As a working alternative to my "tired old eyes" what I would do is as follows. Statistics longa, Eyesight brevis .
I took the 9 annual values for Coventry and obtained and with stats here
I would then estimate this model separately on each of the other 25 candidates and perform a test of significance between Coventry and each of the candidates to assess which one was the MOST DIFFERENT so to speak OR the MOST SIMILAR.
You only shared data from 2009 ..... not 2005 . Also all models are wrong but this one seems useful .
EDITED TO EXPOUND ON THE TEST FOR SIMILARITY BETWEEN COVENTRY AND 1 ONE OTHER CITY:
FOLLOWING GREGORY CHOW 1960 : https://www.jstor.org/stable/1910133?seq=1#page_scan_tab_contents AND here https://en.wikipedia.org/wiki/Chow_test
I took the 9 years from CITY 1 Birmingham and estimate the COVNENTRY MODEL and obtained and and . We add the two error sums of squares and obtain .000263+.004391=.00464.
I then used AUTOBOX ( a piece of software that I had helped to develop ) to estimate 1 set of parameters for the combined time series and obtained and and . Note that the first fitted/expected value for the second series has nothing whatsoever to do with the last observation in the first series.
Now to test the hypothesis of a common set of parameters we construct an F test with 2 and 14 degrees of freedom
NUMERATOR (.015283-.0004654 )/2 = .005315
DENOMINATOR .004654/14 = .000332
Yielding an F value > 16+ which is quite significnt suggesting that COVRNTRY & BIRMINGHAM differ at a very high level probability (which is confirmed by eye)
This can then be performed for each of the other 24 cities .