In 1824,

The Harrisburg Pennsylvanian, a newspaper from a town in Pennsylvania conducted the first known public opinion polls in history, and successfully predicted the result of the vote in the close race between Andrew Jackson and John Quincy. However, opinion polls do not always reflect the opinions of the whole electorate accurately, especially with limited sample sizes and time gaps between polls and actual voting. In 2016, many media outlets failed to predict the results of the Brexit referendum or the US presidential election accurately, for example. Below, poll tracker shows how poll results were misleading, especially in the tight Brexit race.

Since opinion polls have to be conducted with limited sample sizes, there are three key variables that could influence their accuracy.

1 - Sampling accuracy

The distribution of interviewees’ demographics such as location, educational level, gender, age, or religion should resemble the actual diversity of the population, and the sample size should be large enough to increase this accuracy. Like tasting a stew during cooking, as we stir better, all ingredients are mixed evenly, and we can accurately get the right taste of the stew from a small sample size.

2 - Interviewers’ bias

Because people want to avoid confrontations or want to look good in front of others, interviewees may respond with answers that may sound desirable socially or to interviewers. Interviewers may be able to push interviewees to answer certain ways because of how they ask, push polls.

3 - Time

People in general react more to recent occurrences. Electoral polls usually swing drastically with the latest scandals involving candidates, just as movies or music recently released tend to be selected for awards.

When we encounter poll results, it is important to understand these variables more than which side is leading. The context gives us a slightly clearer picture of why one side is leading, and the likelihood of seeing the opposite result. In this post, I collected many visualizations that give context to opinion polls or the vote results of the Brexit referendum and US presidential elections in 2016.

Opinions by Regions

Regions are the first layer representing demographic groups

National elections may be seen as a battle between each region, and their results reflect their different economic, cultural, and demographic backgrounds. Like the New York Times example below, many regions in England and Wales voted to exit, while Scotland and Northern Island, which are far away from the central government voted against. Greater London also voted to remain, reflecting its population consisting of more people with higher education, higher earners, and more foreign-born people, which I’ll discuss more later in the section about demographic distributions.

Geographically inaccurate but electorally accurate maps

The chart below by the Guardian is a more accurate representation of the map by distorting it with the population size of each area. In comparison to the New York Times’ map above, the presence of Greater London in voting counts is clearer.

In the US presidential elections, results in each state are the most important factor since people vote for each state’s representing party, and the number of their seats depends on the population size of each state. Therefore, distorted US maps that represent population size of each state are used often in the US election, instead of ones with geographic accuracy. Many of these maps represent each state using a square with its area representing their electoral vote counts, but it still gives a sense of the geographical relationship between each state (Data Visualization Infographics v.s. Products).

Opinions by Each Demographic Group

The following two charts by the Guardian and Financial Times both try to address trends for the Brexit votes based on demographics. For example, younger voters, degree holders, and higher earners overwhelmingly voted to remain, but younger voters consisted of a small percentage to the overall vote counts, resulting in a smaller impact to the result.

Tracking Opinions Over Time

Changes in supporting parties by demographic groups

The New York Times captured the shift in supporting parties from 2004 by household income levels. Additionally, although lower income groups supported the democratic party historically, education levels correlated even more to shifts in a supporting party for the 2016 election. The Times tracked these shifts by various demographic attributes, such as ethnicity, gender, and education.

Demographic pattern and changes in results

The Economist’s team used the data from YouGov to illustrate how supporters for each party repositioned their opinions since 2017 based on May’s deal proposed in 2019.

Plots of swing counties and their degrees

The Wall Street Journal plotted each county by their velocity of support to a party and how much that shifted from 2012. The chart below illustrates how the 2016 presidential election caused many swing counties.

Supporting parties for each state historically

Swing states, which don’t have a huge gap between their active supporters for either party, are significant in predicting the US presidential elections. The below chart by the Wall Street Journal illustrates the historical differences between each party’s supporters for each state. This chart can inform us of the trend of the support for party and political directions that are significant for each state.

Mapping various poll results over time

Voters’ opinions don’t only change between each election — they may change drastically within a single campaign period. Similar to the opinion poll tracker by the Telegraph from the top of this entry, the Guardian mapped various poll results for the 2015 UK election, and drew median lines out of these plots to estimate positions for each party throughout the campaign.

