synthetic_population_dev

This repository contains the following jupyter notebooks:

SPENSER_notebook_1:

• Merges all persons and households of the study area in two dataframes (df_persons_NE and df_households_NE),
• Generates a unique primary key for each table (persons (PID_AreaMSOA) and households (HID_AreaOA))
• Removes unnecessary persons (those that were not assigned to any households (HIP = (-1)) and those that are assigned to buildings that are not residential (QS420_CELL = -2) or education (QS420_CELL = 26).
• Removes unnecessary households (those buildings that are not residential (QS420_CELL = -2) or education (QS420_CELL = 26).

SPENSER_notebook_2:

• Calculates the following columns:
  • Total people in household: count the number of persons in each household
  • Total children in household: count the number of children in each household
  • Same ethnic: check if there are people with the same ethnicity
  • Adult similar age: check if there are people within +- 10 years
  • Children_dependency: Boolean value that tells if a person has a children dependency
  • Marital_status: classifies each individual in married, couple or single based on their socio-demographic characteristics

SPENSER_notebook_3:

• Calculates the attribute: driving licence (True / False)

SPENSER_notebook_4:

• Calculates the attribute: Economic activity (Employed, Unemployed, Inactive) based on age, gender and OA area. Census 2011 data is projected to 2019 using Regional labour market statistics: HI01 Headline indicators for the North East

SPENSER_notebook_5:

• Calculates the attribute: Occupation for employed and unemployed people only. This column classifies these people in 9 categories

SPENSER_notebook_6:

• Calculates the attribute: Occupation for inactive people only. This column classifies these people in 5 categories: student, looking after family or home, sick, retired and other

SPENSER_notebook_7:

• Calculates the attribute: Income for employed and unemployed people, based on their sex, age and occupation type

SPENSER_notebook_8:

• Swap income values for employed and unemployed people. This is required because it was observed (outputs from SPENSER_notebook_7) that in general, people aged between 40-49 were earning more than expected, while people between 18-21, 22-29, 30-39, 50-59 and 60+ were earning less than expected

SPENSER_notebook_9:

• Calculates the attribute: Bike access based on data from National Travel Survey.

SPENSER_notebook_10:

• Calculates the attribute: Car access.

SPENSER_notebook_11:

• Calculates the column: Income, for inactive people (students, retired, sick, looking after home or family, others)

SPENSER_notebook_12:

• Merges previous generated outputs in one single dataframe

Interconnectons between the notebooks:

Additionally, there is a word file documenting each of the notebooks (e.g., what it does, dependencies and explanation of the main cells).