📜 [專欄新文章] Uniswap v3 Features Explained in Depth
✍️ 田少谷 Shao
📥 歡迎投稿: https://medium.com/taipei-ethereum-meetup #徵技術分享文 #使用心得 #教學文 #medium
Once again the game-changing DEX 🦄 👑
Image source: https://uniswap.org/blog/uniswap-v3/
Outline
0. Intro1. Uniswap & AMM recap2. Ticks 3. Concentrated liquidity4. Range orders: reversible limit orders5. Impacts of v36. Conclusion
0. Intro
The announcement of Uniswap v3 is no doubt one of the most exciting news in the DeFi place recently 🔥🔥🔥
While most have talked about the impact v3 can potentially bring on the market, seldom explain the delicate implementation techniques to realize all those amazing features, such as concentrated liquidity, limit-order-like range orders, etc.
Since I’ve covered Uniswap v1 & v2 (if you happen to know Mandarin, here are v1 & v2), there’s no reason for me to not cover v3 as well ✅
Thus, this article aims to guide readers through Uniswap v3, based on their official whitepaper and examples made on the announcement page. However, one needs not to be an engineer, as not many codes are involved, nor a math major, as the math involved is definitely taught in your high school, to fully understand the following content 😊😊😊
If you really make it through but still don’t get shxt, feedbacks are welcomed! 🙏
There should be another article focusing on the codebase, so stay tuned and let’s get started with some background noise!
1. Uniswap & AMM recap
Before diving in, we have to first recap the uniqueness of Uniswap and compare it to traditional order book exchanges.
Uniswap v1 & v2 are a kind of AMMs (automated market marker) that follow the constant product equation x * y = k, with x & y stand for the amount of two tokens X and Y in a pool and k as a constant.
Comparing to order book exchanges, AMMs, such as the previous versions of Uniswap, offer quite a distinct user experience:
AMMs have pricing functions that offer the price for the two tokens, which make their users always price takers, while users of order book exchanges can be both makers or takers.
Uniswap as well as most AMMs have infinite liquidity¹, while order book exchanges don’t. The liquidity of Uniswap v1 & v2 is provided throughout the price range [0,∞]².
Uniswap as well as most AMMs have price slippage³ and it’s due to the pricing function, while there isn’t always price slippage on order book exchanges as long as an order is fulfilled within one tick.
In an order book, each price (whether in green or red) is a tick. Image source: https://ftx.com/trade/BTC-PERP
¹ though the price gets worse over time; AMM of constant sum such as mStable does not have infinite liquidity
² the range is in fact [-∞,∞], while a price in most cases won’t be negative
³ AMM of constant sum does not have price slippage
2. Tick
The whole innovation of Uniswap v3 starts from ticks.
For those unfamiliar with what is a tick:
Source: https://www.investopedia.com/terms/t/tick.asp
By slicing the price range [0,∞] into numerous granular ticks, trading on v3 is highly similar to trading on order book exchanges, with only three differences:
The price range of each tick is predefined by the system instead of being proposed by users.
Trades that happen within a tick still follows the pricing function of the AMM, while the equation has to be updated once the price crosses the tick.
Orders can be executed with any price within the price range, instead of being fulfilled at the same one price on order book exchanges.
With the tick design, Uniswap v3 possesses most of the merits of both AMM and an order book exchange! 💯💯💯
So, how is the price range of a tick decided?
This question is actually somewhat related to the tick explanation above: the minimum tick size for stocks trading above 1$ is one cent.
The underlying meaning of a tick size traditionally being one cent is that one cent (1% of 1$) is the basis point of price changes between ticks, ex: 1.02 — 1.01 = 0.1.
Uniswap v3 employs a similar idea: compared to the previous/next price, the price change should always be 0.01% = 1 basis point.
However, notice the difference is that in the traditional basis point, the price change is defined with subtraction, while here in Uniswap it’s division.
This is how price ranges of ticks are decided⁴:
Image source: https://uniswap.org/whitepaper-v3.pdf
With the above equation, the tick/price range can be recorded in the index form [i, i+1], instead of some crazy numbers such as 1.0001¹⁰⁰ = 1.0100496621.
As each price is the multiplication of 1.0001 of the previous price, the price change is always 1.0001 — 1 = 0.0001 = 0.01%.
