{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-001",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px 34px;border-radius:20px;background:linear-gradient(135deg,#17343e 0%,#1e5962 63%,#16798a 100%);color:white;\">\n",
    "  <div style=\"font:600 12px/1.2 monospace;letter-spacing:.12em;color:#a9e0dc;\">INFOSCI 301 · WEEK 2 · COLAB COMPANION</div>\n",
    "  <h1 style=\"font:700 42px/1.05 Georgia,serif;margin:14px 0 12px;\">From source rows to a defensible visualization</h1>\n",
    "  <p style=\"max-width:820px;font-size:17px;line-height:1.55;color:#e5f1f1;margin:0;\">Reproduce the Kunshan–Venice case, inspect every transformation, and then replace the instructor example with your own domain, data, tasks, visual idioms, algorithms, and evidence.</p>\n",
    "</div>\n",
    "\n",
    "> **Instructor example—not a submission topic.** Students must replace the domain, community, dataset, tasks, idioms, evidence, and sources with project-specific choices.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-002",
   "metadata": {},
   "source": [
    "## How to use this notebook\n",
    "\n",
    "| 1 · Build verified data | 2 · Reproduce the views | 3 · Critique and redesign |\n",
    "|---|---|---|\n",
    "| Load nine traceable GeoNames rows, verify their fields, and classify data plus tasks. | Create two interactive maps, a population comparison, a source inspector, and an evidence boundary. | Apply the four-level pipeline, explore all 38 idioms and 72 tools, audit governance, and generate replacement prompts. |\n",
    "\n",
    "Run cells from top to bottom. The default mode uses the exact verified subset embedded in this notebook. Switch the data-mode control to re-query the pinned Parquet source when you want to audit the extraction itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-003",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 0 · Setup the Colab environment\n",
    "import importlib.util\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "required = {\"duckdb\": \"duckdb>=1.2,<2\", \"folium\": \"folium>=0.18,<2\"}\n",
    "missing = [spec for module, spec in required.items() if importlib.util.find_spec(module) is None]\n",
    "if missing:\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *missing])\n",
    "\n",
    "import json\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "import duckdb\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML, Markdown, display\n",
    "\n",
    "OUTPUT_DIR = Path(\"infovis_outputs\")\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "COLORS = {\n",
    "    \"ink\": \"#18313B\",\n",
    "    \"muted\": \"#63777D\",\n",
    "    \"river\": \"#14798A\",\n",
    "    \"jiangnan\": \"#C65F49\",\n",
    "    \"venice\": \"#6D6397\",\n",
    "    \"gold\": \"#E4B94D\",\n",
    "    \"line\": \"#CCD8D5\",\n",
    "    \"paper\": \"#F7F5EF\",\n",
    "}\n",
    "plt.rcParams.update({\n",
    "    \"font.family\": \"DejaVu Sans\",\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"axes.edgecolor\": COLORS[\"line\"],\n",
    "    \"axes.labelcolor\": COLORS[\"muted\"],\n",
    "    \"xtick.color\": COLORS[\"muted\"],\n",
    "    \"ytick.color\": COLORS[\"ink\"],\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"axes.facecolor\": \"white\",\n",
    "})\n",
    "print(\"Environment ready · outputs will be written to\", OUTPUT_DIR.resolve())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-004",
   "metadata": {},
   "source": [
    "# 1 · Build the verified data\n",
    "\n",
    "### Three moves\n",
    "\n",
    "1. **Trace the source:** Hugging Face conversion → pinned Parquet revision → original GeoNames export.\n",
    "2. **Validate the subset:** exact identifiers, row count, feature classes, coordinates, and nonzero population fields.\n",
    "3. **Abstract before drawing:** name the dataset/attribute types and express each task as an action + target.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-005",
   "metadata": {},
   "source": [
    "### 1.1 Source contract\n",
    "\n",
    "- **Dataset:** [do-me/Geonames on Hugging Face](https://huggingface.co/datasets/do-me/Geonames)\n",
    "- **Pinned revision:** <code>00c727680a086eaae649043a6ce660a88e57ebea</code>\n",
    "- **Original producer/export:** [GeoNames data dump](https://download.geonames.org/export/dump/)\n",
    "- **Dataset-card license tag:** CC BY 4.0\n",
    "\n",
    "The nine rows are purposively selected for teaching. They are not a probability sample, and feature counts cannot be interpreted as regional prevalence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 1.2 · Load instantly or re-query the pinned source\n",
    "DATA_MODE = \"Use embedded verified subset\" # @param [\"Use embedded verified subset\", \"Re-query pinned Parquet\"]\n",
    "\n",
    "DATASET_SHA = \"00c727680a086eaae649043a6ce660a88e57ebea\"\n",
    "PARQUET_URL = (\n",
    "    \"https://huggingface.co/datasets/do-me/Geonames/resolve/\"\n",
    "    + DATASET_SHA + \"/geonames_23_03_2025.parquet\"\n",
    ")\n",
    "FOCUS = {\n",
    "    1785623: {\"region\": \"Jiangnan\", \"role\": \"focus city\"},\n",
    "    1784074: {\"region\": \"Jiangnan\", \"role\": \"water-town settlement\"},\n",
    "    1812915: {\"region\": \"Jiangnan\", \"role\": \"lake\"},\n",
    "    1793666: {\"region\": \"Jiangnan\", \"role\": \"lake\"},\n",
    "    1886760: {\"region\": \"Jiangnan\", \"role\": \"regional city\"},\n",
    "    3164603: {\"region\": \"Venice\", \"role\": \"focus city\"},\n",
    "    3175933: {\"region\": \"Venice\", \"role\": \"canal\"},\n",
    "    7910672: {\"region\": \"Venice\", \"role\": \"canal\"},\n",
    "    12172719: {\"region\": \"Venice\", \"role\": \"lagoon\"},\n",
    "}\n",
    "COLUMNS = [\n",
    "    \"geonameid\", \"name\", \"asciiname\", \"alternatenames\", \"latitude\",\n",
    "    \"longitude\", \"feature_class\", \"feature_code\", \"country_code\", \"cc2\",\n",
    "    \"admin1_code\", \"admin2_code\", \"admin3_code\", \"admin4_code\",\n",
    "    \"population\", \"elevation\", \"dem\", \"timezone\", \"modification_date\",\n",
    "]\n",
    "\n",
    "embedded_manifest = json.loads(\"{\\n  \\\"source\\\": {\\n    \\\"dataset_name\\\": \\\"do-me/Geonames\\\",\\n    \\\"dataset_card\\\": \\\"https://huggingface.co/datasets/do-me/Geonames\\\",\\n    \\\"dataset_sha\\\": \\\"00c727680a086eaae649043a6ce660a88e57ebea\\\",\\n    \\\"parquet_url\\\": \\\"https://huggingface.co/datasets/do-me/Geonames/resolve/00c727680a086eaae649043a6ce660a88e57ebea/geonames_23_03_2025.parquet\\\",\\n    \\\"upstream\\\": \\\"https://download.geonames.org/export/dump/\\\",\\n    \\\"license\\\": \\\"CC BY 4.0\\\",\\n    \\\"selection_note\\\": \\\"Nine exact GeoNames records selected by stable ID for a paired classroom demonstration. This purposive subset is not exhaustive and feature counts must not be interpreted as regional prevalence.\\\"\\n  },\\n  \\\"schema\\\": [\\n    \\\"geonameid\\\",\\n    \\\"name\\\",\\n    \\\"asciiname\\\",\\n    \\\"alternatenames\\\",\\n    \\\"latitude\\\",\\n    \\\"longitude\\\",\\n    \\\"feature_class\\\",\\n    \\\"feature_code\\\",\\n    \\\"country_code\\\",\\n    \\\"cc2\\\",\\n    \\\"admin1_code\\\",\\n    \\\"admin2_code\\\",\\n    \\\"admin3_code\\\",\\n    \\\"admin4_code\\\",\\n    \\\"population\\\",\\n    \\\"elevation\\\",\\n    \\\"dem\\\",\\n    \\\"timezone\\\",\\n    \\\"modification_date\\\"\\n  ],\\n  \\\"records\\\": [\\n    {\\n      \\\"geonameid\\\": 1784074,\\n      \\\"name\\\": \\\"Zhouzhuang\\\",\\n      \\\"asciiname\\\": \\\"Zhouzhuang\\\",\\n      \\\"alternatenames\\\": \\\"Chou-chuang,Chou-chuang-chen,Chzhouchzhuan,Zhouzhuang,Zhouzhuang Zhen,zhou zhuang,zhou zhuang zhen,\\u0427\\u0436\\u043e\\u0443\\u0447\\u0436\\u0443\\u0430\\u043d,\\u5468\\u5e84,\\u5468\\u5e84\\u9547\\\",\\n      \\\"latitude\\\": 31.11788,\\n      \\\"longitude\\\": 120.84427,\\n      \\\"feature_class\\\": \\\"P\\\",\\n      \\\"feature_code\\\": \\\"PPLA4\\\",\\n      \\\"country_code\\\": \\\"CN\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"04\\\",\\n      \\\"admin2_code\\\": \\\"3205\\\",\\n      \\\"admin3_code\\\": null,\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 22000,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 6,\\n      \\\"timezone\\\": \\\"Asia/Shanghai\\\",\\n      \\\"modification_date\\\": \\\"2021-09-20\\\",\\n      \\\"region\\\": \\\"Jiangnan\\\",\\n      \\\"role\\\": \\\"water-town settlement\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/1784074\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 1785623,\\n      \\\"name\\\": \\\"Kunshan\\\",\\n      \\\"asciiname\\\": \\\"Kunshan\\\",\\n      \\\"alternatenames\\\": \\\"Con Son,C\\u00f4n S\\u01a1n,K'un-shan-ch'eng,K'un-shan-hsien,KVN,Kan-shan,Kun'shan',Kunsanas,Kunshan,Kunshan Shi,Kun\\u0161anas,K\\u2019un-shan-ch\\u2019eng,K\\u2019un-shan-hsien,Yushan,Yushan Zhen,kun shan,kun shan shi,kunsan si,kwnshan,yu shan,yu shan zhen,\\u041a\\u0443\\u043d\\u0448\\u0430\\u043d,\\u041a\\u0443\\u043d\\u044c\\u0448\\u0430\\u043d\\u044c,\\u06a9\\u0648\\u0646\\u0634\\u0627\\u0646,\\u5d11\\u5c71,\\u5d11\\u5c71\\u5e02,\\u6606\\u5c71,\\u6606\\u5c71\\u5e02,\\u7389\\u5c71,\\u7389\\u5c71\\u9547,\\ucfe4\\uc0b0 \\uc2dc\\\",\\n      \\\"latitude\\\": 31.37762,\\n      \\\"longitude\\\": 120.95431,\\n      \\\"feature_class\\\": \\\"P\\\",\\n      \\\"feature_code\\\": \\\"PPLA3\\\",\\n      \\\"country_code\\\": \\\"CN\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"04\\\",\\n      \\\"admin2_code\\\": \\\"3205\\\",\\n      \\\"admin3_code\\\": null,\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 2092496,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 10,\\n      \\\"timezone\\\": \\\"Asia/Shanghai\\\",\\n      \\\"modification_date\\\": \\\"2022-04-01\\\",\\n      \\\"region\\\": \\\"Jiangnan\\\",\\n      \\\"role\\\": \\\"focus city\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/1785623\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 1793666,\\n      \\\"name\\\": \\\"Tai Hu\\\",\\n      \\\"asciiname\\\": \\\"Tai Hu\\\",\\n      \\\"alternatenames\\\": \\\"Danau Taihu,Great Lake,Lac Tai,Lacul Tai,Lago Tai,Lago Taihu,Lake T'ai,Lake Tai,Lake Taihu,Lake T\\u2019ai,Llac Taihu,T'ai-wu,Tai Hu,Tai aintzira,Taihu,Taihu Lake,Taijaervi,Taij\\u00e4rvi,Taj-to,Taj-t\\u00f3,Tajkhu,Tchaj-chu,Thai Ho,Th\\u00e1i H\\u1ed3,T\\u2019ai-wu,Vozera Tajkhu,bhyrt tay,tai ho,tai hu,thale sab thi,\\u0412\\u043e\\u0437\\u0435\\u0440\\u0430 \\u0422\\u0430\\u0439\\u0445\\u0443,\\u0422\\u0430\\u0439\\u0445\\u0443,\\u0628\\u062d\\u064a\\u0631\\u0629 \\u062a\\u0627\\u064a,\\u0e17\\u0e30\\u0e40\\u0e25\\u0e2a\\u0e32\\u0e1a\\u0e44\\u0e17\\u0e48,\\u0f50\\u0f60\\u0f7a\\u0f0b\\u0f67\\u0f74\\u0f60\\u0f74\\u0f0b\\u0f58\\u0f5a\\u0f7a\\u0f60\\u0f74\\u0f0d,\\u592a\\u6e56,\\ud0c0\\uc774 \\ud638\\\",\\n      \\\"latitude\\\": 31.21649,\\n      \\\"longitude\\\": 120.19814,\\n      \\\"feature_class\\\": \\\"H\\\",\\n      \\\"feature_code\\\": \\\"LK\\\",\\n      \\\"country_code\\\": \\\"CN\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"04\\\",\\n      \\\"admin2_code\\\": null,\\n      \\\"admin3_code\\\": null,\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 0,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 0,\\n      \\\"timezone\\\": \\\"Asia/Shanghai\\\",\\n      \\\"modification_date\\\": \\\"2020-12-11\\\",\\n      \\\"region\\\": \\\"Jiangnan\\\",\\n      \\\"role\\\": \\\"lake\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/1793666\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 1812915,\\n      \\\"name\\\": \\\"Dianshan Hu\\\",\\n      \\\"asciiname\\\": \\\"Dianshan Hu\\\",\\n      \\\"alternatenames\\\": \\\"Dianshan Hu,Dianshan Lake,Di\\u00e0nsh\\u0101n H\\u00fa,Tien-shan Hu,dian shan hu,\\u6dc0\\u5c71\\u6e56\\\",\\n      \\\"latitude\\\": 31.11417,\\n      \\\"longitude\\\": 120.96472,\\n      \\\"feature_class\\\": \\\"H\\\",\\n      \\\"feature_code\\\": \\\"LK\\\",\\n      \\\"country_code\\\": \\\"CN\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"00\\\",\\n      \\\"admin2_code\\\": null,\\n      \\\"admin3_code\\\": null,\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 0,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 1,\\n      \\\"timezone\\\": \\\"Asia/Shanghai\\\",\\n      \\\"modification_date\\\": \\\"2024-07-13\\\",\\n      \\\"region\\\": \\\"Jiangnan\\\",\\n      \\\"role\\\": \\\"lake\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/1812915\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 1886760,\\n      \\\"name\\\": \\\"Suzhou\\\",\\n      \\\"asciiname\\\": \\\"Suzhou\\\",\\n      \\\"alternatenames\\\": \\\"SZV,So-chiu-chhi,Soochow,Soutsoou,So\\u0358-chiu-chh\\u012b,Su-chou,Su-chu-su,Su-ciu,Su-cou,Su-\\u010dou,Suchjou,Suchzhou,Sudzhou,Sudzou,Sud\\u017eou,Sugouo,Sutsjou,Suzhou,Suzhou Shi,Suzhou i Jiangsu,Su\\u011do\\u016do,Szucsou,S\\u00fb-ch\\u00fb-s\\u1e73,S\\u016d-ci\\u016d,To Chau,T\\u00f4 Ch\\u00e2u,Wu-hsien,cuco,ssujeou si,su cow,su zhou,su zhou shi,sujho'u,suzu,swgw\\u02bcw,swjw,swzhw,swzhww,\\u03a3\\u03bf\\u03c5\\u03c4\\u03c3\\u03cc\\u03bf\\u03c5,\\u0421\\u0443\\u0434\\u0436\\u043e\\u0443,\\u0421\\u0443\\u0447\\u0436\\u043e\\u0443,\\u0421\\u0443\\u0447\\u0436\\u043e\\u045e,\\u0421\\u0443\\u045f\\u043e\\u0443,\\u0421\\u04af\\u0436\\u043e\\u0443,\\u054d\\u0578\\u0582\\u0579\\u056a\\u0578\\u0578\\u0582,\\u05e1\\u05d5\\u05d2\\u05d5\\u05d0\\u05d5,\\u0633\\u0648\\u062c\\u0648,\\u0633\\u0648\\u0698\\u0648,\\u0633\\u0648\\u0698\\u0648\\u0648,\\u0633\\u06c7\\u062c\\u06c7 \\u0634\\u06d5\\u06be\\u0649\\u0631\\u0649,\\u0938\\u0942\\u091d\\u094b\\u090a,\\u0a38\\u0a42\\u0a1c\\u0a3c\\u0a42,\\u0b9a\\u0bc1\\u0b9a\\u0bcb,\\u0e0b\\u0e39\\u0e42\\u0e08\\u0e27,\\u82cf\\u5dde,\\u82cf\\u5dde\\u5e02,\\u8607\\u5dde,\\u8607\\u5dde\\u5e02,\\uc464\\uc800\\uc6b0 \\uc2dc\\\",\\n      \\\"latitude\\\": 31.30408,\\n      \\\"longitude\\\": 120.59538,\\n      \\\"feature_class\\\": \\\"P\\\",\\n      \\\"feature_code\\\": \\\"PPLA2\\\",\\n      \\\"country_code\\\": \\\"CN\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"04\\\",\\n      \\\"admin2_code\\\": \\\"3205\\\",\\n      \\\"admin3_code\\\": null,\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 6715559,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 10,\\n      \\\"timezone\\\": \\\"Asia/Shanghai\\\",\\n      \\\"modification_date\\\": \\\"2024-03-26\\\",\\n      \\\"region\\\": \\\"Jiangnan\\\",\\n      \\\"role\\\": \\\"regional city\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/1886760\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 3164603,\\n      \\\"name\\\": \\\"Venice\\\",\\n      \\\"asciiname\\\": \\\"Venice\\\",\\n      \\\"alternatenames\\\": \\\"Benatky,Benetia,Benetke,Benezia,Ben\\u00e1tky,Feneyjar,Mleci,V'nise,VCE,Velence,Venecia,Venecia - 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\\\"admin3_code\\\": \\\"027042\\\",\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 51298,\\n      \\\"elevation\\\": 2.0,\\n      \\\"dem\\\": 5,\\n      \\\"timezone\\\": \\\"Europe/Rome\\\",\\n      \\\"modification_date\\\": \\\"2025-02-08\\\",\\n      \\\"region\\\": \\\"Venice\\\",\\n      \\\"role\\\": \\\"focus city\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/3164603\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 3175933,\\n      \\\"name\\\": \\\"Canal Grande\\\",\\n      \\\"asciiname\\\": \\\"Canal Grande\\\",\\n      \\\"alternatenames\\\": \\\"Bueyuek Kanal,B\\u00fcy\\u00fck Kanal,Canal Grande,Canal Grando,Golem kanal,Gran Canal,Gran Canal de Venecia,Grand Canal,Grand-kanal,Granda Kanalo de Venecio,Grande Canal de Veneza,Kanal Grande,Kanal Qrande,Lielais kanals,Lielais kan\\u0101ls,alqnal alkbyr,da yun he,kanal geulande,\\u0413\\u043e\\u043b\\u0435\\u043c \\u043a\\u0430\\u043d\\u0430\\u043b,\\u0413\\u0440\\u0430\\u043d\\u0434-\\u043a\\u0430\\u043d\\u0430\\u043b,\\u041a\\u0430\\u043d\\u0430\\u043b \\u0413\\u0440\\u0430\\u043d\\u0434\\u0435,\\u05d4\\u05ea\\u05e2\\u05dc\\u05d4 \\u05d4\\u05d2\\u05d3\\u05d5\\u05dc\\u05d4,\\u0627\\u0644\\u0642\\u0646\\u0627\\u0644 \\u0627\\u0644\\u0643\\u0628\\u064a\\u0631,\\u30ab\\u30ca\\u30eb\\u30fb\\u30b0\\u30e9\\u30f3\\u30c7,\\u5927\\u8fd0\\u6cb3,\\uce74\\ub0a0 \\uadf8\\ub780\\ub370\\\",\\n      \\\"latitude\\\": 45.43598,\\n      \\\"longitude\\\": 12.33052,\\n      \\\"feature_class\\\": \\\"H\\\",\\n      \\\"feature_code\\\": \\\"CNL\\\",\\n      \\\"country_code\\\": \\\"IT\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"20\\\",\\n      \\\"admin2_code\\\": \\\"VE\\\",\\n      \\\"admin3_code\\\": \\\"027042\\\",\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 0,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 6,\\n      \\\"timezone\\\": \\\"Europe/Rome\\\",\\n      \\\"modification_date\\\": \\\"2018-04-05\\\",\\n      \\\"region\\\": \\\"Venice\\\",\\n      \\\"role\\\": \\\"canal\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/3175933\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 7910672,\\n      \\\"name\\\": \\\"Canal Grande di Murano\\\",\\n      \\\"asciiname\\\": \\\"Canal Grande di Murano\\\",\\n      \\\"alternatenames\\\": null,\\n      \\\"latitude\\\": 45.45602,\\n      \\\"longitude\\\": 12.35462,\\n      \\\"feature_class\\\": \\\"H\\\",\\n      \\\"feature_code\\\": \\\"CNL\\\",\\n      \\\"country_code\\\": \\\"IT\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"20\\\",\\n      \\\"admin2_code\\\": \\\"VE\\\",\\n      \\\"admin3_code\\\": \\\"027042\\\",\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 0,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": 2,\\n      \\\"timezone\\\": \\\"Europe/Rome\\\",\\n      \\\"modification_date\\\": \\\"2011-07-29\\\",\\n      \\\"region\\\": \\\"Venice\\\",\\n      \\\"role\\\": \\\"canal\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/7910672\\\"\\n    },\\n    {\\n      \\\"geonameid\\\": 12172719,\\n      \\\"name\\\": \\\"Venetian Lagoon\\\",\\n      \\\"asciiname\\\": \\\"Venetian Lagoon\\\",\\n      \\\"alternatenames\\\": \\\"Venetian Lagoon\\\",\\n      \\\"latitude\\\": 45.3662,\\n      \\\"longitude\\\": 12.25136,\\n      \\\"feature_class\\\": \\\"H\\\",\\n      \\\"feature_code\\\": \\\"LGN\\\",\\n      \\\"country_code\\\": \\\"IT\\\",\\n      \\\"cc2\\\": null,\\n      \\\"admin1_code\\\": \\\"20\\\",\\n      \\\"admin2_code\\\": \\\"VE\\\",\\n      \\\"admin3_code\\\": \\\"027023\\\",\\n      \\\"admin4_code\\\": null,\\n      \\\"population\\\": 0,\\n      \\\"elevation\\\": null,\\n      \\\"dem\\\": -2,\\n      \\\"timezone\\\": \\\"Europe/Rome\\\",\\n      \\\"modification_date\\\": \\\"2020-10-26\\\",\\n      \\\"region\\\": \\\"Venice\\\",\\n      \\\"role\\\": \\\"lagoon\\\",\\n      \\\"source_url\\\": \\\"https://www.geonames.org/12172719\\\"\\n    }\\n  ]\\n}\")\n",
    "\n",
    "if DATA_MODE == \"Use embedded verified subset\":\n",
    "    manifest = embedded_manifest\n",
    "    records = pd.DataFrame(manifest[\"records\"])\n",
