{"id":3901,"date":"2026-03-08T19:12:49","date_gmt":"2026-03-08T19:12:49","guid":{"rendered":"https:\/\/iranians.global\/news\/?p=3901"},"modified":"2026-03-08T19:40:40","modified_gmt":"2026-03-08T19:40:40","slug":"how-do-scientists-estimate-crowd-sizes-at-public-events-and-why-are-they-often-disputed","status":"publish","type":"post","link":"https:\/\/iranians.global\/news\/how-do-scientists-estimate-crowd-sizes-at-public-events-and-why-are-they-often-disputed\/","title":{"rendered":"How do scientists estimate crowd sizes at public events \u2013 and why are they often disputed?"},"content":{"rendered":"

By Milad Haghani, Associate Professor and Principal Fellow in Urban Risk and Resilience, The University of Melbourne<\/strong><\/a> <\/p>\n

<\/p>\n

\n

Last Sunday, tens of thousands marched across the Sydney Harbour Bridge in support of Gaza. But exactly how many people were there depends on whom you ask.<\/p>\n

Police put it at about 90,000<\/a>. Organisers claimed up to 300,000<\/a>. Other reports and expert estimates landed somewhere in-between<\/a>.<\/p>\n

Why are these accounts so different and how hard is it, really, to estimate the size of a crowd?<\/p>\n

Why people care about crowd sizes<\/h2>\n

It\u2019s far from the first time crowd numbers have been a flashpoint.<\/p>\n

The most infamous modern example is US President Donald Trump\u2019s 2017 inauguration<\/a>, where aerial photos and transit data clashed with claims from the White House officials and Trump himself, sparking controversy<\/a>.<\/p>\n

\n <\/p>\n
\"Aerial<\/div>\n

<\/a>

\n A pair of photos shows a view of the crowd on the National Mall at the inaugurations of President Barack Obama, above, on January 20 2009, and President Donald Trump, below, on January 20 2017.<\/span>
\n AP Photo<\/span><\/span>
\n <\/figcaption><\/figure>\n

In New Zealand last year, the H\u012bkoi march<\/a> to parliament triggered a similar debate<\/a> as vastly different estimates<\/a> circulated.<\/p>\n

Crowd size matters for several reasons \u2013 from symbolic significance to safety implications.<\/p>\n

It can convey the level of support for a social or political cause<\/a>, signal the scale and significance of a spiritual gathering<\/a> or a street party<\/a>. Regimes and revolutions can use crowd sizes as a propaganda tool<\/a>.<\/p>\n

That\u2019s why there are often strong incentives to inflate \u2013 or deflate \u2013 the numbers.<\/p>\n

But crowd estimates are also important for safety reasons. Underestimating can leave infrastructure and logistics unprepared, sometimes leading to catastrophic crowd crushes<\/a>. Overestimating can result in unnecessary restrictions, closures, or even cancellations.<\/p>\n

How are crowd sizes estimated?<\/h2>\n

There\u2019s no single way to count crowds. Experts choose from a toolbox of methods, each suited to different settings and each with its own blind spots.<\/p>\n

Manual visual estimation<\/strong><\/p>\n

The oldest method is also the simplest: estimate the density (people per square metre) in a few sample patches of the crowd (often inferred from aerial images), then multiply by the total area occupied<\/a>. In theory, straightforward; in practice, riddled with problems. <\/p>\n

Human observers (even experienced ones) struggle to distinguish<\/a> between, say, two, three, or four people per square metre. Crowd density is rarely a round number, but as human observers we often tend to infer whole density numbers from the scene. <\/p>\n

Plus, crowd density is rarely uniform: people bunch near focal points and leave gaps elsewhere. So, extrapolating from a sample can lead to very misleading estimates.<\/p>\n

Errors also creep in from misjudging the physical size of the sampled area or overlooking how much of the total space is actually usable. These misjudgements can lead to completely different counts.<\/p>\n

Computer vision<\/strong><\/p>\n

CCTV, aerial photos and drone imagery allow automated counting using image processing techniques<\/a>. These range from texture-based methods<\/a> that work best for low- to medium-density crowds, to object detection models that locate individual heads or bodies<\/a>.<\/p>\n

These can be quite accurate in open spaces with clear sight lines. But shadows, poor lighting, poor weather conditions, obstacles and occlusion in dense gatherings can compromise their accuracy.<\/p>\n

\n