Using Geospatial Data (AQA A Level Geography)

• 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

A choropleth map showing the proportion of people born in the UK from the 2021 census

• 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

Proportional Symbols map showing the results of the 2017 general election 

• 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

• 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
Data is open source so can be used by everyone  Very large sample so statistically reliable  All variables can be compared across space  Some variables can be compared across time	Different questions are asked every decade so not everything can be compared across time  People complete the survey about themselves so much of the data is self-identified, this can be misleading when it comes to data on health  Completing the survey requires reading and writing skills  Some people do not fill it out

• 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

the index of multiple deprivation for the Leeds area in 2019  

• 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