    "    extraction_note = \"Loaded the embedded copy of the verified nine-row manifest.\"\n",
    "else:\n",
    "    aliases = \", \".join(f'\"{i}\" AS {name}' for i, name in enumerate(COLUMNS))\n",
    "    identifiers = \", \".join(map(str, FOCUS))\n",
    "    query = f\"\"\"\n",
    "        SELECT {aliases}\n",
    "        FROM read_parquet('{PARQUET_URL}')\n",
    "        WHERE \"0\" IN ({identifiers})\n",
    "        ORDER BY \"0\"\n",
    "    \"\"\"\n",
    "    connection = duckdb.connect()\n",
    "    records = connection.execute(query).df()\n",
    "    for row_index, geonameid in records[\"geonameid\"].astype(int).items():\n",
    "        records.loc[row_index, [\"region\", \"role\"]] = list(FOCUS[geonameid].values())\n",
    "    records[\"source_url\"] = records[\"geonameid\"].map(\n",
    "        lambda value: f\"https://www.geonames.org/{int(value)}\"\n",
    "    )\n",
    "    manifest = embedded_manifest\n",
    "    extraction_note = \"Re-queried nine IDs from the pinned Hugging Face Parquet source.\"\n",
    "\n",
    "records[\"geonameid\"] = records[\"geonameid\"].astype(int)\n",
    "records[\"population\"] = pd.to_numeric(records[\"population\"], errors=\"coerce\").fillna(0).astype(int)\n",
    "records[\"latitude\"] = pd.to_numeric(records[\"latitude\"], errors=\"raise\")\n",
    "records[\"longitude\"] = pd.to_numeric(records[\"longitude\"], errors=\"raise\")\n",
    "records.to_csv(OUTPUT_DIR / \"geonames_water_towns.csv\", index=False)\n",
    "print(extraction_note)\n",
    "records[[\"name\", \"region\", \"role\", \"feature_class\", \"feature_code\", \"population\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 1.3 · Run the integrity contract\n",
    "expected_ids = set(FOCUS)\n",
    "observed_ids = set(records[\"geonameid\"])\n",
    "populated = records.query(\"feature_class == 'P' and population > 0\")\n",
    "\n",
    "checks = {\n",
    "    \"Exactly nine rows\": len(records) == 9,\n",
    "    \"All stable IDs found\": observed_ids == expected_ids,\n",
    "    \"No duplicate IDs\": records[\"geonameid\"].is_unique,\n",
    "    \"Only H/P feature classes\": set(records[\"feature_class\"]) == {\"H\", \"P\"},\n",
    "    \"Coordinates are valid\": (\n",
    "        records[\"latitude\"].between(-90, 90).all()\n",
    "        and records[\"longitude\"].between(-180, 180).all()\n",
    "    ),\n",
    "    \"Four nonzero settlement values\": len(populated) == 4,\n",
    "}\n",
    "assert all(checks.values()), checks\n",
    "display(pd.DataFrame({\"check\": checks.keys(), \"passed\": checks.values()}))\n",
    "print(\"Integrity contract passed.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-008",
   "metadata": {},
   "source": [
    "### 1.4 Course cheat sheets\n",
    "\n",
    "These are the same course figures used in the tutorial. Click the source links for the editable/high-resolution originals.\n",
    "\n",
    "| What? Data and attributes | Why? Actions and targets |\n",
    "|---|---|\n",
    "| ![Munzner Figure 2.1](attachment:munzner-data-types.png) | ![Munzner Figure 3.1](attachment:munzner-actions-targets.png) |\n",
    "| [Figure 2.1 source PDF](https://www.cs.ubc.ca/~tmm/vadbook/eamonn-figs/fig2.1.pdf) | [Figure 3.1 source PDF](https://www.cs.ubc.ca/~tmm/vadbook/eamonn-figs/fig3.1.pdf) |\n",
    "\n",
    "Source: Tamara Munzner, *Visualization Analysis and Design* (2014), illustrations by Eamonn Maguire, CC BY 4.0.\n"
   ],
   "attachments": {
    "munzner-data-types.png": {
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"
    },
    "munzner-actions-targets.png": {
     "image/png": 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5s2dCodDcwrx5ixb5YnFcXBwR6erpGRgYyK9NJpOFPn0aHR0tKZCY1jVt1bo1j1dYACVMTc3KysoXi9nDN2/esAnTunW55iYjPf358+eJCQk6urpmZmYWDRrgYwYIVwCVxZVLl9pZWxPRI39/1m+YJavY2Fg2Xf7q9uLuenqyCau2barXqMGmPe94Tra3L76wn6/v5g0bFcpCdu7Z3aBhwzEjR3Fz/B/6s4eDhwyZv2hhZmYme8jn870e3OcWy87O/n33nksXL8qPNNPaymrpiuXGxsbcnB3btrPCxRu3bm7d7HLTw4Nbvk9f2wWLFrFqxsI/x8tr62YXFhcZQ0PD85cvcY0iAAAo+PPoMSLqZdNbocvuti1b79y6pTBOkrmFubmFeY+ePVcuW77GedX0WTPlxwZc4ewc+DhAKBRev3qtwmf9ijt75szxo0dTU1LlZ/a2sVmweJGGhgZ7GB8Xx9oa+6m/Nm7SZOnixaIcUYeOHVm4EolE27ds/evKFe7lHTp2nGA3cZr9r0Q05Vd7u0n/bG1UVNTShYui5S5MmdSuvWXbVjbm7dEjR86dOcs9tWj+Ajbx943rLKEd3H/gxPHjuUXDRLFNdV6zGp80qHrw6wq+Pxnp6eHh4SNHjdLT1yMi+WTFXb/6nPV7eXkSkaamZstWrczMzGrXqU1EUZGRxasdtm/d6jjHgUtW7Pwcj8dr0rRpRf6ujIxxo0ZfvHBBJpMpKSnVqVNHRUWFiIICA8eNGv3yxUtuSbWi7LRgnpOHu7t8Ertx7frhg4e4h8+fPVuxdBlLVvXq1TO3MFfl883q1UOyAgAou5UhovETJsrPjwiPOOvm5nroYImDJIWFht65ffuXkSPcTp6S72GlrKw8eOhQIrp4/vwX3MiE+PjUlFRNTc3GjRs3btyYz+cTkYe7+9JFi7llqhVdc4uPj1++ZKko55+tKigomDrFnktWampqSkpKfr6+q1c6F75W7iTdI3//yXZ20dHRfD6/VevWjRo34vF48XFx9pMml6cO0O3U6YP79+fm5vJ4vKaWlia1a/N4PNyFEqoqXLmC78+F8xeIqJVV6/j4+JvuHixN6enrjxs9hohcDx381E5WCm1n3Js4IurYuZOqqioRdf/hhxPH/yQiT0/PiXZ23JK+3j5nTrsRkZKS0vARvwwdPrx27do5OTkvX7zU1NSUSqUXLl9+cP8+K1/s2KnT/IULiUhDU6O0t96xdVtSUhIRNbW03LTFxcDA4P379yuXLffx9haJRBt/++3A4UMsFKmqFn5zQ4KDf+rRw37aVJLJDh046OHuTkSnTpwYN36clrY2EV2+dDk/P5+I5s5zHD5iBBFlZ2crjKYIAADy/ItuBGxs8k/JgEQicZg1y9HJqcTSPq6fVfMWLQIfByj0sGrSpAkRCYXCjPT0z2mh5PXr39/Kqk3nrl1Yu5D09u1U+1+T3r596Of3LCysqaUlFZ3yI6LrV69JpdKWrVp26NiJnTF0O3X69atXRKSnp7dilbN1+/bv379fv2btXS+vwh+IKipcDHPZuEmUIxJUr7577566ZmZEdO/u3UXzF2RlZR09cmSFs/Nke/vRY8bOmjGDVSFu37XT1LQuFY1qeOHcOdZWHj52tEHDhkSUkJDAV+XjkwZVEs5ew/fn4vnz7ayt1dXVe/bqOXzECJasiOinnj1ZE/I5K/cqqgns1r27woTXHU/5Jbds3swmJtjZOTg61qlTR0lJSVNT06qNFRHxeDyjWkbs2hoRVVOvZlTLyKiWUWljA0ZGRLjfuMFe+NumjayOQlNTc826dazA48Xz594PvBXaPDMzsxWrnE1NTU3r1l26fBnr61xQUBDDOnQRJSYksAk9vcLmXEtLq745zhcCAJTK+8EDIhIIBPK9raKjooVC4eCShp+VH8FCWVl55erVZ93c5Bcwq1/YtzY2NvZLbaRFgwZdu3fjyhAMjYz62PYp2p4QNqFc9KxUKu3T1/Z3V9cJdhN79Owpk8mOHj7MnnJauKBDx448Hk9bW3vxsqXcIO9KSoWvvXb1KutDNcV+CktWrGVs2aolEd1098jOztbR0TGqZcS1TTVq1GBNHo/Hk8lkb9++Za2bTtGAHMbGxgrdwwAQrgD+HRHhEUKhcNKUyUSkp68/d54jdxZw6vRpRHTA1TUjPb3C62cdrng8XsNGjdLS0tLS0oyNjVloefXyZUJ8PFtMmJqakJDAcs74iRM+/+969fIVm2jTtm2Noo5eRKSuod61Wzc2Hf66cBklUmIT3X/8gZWCEJEqn9+ocWM2zYUrru/ZmlWrNqz/jZ2nBACAj7Jq2+aD1icivJ21tcJoQCHBwX169uKSVWFyMDEmIvm+uNzFLhYzvhSpVOrn67vGedU0+19HDB12+uQpNj8pqfBdlOQqwP83aTI3nZiYmJ2dTURaWlo//vQTN19HR4drNZSUChuakCfBbCIrK+va1avcP5a+JBIJGwCjNEpKSs1bNGdLThw77uD+A8LUVHy6oApDWSB8Z1jFgmWzZsWfUldXd3Ry2urism3r1lVr1lRg5fFxcazOXiqVjhw2vPgCnnc8x4wbS0Tc8LvGJibyhekVFhkRwSZMapsoPMVKOIgoMiJS4SlNTU35h9xlMa6wfsy4sUGBAYEBgQUFBVcuXbpy6VL7Dh2WLF8mn98AAEBeVEQkEckPqUdE3g8etGzV6oO4FR4xdYo9+7+XO7fFGiOBQBAdGSVfQNjO2vpRUbXhFxEdHe04Z87bxBLSmjhPrDBHWVmZa0qIiI1ey1ocLkQxfDXFar2YmGg2sXf3nhK3JDUlheT+/OIWLFo0Z+aspKSkzMzMg/v3Hzty5OfBg2bNnq3KR2UgVEG4cgXfmZiYaIFA8C4rq8RnBw8dIhAIbrp7VOzilZen18cWKLybsEQiYRN5eXlf9g9kXaTk5eUWvgVP+WNfWCXFGdWqVduxe/e8+fNNahc2qw/9/H6dPLn8t7MEAPiv0TcwIKKszA8amrp1zRTuUC8QGLB7BDcqGk+CIxQKaxoays9JT0v7gluYnJw8a9p0lqz69uu3Y/euS3//NdvBobTlFS648YpK/sRixRZHJpUVa5UK2ESjxo0smzUr/q/4/UIUmNate/j4sbHjx7POwPn5+efOnF25YgU+aVAl4coVfDdCgoPPnztn06dP4OOAcaPHcF2t5IWFhrI7k1QM1+FqtoND8xYtuPmJiQkrli4jomdhz5KSkgwNDc2Lui0lvX0rTE0VVP9I7bhUIi17AfMGhTfRio6KLp4n2YSJsUkF/ihlZeWhw4cNHjrE+/6DdWvWZGVlvU18e/vmrf4DB+BDBQBQHOsu+/RpyLBf/ilhaNKkyfq1a+WHqdDT1z9+8sThQ4dnO8yRTy+sIFBhMAyu3uGLuH/3Hmvs+g3ov3T5cjZTrZpaOV9et54Zm4iPi3v37p22trZ8bFNYuF69eq9evmQtY2srq/KsXyJVbPL09PSmz5zxv8mTzp89t2fXLiLyuuOZEB9vbGKCzxtUMbhyBd9Nspo6xf6mu4fTXMfFy5YS0bjRYxQuT7FliGjfftcKDMeUmpL6LCyMiFT5/J8HDbJsZsn969mrF7vNlEwmu+vpRUSGRkbshCURbduyVX4wdHk6OrqlNVcKmjRpwmozwkJD5Uddf5v41sfbh0137tq14l91Hq9r9279BhQGqiQMGAgAUFq40tMnopvuHvIzzerXY8P9fRjD9OfOc1S4LvQ0JERhMAyuCTA3t/giWxgaGsommjVrzs0sf69aIyMjo1pGRCQWiw+47ufmP7h//2lIiMLCXMXjWbczpTV2RU1eYWl6SlLJTV61atXGjBvbuGiFaIkA4QrgX2NqasqFmRLzla+3D5esKnYH4bteXqzZaNuurbqGusKz3X/8kU143imsDJwzdy6buHP79jT7X2953Hzx/PlDP7+D+/dzzY+hYWGxxPNnz44ePuLn61vaLUFM69YdNHgwm17o5BQYEJifn//ixQvHOXNYCeKAgQMtm1l+6h916uTJ82fPxcfFSaVSYWrqQz+/wgYeNxgBAChFhw4d2IR8lKpVq1Y7a+uVyz9SzCYSiXZt3zHLYY5C3GIT8pezPgcbUZaI3G/cSExMlEqlf125cvniJTaz+IUjxR9/PN70GTOLIpPbPIe5bqdO792127mkv27w0CHs+pKXp+c8h7n+Dx++TXybmJgYFhp25rSb/G2FDYsqIV3/2Hf/3v37d++xh5s3bPS648kutb148SKiqMcXhq6FKgllgfB9YNUX40aPYf87O811dNm29be168aNHjN46NCL58+z+RVOViRXE9i1a7fiz/7w4w+nTpxgbaRQKBQIBD1793r86NGVy5eJKCQ4OCQ4mFvYtm9f1hTVNTNr0rTp82fPiOiP338noukzZ3BD2SqYMXvW05CQ8PDw5OTkmdOm8Xg8aVEDWd/c3MHRsQJ/1OWLl2JjYohIWVmZ6ydm2cxSoaM2AABwWlkVjvv3/Nlz+fsFr1qzuq9Nn3NnzsqXC8qTSCQb1q+vb27e28ZGfj4b253dROSLbOGAnweeO3MmPz//SVDQ0J8Hqaqq5ufn16xZk10iK09n4J69e4WFhbqdOk1Evj4+vj4+RFSvfn0TE5MH9++TXDctNTW1pcuXLZy/IPvdOz9fXz9fX/n1zHZwMCtq1Pr0tb118yYRvXzxcqGTE5/P93pwXygUXrxw4eKFCwot0cjRo3SLRmYHqEpw5Qq+s3xV/PrVAVdXfX19Ryena+43KpysMjMznwQFsenOXUsIHpbNmrG3lkql3D0WFy1dsmXbNgsLC/maEEMjo3fvsrmHS5cvq1evHvewoKCgtG3Q0NA4dOyo/dRfWWUFS1Y6OjrTZ808fOxo8YtpHyWRSCwtLVlbztozVT5/2C/DN2/dyu6PDAAAxamrq7eztiai3/fs4cIAa4a2bNu21cVl5fLlxYcFSkxMtBs3PvBxwKo1qxXmswpD7r4an8/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"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 1.5 · Name the data, attributes, actions, and targets\n",
    "attribute_contract = pd.DataFrame([\n",
    "    [\"Table\", \"Each row is one selected GeoNames record\", \"Dataset type\"],\n",
    "    [\"Geometry\", \"Longitude + latitude become point positions only after spatial encoding\", \"Derived dataset type\"],\n",
    "    [\"Identifier\", \"geonameid\", \"Attribute role\"],\n",
    "    [\"Categorical\", \"name, feature_class/code, country, timezone, region, role\", \"Attribute type\"],\n",
    "    [\"Quantitative\", \"latitude, longitude, population, elevation, DEM\", \"Attribute type\"],\n",
    "], columns=[\"class\", \"case abstraction\", \"course vocabulary\"])\n",
    "\n",
    "task_contract = pd.DataFrame([\n",
    "    [\"Locate\", \"selected named features\", \"Search → spatial target\"],\n",