For example, when i=1, p(1) = 1.0001; when i=2, p(2) = 1.00020001.
p(2) / p(1) = 1.00020001 / 1.0001 = 1.0001
See the connection between the traditional basis point 1 cent (=1% of 1$) and Uniswap v3’s basis point 0.01%?
Image source: https://tenor.com/view/coin-master-cool-gif-19748052
But sir, are prices really granular enough? There are many shitcoins with prices less than 0.000001$. Will such prices be covered as well?
Price range: max & min
To know if an extremely small price is covered or not, we have to figure out the max & min price range of v3 by looking into the spec: there is a int24 tick state variable in UniswapV3Pool.sol.
Image source: https://uniswap.org/whitepaper-v3.pdf
The reason for a signed integer int instead of an uint is that negative power represents prices less than 1 but greater than 0.
24 bits can cover the range between 1.0001 ^ (2²³ — 1) and 1.0001 ^ -(2)²³. Even Google cannot calculate such numbers, so allow me to offer smaller values to have a rough idea of the whole price range:
1.0001 ^ (2¹⁸) = 242,214,459,604.341
1.0001 ^ -(2¹⁷) = 0.000002031888943
I think it’s safe to say that with a int24 the range can cover > 99.99% of the prices of all assets in the universe 👌
⁴ For implementation concern, however, a square root is added to both sides of the equation.
How about finding out which tick does a price belong to?
Tick index from price
The answer to this question is rather easy, as we know that p(i) = 1.0001^i, simply takes a log with base 1.0001 on both sides of the equation⁴:
Image source: https://www.codecogs.com/latex/eqneditor.php
Let’s try this out, say we wanna find out the tick index of 1000000.
Image source: https://ncalculators.com/number-conversion/log-logarithm-calculator.htm
Now, 1.0001¹³⁸¹⁶² = 999,998.678087146. Voila!
⁵ This formula is also slightly modified to fit the real implementation usage.
3. Concentrated liquidity
Now that we know how ticks and price ranges are decided, let’s talk about how orders are executed in a tick, what is concentrated liquidity and how it enables v3 to compete with stablecoin-specialized DEXs (decentralized exchange), such as Curve, by improving the capital efficiency.
Concentrated liquidity means LPs (liquidity providers) can provide liquidity to any price range/tick at their wish, which causes the liquidity to be imbalanced in ticks.
As each tick has a different liquidity depth, the corresponding pricing function x * y = k also won’t be the same!
Each tick has its own liquidity depth. Image source: https://uniswap.org/blog/uniswap-v3/
Mmm… examples are always helpful for abstract descriptions 😂
Say the original pricing function is 100(x) * 1000(y) = 100000(k), with the price of X token 1000 / 100 = 10 and we’re now in the price range [9.08, 11.08].
If the liquidity of the price range [11.08, 13.08] is the same as [9.08, 11.08], we don’t have to modify the pricing function if the price goes from 10 to 11.08, which is the boundary between two ticks.
The price of X is 1052.63 / 95 = 11.08 when the equation is 1052.63 * 95 = 100000.
However, if the liquidity of the price range [11.08, 13.08] is two times that of the current range [9.08, 11.08], balances of x and y should be doubled, which makes the equation become 2105.26 * 220 = 400000, which is (1052.63 * 2) * (110 * 2) = (100000 * 2 * 2).
We can observe the following two points from the above example:
Trades always follow the pricing function x * y = k, while once the price crosses the current price range/tick, the liquidity/equation has to be updated.
√(x * y) = √k = L is how we represent the liquidity, as I say the liquidity of x * y = 400000 is two times the liquidity of x * y = 100000, as √(400000 / 100000) = 2.
What’s more, compared to liquidity on v1 & v2 is always spread across [0,∞], liquidity on v3 can be concentrated within certain price ranges and thus results in higher capital efficiency from traders’ swapping fees!
Let’s say if I provide liquidity in the range [1200, 2800], the capital efficiency will then be 4.24x higher than v2 with the range [0,∞] 😮😮😮 There’s a capital efficiency comparison calculator, make sure to try it out!
Image source: https://uniswap.org/blog/uniswap-v3/
It’s worth noticing that the concept of concentrated liquidity was proposed and already implemented by Kyper, prior to Uniswap, which is called Automated Price Reserve in their case.⁵
⁶ Thanks to Yenwen Feng for the information.
