Amberwood North - human settlement in Arizona, United States of America. Article “北安伯伍德 (亞利桑那州)” in Chinese Wikipedia has 22.1 points for quality (as of July 1, 2025). The article contains 3 references and 2 sections.
Since the creation of article “北安伯伍德 (亞利桑那州)”, its content was written by 1 registered users of Chinese Wikipedia and edited by 1 registered Wikipedia users in all languages.
The article is cited 126 times in Chinese Wikipedia and cited 126 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from July 1, 2025 (including revision history and pageviews for previous years).
| Best Rank | Chinese: #703328 11.2019 |
Global: #6763080 03.2025 |
| Best Rank | Chinese: #21931 10.2018 |
Global: #912083 10.2018 |
On 12 August 2025 in multilingual Wikipedia, Internet users most often read articles on the following topics: Cristiano Ronaldo, Wednesday, Georgina Rodríguez, ChatGPT, Weapons, Jenna Ortega, deaths in 2025, Taylor Swift, Miguel Uribe Turbay, 2025–26 UEFA Champions League.
In Chinese Wikipedia the most popular articles on that day were: 朱由𣜬, 林岱樺, 林逸欣, 江油未成年人欺凌事件, 鬼滅之刃, 黃士修, 鬼滅之刃角色列表, 锦月如歌, 2025年亞洲盃籃球賽, 鬼滅之刃劇場版 無限城篇.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from July, 2025. When calculating current popularity and AI of articles data from June 2025 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2025... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(5.86+2.97+33.33+16.67+51.85)$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