    "    [\"Browse\", \"available source fields\", \"Search → item/attribute target\"],\n",
    "    [\"Compare\", \"population for four settlement rows\", \"Query → quantitative attribute target\"],\n",
    "    [\"Identify\", \"unsupported cultural/environmental claims\", \"Query → evidence boundary\"],\n",
    "], columns=[\"action\", \"target\", \"action + target expression\"])\n",
    "\n",
    "display(attribute_contract.style.hide(axis=\"index\"))\n",
    "display(task_contract.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 1.6 · Inspect the exact source rows\n",
    "source_columns = [\n",
    "    \"geonameid\", \"name\", \"region\", \"role\", \"feature_class\", \"feature_code\",\n",
    "    \"latitude\", \"longitude\", \"population\", \"modification_date\", \"source_url\",\n",
    "]\n",
    "source_view = records[source_columns].sort_values([\"region\", \"feature_class\", \"name\"])\n",
    "display(\n",
    "    source_view.style\n",
    "    .format({\"latitude\": \"{:.5f}\", \"longitude\": \"{:.5f}\", \"population\": \"{:,.0f}\"})\n",
    "    .hide(axis=\"index\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-011",
   "metadata": {},
   "source": [
    "# 2 · Reproduce the demo views\n",
    "\n",
    "### Three linked views\n",
    "\n",
    "1. **Map:** two local geographic views answer *where* without forcing labels to overlap.\n",
    "2. **Population:** aligned log position and exact labels answer *how much* more accurately than area alone.\n",
    "3. **Records + limits:** the source inspector and evidence table prevent geographic context from becoming a cultural conclusion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-012",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 2.1 · Build the two interactive maps\n",
    "MAP_FILTER = \"All records\" # @param [\"All records\", \"Water features\", \"Settlements\"]\n",
    "\n",
    "filter_code = {\"All records\": None, \"Water features\": \"H\", \"Settlements\": \"P\"}[MAP_FILTER]\n",
    "map_rows = records if filter_code is None else records.query(\"feature_class == @filter_code\")\n",
    "settlement_values = records.query(\"feature_class == 'P' and population > 0\")[\"population\"]\n",
    "log_low, log_high = np.log10(settlement_values.min()), np.log10(settlement_values.max())\n",
    "\n",
    "def graduated_radius(row):\n",
    "    if row.feature_class != \"P\" or row.population <= 0:\n",
    "        return 7\n",
    "    position = (np.log10(row.population) - log_low) / (log_high - log_low)\n",
    "    return float(8 + 18 * np.sqrt(max(0, position)))\n",
    "\n",
    "def region_map(region):\n",
    "    all_region = records.query(\"region == @region\")\n",
    "    shown = map_rows.query(\"region == @region\")\n",
    "    center = [all_region[\"latitude\"].mean(), all_region[\"longitude\"].mean()]\n",
    "    view = folium.Map(\n",
    "        location=center, tiles=\"OpenStreetMap\", control_scale=True,\n",
    "        zoom_control=True, scrollWheelZoom=False, width=\"100%\", height=420,\n",
    "    )\n",
    "    for row in shown.itertuples():\n",
    "        water = row.feature_class == \"H\"\n",
    "        color = COLORS[\"river\"] if water else (\n",
    "            COLORS[\"jiangnan\"] if row.region == \"Jiangnan\" else COLORS[\"venice\"]\n",
    "        )\n",
    "        population_text = f\"{row.population:,}\" if row.population > 0 else \"not applicable / 0\"\n",
    "        popup = folium.Popup(\n",
    "            f\"<b>{row.name}</b><br>Class/code: {row.feature_class}/{row.feature_code}\"\n",
    "            f\"<br>Population field: {population_text}\"\n",
    "            f'<br><a href=\"{row.source_url}\" target=\"_blank\">Open GeoNames record</a>',\n",
    "            max_width=280,\n",
    "        )\n",
    "        folium.CircleMarker(\n",
    "            [row.latitude, row.longitude],\n",
    "            radius=graduated_radius(row),\n",
    "            color=COLORS[\"river\"] if water else \"white\",\n",
    "            weight=3 if water else 2,\n",
    "            fill=True,\n",
    "            fill_color=COLORS[\"river\"] if water else color,\n",
    "            fill_opacity=0.35 if water else 0.82,\n",
    "            tooltip=f\"{row.name} · {row.feature_class}/{row.feature_code}\",\n",
    "            popup=popup,\n",
    "        ).add_to(view)\n",
    "    view.fit_bounds([\n",
    "        [all_region[\"latitude\"].min(), all_region[\"longitude\"].min()],\n",
    "        [all_region[\"latitude\"].max(), all_region[\"longitude\"].max()],\n",
    "    ], padding=(24, 24))\n",
    "    return view\n",
    "\n",
    "jiangnan_map = region_map(\"Jiangnan\")\n",
    "venice_map = region_map(\"Venice\")\n",
    "jiangnan_map.save(OUTPUT_DIR / \"jiangnan_map.html\")\n",
    "venice_map.save(OUTPUT_DIR / \"venice_map.html\")\n",
    "\n",
    "display(HTML(\n",
    "    '<div style=\"display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:14px;\">'\n",
    "    '<div><h3 style=\"font-family:Georgia;color:#18313b;\">Kunshan, Suzhou & nearby waters</h3>'\n",
    "    + jiangnan_map._repr_html_() + '</div>'\n",
    "    '<div><h3 style=\"font-family:Georgia;color:#18313b;\">Venice & its lagoon</h3>'\n",
    "    + venice_map._repr_html_() + '</div></div>'\n",
    "    '<p style=\"color:#63777d;font-size:13px;\">'\n",
    "    'Map bubbles are a graduated cue; use the comparison chart below for exact values. '\n",
    "    'Basemap © OpenStreetMap contributors.</p>'\n",
    "))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-013",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 2.2 · Draw and export the population comparison\n",
    "population = (\n",
    "    records.query(\"feature_class == 'P' and population > 0\")\n",
    "    .sort_values(\"population\", ascending=True)\n",
    "    .copy()\n",
    ")\n",
    "population[\"color\"] = population[\"region\"].map({\n",
    "    \"Jiangnan\": COLORS[\"jiangnan\"], \"Venice\": COLORS[\"venice\"]\n",
    "})\n",
    "population[\"bubble_area\"] = 90 + 560 * np.sqrt(\n",
    "    population[\"population\"] / population[\"population\"].max()\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10.5, 5.7))\n",
    "y = np.arange(len(population))\n",
    "ax.hlines(y, 1e4, population[\"population\"], color=COLORS[\"line\"], linewidth=2, zorder=1)\n",
    "ax.scatter(\n",
    "    population[\"population\"], y,\n",
    "    s=population[\"bubble_area\"], c=population[\"color\"],\n",
    "    edgecolor=\"white\", linewidth=2.5, zorder=3,\n",
    ")\n",
    "for index, row in enumerate(population.itertuples()):\n",
    "    ax.annotate(\n",
    "        f\"{row.population:,}\",\n",
    "        (row.population, index),\n",
    "        xytext=(-13 if row.population > 1_000_000 else 13, 0),\n",
    "        textcoords=\"offset points\",\n",
    "        ha=\"right\" if row.population > 1_000_000 else \"left\",\n",
    "        va=\"center\", fontsize=10, fontweight=\"bold\", color=COLORS[\"ink\"],\n",
    "    )\n",
    "\n",
    "labels = [f\"{row.name}\\n{row.region} · {row.role}\" for row in population.itertuples()]\n",
    "ax.set_yticks(y, labels)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlim(1e4, 1.15e7)\n",
    "ax.set_xticks([1e4, 1e5, 1e6, 1e7], [\"10K\", \"100K\", \"1M\", \"10M\"])\n",
    "ax.grid(axis=\"x\", color=COLORS[\"line\"], linewidth=.8)\n",
    "ax.set_axisbelow(True)\n",
    "ax.set_xlabel(\"Population field recorded by GeoNames · logarithmic scale\")\n",
    "fig.suptitle(\n",
    "    \"Four settlement records on one comparable scale\",\n",
    "    x=0.235, y=0.965, ha=\"left\", fontsize=19, fontweight=\"bold\", color=COLORS[\"ink\"],\n",
    ")\n",
    "fig.text(\n",
    "    0.235, 0.91,\n",
    "    \"Position supports comparison; bubble area is secondary; exact values are printed.\",\n",
    "    ha=\"left\", color=COLORS[\"muted\"], fontsize=10,\n",
    ")\n",
    "for spine in [\"top\", \"right\", \"left\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "ax.tick_params(axis=\"y\", length=0)\n",
    "fig.subplots_adjust(left=0.235, right=0.97, bottom=0.15, top=0.84)\n",
    "\n",
    "for extension in [\"png\", \"svg\", \"pdf\"]:\n",
    "    fig.savefig(\n",
    "        OUTPUT_DIR / f\"population_comparison.{extension}\",\n",
    "        dpi=220 if extension == \"png\" else None,\n",
    "        bbox_inches=\"tight\",\n",
    "    )\n",
    "plt.show()\n",
    "\n",
    "display(Markdown(\n",
    "    \"**Comparability note.** These are the values in four selected GeoNames rows. \"\n",
    "    \"The source does not provide harmonized reference dates or definitions, so this \"\n",
    "    \"figure is descriptive of the recorded field—not a synchronized census comparison.\"\n",
    "))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 2.3 · Inspect one record without leaving the notebook\n",
    "SELECTED_RECORD = \"Zhouzhuang\" # @param [\"Zhouzhuang\", \"Kunshan\", \"Suzhou\", \"Tai Hu\", \"Dianshan Hu\", \"Venice\", \"Canal Grande\", \"Canal Grande di Murano\", \"Venetian Lagoon\"]\n",
    "\n",
    "selected = records.loc[records[\"name\"].eq(SELECTED_RECORD)].iloc[0]\n",
    "detail = pd.DataFrame({\n",
    "    \"field\": [\n",
    "        \"GeoNames ID\", \"role\", \"feature class / code\", \"coordinates\",\n",
    "        \"population field\", \"elevation / DEM\", \"timezone\", \"modified\", \"source\",\n",
    "    ],\n",
    "    \"value\": [\n",
