3.2.2.2 Prepare source file for district level statistics
Next you need to prepare your statistics source file for the district level. This is a csv file for the statistics parameters that are used to calculate performance metrics and completeness rates. We only need to create this file for districts because our code will scale them appropriately for the parent state. You can import and create csv files in Excel or Google Drive. Using our Farajaland statistics.csv file as an example, update it with the second-level administrative statistical information for your country - do not move the file from this location. Our statistics.csv file looks like this:
statisticalID
name
male_population_2007
female_population_2007
population_2007
crude_birth_rate_2007
male_population_2008
female_population_2008
population_2008
crude_birth_rate_2008
male_population_2009
female_population_2009
population_2009
crude_birth_rate_2009
male_population_2010
female_population_2010
population_2010
crude_birth_rate_2010
male_population_2011
female_population_2011
population_2011
crude_birth_rate_2011
male_population_2012
female_population_2012
population_2012
crude_birth_rate_2012
male_population_2013
female_population_2013
population_2013
crude_birth_rate_2013
male_population_2014
female_population_2014
population_2014
crude_birth_rate_2014
male_population_2015
female_population_2015
population_2015
crude_birth_rate_2015
male_population_2016
female_population_2016
population_2016
crude_birth_rate_2016
male_population_2017
female_population_2017
population_2017
crude_birth_rate_2017
male_population_2018
female_population_2018
population_2018
crude_birth_rate_2018
male_population_2019
female_population_2019
population_2019
crude_birth_rate_2019
male_population_2020
female_population_2020
population_2020
crude_birth_rate_2020
male_population_2021
female_population_2021
population_2021
crude_birth_rate_2021
oEBf29y8JP8
Ibombo
40971
39344
80323
18.2
42668
41823
84511
21
45008
43843
88871
21.8
45150
43903
89073
16.2
45513
43884
89409
12.6
47965
46643
94631
20.6
25192
24542
49734
17.5
25659
25277
50936
18.9
32924
32808
65731
16.5
34031
34013
68044
12.6
33472
32968
66440
16.7
32876
28563
61440
13.8
36647
27595
64242
11.1
39380
32952
72332
11.7
46278
27705
73983
10.5
HPGiE9Jjh2r
Isamba
69782
64619
134371
19.5
67498
64096
131590
18.6
68665
64425
133069
14.7
69810
65957
135754
18.4
70091
66079
136153
27.2
71602
66857
138426
19.4
65834
63461
129296
17.7
53334
54067
107402
19.6
190512
193439
383952
16.5
192934
195623
388557
17.2
179651
179636
359287
19.5
200566
199729
400295
17.9
235281
197258
432538
14.6
249443
211685
461128
12.3
245007
184189
429196
13.9
BxrIbNW7f3K
Itambo
60206
53830
113974
20.7
59009
52811
111762
23.1
58243
52139
110310
17.3
58014
52207
110151
17.6
60815
54380
115111
19.8
60648
53659
114206
18.2
51151
47880
99033
21.2
41440
39577
81016
21.7
68791
66503
135293
22.7
73056
70325
143382
22
73629
70894
144524
12.4
70410
64749
135159
12.5
75221
56581
131801
12.2
60738
60791
121529
13.5
70895
61882
132777
15.2
SQT8xjbvWwf
Ezhi-tezhi
43316
42961
86309
21
38253
37439
75709
21.2
53334
51972
105329
18.4
45577
45721
91349
17.8
46149
45840
92033
18.7
44235
43929
88208
21.3
20360
19571
39932
19.2
20241
19974
40215
20.6
29049
28792
57841
17.6
29248
28758
58006
18.6
28260
27756
56016
17.2
25545
27201
52746
18.8
25659
26793
52452
19.4
23434
24550
47984
17.2
24982
25295
50278
16.3
ntoX1PkiWri
Ilanga
39262
37358
76621
19.3
40044
38649
78701
20.2
41564
39172
80727
19
41392
39070
80453
18.7
41508
39270
80770
15.5
42314
39919
82223
10.9
39941
39287
79228
15
38917
38045
76962
15.9
39040
39384
78424
18.8
39520
39049
78569
17.7
40754
40597
81351
18.3
46524
41900
88424
17.3
54068
46751
100819
19.3
64557
50394
114952
21.9
56918
41369
98287
24
SvFNQpplnch
Irundu
41723
38091
79785
18.4
43345
38834
82137
21.2
44064
40065
84088
18.8
43897
40288
84150
20
44647
41206
85821
19.7
45958
41839
87749
20.2
30223
29491
59714
17.8
30029
30017
60047
20.1
34387
34904
69291
21.8
34115
34323
68439
27.8
32550
32630
65180
23.3
30169
35643
65813
22.7
32508
29253
61761
19.6
33079
30747
63826
16.9
36354
27886
64240
19.4
NLjvK1QsrN3
Zobwe
68976
65286
134256
16.9
70717
66503
137205
22.8
72297
68143
140423
17.6
72625
68558
141169
18.5
73523
69672
143185
17.8
74485
71308
145801
18.3
74706
72935
147641
18
75411
74447
149858
15.6
74552
73697
148248
13.5
72687
72264
144951
18.4
71591
71303
142894
16.9
62110
84182
146292
13.7
69080
90774
159854
11.8
58136
102480
160616
11.6
60655
112231
172886
11.4
hywxVKv48Xp
Afue
52065
48359
100403
16.2
52059
48619
100661
12
52813
49667
102465
15.8
53526
50534
104050
18.4
54076