How each county swung in 2018 from 2016

The Guardian used a shifting arrow map to illustrate Democrats’ gain for the House of Representatives by showing how each region swung for the 2018 midterm since the last election. Democrats increased their support in large areas where Donald Trump dominated in 2016. Larger blue arrows demonstrate greater regrets in 2016, and tracking these velocities similarly by polls could suggest the next electoral results.

Chance of Misleading Poll Results

In 1948, Gallup predicted that 49.5% of the public would vote Thomas Dewey for the presidency, but the real result was almost the reverse: Truman for 49.6% and Dewey for 45.1%. Chicago Daily Tribune published the famous headline “Dewey Defeats Truman” based on the polling data. Although Gallup mentions that their accuracy improved dramatically after the 60s, they were wrong in recent elections including 2000, 2012, and 2016.

Nowadays, voters are online and have closer access to information including numbers of opinion polls conducted by various media outlets. If poll results suggest your supporting side was going to win, these poll results may discourage you to bother going to vote. Closer access to data is also true and crucial for candidates — last minute stories drastically influence voters’ minds, and this velocity is getting greater as modern campaigns become more online with the greater access to real-time data.

Illustrating the likelihood to swing participants’ opinions

The Economist’s team used a “ternary” plot instead of the common two-dimentional plot for the Brexit poll data — their attempt was to portray how poll participants were likely to position for supporting remain, leave, or leave without the deal based on their responses and their likelihood to change their opinions based on their demographics.

Elections also do not always represent the public opinion accurately

The Economist’s analytical team ran a model based on various polls to suggest how the 2016 US election would have resulted if all Americans voted. The simulation suggests Clinton would win over Trump, which was also suggested by predictions based on polls before the actual election.

在1824年，

賓夕法尼亞州一個小鎮的報紙《哈里斯堡賓夕法尼亞州》進行了歷史上首次已知的民意測驗，并成功預測了安德魯·杰克遜和約翰·昆西之間親密比賽的投票結果。 但是，民意調查并不總是能準確反映出全體選民的意見，特別是在樣本量有限以及民意調查與實際投票之間的時間間隔有限的情況下。 例如，2016年，許多媒體未能準確預測英國退歐公投或美國總統大選的結果。 下面，民意調查跟蹤器顯示民意調查結果如何產生誤導，尤其是在激烈的英國退歐競賽中。

由于民意測驗必須以有限的樣本量進行，因此存在三個可能影響其準確性的關鍵變量。

1-采樣精度

受訪者的人口統計信息(例如位置，教育水平，性別，年齡或宗教信仰)的分布應類似于人口的實際多樣性，并且樣本量應足夠大以提高準確性。 就像在烹飪過程中品嘗燉肉一樣，隨著我們更好地攪拌，所有成分均被混合均勻，并且我們可以從少量樣品中準確地獲得燉菜的正確口味。

2- 觀眾的偏見

因為人們想要避免對抗或想在別人面前看起來很好，所以受訪者可能會做出聽起來可能是社會上或受訪者希望的答案。 采訪者可能會因為他們的詢問方式， 推動民意測驗而促使受訪者回答某些問題。

3-時間

人們通常對最近發生的事情有更多的React。 選舉通常與涉及候選人的最新丑聞大相徑庭，就像最近發布的電影或音樂往往被選為獎項一樣。

當我們遇到民意測驗結果時，重要的是要了解這些變量，而不是領先于哪一方。 通過上下文，我們可以更清楚地了解到一側為何領先以及看到相反結果的可能性。 在這篇文章中，我收集了許多可視化內容，這些內容為民意調查或英國退歐公投和2016年美國總統選舉的投票結果提供了背景信息。

各地區意見

區域是代表人口群體的第一層

全國大選可以看作是每個地區之間的斗爭，其選舉結果反映了不同的經濟，文化和人口背景。 就像下面的《紐約時報》的例子一樣，英格蘭和威爾士的許多地區投票退出，而遠離中央政府的蘇格蘭和北島投票反對。 大倫敦地區也投票決定保留，以反映其人口，其中包括更多受過高等教育的人，收入更高的人以及更多在外國出生的人，我將在后面有關人口分布的部分中討論更多。

