These four CSVs (located in this repo) were used for the production of the interactive plotly graphs. There data was originally taken from the London Datastore and subsequently modified to remove unnecessary data (identified as outside of the data range scope for this task)
- ChildPovertyRatesByBorough.csv
- ChildImmunisationRatesBy5thBirthdayByBorough.csv
- FreeSchoolMealsAndChildDevelopmentByBorough.csv
- FreeSchoolMealsUptakeByBorough.csv
The following graphs were created using a combination of the pandas and plotly modules in Python. The data used compares all boroughs within London.
- Data shows there is a strong positive correlation
- Areas with higher child poverty rates tend to have increased uptake in Free School Meals
- Tower Hamlets: 48% child poverty (AHC), 31.3% free school meal uptake in primary schools
- Richmond upon Thames: 12% child poverty (AHC), 7.7% free school meal uptake in primary schools
- Data shows there is a negative correlation
- Areas with higher child poverty rates tend to have lower immunisation rates
- While the correlation is not super strong, the trend is generally consistent across boroughs
- Data shows there is a strong correlation
- For all boroughs, children who are on Free School Meals have a lower Child Development score compared to children who are not on Free School Meals.
- Across all boroughs, the average percentage difference (disparity) between those on FSM and those not, is 12.09%
- Pupils eligible for free school meals: 59% achieve a good level of development
- All other pupils: 80% achieve a good level of development
Following the creation of the Child Development by Free School Meal Eligibility graph, I created a Python script to display the average disparity in 'good level of development' scores across all borough (see FSMVsChildDevelopmentDisparityCalc)
This result of this was 12.09 percentage points