    "        selected.geonameid, selected.role,\n",
    "        f\"{selected.feature_class} / {selected.feature_code}\",\n",
    "        f\"{selected.latitude:.5f}, {selected.longitude:.5f}\",\n",
    "        f\"{selected.population:,}\" if selected.population else \"not applicable / 0\",\n",
    "        f\"{selected.elevation} / {selected.dem}\", selected.timezone,\n",
    "        selected.modification_date, selected.source_url,\n",
    "    ],\n",
    "})\n",
    "display(detail.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-015",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 2.4 · State the evidence boundary\n",
    "evidence_boundary = pd.DataFrame([\n",
    "    [\n",
    "        \"Supported\",\n",
    "        \"Named geographic context\",\n",
    "        \"Names, coordinates, feature class/code, and selected administrative fields\",\n",
    "        \"Locate these nine rows and inspect their available attributes\",\n",
    "    ],\n",
    "    [\n",
    "        \"Partly supported\",\n",
    "        \"Population and water-system framing\",\n",
    "        \"Population and class-H fields exist; reference dates, definitions, connectivity, condition, use, and change do not\",\n",
    "        \"Describe recorded fields with explicit limitations\",\n",
    "    ],\n",
    "    [\n",
    "        \"Not supported\",\n",
    "        \"Cultural meaning or community need\",\n",
    "        \"No evidence of heritage value, exchange, authority, tourism pressure, or a historical route\",\n",
    "        \"Do not infer; add authoritative and participatory evidence\",\n",
    "    ],\n",
    "], columns=[\"status\", \"claim area\", \"what the rows contain\", \"permitted use\"])\n",
    "display(evidence_boundary.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-016",
   "metadata": {},
   "source": [
    "# 3 · Critique and redesign\n",
    "\n",
    "### Three questions\n",
    "\n",
    "1. **Pipeline:** Is the domain supported by the data/tasks, idiom, and algorithm?\n",
    "2. **Evidence:** Can the source be traced, is it fit for the claim, and who controls or bears risk?\n",
    "3. **Redesign:** Which additional evidence or visual decision repairs a named gap?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.1 · Apply the four-level pipeline to the case\n",
    "pipeline = pd.DataFrame([\n",
    "    [\n",
    "        \"Domain\",\n",
    "        \"Whose real question should the artifact serve?\",\n",
    "        \"Intercultural inquiry around two water systems; local need remains an open question\",\n",
    "        \"Keep the interface framed as a starting point, not a community contribution\",\n",
    "    ],\n",
    "    [\n",
    "        \"Data / task\",\n",
    "        \"Which dataset/attribute types and action + target pairs are present?\",\n",
    "        \"Table → point geometry; categorical + quantitative attributes; locate, browse, compare, identify\",\n",
    "        \"Limit comparison to recorded fields and mark absent evidence\",\n",
    "    ],\n",
    "    [\n",
    "        \"Idiom\",\n",
    "        \"Which form follows from the task?\",\n",
    "        \"Two maps for locate; log bubble-lollipop for compare; table for inspect\",\n",
    "        \"Do not rely on map-circle area for exact comparison\",\n",
    "    ],\n",
    "    [\n",
    "        \"Algorithm\",\n",
    "        \"Which transformations and tools produce the idiom?\",\n",
    "        \"Pinned-ID filter → field mapping → validation → Folium maps + Matplotlib chart\",\n",
    "        \"Export source mapping and PNG/SVG/PDF; retain failure and comparability notes\",\n",
    "    ],\n",
    "], columns=[\"level\", \"question\", \"worked-case answer\", \"visible design consequence\"])\n",
    "display(pipeline.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-018",
   "metadata": {},
   "source": [
    "### 3.2 Idiom decision tree\n",
    "\n",
    "Choose an idiom only after naming the action and target. The next cell includes every option shown in the tutorial:\n",
    "\n",
    "- **Compare:** values/ranks, change/groups, composition/profiles\n",
    "- **Understand variation:** distributions, relationships/density, uncertainty/models\n",
    "- **Reveal structure:** hierarchy/flow, cycles/opposition, arranged tables\n",
    "\n",
    "For this case: **Locate → map** and **Compare → log-scaled bubble-lollipop**. Treemap is rejected because these rows contain no hierarchy; population pyramid is rejected because they contain no opposing populations over shared ordered categories.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.3 · Explore all 38 demonstrated idioms\n",
    "IDIOM_ROOT = \"Compare\" # @param [\"Compare\", \"Understand variation\", \"Reveal structure\"]\n",
    "\n",
    "idiom_catalog = pd.DataFrame([{'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Sorted bar', 'selection_cue': 'Compare magnitudes on a shared zero baseline.', 'caution': ''}, {'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Dot plot', 'selection_cue': 'Compare positions with less ink than bars.', 'caution': ''}, {'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Lollipop', 'selection_cue': 'Emphasize endpoints while retaining a baseline.', 'caution': ''}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Slope graph', 'selection_cue': 'Compare two time points across groups.', 'caution': ''}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Dumbbell', 'selection_cue': 'Compare paired values and the gap between them.', 'caution': ''}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Line chart', 'selection_cue': 'Follow ordered change; do not connect unordered categories.', 'caution': ''}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Small multiples', 'selection_cue': 'Compare repeated panels with aligned scales.', 'caution': ''}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Stacked bar', 'selection_cue': 'Compare totals and a limited number of parts.', 'caution': ''}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': '100% stacked', 'selection_cue': 'Compare proportions when totals are secondary.', 'caution': ''}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Donut', 'selection_cue': 'Use only for a few parts; angles are imprecise.', 'caution': 'Use sparingly'}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Aligned profile', 'selection_cue': 'Compare many measures on shared aligned axes.', 'caution': ''}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Radar', 'selection_cue': 'Only for a small, common scale; aligned profiles are often clearer.', 'caution': 'Requires justification'}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Histogram', 'selection_cue': 'Show binned distribution shape; test bin sensitivity.', 'caution': ''}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'KDE', 'selection_cue': 'Show smoothed density; disclose bandwidth.', 'caution': ''}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'ECDF', 'selection_cue': 'Show every observation without bin or bandwidth choice.', 'caution': ''}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Box plot', 'selection_cue': 'Compare robust summaries; show raw data when sample size matters.', 'caution': ''}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Violin', 'selection_cue': 'Compare distribution shape; include scale and sample context.', 'caution': ''}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Raincloud', 'selection_cue': 'Combine density, summary, and observations.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Scatterplot', 'selection_cue': 'Inspect two quantitative attributes.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Regression view', 'selection_cue': 'Show fitted relationship with uncertainty and assumptions.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Hexbin', 'selection_cue': 'Aggregate dense points to reveal concentration.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Pair plot', 'selection_cue': 'Scan many pairwise relationships; avoid tiny unreadable panels.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Correlation heatmap', 'selection_cue': 'Reveal a matrix pattern after justified ordering.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': '2D contour', 'selection_cue': 'Show a continuous surface without unnecessary 3D perspective.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Interval / forest', 'selection_cue': 'Compare estimates and uncertainty on aligned axes.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Fan chart', 'selection_cue': 'Show forecast uncertainty expanding through time.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Calibration', 'selection_cue': 'Compare predicted probability with observed frequency.', 'caution': ''}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Residual view', 'selection_cue': 'Diagnose model error, structure, and outliers.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Treemap', 'selection_cue': 'Encode a genuine part-to-whole hierarchy through nested area.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Sunburst', 'selection_cue': 'Show hierarchical depth radially; labels can become difficult.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Sankey', 'selection_cue': 'Show aggregated flows with conserved quantities.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Alluvial', 'selection_cue': 'Compare category flows across ordered stages.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Cycles and opposition', 'idiom': 'Polar / Burtin', 'selection_cue': 'Use only for genuinely cyclic variables.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Cycles and opposition', 'idiom': 'Population pyramid', 'selection_cue': 'Compare two opposing populations across the same ordered categories.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Ordered heatmap', 'selection_cue': 'Reveal table structure through meaningful row and column order.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Faceted table', 'selection_cue': 'Keep exact values while arranging meaningful groups.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Missingness matrix', 'selection_cue': 'Expose which variables or groups lack evidence.', 'caution': ''}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'UpSet matrix', 'selection_cue': 'Compare intersections when a Venn diagram no longer scales.', 'caution': ''}])\n",