4. Range orders: reversible limit orders
As explained in the above section, LPs of v3 can provide liquidity to any price range/tick at their wish. Depending on the current price and the targeted price range, there are three scenarios:
current price < the targeted price range
current price > the targeted price range
current price belongs to the targeted price range
The first two scenarios are called range orders. They have unique characteristics and are essentially fee-earning reversible limit orders, which will be explained later.
The last case is the exact same liquidity providing mechanism as the previous versions: LPs provide liquidity in both tokens of the same value (= amount * price).
There’s also an identical product to the case: grid trading, a very powerful investment tool for a time of consolidation. Dunno what’s grid trading? Check out Binance’s explanation on this, as this topic won’t be covered!
In fact, LPs of Uniswap v1 & v2 are grid trading with a range of [0,∞] and the entry price as the baseline.
Range orders
To understand range orders, we’d have to first revisit how price is discovered on Uniswap with the equation x * y = k, for x & y stand for the amount of two tokens X and Y and k as a constant.
The price of X compared to Y is y / x, which means how many Y one can get for 1 unit of X, and vice versa the price of Y compared to X is x / y.
For the price of X to go up, y has to increase and x decrease.
With this pricing mechanism in mind, it’s example time!
Say an LP plans to place liquidity in the price range [15.625, 17.313], higher than the current price of X 10, when 100(x) * 1000(y) = 100000(k).
The price of X is 1250 / 80 = 15.625 when the equation is 80 * 1250 = 100000.
The price of X is 1315.789 / 76 = 17.313 when the equation is 76 * 1315.789 = 100000.
If now the price of X reaches 15.625, the only way for the price of X to go even higher is to further increase y and decrease x, which means exchanging a certain amount of X for Y.
Thus, to provide liquidity in the range [15.625, 17.313], an LP needs only to prepare 80 — 76 = 4 of X. If the price exceeds 17.313, all 4 X of the LP is swapped into 1315.789 — 1250 = 65.798 Y, and then the LP has nothing more to do with the pool, as his/her liquidity is drained.
What if the price stays in the range? It’s exactly what LPs would love to see, as they can earn swapping fees for all transactions in the range! Also, the balance of X will swing between [76, 80] and the balance of Y between [1250, 1315.789].
This might not be obvious, but the example above shows an interesting insight: if the liquidity of one token is provided, only when the token becomes more valuable will it be exchanged for the less valuable one.
…wut? 🤔
Remember that if 4 X is provided within [15.625, 17.313], only when the price of X goes up from 15.625 to 17.313 is 4 X gradually swapped into Y, the less valuable one!
What if the price of X drops back immediately after reaching 17.313? As X becomes less valuable, others are going to exchange Y for X.
The below image illustrates the scenario of DAI/USDC pair with a price range of [1.001, 1.002] well: the pool is always composed entirely of one token on both sides of the tick, while in the middle 1.001499⁶ is of both tokens.
Image source: https://uniswap.org/blog/uniswap-v3/
Similarly, to provide liquidity in a price range < current price, an LP has to prepare a certain amount of Y for others to exchange Y for X within the range.
To wrap up such an interesting feature, we know that:
Only one token is required for range orders.
Only when the current price is within the range of the range order can LP earn trading fees. This is the main reason why most people believe LPs of v3 have to monitor the price more actively to maximize their income, which also means that LPs of v3 have become arbitrageurs 🤯
I will be discussing more the impacts of v3 in 5. Impacts of v3.
⁷ 1.001499988 = √(1.0001 * 1.0002) is the geometric mean of 1.0001 and 1.0002. The implication is that the geometric mean of two prices is the average execution price within the range of the two prices.
Reversible limit orders
As the example in the last section demonstrates, if there is 4 X in range [15.625, 17.313], the 4 X will be completely converted into 65.798 Y when the price goes over 17.313.
We all know that a price can stay in a wide range such as [10, 11] for quite some time, while it’s unlikely so in a narrow range such as [15.625, 15.626].
Thus, if an LP provides liquidity in [15.625, 15.626], we can expect that once the price of X goes over 15.625 and immediately also 15.626, and does not drop back, all X are then forever converted into Y.
