Traditional Measures
|
 |
| Assume that we want to target only the worst 5% of cases. Let us look initially at a map of the number of males unemployed in the pre-1984 County of Humberside. The blue areas show the worst 5% of areas. The largest blob is Hull to the north of the Humber Estuary. The northern most blob of blue is Bridlington.The distribution of worst cases is very similar to the distribution of population with concentrations in the urban centres. Numbers unemployed is known to have an urban bias because the numbers are constrained by the number of economically active males. This is why ratios are widely used in comparative studies. They are believed to standardise data against the base population. |
|
|
 |
| However, if we look at a map of % unemployed males, it is quite obvious that we now have a rural bias. This was pointed out by a Polish geomorphologist, Choynowski, in 1959. It is easier to see the urban and rural biases if we look at the scatterplot below of % unemployed against numbers unemployed.
|
|
|
 |
| Here we note two extreme ratios which stand out as outliers. If we were able to point to such outliers and locate them on the above maps, we will see that they are the ones with the two largest squares in the ratio map. If we could also retrieve the topographic maps for 1971, we will soon find that these squares had male borstals in 1971. So the outliers are due to errors in data entry.Such errors in data entry are not uncommon in large data sets. We need procedures for detecting and correcting them. Instead, data are aggregated up for for wards, districts and counties, where errors remain averaged and hidden in many analyses. |
|
If we ignore the 2 extreme values, we see two trends reflecting the existence of rural and urban unemployment. They are measured respectively by % unemployed and number unemployed .
|
|