    "selected_idioms = idiom_catalog.query(\"root == @IDIOM_ROOT\")\n",
    "display(selected_idioms.style.hide(axis=\"index\"))\n",
    "print(\n",
    "    f\"Showing {len(selected_idioms)} of {len(idiom_catalog)} idioms · \"\n",
    "    f\"roots: {', '.join(idiom_catalog['root'].unique())}\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-020",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.4 · Search all 72 Python and R visualization tools\n",
    "LANGUAGE = \"Python\" # @param [\"Python\", \"R\"]\n",
    "TOOL_CATEGORY = \"All\" # @param [\"All\", \"grammar\", \"statistics\", \"specialized\"]\n",
    "TOOL_SEARCH = \"map\" # @param {type:\"string\"}\n",
    "\n",
    "tool_catalog = pd.DataFrame([{'name': 'Matplotlib', 'language': 'Python', 'category': 'grammar', 'use': 'Foundational static, animated, and publication figures.', 'tags': 'bar line scatter animation', 'official_url': 'https://matplotlib.org/stable/gallery/index.html'}, {'name': 'Seaborn', 'language': 'Python', 'category': 'grammar', 'use': 'Statistical graphics with concise data-aware defaults.', 'tags': 'distribution relational categorical', 'official_url': 'https://seaborn.pydata.org/examples/index.html'}, {'name': 'Plotly', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive browser charts, maps, 3D, and dashboards.', 'tags': 'interactive hover map', 'official_url': 'https://plotly.com/python/'}, {'name': 'Altair', 'language': 'Python', 'category': 'grammar', 'use': 'Declarative Vega-Lite grammar for composable charts.', 'tags': 'declarative grammar interactive', 'official_url': 'https://altair-viz.github.io/gallery/index.html'}, {'name': 'Bokeh', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive linked plots and browser applications.', 'tags': 'interactive linked brushing server', 'official_url': 'https://docs.bokeh.org/en/latest/docs/gallery.html'}, {'name': 'HoloViews', 'language': 'Python', 'category': 'grammar', 'use': 'High-level declarative views across plotting backends.', 'tags': 'declarative linked data', 'official_url': 'https://holoviews.org/reference/index.html'}, {'name': 'hvPlot', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive plotting API for pandas, xarray, and more.', 'tags': 'pandas xarray interactive', 'official_url': 'https://hvplot.holoviz.org/reference/index.html'}, {'name': 'plotnine', 'language': 'Python', 'category': 'grammar', 'use': 'Grammar-of-graphics implementation inspired by ggplot2.', 'tags': 'grammar layers facets', 'official_url': 'https://plotnine.org/gallery.html'}, {'name': 'Lets-Plot', 'language': 'Python', 'category': 'grammar', 'use': 'Grammar-of-graphics plots for notebooks and web output.', 'tags': 'grammar interactive notebooks', 'official_url': 'https://lets-plot.org/python/pages/gallery.html'}, {'name': 'Pygal', 'language': 'Python', 'category': 'grammar', 'use': 'Lightweight SVG charts with browser-friendly output.', 'tags': 'svg browser simple', 'official_url': 'https://www.pygal.org/en/stable/documentation/types/index.html'}, {'name': 'pyecharts', 'language': 'Python', 'category': 'grammar', 'use': 'Python bindings for the Apache ECharts ecosystem.', 'tags': 'echarts interactive web', 'official_url': 'https://gallery.pyecharts.org/'}, {'name': 'Panel', 'language': 'Python', 'category': 'grammar', 'use': 'Compose plots, widgets, and data apps across libraries.', 'tags': 'dashboard widgets app', 'official_url': 'https://panel.holoviz.org/gallery/index.html'}, {'name': 'pandas plotting', 'language': 'Python', 'category': 'statistics', 'use': 'Quick plots directly from Series and DataFrames.', 'tags': 'dataframe quick exploratory', 'official_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html'}, {'name': 'statsmodels graphics', 'language': 'Python', 'category': 'statistics', 'use': 'Regression, diagnostic, time-series, and model plots.', 'tags': 'regression residual diagnostic', 'official_url': 'https://www.statsmodels.org/stable/graphics.html'}, {'name': 'scikit-learn Displays', 'language': 'Python', 'category': 'statistics', 'use': 'Model evaluation displays with estimator integration.', 'tags': 'machine learning calibration roc confusion', 'official_url': 'https://scikit-learn.org/stable/visualizations.html'}, {'name': 'Yellowbrick', 'language': 'Python', 'category': 'statistics', 'use': 'Visual diagnostics for machine-learning workflows.', 'tags': 'machine learning diagnostic residual', 'official_url': 'https://www.scikit-yb.org/en/latest/gallery.html'}, {'name': 'ArviZ', 'language': 'Python', 'category': 'statistics', 'use': 'Exploratory analysis and diagnostics for Bayesian models.', 'tags': 'bayesian posterior interval', 'official_url': 'https://python.arviz.org/en/stable/examples/index.html'}, {'name': 'corner.py', 'language': 'Python', 'category': 'statistics', 'use': 'Multidimensional posterior and parameter distributions.', 'tags': 'bayesian pairplot distribution', 'official_url': 'https://corner.readthedocs.io/en/latest/pages/quickstart/'}, {'name': 'missingno', 'language': 'Python', 'category': 'statistics', 'use': 'Missing-data matrices, bars, heatmaps, and dendrograms.', 'tags': 'missingness data quality', 'official_url': 'https://github.com/ResidentMario/missingno'}, {'name': 'UpSetPlot', 'language': 'Python', 'category': 'statistics', 'use': 'Scalable set-intersection visualization.', 'tags': 'sets intersections upset', 'official_url': 'https://upsetplot.readthedocs.io/en/stable/auto_examples/index.html'}, {'name': 'JoyPy', 'language': 'Python', 'category': 'statistics', 'use': 'Ridgeline distribution plots built on Matplotlib.', 'tags': 'ridgeline density distribution', 'official_url': 'https://github.com/leotac/joypy'}, {'name': 'PtitPrince', 'language': 'Python', 'category': 'statistics', 'use': 'Raincloud plots combining density, box, and points.', 'tags': 'raincloud distribution', 'official_url': 'https://github.com/pog87/PtitPrince'}, {'name': 'SciencePlots', 'language': 'Python', 'category': 'statistics', 'use': 'Matplotlib styles for scientific publication contexts.', 'tags': 'publication style journal', 'official_url': 'https://github.com/garrettj403/SciencePlots'}, {'name': 'statannotations', 'language': 'Python', 'category': 'statistics', 'use': 'Statistical annotations for seaborn and Matplotlib plots.', 'tags': 'significance annotation box', 'official_url': 'https://github.com/trevismd/statannotations'}, {'name': 'GeoPandas', 'language': 'Python', 'category': 'specialized', 'use': 'Geospatial vector data analysis and mapping.', 'tags': 'map geometry spatial', 'official_url': 'https://geopandas.org/en/stable/docs/user_guide/mapping.html'}, {'name': 'Cartopy', 'language': 'Python', 'category': 'specialized', 'use': 'Projected maps and geospatial data transformations.', 'tags': 'map projection geospatial', 'official_url': 'https://cartopy.readthedocs.io/stable/gallery/index.html'}, {'name': 'Folium', 'language': 'Python', 'category': 'specialized', 'use': 'Interactive Leaflet maps from Python.', 'tags': 'interactive map leaflet', 'official_url': 'https://python-visualization.github.io/folium/latest/getting_started.html'}, {'name': 'pydeck', 'language': 'Python', 'category': 'specialized', 'use': 'Large-scale layered geospatial visualizations with deck.gl.', 'tags': 'map layers gpu spatial', 'official_url': 'https://deckgl.readthedocs.io/en/latest/gallery/index.html'}, {'name': 'Datashader', 'language': 'Python', 'category': 'specialized', 'use': 'Rasterize and aggregate massive point or line datasets.', 'tags': 'large data density raster', 'official_url': 'https://datashader.org/user_guide/index.html'}, {'name': 'NetworkX', 'language': 'Python', 'category': 'specialized', 'use': 'Network analysis, layouts, and graph drawing.', 'tags': 'network graph topology', 'official_url': 'https://networkx.org/documentation/stable/auto_examples/index.html'}, {'name': 'PyVis', 'language': 'Python', 'category': 'specialized', 'use': 'Interactive browser-based network visualization.', 'tags': 'network interactive browser', 'official_url': 'https://pyvis.readthedocs.io/en/latest/'}, {'name': 'OSMnx', 'language': 'Python', 'category': 'specialized', 'use': 'Download, analyze, and visualize street networks.', 'tags': 'network street map osm', 'official_url': 'https://osmnx.readthedocs.io/en/stable/'}, {'name': 'contextily', 'language': 'Python', 'category': 'specialized', 'use': 'Add web basemaps to geospatial plots.', 'tags': 'basemap tiles spatial', 'official_url': 'https://contextily.readthedocs.io/en/latest/intro_guide.html'}, {'name': 'GeoViews', 'language': 'Python', 'category': 'specialized', 