51129
105195
15.3
54833
52506
107345
15.3
47968
45428
93397
14.9
47793
45161
92953
13.5
51881
50619
102500
14.9
52210
50859
103069
12.8
47997
47388
95386
16.7
46462
38863
85325
17.1
39956
33855
73811
18.5
32659
34539
67199
22.1
38938
40308
79246
23.2
QTtxiWj8ONP
Embe
44191
44208
88438
22.1
44862
44626
89520
18.4
45647
45329
91014
18
46251
45707
91996
18.9
47156
46207
93393
18.3
48513
47973
96529
19.3
19871
20162
40032
18.1
20207
20830
41037
22.6
17181
17473
34654
19.4
17381
18022
35403
17.8
17447
17663
35110
17.6
16251
16120
32371
14.3
13111
16234
29345
17
10795
16609
27404
19
9870
19882
29752
17.7
Oapn6R4rt3D
Ienge
45749
43132
88875
19.5
46372
43244
89601
20
47251
43696
90919
17.6
47497
44443
91922
16.8
50903
47897
98785
17.1
48164
45299
93449
13.2
56005
54088
110093
14.9
55970
54585
110555
14.7
50231
49359
99591
19.2
51345
50087
101432
15.6
49516
48123
97640
19.8
41502
52584
94086
22.9
46480
47181
93662
20.5
46726
56442
103167
18.4
43100
55627
98727
20.7
idR0pJWLqcR
Funabuli
69454
65596
135042
20.1
67792
63091
130857
20.8
71984
67077
139029
19.5
72715
68038
140724
18.1
74718
68937
143602
16.1
76357
70161
146455
17.1
91749
89408
181157
18
93096
92563
185659
18.1
86496
85278
171774
18.4
87701
87432
175134
19
84150
83384
167534
20.7
99309
79972
179282
20.1
98410
92248
190658
18.4
98251
96108
194359
20
82404
82479
164883
19.9
jCJxK8sFH40
Pili
40322
37127
77427
17.9
38697
35747
74425
18.3
39905
37213
77101
18.6
40098
37272
77349
20.5
41417
38862
80265
21.9
41089
38521
79595
22.4
24835
23996
48831
17.6
25468
24388
49855
22.9
23145
22402
45546
20.4
18959
18324
37283
25.5
21672
21166
42838
20.9
20045
17459
37504
22.7
22825
16121
38946
18.2
25150
13315
38465
19
25727
11500
37227
20.2
ydyJb1RAy4U
Ama
53131
49864
102983
17.8
54102
50457
104541
18.8
55868
52772
108629
16.4
56458
53491
109942
16.6
57131
54567
111701
20.1
58741
55824
114561
20.6
58821
57242
116064
19.8
60881
57907
118787
19.8
59867
57145
117011
16
60854
57961
118815
18.4
61186
58969
120156
20
59417
52603
112019
22.2
49048
53876
102925
21.7
48659
48973
97632
17.9
44180
40980
85159
15.1
nO68NTtcdw2
Nsali
43774
40176
83924
18.7
43864
41010
84860
24.6
45064
41631
86667
19.4
45831
41776
87565
19.3
47708
43837
91507
17.1
48050
43752
91753
23.2
35210
33492
68703
21.6
36236
34510
70745
20.5
30377
29046
59423
21.4
30716
29371
60087
25.9
31012
29510
60523
17.7
31506
32908
64414
16.4
33228
33203
66431
17.5
35994
31242
67236
19
31786
29832
61617
17.6
FuvOOwvBw0p
Soka
47198
44934
92133
26.3
48402
45723
94119
23.9
50237
47581
97811
21.6
50511
47479
97976
23.8
51644
48765
100398
23.2
52933
49975
102896
18.3
49704
48828
98532
17.9
51273
50723
101996
22.2
50680
49951
100631
20.7
51387
50356
101743
20.8
49360
47934
97295
20.2
53407
40728
94135
22.3
58185
46204
104390
18.1
52587
43979
96565
16.1
46290
51781
98071
17.8
R22tRIhdm5Y
Chibiya
43692
40928
84609
23.4
44158
40481
84611
25.7
45432
42704
88123
22.9
46664
43172
89807
27.1
47827
43650
91431
22.4
49011
44397
93352
28.9
27986
26961
54946
20.8
28103
27529
55632
20
29904
28378
58281
18.3
27670
26223
53893
18.7
26429
25039
51470
21.2
27443
30018
57460
19.6
23001
35855
58856
21.1
18508
34629
53137
24.7
21629
37207
58836
24.1
Do not change or re-format the header row as the code requires these names to be precise in order to parse the csv. Do not use commas in any cell and do not have any empty rows.
Each row will represent a unique set of statistics for a second level administrative division in your country.
Notice that the statisticalD and name columns are identical to those created in the districts.csv file in the previous step.
The next 4 columns are repeated for however many years you are able to source data from your statistical department. Each year MUST contain a minimum of 4 columns:
  • male_population_<year>
  • female_population_<year>
  • population_<year>
  • crude_birth_rate_<year>
Be careful when adding extra years to not have any spaces in your headers between the name of the header and the year key.
You can remove year blocks of 4 columns if you do not have this data, but make sure the available years that you do have run consecutively without any time gaps.
Note: crude death rate is applied globally in the "required settings" step later in this documentation.
Note: At this stage, there is not too much point in you entering data more than 5 years back in time as OpenCRVS can only calculate completeness rates based on the registration information it has available. In future versions, we plan to introduce legacy data import to include historical registrations. At that point, a history of population data becomes really powerful.
Copy link