The chart below shows the movement of people from rural to urban areas in three countries and predictions for future years.
The given line graph compares three different countries in terms of the people migrated and forecasted to migrated from countryside to cities of this three countries over the period of quarter century. Units are measured in millions.
It is clear that, although Iran had the least number of migrants at the beginning, the trend intersected with the figure for Russia in 2020 and forecasted to pose the highest amount of migrants in 2025. Interestingly, while all three countries have seen growth up to 2020, only the figures for countries Iran and Russia are predicted to see continued growth up to 2025.
All the three countries began the period approximately with the similar numbers of urban migrants. Russia started with the most migrants at around 15 million and saw significant changes particularly between 2010 and 2015 rose by 28 million. Also the trend is predicted to reach approximately 86 million by 2025.Similarly, Iran after a slow start without any considerable changes between 2000 and 2005, saw a dramatic rise in urban migration. In 2020, that of Iran surpassed the Russia’s figures and forecasted to reach its highest point just under 100 million by 2025.
From 2000 to 2020, the figures for Indonesia witnessed gradual increase and reached a plateau in 2020 and forecasted to remain unchanged by the end of the period.
Post date | Users | Rates | Link to Content |
---|---|---|---|
2023-05-30 | preetsidhu29500@gmail.com | 67 | view |
2022-06-18 | nttn24905_vkdekutsuki | 78 | view |
2021-07-08 | maiphuong0610 | 73 | view |
2021-07-08 | maiphuong0610 | 73 | view |
2021-06-02 | Ismoilkhon | 73 | view |
- The graph below shows the number of hours per day on average that children spent watching television between 1950 and 2010
- The given line graph below illustrates the number of people moved from rural to urban areas of three different countries 78
- The chart below shows the changes that took place in three different areas of crime in Panama City from 2010 to 2019 67
- The graph below shows the amounts of waste produced by three companies over a period of 15 years 67
- The chart below shows the movement of people from rural to urban areas in three countries and predictions for future years 73
Grammar and spelling errors:
Line 3, column 241, Rule ID: SENT_START_CONJUNCTIVE_LINKING_ADVERB_COMMA[1]
Message: Did you forget a comma after a conjunctive/linking adverb?
Suggestion: Also,
...tween 2010 and 2015 rose by 28 million. Also the trend is predicted to reach approxi...
^^^^
Line 3, column 311, Rule ID: SENTENCE_WHITESPACE
Message: Add a space between sentences
Suggestion: Similarly
... reach approximately 86 million by 2025.Similarly, Iran after a slow start without any co...
^^^^^^^^^
Transition Words or Phrases used:
also, if, similarly, so, while
Attributes: Values AverageValues Percentages(Values/AverageValues)% => Comments
Performance on Part of Speech:
To be verbs : 4.0 7.0 57% => More to be verbs wanted.
Auxiliary verbs: 0.0 1.00243902439 0% => OK
Conjunction : 9.0 6.8 132% => OK
Relative clauses : 2.0 3.15609756098 63% => OK
Pronoun: 5.0 5.60731707317 89% => OK
Preposition: 45.0 33.7804878049 133% => OK
Nominalization: 1.0 3.97073170732 25% => More nominalizations (nouns with a suffix like: tion ment ence ance) wanted.
Performance on vocabulary words:
No of characters: 1125.0 965.302439024 117% => OK
No of words: 221.0 196.424390244 113% => OK
Chars per words: 5.09049773756 4.92477711251 103% => OK
Fourth root words length: 3.85565412703 3.73543355544 103% => OK
Word Length SD: 2.79895618576 2.65546596893 105% => OK
Unique words: 120.0 106.607317073 113% => OK
Unique words percentage: 0.542986425339 0.547539520022 99% => OK
syllable_count: 323.1 283.868780488 114% => OK
avg_syllables_per_word: 1.5 1.45097560976 103% => OK
A sentence (or a clause, phrase) starts by:
Pronoun: 2.0 1.53170731707 131% => OK
Article: 3.0 4.33902439024 69% => OK
Subordination: 2.0 1.07073170732 187% => OK
Conjunction: 0.0 0.482926829268 0% => OK
Preposition: 2.0 3.36585365854 59% => More preposition wanted as sentence beginning.
Performance on sentences:
How many sentences: 9.0 8.94146341463 101% => OK
Sentence length: 24.0 22.4926829268 107% => OK
Sentence length SD: 53.3187480056 43.030603864 124% => OK
Chars per sentence: 125.0 112.824112599 111% => OK
Words per sentence: 24.5555555556 22.9334400587 107% => OK
Discourse Markers: 3.33333333333 5.23603664747 64% => OK
Paragraphs: 4.0 3.83414634146 104% => OK
Language errors: 2.0 1.69756097561 118% => OK
Sentences with positive sentiment : 5.0 3.70975609756 135% => OK
Sentences with negative sentiment : 1.0 1.13902439024 88% => OK
Sentences with neutral sentiment: 3.0 4.09268292683 73% => OK
What are sentences with positive/Negative/neutral sentiment?
Coherence and Cohesion:
Essay topic to essay body coherence: 0.15979004919 0.215688989381 74% => OK
Sentence topic coherence: 0.066276029691 0.103423049105 64% => OK
Sentence topic coherence SD: 0.0640983394891 0.0843802449381 76% => OK
Paragraph topic coherence: 0.10178353843 0.15604864568 65% => OK
Paragraph topic coherence SD: 0.0402701482223 0.0819641961636 49% => Paragraphs are similar to each other. Some content may get duplicated or it is not exactly right on the topic.
Essay readability:
automated_readability_index: 14.8 13.2329268293 112% => OK
flesch_reading_ease: 55.58 61.2550243902 91% => OK
smog_index: 8.8 6.51609756098 135% => OK
flesch_kincaid_grade: 11.5 10.3012195122 112% => OK
coleman_liau_index: 12.54 11.4140731707 110% => OK
dale_chall_readability_score: 8.18 8.06136585366 101% => OK
difficult_words: 47.0 40.7170731707 115% => OK
linsear_write_formula: 13.5 11.4329268293 118% => OK
gunning_fog: 11.6 10.9970731707 105% => OK
text_standard: 12.0 11.0658536585 108% => OK
What are above readability scores?
---------------------
Rates: 73.0337078652 out of 100
Scores by essay e-grader: 6.5 Out of 9
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Note: the e-grader does NOT examine the meaning of words and ideas. VIP users will receive further evaluations by advanced module of e-grader and human graders.