'use': 'Geographic visualization built on HoloViews.', 'tags': 'map geographic interactive', 'official_url': 'https://geoviews.org/gallery/index.html'}, {'name': 'Graphviz', 'language': 'Python', 'category': 'specialized', 'use': 'Graph and hierarchy layout through the Graphviz engine.', 'tags': 'network tree layout', 'official_url': 'https://graphviz.readthedocs.io/en/stable/examples.html'}, {'name': 'pyCirclize', 'language': 'Python', 'category': 'specialized', 'use': 'Circular genome, chord, and sector visualizations.', 'tags': 'circular chord polar', 'official_url': 'https://moshi4.github.io/pyCirclize/'}, {'name': 'ggplot2', 'language': 'R', 'category': 'grammar', 'use': 'Layered grammar of graphics for analytical and publication figures.', 'tags': 'grammar layers facets', 'official_url': 'https://ggplot2.tidyverse.org/'}, {'name': 'plotly for R', 'language': 'R', 'category': 'grammar', 'use': 'Interactive Plotly figures and ggplotly conversion.', 'tags': 'interactive hover web', 'official_url': 'https://plotly.com/r/'}, {'name': 'Shiny', 'language': 'R', 'category': 'grammar', 'use': 'Reactive interactive applications and dashboards.', 'tags': 'app reactive dashboard', 'official_url': 'https://shiny.posit.co/r/gallery/'}, {'name': 'highcharter', 'language': 'R', 'category': 'grammar', 'use': 'Interactive Highcharts visualizations from R.', 'tags': 'interactive web time series', 'official_url': 'https://jkunst.com/highcharter/'}, {'name': 'echarts4r', 'language': 'R', 'category': 'grammar', 'use': 'R interface to Apache ECharts with rich interaction.', 'tags': 'interactive echarts web', 'official_url': 'https://echarts4r.john-coene.com/'}, {'name': 'ggiraph', 'language': 'R', 'category': 'grammar', 'use': 'Interactive SVG output for ggplot2 graphics.', 'tags': 'ggplot interactive svg', 'official_url': 'https://davidgohel.github.io/ggiraph/'}, {'name': 'lattice', 'language': 'R', 'category': 'grammar', 'use': 'Trellis graphics for multivariable conditioning.', 'tags': 'facets conditioning multivariate', 'official_url': 'https://lattice.r-forge.r-project.org/'}, {'name': 'r2d3', 'language': 'R', 'category': 'grammar', 'use': 'Build custom D3 visualizations from R.', 'tags': 'd3 custom interactive', 'official_url': 'https://rstudio.github.io/r2d3/'}, {'name': 'htmlwidgets', 'language': 'R', 'category': 'grammar', 'use': 'Framework connecting JavaScript visualization libraries to R.', 'tags': 'html javascript interactive', 'official_url': 'https://www.htmlwidgets.org/showcase_leaflet.html'}, {'name': 'vegawidget', 'language': 'R', 'category': 'grammar', 'use': 'Render and compose Vega and Vega-Lite specifications.', 'tags': 'vega declarative grammar', 'official_url': 'https://vegawidget.github.io/vegawidget/'}, {'name': 'dygraphs', 'language': 'R', 'category': 'grammar', 'use': 'Interactive time-series charts for R and Shiny.', 'tags': 'interactive time series', 'official_url': 'https://rstudio.github.io/dygraphs/'}, {'name': 'reactable', 'language': 'R', 'category': 'grammar', 'use': 'Interactive data tables with sorting and grouping.', 'tags': 'table interactive arranged', 'official_url': 'https://glin.github.io/reactable/articles/examples.html'}, {'name': 'patchwork', 'language': 'R', 'category': 'statistics', 'use': 'Compose multiple ggplot2 figures with a layout grammar.', 'tags': 'composition panels publication', 'official_url': 'https://patchwork.data-imaginist.com/'}, {'name': 'cowplot', 'language': 'R', 'category': 'statistics', 'use': 'Align, arrange, annotate, and theme publication plots.', 'tags': 'publication arrange annotation', 'official_url': 'https://wilkelab.org/cowplot/'}, {'name': 'ggridges', 'language': 'R', 'category': 'statistics', 'use': 'Ridgeline density plots for grouped distributions.', 'tags': 'ridgeline distribution density', 'official_url': 'https://wilkelab.org/ggridges/'}, {'name': 'ggdist', 'language': 'R', 'category': 'statistics', 'use': 'Uncertainty and distribution visualization for ggplot2.', 'tags': 'uncertainty interval distribution', 'official_url': 'https://mjskay.github.io/ggdist/'}, {'name': 'ggbeeswarm', 'language': 'R', 'category': 'statistics', 'use': 'Non-overlapping point plots for distributions.', 'tags': 'beeswarm points distribution', 'official_url': 'https://eclarke.github.io/ggbeeswarm/'}, {'name': 'GGally', 'language': 'R', 'category': 'statistics', 'use': 'Pairs plots and extensions to ggplot2.', 'tags': 'pairplot correlation multivariate', 'official_url': 'https://ggobi.github.io/ggally/'}, {'name': 'ggstatsplot', 'language': 'R', 'category': 'statistics', 'use': 'ggplot2 figures integrated with statistical details.', 'tags': 'statistics annotation inference', 'official_url': 'https://indrajeetpatil.github.io/ggstatsplot/'}, {'name': 'forestplot', 'language': 'R', 'category': 'statistics', 'use': 'Customizable forest plots and confidence intervals.', 'tags': 'forest interval meta analysis', 'official_url': 'https://cran.r-project.org/package=forestplot'}, {'name': 'survminer', 'language': 'R', 'category': 'statistics', 'use': 'Publication-ready survival curves and diagnostics.', 'tags': 'survival model curve', 'official_url': 'https://rpkgs.datanovia.com/survminer/'}, {'name': 'ggforce', 'language': 'R', 'category': 'statistics', 'use': 'Geometric extensions, facets, and annotations for ggplot2.', 'tags': 'geometry facet annotation', 'official_url': 'https://ggforce.data-imaginist.com/'}, {'name': 'ggthemes', 'language': 'R', 'category': 'statistics', 'use': 'Additional complete themes, scales, and palettes for ggplot2.', 'tags': 'theme publication palette', 'official_url': 'https://jrnold.github.io/ggthemes/'}, {'name': 'ggtext', 'language': 'R', 'category': 'statistics', 'use': 'Rich text rendering inside ggplot2 figures.', 'tags': 'typography annotation publication', 'official_url': 'https://wilkelab.org/ggtext/'}, {'name': 'sf', 'language': 'R', 'category': 'specialized', 'use': 'Simple-features vector data operations and mapping.', 'tags': 'map geometry spatial', 'official_url': 'https://r-spatial.github.io/sf/'}, {'name': 'terra', 'language': 'R', 'category': 'specialized', 'use': 'Spatial raster and vector analysis with plotting support.', 'tags': 'raster map spatial', 'official_url': 'https://rspatial.github.io/terra/'}, {'name': 'tmap', 'language': 'R', 'category': 'specialized', 'use': 'Thematic static and interactive maps.', 'tags': 'thematic map interactive', 'official_url': 'https://r-tmap.github.io/tmap/'}, {'name': 'leaflet', 'language': 'R', 'category': 'specialized', 'use': 'Interactive Leaflet maps from R and Shiny.', 'tags': 'interactive map tiles', 'official_url': 'https://rstudio.github.io/leaflet/'}, {'name': 'mapview', 'language': 'R', 'category': 'specialized', 'use': 'Rapid interactive viewing of spatial objects.', 'tags': 'interactive map exploratory', 'official_url': 'https://r-spatial.github.io/mapview/'}, {'name': 'ggraph', 'language': 'R', 'category': 'specialized', 'use': 'Grammar-of-graphics approach to networks and trees.', 'tags': 'network tree grammar', 'official_url': 'https://ggraph.data-imaginist.com/'}, {'name': 'igraph', 'language': 'R', 'category': 'specialized', 'use': 'Network analysis, layout, and plotting.', 'tags': 'network topology graph', 'official_url': 'https://r.igraph.org/'}, {'name': 'ComplexHeatmap', 'language': 'R', 'category': 'specialized', 'use': 'Highly composable heatmaps with annotations.', 'tags': 'heatmap matrix annotation', 'official_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/'}, {'name': 'ggalluvial', 'language': 'R', 'category': 'specialized', 'use': 'Alluvial plots for categorical flows in ggplot2.', 'tags': 'alluvial sankey flow', 'official_url': 'https://corybrunson.github.io/ggalluvial/'}, {'name': 'circlize', 'language': 'R', 'category': 'specialized', 'use': 'Circular, chord, genomic, and sector visualizations.', 'tags': 'circular chord polar', 'official_url': 'https://jokergoo.github.io/circlize_book/book/'}, {'name': 'networkD3', 'language': 'R', 'category': 'specialized', 'use': 'Interactive D3 networks, trees, and Sankey diagrams.', 'tags': 'network sankey interactive', 'official_url': 'https://christophergandrud.github.io/networkD3/'}, {'name': 'treemapify', 'language': 'R', 'category': 'specialized', 'use': 'Treemaps in the ggplot2 ecosystem.', 'tags': 'treemap hierarchy area', 'official_url': 'https://wilkox.org/treemapify/'}])\n",
    "selected_tools = tool_catalog.query(\"language == @LANGUAGE\").copy()\n",
    "if TOOL_CATEGORY != \"All\":\n",
    "    selected_tools = selected_tools.query(\"category == @TOOL_CATEGORY\")\n",
    "if TOOL_SEARCH.strip():\n",
    "    query = TOOL_SEARCH.lower().strip()\n",
    "    selected_tools = selected_tools[\n",
    "        selected_tools.apply(\n",
    "            lambda row: query in \" \".join(map(str, row)).lower(), axis=1\n",
    "        )\n",
    "    ]\n",
    "display(selected_tools[[\"name\", \"language\", \"category\", \"use\", \"official_url\"]].style.hide(axis=\"index\"))\n",
    "print(\n",
    "    \"Complete atlas:\",\n",
    "    len(tool_catalog), \"tools ·\",\n",
    "    (tool_catalog[\"language\"] == \"Python\").sum(), \"Python ·\",\n",
    "    (tool_catalog[\"language\"] == \"R\").sum(), \"R\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-021",
   "metadata": {},
   "source": [
    "### 3.5 Open science and data governance—three paper-anchored questions\n",
    "\n",
    "| Question | Inspect | Course anchors |\n",
    "|---|---|---|\n",
