Newcastle upon Tyne County Borough (CB/MB) : Persons Aged over 10 by Sex, Age & 1911 Occupational Order

Persons Aged over 10 by Sex, Age & 1911 Occupational Order

Data cube chart.

Data cube chart.

Data for 1911:

Sex = Male 1911 Occupation Tables Age Groups
1911 Occupational Classification 10-12 13 14 15 16 17 18 19 20-24 25-34 35-44 45-54 55-64 65 up
Government 0 Show data context 2 Show data context 55 Show data context 65 Show data context 35 Show data context 24 Show data context 21 Show data context 34 Show data context 248 Show data context 536 Show data context 415 Show data context 242 Show data context 77 Show data context 24 Show data context
Defence 0 Show data context 0 Show data context 0 Show data context 0 Show data context 1 Show data context 63 Show data context 126 Show data context 77 Show data context 293 Show data context 288 Show data context 152 Show data context 19 Show data context 4 Show data context 0 Show data context
Professional Occupations 1 Show data context 2 Show data context 24 Show data context 25 Show data context 63 Show data context 62 Show data context 67 Show data context 52 Show data context 379 Show data context 770 Show data context 643 Show data context 395 Show data context 221 Show data context 93 Show data context
Domestic Services 0 Show data context 1 Show data context 6 Show data context 18 Show data context 18 Show data context 21 Show data context 19 Show data context 28 Show data context 150 Show data context 281 Show data context 233 Show data context 215 Show data context 131 Show data context 56 Show data context
Commercial Occupations 0 Show data context 3 Show data context 55 Show data context 130 Show data context 211 Show data context 264 Show data context 241 Show data context 219 Show data context 1,104 Show data context 1,796 Show data context 1,198 Show data context 799 Show data context 454 Show data context 178 Show data context
Transport 38 Show data context 66 Show data context 707 Show data context 604 Show data context 341 Show data context 293 Show data context 212 Show data context 222 Show data context 1,212 Show data context 2,823 Show data context 2,226 Show data context 1,443 Show data context 832 Show data context 279 Show data context
Agriculture 0 Show data context 0 Show data context 4 Show data context 7 Show data context 9 Show data context 2 Show data context 11 Show data context 17 Show data context 48 Show data context 99 Show data context 82 Show data context 71 Show data context 77 Show data context 49 Show data context
Mines & Quarries
Metals, Machines 0 Show data context 2 Show data context 127 Show data context 210 Show data context 223 Show data context 200 Show data context 200 Show data context 164 Show data context 722 Show data context 1,295 Show data context 905 Show data context 534 Show data context 258 Show data context 92 Show data context
Jewelry, Instruments 0 Show data context 1 Show data context 220 Show data context 458 Show data context 700 Show data context 731 Show data context 786 Show data context 818 Show data context 3,346 Show data context 6,122 Show data context 5,387 Show data context 3,652 Show data context 2,187 Show data context 773 Show data context
Building 0 Show data context 0 Show data context 8 Show data context 10 Show data context 18 Show data context 17 Show data context 18 Show data context 13 Show data context 59 Show data context 127 Show data context 103 Show data context 77 Show data context 44 Show data context 13 Show data context
Wood, Furniture 0 Show data context 0 Show data context 32 Show data context 63 Show data context 131 Show data context 125 Show data context 106 Show data context 127 Show data context 787 Show data context 1,791 Show data context 1,490 Show data context 1,146 Show data context 768 Show data context 259 Show data context
Brick, Cement 0 Show data context 1 Show data context 15 Show data context 41 Show data context 39 Show data context 57 Show data context 54 Show data context 56 Show data context 289 Show data context 551 Show data context 417 Show data context 317 Show data context 227 Show data context 85 Show data context
Chemicals 0 Show data context 1 Show data context 25 Show data context 23 Show data context 37 Show data context 27 Show data context 41 Show data context 27 Show data context 112 Show data context 228 Show data context 161 Show data context 99 Show data context 62 Show data context 23 Show data context
Skins, Leather 0 Show data context 0 Show data context 20 Show data context 21 Show data context 22 Show data context 29 Show data context 32 Show data context 25 Show data context 128 Show data context 307 Show data context 220 Show data context 174 Show data context 98 Show data context 40 Show data context
Paper, Prints 0 Show data context 0 Show data context 6 Show data context 5 Show data context 17 Show data context 9 Show data context 13 Show data context 15 Show data context 55 Show data context 130 Show data context 110 Show data context 109 Show data context 86 Show data context 37 Show data context
Textile Fabrics 0 Show data context 1 Show data context 17 Show data context 39 Show data context 46 Show data context 28 Show data context 32 Show data context 39 Show data context 172 Show data context 356 Show data context 299 Show data context 216 Show data context 107 Show data context 42 Show data context
Dress 0 Show data context 1 Show data context 15 Show data context 22 Show data context 38 Show data context 47 Show data context 38 Show data context 25 Show data context 141 Show data context 223 Show data context 174 Show data context 130 Show data context 83 Show data context 27 Show data context
Food, Drink 7 Show data context 9 Show data context 33 Show data context 49 Show data context 82 Show data context 74 Show data context 69 Show data context 65 Show data context 297 Show data context 576 Show data context 552 Show data context 401 Show data context 255 Show data context 149 Show data context
Gas, Water 0 Show data context 2 Show data context 81 Show data context 130 Show data context 197 Show data context 226 Show data context 221 Show data context 217 Show data context 919 Show data context 1,730 Show data context 1,319 Show data context 801 Show data context 472 Show data context 167 Show data context
