{ "cells": [ { "cell_type": "markdown", "id": "084f60ed", "metadata": {}, "source": [ "# From survey participants to agents\n", "\n", "In the social sciences, truly relational datasets are relatively scarce, while individual-centered survey data are far more common.\n", "Pop2net provides methods for generating networks from such survey data by first creating actors from survey participants and then connecting them based on their empirical attributes.\n", "This approach is particularly effective with datasets that include quasi-network information—for example, household membership data found in surveys like the German Socio-Economic Panel (SOEP).\n", "\n", "The following basic example illustrates how Pop2net can be used to construct networks from survey data.\n", "\n", "Let’s begin by creating an artificial example dataset:" ] }, { "cell_type": "code", "execution_count": 1, "id": "550ea507", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agegenderwork_hours_daynace2_divisionhidpid
020.0male0.000000-22201583
144.0female0.000000-22201868
232.0female0.000000-21033262
398.0female0.000000-21033121
495.0female12.613089868117780
538.0male0.000000-28117461
652.0male6.528070648117484
780.0female0.000000-26219808
844.0male1.889185886219215
981.0female0.000000-2621997
104.0male0.000000-26219500
1148.0male0.000000-2464915
1220.0female5.52471286464856
1365.0female0.000000-2464400
1466.0male0.000000-2464444
1585.0male0.000000-2464623
1632.0female0.000000-234713
1749.0female7.1139688434457
1842.0male0.000000-234273
1997.0female0.000000-23748606
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" ], "text/plain": [ " age gender work_hours_day nace2_division hid pid\n", "0 20.0 male 0.000000 -2 2201 583\n", "1 44.0 female 0.000000 -2 2201 868\n", "2 32.0 female 0.000000 -2 1033 262\n", "3 98.0 female 0.000000 -2 1033 121\n", "4 95.0 female 12.613089 86 8117 780\n", "5 38.0 male 0.000000 -2 8117 461\n", "6 52.0 male 6.528070 64 8117 484\n", "7 80.0 female 0.000000 -2 6219 808\n", "8 44.0 male 1.889185 88 6219 215\n", "9 81.0 female 0.000000 -2 6219 97\n", "10 4.0 male 0.000000 -2 6219 500\n", "11 48.0 male 0.000000 -2 464 915\n", "12 20.0 female 5.524712 86 464 856\n", "13 65.0 female 0.000000 -2 464 400\n", "14 66.0 male 0.000000 -2 464 444\n", "15 85.0 male 0.000000 -2 464 623\n", "16 32.0 female 0.000000 -2 34 713\n", "17 49.0 female 7.113968 84 34 457\n", "18 42.0 male 0.000000 -2 34 273\n", "19 97.0 female 0.000000 -2 3748 606" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pop2net as p2n\n", "from pop2net.data_fakers.soep import soep_faker\n", "\n", "df_soep = soep_faker.soep(size=100, seed=1)\n", "df_soep.head(20)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d37d199a", "metadata": {}, "outputs": [], "source": [ "env = p2n.Environment()\n", "creator = p2n.Creator(env)" ] }, { "cell_type": "markdown", "id": "6014537a", "metadata": {}, "source": [ "Using the creator, we can sample from the dataset above.\n", "While doing so, we can specify a column to serve as the sampling unit.\n", "In this example, we use the household identifier \"household_id\" as the sample_level.\n", "Additionally, it is possible to oversample the data, meaning we can generate more sampled actors than there are original rows.\n", "Here, we set the number of samples to 500." ] }, { "cell_type": "code", "execution_count": 3, "id": "4bef6b97", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agegenderwork_hours_daynace2_divisionhidpidhid_original
095.0female12.6130898617808117
138.0male0.000000-214618117
252.0male6.5280706414848117
336.0female0.000000-225626014
465.0female9.1028879927206014
538.0other6.8345178627956014
651.0male8.0000006926916014
753.0female0.000000-227566014
828.0female3.9636278423846014
997.0female0.000000-236063748
102.0female0.000000-239683748
1132.0male0.000000-244501416
1237.0female0.000000-246801416
1361.0male5.0490226445211416
1453.0male8.0000009959459116
155.0male0.000000-256589116