    "| Can we trace and reproduce it? | Producer, original source, revision, schema, license, selection, transformation | [FAIR — Wilkinson et al. (2016)](https://doi.org/10.1038/sdata.2016.18); [Croissant — Akhtar et al. (2024)](https://proceedings.neurips.cc/paper_files/paper/2024/hash/9547b09b722f2948ff3ddb5d86002bc0-Abstract-Datasets_and_Benchmarks_Track.html) |\n",
    "| Is it fit for this claim? | Coverage, measurement, missingness, uncertainty, visual implication, non-inference | [Lan & Liu (2024)](https://doi.org/10.1109/TVCG.2024.3456341); [Ziman et al. (2026)](https://doi.org/10.1109/TVCG.2025.3633883) |\n",
    "| Who benefits, controls, and bears risk? | Authority, consent, context, correction, withdrawal, intended use | [CARE + FAIR — Carroll et al. (2021)](https://doi.org/10.1038/s41597-021-00892-0) |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.6 · Model a citation-based governance critique\n",
    "governance_audit = pd.DataFrame([\n",
    "    [\n",
    "        \"Observed\",\n",
    "        \"The Dataset Card identifies a public Parquet conversion and CC BY 4.0; the upstream dump documents the positional schema.\",\n",
    "        \"Dataset Card + GeoNames export\",\n",
    "        \"Record the exact SHA, field mapping, IDs, and transformations.\",\n",
    "    ],\n",
    "    [\n",
    "        \"Inferred\",\n",
    "        \"The rows make both locations geographically relevant but cannot establish cultural equivalence or environmental change.\",\n",
    "        \"Lan & Liu (2024); Ziman et al. (2026)\",\n",
    "        \"Keep unsupported claims outside the visual conclusion.\",\n",
    "    ],\n",
    "    [\n",
    "        \"Open question\",\n",
    "        \"The source does not establish whether local communities defined the comparison, benefits, or acceptable reuse.\",\n",
    "        \"Carroll et al. (2021)\",\n",
    "        \"Validate purpose, authority, and possible harm with affected communities.\",\n",
    "    ],\n",
    "], columns=[\"evidence status\", \"claim\", \"basis\", \"design consequence\"])\n",
    "display(governance_audit.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-023",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.7 · Add evidence only to repair a named gap\n",
    "additional_evidence = pd.DataFrame([\n",
    "    [\n",
    "        \"Cultural authority\",\n",
    "        \"UNESCO Venice and its Lagoon\",\n",
    "        \"https://whc.unesco.org/en/list/394/\",\n",
    "        \"Designation context; not everyday community experience or Kunshan priorities\",\n",
    "    ],\n",
    "    [\n",
    "        \"Environmental change\",\n",
    "        \"JRC Global Surface Water\",\n",
    "        \"https://global-surface-water.appspot.com/download\",\n",
    "        \"Water occurrence/change; not cause, quality, or cultural meaning\",\n",
    "    ],\n",
    "    [\n",
    "        \"Spatial context\",\n",
    "        \"OpenStreetMap\",\n",
    "        \"https://www.openstreetmap.org/copyright\",\n",
    "        \"Waterways and places; contributor coverage is uneven and requires verification\",\n",
    "    ],\n",
    "], columns=[\"gap repaired\", \"candidate source\", \"direct URL\", \"evidence boundary\"])\n",
    "display(additional_evidence.style.hide(axis=\"index\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-024",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.8 · Replace the instructor case and generate three working prompts\n",
    "student_project = {\n",
    "    \"title\": \"REPLACE\",\n",
    "    \"domain_question\": \"REPLACE\",\n",
    "    \"intended_community\": \"REPLACE\",\n",
    "    \"dataset_url\": \"REPLACE\",\n",
    "    \"dataset_and_attribute_types\": \"REPLACE\",\n",
    "    \"actions_and_targets\": \"REPLACE\",\n",
    "    \"idiom_decision\": \"REPLACE\",\n",
    "    \"algorithm_and_tools\": \"REPLACE\",\n",
    "    \"evidence_boundary\": \"REPLACE\",\n",
    "    \"additional_source\": \"REPLACE\",\n",
    "}\n",
    "\n",
    "generation_prompt = f\"\"\"\n",
    "Create one complete index.html for {student_project['title']}.\n",
    "Domain question: {student_project['domain_question']}\n",
    "Community: {student_project['intended_community']}\n",
    "Dataset: {student_project['dataset_url']}\n",
    "Data/attributes: {student_project['dataset_and_attribute_types']}\n",
    "Tasks as action + target: {student_project['actions_and_targets']}\n",
    "Use verified source rows, visible attribution, accessible browser-compatible code,\n",
    "explicit loading/error states, and no unsupported claims.\n",
    "\"\"\"\n",
    "\n",
    "deployment_prompt = \"\"\"\n",
    "Convert the attached index.html into a zero-build Hugging Face Static Space.\n",
    "Preserve interactions, use HTTPS resources, add visible provenance and limitations,\n",
    "and return complete index.html plus README.md.\n",
    "\"\"\"\n",
    "\n",
    "critique_prompt = f\"\"\"\n",
    "Critique the deployed Space at four levels: domain; data/task; idiom; algorithm.\n",
    "Apply the assigned FAIR, CARE, Croissant, and visualization papers to direct source\n",
    "evidence. Separate Observed, Inferred, and Open Question claims. Redesign using:\n",
    "idiom={student_project['idiom_decision']};\n",
    "algorithm={student_project['algorithm_and_tools']};\n",
    "evidence boundary={student_project['evidence_boundary']};\n",
    "additional source={student_project['additional_source']}.\n",
    "\"\"\"\n",
    "\n",
    "if any(value == \"REPLACE\" for value in student_project.values()):\n",
    "    print(\"Instructor example mode · replace every REPLACE field before using these prompts.\")\n",
    "display(HTML(\"<h3>Generation prompt</h3><pre>\" + generation_prompt.strip() + \"</pre>\"))\n",
    "display(HTML(\"<h3>Static-Space prompt</h3><pre>\" + deployment_prompt.strip() + \"</pre>\"))\n",
    "display(HTML(\"<h3>Critique/redesign prompt</h3><pre>\" + critique_prompt.strip() + \"</pre>\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title 3.9 · Export the reproducibility bundle\n",
    "attribute_contract.to_csv(OUTPUT_DIR / \"data_attribute_contract.csv\", index=False)\n",
    "task_contract.to_csv(OUTPUT_DIR / \"action_target_contract.csv\", index=False)\n",
    "evidence_boundary.to_csv(OUTPUT_DIR / \"evidence_boundary.csv\", index=False)\n",
    "pipeline.to_csv(OUTPUT_DIR / \"four_level_pipeline.csv\", index=False)\n",
    "idiom_catalog.to_csv(OUTPUT_DIR / \"idiom_catalog_38.csv\", index=False)\n",
    "tool_catalog.to_csv(OUTPUT_DIR / \"python_r_tool_atlas_72.csv\", index=False)\n",
    "governance_audit.to_csv(OUTPUT_DIR / \"governance_audit.csv\", index=False)\n",
    "additional_evidence.to_csv(OUTPUT_DIR / \"additional_evidence_plan.csv\", index=False)\n",
    "(OUTPUT_DIR / \"student_project.json\").write_text(\n",
    "    json.dumps(student_project, indent=2, ensure_ascii=False), encoding=\"utf-8\"\n",
    ")\n",
    "\n",
    "bundle = shutil.make_archive(\"INFOSCI301_INFOVIS_Redesign_Colab_Outputs\", \"zip\", OUTPUT_DIR)\n",
    "expected = {\n",
    "    \"geonames_water_towns.csv\",\n",
    "    \"jiangnan_map.html\",\n",
    "    \"venice_map.html\",\n",
    "    \"population_comparison.png\",\n",
    "    \"population_comparison.svg\",\n",
    "    \"population_comparison.pdf\",\n",
    "    \"four_level_pipeline.csv\",\n",
    "    \"idiom_catalog_38.csv\",\n",
    "    \"python_r_tool_atlas_72.csv\",\n",
    "    \"governance_audit.csv\",\n",
    "}\n",
    "present = {path.name for path in OUTPUT_DIR.iterdir()}\n",
    "assert expected <= present, sorted(expected - present)\n",
    "print(\"Export complete:\", Path(bundle).resolve())\n",
    "print(\"Verified:\", len(records), \"source rows ·\", len(idiom_catalog), \"idioms ·\", len(tool_catalog), \"tools\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-026",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Finish with three judgments\n",
    "\n",
    "1. **Claim:** What can the current evidence support—and what must remain explicitly unclaimed?\n",
    "2. **Choice:** Which human decision changed the data, idiom, algorithm, or interaction after critique?\n",
    "3. **Consequence:** Whose understanding, authority, benefit, or risk should the next test evaluate?\n",
    "\n",
    "### References\n",
    "\n",
    "**Visualization:** Munzner (2014), [book companion](https://www.cs.ubc.ca/~tmm/vadbook/); Pu & Kay (2023), [CHI paper](https://doi.org/10.1145/3544548.3580837); Lan & Liu (2024), [TVCG paper](https://doi.org/10.1109/TVCG.2024.3456341); Ziman et al. (2026), [TVCG paper](https://doi.org/10.1109/TVCG.2025.3633883).\n",
    "\n",
    "**Open science and governance:** Wilkinson et al. (2016), [FAIR](https://doi.org/10.1038/sdata.2016.18); Carroll et al. (2021), [CARE + FAIR](https://doi.org/10.1038/s41597-021-00892-0); Akhtar et al. (2024), [Croissant](https://proceedings.neurips.cc/paper_files/paper/2024/hash/9547b09b722f2948ff3ddb5d86002bc0-Abstract-Datasets_and_Benchmarks_Track.html).\n",
    "\n",
    "**Data and implementation:** [do-me/Geonames](https://huggingface.co/datasets/do-me/Geonames); [GeoNames export](https://download.geonames.org/export/dump/); [Folium documentation](https://python-visualization.github.io/folium/latest/); [OpenStreetMap tile policy](https://operations.osmfoundation.org/policies/tiles/).\n"
   ]
  }
 ],
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  "kernelspec": {
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