Table:
Shaoyang administrative and population data

Division

Area[1]
(km2)

Permanent residents[2]
(2010 Census)

Hukou permits[3]
(late 2009)

Divisions[4]

Subdistricts

Towns

Townships

Including
ethnic
townships

Residential
communities

Villages

SHAOYANG CITY
20,829.63
7,071,741
7,705,957
21
97
99
21
335
5551

Shuangqing District

137.08
307,980
270,458
6
2
4

54
63

Daxiang District

214.66
340,605
309,752
6
2
4

44
81

Beita District

84.39
104,609
87,714
2

3

14
40

Shaodong County

1,776.38
896,625
1,260,966
3
16
9

33
977

Xinshao County

1,762.97
743,073
764,905

11
4

28
652

Shaoyang County

1,996.88
915,600
999,148

12
10

23
633

Longhui County

2,871.16
1,095,392
1,156,917

15
11
2
41
964

Dongkou County

2184.01
770,473
833,003

10
12
3
23
566

Suining County

2,898.89
351,139
363,740

6
19
14
14
348

Xinning County

2,751.31
560,742
607,055

9
9
2
15
474

Chengbu Miao Autonomous County

2,619.76
250,633
267,921

6
5

21
271

Wugang City

1,532.15
734,870
784,378
4
8
9

25
482

In the context of a table, we can utilize the json format {'value': '<cell value>', 'location': (row_id, column_id)} to express a cell's position, where both row and column id commence from 1. Examining the provided table image, identify the locations of the following cells using this format.
Values to locate:
1. 'xinshao county'
2. '130.3'
3. '33'
4. '743,073'
5. '139'
6. '214.66'
7. '964'
Denote the location as 'DOES NOT EXIST' if a cell does not exist in the table.

The coordinates of target cells are listed below.
1. {'value': 'xinshao county', 'location': (8, 1)}
2. {'value': '130.3', 'location': 'DOES NOT EXIST'}
3. {'value': '33', 'location': (7, 9)}
4. {'value': '743,073', 'location': (8, 3)}
5. {'value': '139', 'location': 'DOES NOT EXIST'}
6. {'value': '214.66', 'location': (5, 2)}
7. {'value': '964', 'location': (10, 10)}