The concept of having a targeted price and the order will be executed after the price is crossed is exactly the concept of limit orders! The only difference is that if the range of a range order is not narrow enough, it’s highly possible that the conversion of tokens will be reverted once the price falls back to the range.
As price ranges follow the equation p(i) = 1.0001 ^ i, the range can be quite narrow and a range order can thus effectively serve as a limit order:
When i = 27490, 1.0001²⁷⁴⁹⁰ = 15.6248.⁸
When i = 27491, 1.0001²⁷⁴⁹¹ = 15.6264.⁸
A range of 0.0016 is not THAT narrow but can certainly satisfy most limit order use cases!
⁸ As mentioned previously in note #4, there is a square root in the equation of the price and index, thus the numbers here are for explantion only.
5. Impacts of v3
Higher capital efficiency, LPs become arbitrageurs… as v3 has made tons of radical changes, I’d like to summarize my personal takes of the impacts of v3:
Higher capital efficiency makes one of the most frequently considered indices in DeFi: TVL, total value locked, becomes less meaningful, as 1$ on Uniswap v3 might have the same effect as 100$ or even 2000$ on v2.
The ease of spot exchanging between spot exchanges used to be a huge advantage of spot markets over derivative markets. As LPs will take up the role of arbitrageurs and arbitraging is more likely to happen on v3 itself other than between DEXs, this gap is narrowed … to what extent? No idea though.
LP strategies and the aggregation of NFT of Uniswap v3 liquidity token are becoming the blue ocean for new DeFi startups: see Visor and Lixir. In fact, this might be the turning point for both DeFi and NFT: the two main reasons of blockchain going mainstream now come to the alignment of interest: solving the $$ problem 😏😏😏
In the right venue, which means a place where transaction fees are low enough, such as Optimism, we might see Algo trading firms coming in to share the market of designing LP strategies on Uniswap v3, as I believe Algo trading is way stronger than on-chain strategies or DAO voting to add liquidity that sort of thing.
After reading this article by Parsec.finance: The Dex to Rule Them All, I cannot help but wonder: maybe there is going to be centralized crypto exchanges adopting v3’s approach. The reason is that since orders of LPs in the same tick are executed pro-rata, the endless front-running speeding-competition issue in the Algo trading world, to some degree, is… solved? 🤔
Anyway, personal opinions can be biased and seriously wrong 🙈 I’m merely throwing out a sprat to catch a whale. Having a different voice? Leave your comment down below!
6. Conclusion
That was kinda tough, isn’t it? Glad you make it through here 🥂🥂🥂
There are actually many more details and also a huge section of Oracle yet to be covered. However, since this article is more about features and targeting normal DeFi users, I’ll leave those to the next one; hope there is one 😅
If you have any doubt or find any mistake, please feel free to reach out to me and I’d try to reply AFAP!
Stay tuned and in the meantime let’s wait and see how Uniswap v3 is again pioneering the innovation of DeFi 🌟
Uniswap v3 Features Explained in Depth was originally published in Taipei Ethereum Meetup on Medium, where people are continuing the conversation by highlighting and responding to this story.
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同時也有10000部Youtube影片,追蹤數超過2,910的網紅コバにゃんチャンネル,也在其Youtube影片中提到,...
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根據計算,100萬人遊行隊伍要從維多利亞公園排到廣東;200萬人遊行則要排到泰國。
順道一提香港15~30歲人口約莫100出頭萬人。以照片人群幾乎都是此年齡帶來看,兩個數字都是明顯誇大太多了。
另一個可以參考的是1969年的Woodstock Music & Art Fair,幾天內湧進40萬人次,照片看起來也是滿山滿谷的人。(http://sites.psu.edu/…/upl…/sites/851/2013/01/Woodstock3.jpg)
當年40萬人次引發驚人的大塞車,幾乎花十幾個小時才逐漸清場。
而香港遊行清場速度明顯快得多。
順道一提,因此運動而認定「你的父母不愛你」的白痴論述也如同文化大革命時的「爹親娘親不如毛主席親」般開始出現:
https://www.facebook.com/SaluteToHKPolice/videos/350606498983830/UzpfSTUyNzM2NjA3MzoxMDE1NjMyMTM4NjY3MTA3NA/
EVERY MAJOR NEWS outlet in the world is reporting that two million people, well over a quarter of our population, joined a single protest.