Other & Undefined 0 Show data context 0 Show data context 5 Show data context 8 Show data context 8 Show data context 22 Show data context 14 Show data context 12 Show data context 106 Show data context 248 Show data context 257 Show data context 174 Show data context 124 Show data context 27 Show data context
Unoccupied 30 Show data context 45 Show data context 49 Show data context 62 Show data context 104 Show data context 115 Show data context 124 Show data context 139 Show data context 605 Show data context 1,297 Show data context 1,141 Show data context 869 Show data context 502 Show data context 220 Show data context
Sex = Female 1911 Occupation Tables Age Groups
1911 Occupational Classification 10-12 13 14 15 16 17 18 19 20-24 25-34 35-44 45-54 55-64 65 up
Government 0 Show data context 0 Show data context 0 Show data context 3 Show data context 3 Show data context 4 Show data context 9 Show data context 13 Show data context 82 Show data context 103 Show data context 63 Show data context 19 Show data context 3 Show data context 3 Show data context
Defence
Professional Occupations 1 Show data context 0 Show data context 3 Show data context 8 Show data context 20 Show data context 47 Show data context 67 Show data context 71 Show data context 544 Show data context 877 Show data context 382 Show data context 192 Show data context 80 Show data context 23 Show data context
Domestic Services 4 Show data context 5 Show data context 177 Show data context 388 Show data context 515 Show data context 618 Show data context 719 Show data context 750 Show data context 2,964 Show data context 2,296 Show data context 1,190 Show data context 836 Show data context 415 Show data context 161 Show data context
Commercial Occupations 0 Show data context 0 Show data context 14 Show data context 48 Show data context 75 Show data context 108 Show data context 130 Show data context 164 Show data context 554 Show data context 366 Show data context 83 Show data context 31 Show data context 13 Show data context 3 Show data context
Transport 4 Show data context 0 Show data context 40 Show data context 41 Show data context 29 Show data context 24 Show data context 27 Show data context 18 Show data context 42 Show data context 41 Show data context 12 Show data context 9 Show data context 5 Show data context 3 Show data context
Agriculture 0 Show data context 0 Show data context 1 Show data context 2 Show data context 2 Show data context 1 Show data context 1 Show data context 4 Show data context 14 Show data context 13 Show data context 5 Show data context 6 Show data context 6 Show data context 3 Show data context
Mines & Quarries
Metals, Machines 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 1 Show data context 1 Show data context 3 Show data context 2 Show data context 3 Show data context 5 Show data context 1 Show data context
Jewelry, Instruments 0 Show data context 0 Show data context 15 Show data context 30 Show data context 51 Show data context 65 Show data context 73 Show data context 45 Show data context 187 Show data context 124 Show data context 32 Show data context 7 Show data context 0 Show data context 2 Show data context
Building 0 Show data context 0 Show data context 2 Show data context 4 Show data context 4 Show data context 3 Show data context 6 Show data context 6 Show data context 13 Show data context 14 Show data context 6 Show data context 1 Show data context 2 Show data context 2 Show data context
Wood, Furniture 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 1 Show data context 1 Show data context 0 Show data context 0 Show data context
Brick, Cement 0 Show data context 0 Show data context 6 Show data context 10 Show data context 8 Show data context 16 Show data context 17 Show data context 12 Show data context 54 Show data context 36 Show data context 16 Show data context 13 Show data context 10 Show data context 5 Show data context
Chemicals 0 Show data context 0 Show data context 14 Show data context 34 Show data context 38 Show data context 54 Show data context 48 Show data context 46 Show data context 135 Show data context 72 Show data context 31 Show data context 14 Show data context 8 Show data context 4 Show data context
Skins, Leather 0 Show data context 0 Show data context 3 Show data context 10 Show data context 19 Show data context 20 Show data context 26 Show data context 25 Show data context 97 Show data context 67 Show data context 19 Show data context 6 Show data context 4 Show data context 2 Show data context
Paper, Prints 0 Show data context 0 Show data context 10 Show data context 13 Show data context 19 Show data context 27 Show data context 30 Show data context 30 Show data context 74 Show data context 58 Show data context 51 Show data context 26 Show data context 12 Show data context 3 Show data context
Textile Fabrics 1 Show data context 0 Show data context 26 Show data context 51 Show data context 49 Show data context 45 Show data context 56 Show data context 43 Show data context 179 Show data context 118 Show data context 47 Show data context 29 Show data context 10 Show data context 7 Show data context
Dress 0 Show data context 0 Show data context 19 Show data context 38 Show data context 48 Show data context 60 Show data context 58 Show data context 53 Show data context 244 Show data context 197 Show data context 64 Show data context 44 Show data context 25 Show data context 9 Show data context
Food, Drink 2 Show data context 2 Show data context 158 Show data context 257 Show data context 259 Show data context 299 Show data context 238 Show data context 234 Show data context 902 Show data context 825 Show data context 423 Show data context 243 Show data context 132 Show data context 70 Show data context
Gas, Water 1 Show data context 1 Show data context 45 Show data context 120 Show data context 150 Show data context 183 Show data context 204 Show data context 213 Show data context 885 Show data context 858 Show data context 578 Show data context 438 Show data context 314 Show data context 184 Show data context
Other & Undefined 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context 3 Show data context 1 Show data context 0 Show data context 0 Show data context 0 Show data context 0 Show data context
Unoccupied 0 Show data context 0 Show data context 27 Show data context 57 Show data context 66 Show data context 58 Show data context 77 Show data context 63 Show data context 240 Show data context 213 Show data context 161 Show data context 105 Show data context 99 Show data context 40 Show data context

