Using Geospatial Data (AQA A Level Geography)
Revision Note
Written by: Alex Lippa
Reviewed by: Bridgette Barrett
Value of Big Data
Geospatial data is data information that has a location
Much of this data come from big data sets
The census is an example of a big data set as it surveys the entire country and needs computational analysis to start making sense of the patterns
The census asks a number of questions which creates lots of complexity in the data, another feature of big data sets
Understanding geospatial data and its different forms allows geographers to infer spatial patterns and see the relationship between people, environment and place
A spatial pattern simply means that there is a pattern in the data based on the place
For example, many data indicators in the indicators reveal a north-south divide which is a spatial pattern
Geospatial data can be qualitative or quantitative
When analysing geospatial data it is important to compare one source with another to check for reliability
Maps are an excellent example of geospatial data but when using their consideration must be given to:
Whether the map is choropleth or proportional
How this will affect the visual representation of the spread
The choropleth map from the UK 2021 census shows the proportion of people born in the UK. A map like this has abrupt boundaries that suggest a significant change as soon as a country boundary is crossed which is not likely to be the case in reality
The proportional symbols map plots the results of the 2017 general election, this type of geospatial data illustrates the difference between many places very well and shows data associated with a more specific location than a choropleth map can
However, the proportional symbols make it very difficult to calculate the actual value, even if there was a key, and the size of the symbols can obstruct the map underneath, making the positioning less accurate
Examiner Tips and Tricks
When approaching your data analysis six markers you have to look to see if you find a relationship between the variable or figures you are shown. It is not just about describing what you see in the figure but analysing if a relationship exists, if it is a strong relationship and if there are any outliers or anomalies to the relationship.
Things to look for:
The general pattern, is a headline that could describe the figure in one statement
The most and least, are they the same in both figures?
If there is a relationship between the two figures is it a positive or negative one?
How strong the relationship is
The outliers or anomalies that do not fit the pattern or relationship
Quantitative Sources of Data
Quantitative sources of data are numerical
They are objectively measured and can therefore be compared across space and often across time as well
Much of the quantitative data we have in the UK comes from local councils and the census
The census is a nationwide survey that is taken every ten years to collect information that creates a picture of all the households and people in England and Wales
Scotland has a separate census
The first modern census was taken in 1841
Advantages of the Census | Disadvantages of the Census |
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Another very popular quantitative data source to understand place characteristics is the Index of Multiple Deprivation (IMD)
To create the IMD seven components of deprivation are considered and put together to create a single score of deprivation
These are: income, employment, education, health, crime, barriers to housing and services and living environment
A composite measure like this captures a full range of variables that contribute to deprivation in an area and recognises that one measure is not enough to truly represent a place
It is easy to see which areas are in the most deprived 10% of the country and then in the other deciles
IMD maps are choropleth maps using small areas called Lower Super Output Areas
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