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It’s an astonishing thought that filled an enthusiastic old marcher like me with pride. Unfortunately, it’s almost certainly not true.
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A march of two million people would fill a street that was 58 kilometers long, starting at Victoria Park in Hong Kong and ending in Tanglangshan Country Park in Guangdong, according to one standard crowd estimation technique.
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If the two million of us stood in a queue, we’d stretch 914 kilometers (568 miles), from Victoria Park to Thailand. Even if all of us marched in a regiment 25 people abreast, our troop would stretch towards the Chinese border.
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Yes, there was a very large number of us there. But getting key facts wrong helps nobody. Indeed, it could hurt the protesters more than anyone.
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For math geeks only, here’s a discussion of the actual numbers that I hope will interest you whatever your political views.
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DO NUMBERS MATTER?
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People have repeatedly asked me to find out “the real number” of people at the recent mass rallies in Hong Kong.
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I declined for an obvious reason: There was a huge number of us. What does it matter whether it was hundreds of thousands or a million? That’s not important.
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But my critics pointed out that the word “million” is right at the top of almost every report about the marches. Clearly it IS important.
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FIRST, THE SCIENCE
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In the west, drone photography is analyzed to estimate crowd sizes.
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This reporter apologizes for not having found a comprehensive database of drone images of the Hong Kong protests.
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But we can still use related methods, such as density checks, crowd-flow data and impact assessments. Universities which have gathered Hong Kong protest march data using scientific methods include Hong Kong Polytechnic University, Hong Kong University of Science and Technology, University of Hong Kong, and Hong Kong Baptist University.
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DENSITY CHECKS
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Figures gathered in the past by Hong Kong Polytechnic specialists using satellite photo analysis found a density level of one square meter per marcher. Modern analysis suggests this remains roughly accurate.
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I know from experience that Hong Kong marches feature long periods of normal spacing (one square meter or one and half per person, walking) and shorter periods of tight spacing (half a square meter or less per person, mostly standing).
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JOINERS AND SPEED
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We need to include people who join halfway. In the past, a Hong Kong University analysis using visual counting methods cross-referenced with one-on-one interviews indicated that estimates should be boosted by 12% to accurately reflect late joiners. These days, we’re much more generous in estimating joiners.
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As for speed, a Hong Kong Baptist University survey once found a passing rate of 4,000 marchers every ten minutes.
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Videos of the recent rallies indicates that joiner numbers and stop-start progress were highly erratic and difficult to calculate with any degree of certainty.
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DISTANCE MULTIPLIED BY DENSITY
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But scientists have other tools. We know the walking distance between Victoria Park and Tamar Park is 2.9 kilometers. Although there was overspill, the bulk of the marchers went along Hennessy Road in Wan Chai, which is about 25 meters (or 82 feet) wide, and similar connected roads, some wider, some narrower.
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Steve Doig, a specialist in crowd analysis approached by the Columbia Journalism Review (CJR), analyzed an image of Hong Kong marchers to find a density level of 7,000 people in a 210-meter space. Although he emphasizes that crowd estimates are never an exact science, that figure means one million Hong Kong marchers would need a street 18.6 miles long – which is 29 kilometers.
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Extrapolating these figures for the June 16 claim of two million marchers, you’d need a street 58 kilometers long.
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Could this problem be explained away by the turnover rate of Hong Kong marchers, which likely allowed the main (three kilometer) route to be filled more than once?
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The answer is yes, to some extent. But the crowd would have to be moving very fast to refill the space a great many times over in a single afternoon and evening. It wasn’t. While I can walk the distance from Victoria Park to Tamar in 41 minutes on a quiet holiday afternoon, doing the same thing during a march takes many hours.
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More believable: There was a huge number of us, but not a million, and certainly not two million.
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IMPACT MEASUREMENTS
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A second, parallel way of analyzing the size of the crowd is to seek evidence of the effects of the marchers’ absence from their normal roles in society.
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If we extract two million people out of a population of 7.4 million, many basic services would be severely affected while many others would grind to a complete halt.
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Manpower-intensive sectors of society, such as transport, would be badly affected by mass absenteeism. Industries which do their main business on the weekends, such as retail, restaurants, hotels, tourism, coffee shops and so on would be hard hit. Round-the-clock operations such as hospitals and emergency services would be severely troubled, as would under-the-radar jobs such as infrastructure and utility maintenance.
