Location Quotient

Introduction

Location Quotients are proportional measures which show how much the incidence of something in an area or spatial unit differs from a larger area or region in which it sits. It may be used, for example, to show the reliance on an industry within a city compared to the country as a whole, or the percentage of people residing within a local government area, compared to the state as a whole.

The standard output of the LQ is a ratio, indicating how much more or less the spatial unit’s incidence is, compared to the larger area.

For example, an LQ of 1 indicates no difference between area of interest and the overall incidence; an LQ of 0.65 would indicate an incidence in the area of interest is 35% lower than the overall incidence; an LQ of 1.35 would indicate an incidence 35% higher than the overall incidence.

See “Advanced” for the formulae used in this implementation of LQ.

Inputs

For this analysis, we will calculate the location quotients for two industries in Western Australia. We first need to select our area and data:

  • Select Western Australia as your area
  • Select LGA Industry Sectors 2001 – 2006 – 2011 for Australia (UQeresearch) as your dataset
  • Select LGA Code; LGA Name; Agriculture, forestry and fishing (2011); Mining (2011); and Total persons (2011) as your attributes (click Add and Open) to load the data

We now want to open the Location Quotient tool (Tools → Indicies → Location Quotient). Enter your parameters as shown below (the meanings of all these inputs are explained below), and click Add and Run.

locationquotientinput

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  • Dataset Input: The dataset that you would like to run the location quotient tool on
  • Location Quotient Key Column: The region or spatial unit key for the areas of interest
  • Location Quotient Region Variable: The attributes you would like to analyse, to see if their incidence is higher or lower in each area than overall
  • Location Quotient Region Total: The total count for each region (usually people)
  • Location Quotient Number of Intervals: This is used to specify how many “groups” of LQ values you want to create – the tool will produce an LQ value for each area; this input allows you to group those into a set number of classes or groups.
  • Location Quotient Upper Limit of Intervals: This is used to specify the boundaries of the groups that you want to create. For example if you wanted the group of the lowest LQ values to be between 0 and 0.5, you would specify “0.5” and so on. Each value needs to be separated by a comma, and you need to enter the same number of values as the number of intervals that you specified in the parameter above i.e. if you set the number of intervals to 4, you need to have 4 upper limits, each separated by a comma.

Outputs

Once your analysis has run a dialogue box will open. Click Display to open the output table. For the Western Australian example used here, it should look something like the image below:

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The output of the Location Quotient tool is a table. The table has the Location Quotient for each variable (xxx_LQ). In the example here, these are Agriculture, forestry and fishing 2011 (a_f_f_11) and Mining 2011 (min_11). In addition, each of the variables is placed into a group, based on it’s falling into one of the interval ranges (xxx_range).

These tables can be downloaded as a csv to your desktop

Advanced

The following formula is used in the implementation of the Location Quotient.

\(LQ = \left({{p_i}/{P_i}}\over{{p_n}/{P_n}}\right)\)

Where:
\(p_i\) = count of the phenomenon in spatial unit i
\(P_i\) = total population within spatial unit i
\(p_n\) = count of the phenomenon in larger area n
\(P_n\) = total population within larger area n

References

  1. Stimson, R., S. Baum, J. Mangan, Y. v. Gellecum, T-K Shyy and T. Yigitcanlar (2003) Analysing Spatial Patterns in the Characteristics of Work, Employment and Skills in Australia’s Capital Cities and Across Its Regional Cities and Towns: Modelling Community Opportunity and Vulnerability, Prepared for The Australian National Training Authority (ANTA) National Project.
  2. Tidswell, W.V. and S.M. Barker (1971) Quantitative Methods: An Approach to Socio-Economic Geography, University Tutorial Press Ltd., London.