1653.0male0.000000-269241674
1790.0male0.000000-263261674
1867.0male0.000000-275206915
1949.0female0.000000-278506915
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" ], "text/plain": [ " age gender work_hours_day nace2_division hid pid hid_original\n", "0 95.0 female 12.613089 86 1 780 8117\n", "1 38.0 male 0.000000 -2 1 461 8117\n", "2 52.0 male 6.528070 64 1 484 8117\n", "3 36.0 female 0.000000 -2 2 562 6014\n", "4 65.0 female 9.102887 99 2 720 6014\n", "5 38.0 other 6.834517 86 2 795 6014\n", "6 51.0 male 8.000000 69 2 691 6014\n", "7 53.0 female 0.000000 -2 2 756 6014\n", "8 28.0 female 3.963627 84 2 384 6014\n", "9 97.0 female 0.000000 -2 3 606 3748\n", "10 2.0 female 0.000000 -2 3 968 3748\n", "11 32.0 male 0.000000 -2 4 450 1416\n", "12 37.0 female 0.000000 -2 4 680 1416\n", "13 61.0 male 5.049022 64 4 521 1416\n", "14 53.0 male 8.000000 99 5 945 9116\n", "15 5.0 male 0.000000 -2 5 658 9116\n", "16 53.0 male 0.000000 -2 6 924 1674\n", "17 90.0 male 0.000000 -2 6 326 1674\n", "18 67.0 male 0.000000 -2 7 520 6915\n", "19 49.0 female 0.000000 -2 7 850 6915" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_sample = creator.draw_sample(\n", " df=df_soep, # set the dataset to be sampled from\n", " n=500, # set the number of lines to be sampled\n", " sample_level=\"hid\", # set the unit of sampling\n", " sample_weight=None, # if needed, set a column as the sampling weight\n", ")\n", "\n", "df_sample.head(20)" ] }, { "cell_type": "markdown", "id": "5ee9497c", "metadata": {}, "source": [ "If you look closely, the method used overwrites the column specified as the sample level (e.g., \"hid\") with new, unique values. The original values are preserved in a separate column (e.g., \"hid_original\"). This ensures that the sample level can still be used to connect actors, without introducing artifacts caused by duplicated samples.\n", "If desired, this behavior can be disabled by setting the `replace_sample_level_column argument` to `False`.\n", "\n", "Now we create the actors from the sampled data:" ] }, { "cell_type": "code", "execution_count": 4, "id": "a7d396f3", "metadata": {}, "outputs": [], "source": [ "_ = creator.create_actors(df=df_sample)" ] }, { "cell_type": "markdown", "id": "37dc7910", "metadata": {}, "source": [ "Now let's use two types of locations (households and work places) to connect the created actors based on their empirical attributes:" ] }, { "cell_type": "code", "execution_count": 5, "id": "4c213c25", "metadata": {}, "outputs": [], "source": [ "class Household(p2n.LocationDesigner):\n", " def split(self, actor):\n", " \"\"\"Build one household location for each hid.\"\"\"\n", " return actor.hid\n", "\n", "\n", "class Work(p2n.LocationDesigner):\n", " n_actors = 10 # set the number of actors per work place to 10\n", "\n", " def split(self, actor):\n", " \"\"\"Build work places separated by industry.\"\"\"\n", " return actor.nace2_division\n", "\n", " def filter(self, actor):\n", " \"\"\"Connect only actors with valid industry codes.\"\"\"\n", " return actor.nace2_division > 0\n", "\n", " def weight(self, actor):\n", " \"\"\"Weight the connection between the actor and the location\n", " by the empirical working time.\"\"\"\n", " return actor.work_hours_day" ] }, { "cell_type": "code", "execution_count": 6, "id": "c1c74702", "metadata": {}, "outputs": [], "source": [ "_ = creator.create_locations(location_designers=[Household, Work])" ] }, { "cell_type": "code", "execution_count": 7, "id": "8a2485cb", "metadata": {}, "outputs": [ { "data": { "application/javascript": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; 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\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
start:@start
end:@end
weight:@weight
index:@index
label:@label
type:@type
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print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
start:@start
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