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There seems to be no evidence that any of that happened in Hong Kong.
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HOW DID WE GET INTO THIS MESS?
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To understand that, a bit of historical context is necessary.
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In 2003, a very large number of us walked from Victoria Park to Central. The next day, newspapers gave several estimates of crowd size.
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The differences were small. Academics said it was 350,000 plus. The police counted 466,000. The organizers, a group called the Civil Rights Front, rounded it up to 500,000.
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No controversy there. But there was trouble ahead.
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THINGS FALL APART
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At a repeat march the following year, it was obvious to all of us that our numbers were far lower that the previous year. The people counting agreed: the academics said 194,000 and the police said 200,000.
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But the Civil Rights Front insisted that there were MORE than the previous year’s march: 530,000 people.
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The organizers lost credibility even with us, their own supporters. To this day, we all quote the 2003 figure as the high point of that period, ignoring their 2004 invention.
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THE TRUTH COUNTS
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The organizers had embarrassed the marchers. The following year several organizations decided to serve us better, with detailed, scientific counts.
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After the 2005 march, the academics said the headcount was between 60,000 and 80,000 and the police said 63,000. Separate accounts by other independent groups agreed that it was below 100,000.
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But the organizers? The Civil Rights Front came out with the awkward claim that it was a quarter of a million. Ouch. (This data is easily confirmed from multiple sources in newspaper archives.)
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AN UNEXPECTED TWIST
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But then came a twist. Some in the Western media chose to present ONLY the organizer’s “outlier” claim.
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“Dressed in black and chanting ‘one man, one vote’, a quarter of a million people marched through Hong Kong yesterday,” said the Times of London in 2005.
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“A quarter of a million protesters marched through Hong Kong yesterday to demand full democracy from their rulers in Beijing,” reported the UK Independent.
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It became obvious that international media outlets were committed to emphasizing whichever claim made the Hong Kong government (and by extension, China) look as bad as possible. Accuracy was nowhere in the equation.
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STRATEGICALLY CHOSEN
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At universities in Hong Kong, there were passionate discussions about the apparent decision to pump up the numbers as a strategy, with the international media in mind. Activists saw two likely positive outcomes.
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First, anyone who actually wanted the truth would choose a middle point as the “real” number: thus it was worth making the organizers’ number as high as possible. (The police could be presented as corrupt puppets of Beijing.)
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Second, international reporters always favored the largest number, since it implicitly criticized China. Once the inflated figure was established in the Western media, it would become the generally accepted figure in all publications.
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Both of the activists’ predictions turned out to be bang on target. In the following years, headcounts by social scientists and police were close or even impressively confirmed the other—but were ignored by the agenda-driven international media, who usually printed only the organizers’ claims.
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SKIP THIS SECTION
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Skip this section unless you want additional examples to reinforce the point.
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In 2011, researchers and police said that between 63,000 and 95,000 of us marched. Our delightfully imaginative organizers multiplied by four to claim there were 400,000 of us.
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In 2012, researchers and police produced headcounts similar to the previous year: between 66,000 and 97,000. But the organizers claimed that it was 430,000. (These data can also be easily confirmed in any newspaper archive.)
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SKIP THIS SECTION TOO
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Unless you’re interested in the police angle. Why are police figures seen as lower than others? On reviewing data, two points emerge.
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First, police estimates rise and fall with those of independent researchers, suggesting that they function correctly: they are not invented. Many are slightly lower, but some match closely and others are slightly higher. This suggests that the police simply have a different counting method.
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Second, police sources explain that live estimates of attendance are used for “effective deployment” of staff. The number of police assigned to work on the scene is a direct reflection of the number of marchers counted. Thus officers have strong motivation to avoid deliberately under-estimating numbers.
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RECENT MASS RALLIES
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Now back to the present: this hot, uncomfortable summer.
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Academics put the 2019 June 9 rally at 199,500, and police at 240,000. Some people said the numbers should be raised or even doubled to reflect late joiners or people walking on parallel roads. Taking the most generous view, this gave us total estimates of 400,000 to 480,000.
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But the organizers, God bless them, claimed that 1.03 million marched: this was four times the researchers’ conservative view and more than double the generous view.
