This repo is for Python scripts for "Capturing urban diversity through languages: long-term changes in multilingual residential neighbourhoods in the Helsinki Metropolitan Area" published in Population, Space and Place.
- Apply for access to individual-level register data of Statistics Finland
- Calculate linguistic diversity of register data per grid cell with scikit-bio similar to how it was done in a previous article or with language family information in this article's appendices with calculate_langfam_diversity.py.
| Step | Script | Description | Input | Output |
|---|---|---|---|---|
| 1 | calculate_langfam_diversity.py | Calculates diversity of language families per grid cell | Output from Step 1 | Geopackages |
| 2 | add_langfam_genus.py | Adds information on the family and genus per each language | First language information per individual in a CSV file | Language family and genus information per individual |
| 3 | calculate_speakers.py | Calculates the number of speakers per a grid cell across the language groups used in the article | First language information per individual in a CSV file | Geopackage |
| 4 | calculate_language_changes.py | Calculates how many times individuals have changed their language | First language information per individual in a CSV file | Pickled dataframe on changes |
| 5 | HMA_overall_diversityplot.py | Plots subplots in Figure 1 and full Figure 2 | Individual-level data from Statistics Finland | PDF Figures |
| 6 | get_gridcell_histories.py | Gets annual linguistic diversity values per grid cell | Geopackage of diversity information per grid cell | Geopackage of metric-specific grid cell histories |
| 7 | calculate_livingspace_commutes.py | Aggregates the records on living space and commute distances to grid cells | FOLK commuting and employment statistics | Pickled data frame |
| 8 | add_livingspace_commute_to_HMAgrid.py | Adds the calculated values from Step 5 to the spatial grid data | Step 5 | Geopackage |
| 9 | plot_diversities_in_new_old_stable_grids.py | Provides non-normalized plots for Figure 5 | Output from step 4 | PDF Graph and pickled dataframes |
| 10 | norm_grid_trajectories.py | Calculates the normalized values of grid trajectories | Geopackage of diversity information per grid cell | PDF Graphs and pickled dataframes |
| 11 | popweigh_grid_trajectories.py | Calculates the population-weighted average values of grid trajectories | Geopackage of diversity information per grid cell | PDF Graphs and pickled dataframes |
| 12 | plot_grid_diversitiyes_figure5.py | Plots final Figure 5 | Outputs from steps 8 and 9 | PDF Graph |
| 13 | plot_divs_est_som_87-19.py | Plots figures 9 and 10, yields outputs | Output from Step 1 | Figures 9 and 10, dataframes |
| 14 | plot_weighted_divs_est_som_87-19.py | Plots Figure 4 | Output from Step 2 | Geopackage with stability classficiations |
| 15 | calculate_spatial_markov.py | Calculate and plot Markov chain matrices | Output from Step 5 | PDF matrices (Figure 6) and matrices as pickled dataframes |
| 16 | compare_markov.py | Compares Markov probability matrices with Jensen-Shannon distances | Outputs Figure 7 | PNG file |
Some of these scripts have not been formatted to be flexibly used on all computers. You might have to alter some hard-coded filepaths.