Urban Informal Settlements Survey 2016
Bangladesh, 2016
Get Microdata
Reference ID
BGD_2016_UIS_v01_M
Producer(s)
Monica Yanez-Pagans
Metadata
Created on
Jul 20, 2017
Last modified
Jul 20, 2017
Page views
79926
Downloads
45600
District code prior to coming to slum area (a1_9)
Data file: hh_sec_1_000_rev
Overview
Valid:
2516
Invalid:
0
Minimum:
1
Maximum:
99
Type:
Discrete
Decimal:
0
Start:
41
End:
42
Width:
2
Range:
1 - 99
Format:
Numeric
Questions and instructions
Literal question
District code and upazila name prior to coming to slum area
Categories
| Value | Category | Cases | |
|---|---|---|---|
| 1 | Dinajpur | 10 |
0.4%
|
| 2 | Panchagarh | 1 |
0%
|
| 3 | Thakurgaon | 0 |
0%
|
| 4 | Rangpur | 25 |
1%
|
| 5 | Nilfamari | 9 |
0.4%
|
| 6 | Lalmonirhaat | 0 |
0%
|
| 7 | Kurigram | 15 |
0.6%
|
| 8 | Gaibandha | 7 |
0.3%
|
| 9 | Bogra | 7 |
0.3%
|
| 10 | Jaipurhaat | 2 |
0.1%
|
| 11 | Rajshahi | 9 |
0.4%
|
| 12 | Naogaon | 0 |
0%
|
| 13 | Chapainababgan | 2 |
0.1%
|
| 14 | Natore | 4 |
0.2%
|
| 15 | Pabna | 6 |
0.2%
|
| 16 | Sirajganj | 3 |
0.1%
|
| 17 | Dhaka | 138 |
5.5%
|
| 18 | Gazipur | 23 |
0.9%
|
| 19 | Manikgank | 2 |
0.1%
|
| 20 | Munshiganj | 20 |
0.8%
|
| 21 | Narayanganj | 15 |
0.6%
|
| 22 | Narshingdhi | 13 |
0.5%
|
| 23 | Faridpur | 13 |
0.5%
|
| 24 | Madaripur | 20 |
0.8%
|
| 25 | Shariatpur | 18 |
0.7%
|
| 26 | Rajbari | 0 |
0%
|
| 27 | Gopalganj | 1 |
0%
|
| 28 | Mymensingh | 39 |
1.6%
|
| 29 | Netrokona | 54 |
2.1%
|
| 30 | Kishoreganj | 126 |
5%
|
| 31 | Tangail, | 3 |
0.1%
|
| 32 | Jamalpur | 9 |
0.4%
|
| 33 | Sherpur | 9 |
0.4%
|
| 34 | Khulna | 3 |
0.1%
|
| 35 | Shathkhira | 9 |
0.4%
|
| 36 | Bagherhaat | 4 |
0.2%
|
| 37 | Jessore | 3 |
0.1%
|
| 38 | Narail | 0 |
0%
|
| 39 | Magura | 0 |
0%
|
| 40 | Jhinaidaha | 2 |
0.1%
|
| 41 | Kushtia, | 2 |
0.1%
|
| 42 | Chuadanga | 0 |
0%
|
| 43 | Meherpur | 0 |
0%
|
| 44 | Barisal | 34 |
1.4%
|
| 45 | Jhalakathi | 0 |
0%
|
| 46 | Pirojpur | 5 |
0.2%
|
| 47 | Bhola | 121 |
4.8%
|
| 48 | Patuakhali | 57 |
2.3%
|
| 49 | Barguna | 8 |
0.3%
|
| 50 | Chattagram | 1 |
0%
|
| 51 | Cox?sbazar | 0 |
0%
|
| 52 | Rangamati | 0 |
0%
|
| 53 | Khagrachori | 2 |
0.1%
|
| 54 | Bandarban | 0 |
0%
|
| 55 | Noakhali | 7 |
0.3%
|
| 56 | Lakhhipur | 8 |
0.3%
|
| 57 | Feni | 0 |
0%
|
| 58 | Comilla | 13 |
0.5%
|
| 59 | Chandpur | 9 |
0.4%
|
| 60 | Brammanbaria | 8 |
0.3%
|
| 61 | Sylhet | 2 |
0.1%
|
| 62 | Moulavibazar | 2 |
0.1%
|
| 63 | Habiganj, | 5 |
0.2%
|
| 64 | Sunamganj | 32 |
1.3%
|
| 77 | Abroad | 3 |
0.1%
|
| 88 | Come from other slum in the district | 1034 |
41.1%
|
| 99 | Born in this slum | 539 |
21.4%
|
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.
Interviewer instructions
If born in this slum write ‘99’, if came from different slum of this district write ‘88’ in district code and leave blank upazila name
Question post text
If born in this slum write ‘99’, if came from different slum of this district write ‘88’ in district code and leave blank upazila name