Value |
Category |
1 |
magomeni |
2 |
ukonga |
3 |
kigamboni |
4 |
makurumla |
5 |
pugu |
6 |
vijibweni |
7 |
ndugumbi |
8 |
msongola |
9 |
kibada |
10 |
tandale |
11 |
tabata |
12 |
kisarawe ii |
13 |
mwananyamala |
14 |
kinyerezi |
15 |
somangila |
16 |
msasani |
17 |
ilala |
18 |
kimbiji |
19 |
kinondoni |
20 |
mchikichini |
21 |
mbagala |
22 |
mzimuni |
23 |
vingunguti |
24 |
chamazi |
25 |
kipawa |
26 |
kigogo |
27 |
yombo vituka |
28 |
mabibo |
29 |
buguruni |
30 |
charambe |
31 |
manzese |
32 |
kariakoo |
33 |
toangoma |
34 |
ubungo |
35 |
jangwani |
36 |
miburani |
37 |
kibamba |
38 |
gerezani |
39 |
temeke |
40 |
goba |
41 |
kisutu |
42 |
mtoni |
43 |
kawe |
44 |
mchafukoge |
45 |
keko |
46 |
kunduchi |
47 |
upanga mashariki |
48 |
kurasini |
49 |
mbweni |
50 |
upanga magharibi |
51 |
azimio |
52 |
bunju |
53 |
kivukoni |
54 |
tandika |
55 |
kiwalani |
56 |
makuburi |
57 |
sandali |
58 |
segerea |
59 |
mburahati |
60 |
changombe |
61 |
kitunda |
62 |
makumbusho |
63 |
mbagala kuu |
64 |
chanika |
65 |
sinza |
66 |
makangarawe |
67 |
kijitonyama |
68 |
kivule |
69 |
pemba mnazi |
70 |
kimara |
71 |
gongolamboto |
72 |
mjimwema |
73 |
majohe |
74 |
mikocheni |
75 |
tungi |
76 |
kimanga |
77 |
mbezi |
78 |
kijichi |
79 |
hananasifu |
80 |
mianzini |
81 |
saranga |
82 |
kiburugwa |
83 |
kwembe |
84 |
buza |
85 |
msigani |
86 |
kilakala |
87 |
mbezi juu |
88 |
makongo |
89 |
mabwepande |
90 |
wazo |
Sysmiss |
|
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.