地理上不準確但選舉上準確的地圖

《衛報》下方的圖表通過將每個區域的人口規模進行扭曲來更準確地表示地圖。 與上面的《紐約時報》的地圖相比，大倫敦的投票人數更加清楚。

在美國總統選舉中，自從人們投票支持每個州的代表黨以來，每個州的選舉結果都是最重要的因素，其席位數量取決于每個州的人口規模。 因此，代表美國各州人口規模的失真的美國地圖通常會在美國大選中使用，而不是使用具有地理準確性的地圖。 這些地圖中的許多地圖都使用正方形來表示每個州，其面積代表其選舉人的票數，但仍然可以看出每個州之間的地理關系( 數據可視化圖表與產品 )。

每個人口群體的意見

《衛報》和《金融時報》的以下兩張圖表都試圖根據人口統計數據來解決英國退歐投票的趨勢。 例如，年輕的選民，學位持有者和收入較高的選民壓倒性地選擇留下，但年輕的選民在總投票數中所占的比例很小，對結果的影響較小。

隨時間跟蹤意見

人口統計群體對支持方的變化

《紐約時報》從2004年開始根據家庭收入水平反映了支持政黨的轉變。 此外，盡管低收入群體在歷史上一直支持民主黨，但教育水平與2016年大選支持黨的轉變甚至更多相關。 泰晤士報通過各種人口統計屬性(例如種族，性別和教育)跟蹤了這些變化。

人口特征和結果變化

《經濟學人》團隊使用YouGov的數據來說明自2019年以來，各方的支持者如何根據2019年5月提出的交易重新定位自己的觀點。

搖擺縣的情節及其程度

《華爾街日報》根據每個縣對政黨的支持速度以及自2012年以來的變化情況來繪制每個縣。下圖說明了2016年總統大選如何導致許多搖擺縣。

歷史上每個州的支持方

搖擺不定的州在對任何一方的積極支持者之間沒有很大差距，對預測美國總統大選具有重要意義。 《華爾街日報》(Wall Street Journal)下圖顯示了各州支持者之間各州之間的歷史差異。 該圖可以告訴我們支持對每個州都重要的政黨和政治方向的趨勢。

隨時間映射各種民意調查結果

選民的意見不僅會在每次選舉之間發生變化，而且可能在單個競選期間發生巨大變化。 與《電訊報》從頂部開始的民意測驗追蹤器類似，《衛報》繪制了2015年英國大選的各種民意測驗結果，并從這些情節中繪制了中位數線，以估計整個競選期間各方的立場。

從2016年開始，每個縣在2018年如何變化

《衛報》使用不斷變化的箭頭地圖，通過顯示自上次大選以來各地區在2018年中期選舉中的變動情況，來說明民主黨在眾議院的利益。 民主黨人在2016年唐納德·特朗普(Donald Trump)統治的廣大地區增加了支持。較大的藍色箭頭在2016年表示更大的遺憾，而通過民意調查追蹤這些速度可能暗示下一次選舉結果。

產生誤導性投票結果的機會

蓋洛普(Gallup)在1948年預測，有49.5％的公眾將投票選舉托馬斯·杜威(Thomas Dewey)為總統，但真正的結果幾乎是相反的：杜魯門(49.6％)和杜威(45.1％)。 根據民意調查數據，《芝加哥每日論壇報》發表了著名的標題“杜威擊敗杜魯門”。 盡管蓋洛普(Gallup)提到他們的準確性在60年代后大為提高，但在包括2000年，2012年和2016年在內的最近選舉中，他們的說法是錯誤的。

如今，選民已經上網，可以更緊密地訪問各種媒體所進行的民意調查等信息。 如果民意調查結果表明您的支持方將獲勝，這些民意調查結果可能會阻止您去投票。 對候選人而言，更緊密地訪問數據也至關重要，這是至關重要的-最后一刻的故事會極大地影響選民的思想，并且隨著現代競選活動越來越在線化，對實時數據的訪問越來越多，這一速度越來越大。

說明擺動參與者意見的可能性

經濟學家團隊使用“三元”圖代替通用的二維圖來獲得英國脫歐民意測驗數據–他們的嘗試是根據受訪者的React描繪民意測驗參與者在未達成協議的情況下如何支持留任 ， 休假或休假的立場以及他們根據人口統計資料改變看法的可能性。

選舉也并不總是能準確地代表民意

《經濟學人》的分析團隊根據各種民意測驗運行了一個模型，以表明如果所有美國人都投票，2016年美國大選將會如何。 模擬表明克林頓將贏得特朗普，這是根據在實際選舉之前的民意測驗得出的預測。