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The addition of the “.03m” caused a bit of mirth among social scientists. Even an academic writing in the rabidly pro-activist Hong Kong Free Press struggled to accept it. “Undoubtedly, the anti-amendment group added the extra .03 onto the exact one million figure in order to give their estimate a veneer of accuracy,” wrote Paul Stapleton.
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MIND-BOGGLING ESTIMATE
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But the vast majority of international media and social media printed ONLY the organizers’ eyebrow-raising claim of a million plus—and their version soon fed back into the system and because the “accepted” number. (Some mentioned other estimates in early reports and then dropped them.)
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The same process was repeated for the following Sunday, June 16, when the organizers’ frankly unbelievable claim of “about two million” was taken as gospel in the majority of international media.
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“Two million people in Hong Kong protest China's growing influence,” reported Fox News.
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“A record two million people – over a quarter of the city’s population” joined the protest, said the Guardian this morning.
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“Hong Kong leader apologizes as TWO MILLION take to the streets,” said the Sun newspaper in the UK.
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Friends, colleagues, fellow journalists—what happened to fact-checking? What happened to healthy skepticism? What happened to attempts at balance?
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CONCLUSIONS?
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I offer none. I prefer that you do your own research and draw your own conclusions. This is just a rough overview of the scientific and historical data by a single old-school citizen-journalist working in a university coffee shop.
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I may well have made errors on individual data points, although the overall message, I hope, is clear.
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Hong Kong people like to march.
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We deserve better data.
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We need better journalism. Easily debunked claims like “more than a quarter of the population hit the streets” help nobody.
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International media, your hostile agendas are showing. Raise your game.
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Organizers, stop working against the scientists and start working with them.
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Hong Kong people value truth.
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We’re not stupid. (And we’re not scared of math!)
equation of two points 在 943就是省的超值好康分享團 Facebook 的精選貼文
腳踏車真萬能~節能省碳又不花汽油錢... :D
左下角和右中那2位是騎車環遊世界嗎?XD
The Real Reason Bicycles Are The Key To Better Cities:
"We all know the talking points. The benefits of bicycles have been tirelessly elaborated upon; bicycles improve health, ease congestion, save money, use less space, and provide efficient transportation with zero fuel consumption and zero carbon emissions. The culmination of a population on two wheels can have a drastic impact on the overall wellbeing of a city. However, none of these come close to the most meaningful aspect of cycling, a factor that cannot be quantified but has endless value to those fighting to improve their communities.
The most vital element for the future of our cities is that the bicycle is an instrument of experiential understanding.
On a bicycle, citizens experience their city with deep intimacy, often for the first time. For a regular motorist to take that two or three mile trip by bicycle instead is to decimate an enormous wall between them and their communities.
In a car, the world is reduced to mere equation; “What is the fastest route from A to B?” one will ask as they start their engine. This invariably leads to a cascade of freeway concrete flying by at incomprehensible speeds. Their environment, the neighborhoods that compose their communities, the beauty of architecture, the immense societal problems in distressed areas, the faces of neighbors… all of this becomes a conceptually abstract blur from the driver’s seat.
One cannot turn a blind eye on a bicycle - they must acknowledge their community, all of it.
Here lies the secret weapon of the urban renaissance.
No one wants to be told that they must radically alter their lifestyle, no matter how well you sell it.
The bicycle doesn’t need to be sold. It’s economical, it’s fun, it’s sexy, and just about everyone already has one hiding somewhere in their garage.
Invite a motorist for a bike ride through your city and you’ll be cycling with an urbanist by the end of the day. Even the most eloquent of lectures about livable cities and sustainable design can’t compete with the experience from atop a bicycle saddle.
“These cars are going way too fast,” they may mutter beneath their breath.
“How are we supposed to get across the highway?”
“Wow, look at that cathedral! I didn’t know that was there.”
“I didn’t realize there were so many vacant lots in this part of town.”
“Hey, let’s stop at this cafe for a drink.”
Suddenly livability isn’t an abstract concept, it’s an experience. Human scale, connectivity, land use efficiency, urban fabric, complete streets… all the codewords, catchphrases, and academic jargon can be tossed out the window because now they are one synthesized moment of appreciation. Bicycles matter because they are a catalyst of understanding - become hooked on the thrill of cycling, and everything else follows."
Full Article: The Real Reason Bicycles Are The Key To Better Cities http://bit.ly/mv46as
~BP