A description of the MLID
Posted: Sat Feb 08, 2025 7:20 am
Whereas other approaches may take hours or days to run and/or are limited to small data sets and study regions, the MLID operates in the order of minutes on small area census data for the whole of England and Wales.
Even if it used as a pre-cursor to more advanced approaches, it offers more ‘interactivity’ with the data at the early stages of analysis, helping to judge whether more complex approaches are greece rcs data warranted. its theoretical derivation, its interpretation and its implementation in R are available in this open access article.
To consider its usefulness, consider the following example. Standard indices of segregation treat the four patterns shown in Figure 1 as the same – more accurately, they don’t consider them at all: numerically the amount of segregation is the same in each case. But clearly the patterns and therefore the nature of the segregation is not the same in all four occurrences. The MLID captures the differences – it is a measure of clustering as well as unevenness across the study region.
Figure 1. Standard indices of segregation cannot differentiate between these patterns but the multilevel index of dissimilarity (MLID) can.
Because it is able to differentiate between the numeric and geographic scales of segregation, the MLID can be used to quantify a measure of spatial diffusion, as in the example below.
Even if it used as a pre-cursor to more advanced approaches, it offers more ‘interactivity’ with the data at the early stages of analysis, helping to judge whether more complex approaches are greece rcs data warranted. its theoretical derivation, its interpretation and its implementation in R are available in this open access article.
To consider its usefulness, consider the following example. Standard indices of segregation treat the four patterns shown in Figure 1 as the same – more accurately, they don’t consider them at all: numerically the amount of segregation is the same in each case. But clearly the patterns and therefore the nature of the segregation is not the same in all four occurrences. The MLID captures the differences – it is a measure of clustering as well as unevenness across the study region.
Figure 1. Standard indices of segregation cannot differentiate between these patterns but the multilevel index of dissimilarity (MLID) can.
Because it is able to differentiate between the numeric and geographic scales of segregation, the MLID can be used to quantify a measure of spatial diffusion, as in the example below.