Getting started with the Amsterdam Transit Network

Install

conda env create --file environment.yml

Run

python main.py

Extracting Socio-economic Status data per Neighbourhood

1. Read the excel file nl_fig = pd.read_excel('./input/nb_main_figures.xls')
2. Keep only Amsterdam Neigbhorhoods ams_fig = nl_fig[np.isin(nl_fig['gwb_code_10'], ams_nb['BU_CODE'].unique())]
3. Join with the amsterdam neigbhorhoods dataframe ams_nb = pd.merge(ams_nb, ams_fig, on='BU_CODE', how='left')

nb_main_figures_explainer.pdf is a guide for the labels of nb_main_figures. Some examples:

• a_inw: total number of inhabitants.
• a_hh: total number of households.
• p_hh_lkk: percentage of household with low income
• p_hh_li: percentage of households with the lowest 20% income.
• a_w_all: nr of people with western migration background.
• a_nw_all: nr of people with non-western migration background.