Click on the triangles for all about a particular number

nCube definition

This website exists to help people doing personal research projects on particular areas within a locality. So long as you are using our data for only a small number of units, you are not making money out of what you are doing, and you are not systematically re-publishing our data, you do not need to request permission from us, but you do need to acknowledge us as your source with the wording:

"This work is based on data provided through www.VisionofBritain.org.uk and uses historical material which is copyright of the Great Britain Historical GIS Project and the University of Portsmouth".

Where the above statement is included in a web page or similar online resource, the reference to "www.VisionofBritain.org.uk" must be a working hyperlink.

nCube definition


This classification is designed to permit data from the 1801 Crop Returns to be included with data from the annual Agricultural Census from 1866 onwards. The 1801 Returns were highly simplified, and varied in what they reported from one parish to another, so in practice we can consistently identify only three individual crops (wheat, rye and potatoes) and two pairings ("Barley and Oats", and "Peas and Beans"). Everything else has to be included in "Other". The 1801 returns also provided limited geographical cov...


erage: a great many parishes were missing from the data, and some additional parish names could not be identified, matched more than one parish within the named county, or lacked information on the county they were in. Nottinghamshire is completely missing. County totals include data for parishes that could not be identified. The national total for England includes parishes with missing county information. The national total for England is also used as the total for England and Wales, to permit the web site to present comparisons with national totals, even though the 1801 returns contain no data on Wales.


How to reference this page:

GB Historical GIS / University of Portsmouth, Newcastle upon Tyne County Borough (CB/MB) through time | Historical Statistics on Industry | Persons Aged over 10 by Sex, Age & 1911 Occupational Order, A Vision of Britain through Time.

URL: https://www.visionofbritain.org.uk/unit/10108913/cube/OCC_ORD1911_AGESEX

Date accessed: 05th June 2026