.

## Transforming Between Coordinate Systems

While spherical and ellipsoidal models may suit different applications, the need often arises to convert coordinates measured in one system into the other system. Rather than attempting to re-survey sites, which is cost prohibitive, mathematical transformations provide the solution.

A number of established geodetic algorithms and code samples exist to perform accurate conversions between spherical, ellipsoidal, and even planar coordinates. Proper methodology depends on direction of conversion and underlying datum specifics. We will demonstrate sample Python and R scripts covering typical transformation use cases.

### Sample Python Code for Transformations

Python offers versatile geospatial libraries to carry out coordinate conversions. Below functions first transform latitude/longitude points to Cartesian triples on a WGS84 ellipsoid, then convert to a spherical representation via inverse projection.

import pyproj def ellipsoid_to_sphere(lat, lon, R=6371): #Define source and target coordinate systems wgs84 = pyproj.Proj(proj="latlong",datum="WGS84",ellps="WGS84") sphere = pyproj.Proj(proj="laea",lat_0=lat,lon_0=lon,a=R,b=R) #Convert lat/lon points to x/y/z cartesian coordinates on WGS84 ellipsoid x,y,z = pyproj.transform(wgs84,sphere,lon,lat,0,radians=False) #Then inverse project back to lat/lon on sphere lon, lat = pyproj.transform(sphere,wgs84,x,y,radians=False) return lat, lon def sphere_to_ellipsoid(lat, lon, R=6371): #Define source and target CRS the opposite way sphere = pyproj.Proj(proj="laea",lat_0=lat,lon_0=lon,a=R,b=R) wgs84 = pyproj.Proj(proj="latlong",datum="WGS84",ellps="WGS84") #Inverse project lat/lon on sphere to cartesian x,y = pyproj.transform(wgs84,sphere,lon,lat,radians=False) #Convert cartesian coordinates back to lat/lon on ellipsoid lon, lat = pyproj.transform(sphere,wgs84,x,y,radians=False) return lat, lon

These snippets demonstrate forward and inverse conversion flows for common analysis needs. Variations are possible via substituting alternate projections, datums, ellipsoids, and parameter tweaks.

### Sample R Code for Transformations

Similar coordinate system transformations are achievable in R using the proj4 and sf packages for spatial data manipulation.

library(sf) library(proj4) ellipsoid_to_sphere <- function(long, lat, R=6371){ # Define source and destination CRS objects wgs84 <- st_crs(4326) sphere <- st_crs(paste0("+proj=laea +lat_0=",lat,"+lon_0=",long,"+a=",R,"b=",R)) # Convert lat/long points to cartesian x/y on ellipsoid pts <- st_sfc(st_point(c(long, lat)), crs=wgs84) cartesian <- st_transform(pts, sphere) # Inverse project cartesian coords to lat/long on sphere transformed <- st_transform(cartesian, wgs84) return(st_coordinates(transformed)) } sphere_to_ellipsoid <- function(long, lat, R=6371){ sphere <- st_crs(paste0("+proj=laea +lat_0=",lat,"+lon_0=",long,"+a=",R,"b=",R)) wgs84 <- st_crs(4326) # Forward project lat/long on sphere to cartesian pts <- st_sfc(st_point(c(long, lat)), crs=sphere) cartesian <- st_transform(pts, wgs84) # Convert cartesian coordinates back to lat/long on ellipsoid transformed <- st_transform(cartesian, wgs84) return(st_coordinates(transformed)) }

As with the Python code, simply switching the source and destination CRS inputs facilitates conversion in either direction between spherical and ellipsoidal representations.

## Recommendations Based on Use Case

With an understanding of key differences between spherical and ellipsoidal models, when each is best utilized, and how to convert between the two, we now suggest recommendations based on several common use case scenarios.

### Local Analysis

For local scale analysis focused on areas under 1,000 square kilometers, utilizing a spherical model is reasonable given accuracy needs can be met. Examples include flood risk prediction for a city region, infrastructure capacity planning across a county, or even state wide simulations provided precision requirements are low.

Computational performance gains justify use of simplified spherical math for contained areas. Standard datums can be adopted for continental positioning uniformity. Easy output visualization also promotes spheres at local levels. Just be cognizant that accuracy degrades past 1,000 kilometer extents.

### Regional or Continental Analysis

Expanding scope to regional, country, or continental sized areas makes adoption of an ellipsoidal Earth model critical for meaningful analysis and mapping. Precision errors from spheres become substantial at thesebreadths.

An aligned reference ellipsoid will aptly represent target geography and enable kilometer or sub-kilometer accuracy needed for most applications. For example, modeling avian migration patterns across North America necessitates an ellipsoid like NAD83 aligned to the continent. Similarly, accurate national mapping relies on locally fitted spheroids.

Ensure proper geodetic datum choice for target areas. Conversion to spherical output projections may still occur after ellipsoidal processing if visualization mandates it.

### Global Analysis

For global spanning analysis and simulation, utilization of WGS84 as the reference spheroid is recommended to enable ubiquitous positional accuracy. WGS84 exhibits under one meter average error worldwide, providing a versatile geospatial foundation for Earth representation.

Whether calculating shipping routes, airline distances, real-time telemetry, plate tectonics movements, or any other global scale modeling, WGS84 delivers consistency. Adjustments to different datums and projections can always occur downstream if required for specific applications or visual outputs.

Certain exceptions exist where a global sphere is acceptable, namely computationally demanding simulations that simplify processes to 2D. But understand positional accuracy suffers significantly. In most cases WGS84 satisfies global needs.