Duplicate from tmquan/sapnhap-bando-vn

Co-authored-by: Tran Minh Quan <tmquan@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,61 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+geo/communes.geojson filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,344 @@
+---
+language:
+- vi
+- en
+license: cc-by-nc-4.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- tabular-classification
+- tabular-regression
+- text-classification
+tags:
+- vietnamese
+- geography
+- administrative-units
+- post-merger-2025
+- nq-202-2025-qh15
+- gso
+configs:
+- config_name: all
+  data_files:
+  - split: train
+    path: data/all.parquet
+  default: true
+- config_name: provinces
+  data_files:
+  - split: train
+    path: data/provinces.parquet
+- config_name: communes
+  data_files:
+  - split: train
+    path: data/communes.parquet
+- config_name: committees
+  data_files:
+  - split: train
+    path: data/committees.parquet
+---
+
+# sapnhap-bando-vn — Vietnam's 2025 administrative-merger atlas
+
+> 🇻🇳 **Tóm tắt.** Một bản sao đầy đủ, có cấu trúc của
+> <https://sapnhap.bando.com.vn/> — bộ atlas chính thức của
+> **Nhà Xuất Bản Tài Nguyên - Môi Trường và Bản Đồ Việt Nam** về **các
+> đơn vị hành chính sau sáp nhập** theo **Nghị quyết 202/2025/QH15**
+> ngày 12/06/2025 của Quốc hội và **34 nghị quyết** của Ủy ban Thường
+> vụ Quốc hội ban hành ngày 16/06/2025. Mỗi cấp được phơi bày ở dạng
+> *parquet*, *GeoJSON*, biểu đồ tương tác, và embedding 384-d UMAP đã
+> tính sẵn.
+>
+> 🇬🇧 **Summary.** A complete, table-by-table mirror of
+> <https://sapnhap.bando.com.vn/> — the official atlas of
+> **post-merger administrative units** introduced by **National Assembly
+> Resolution 202/2025/QH15** of 12 June 2025 and the **34 follow-up
+> Standing Committee resolutions** of 16 June 2025. Every level is
+> exposed as *parquet*, *GeoJSON*, interactive figures, and a
+> pre-computed 384-d UMAP embedding.
+
+## Bối cảnh · Context
+
+🇻🇳 Cuộc cải tổ năm 2025 thu gọn Việt Nam từ **63 đơn vị hành chính cấp
+một xuống còn 34** (28 tỉnh + 6 thành phố trực thuộc trung ương) và sắp
+xếp lại tầng dưới: từ **705 huyện ÷ 10 599 xã/phường** giảm còn **3 321
+xã / phường / đơn vị hành chính đặc biệt**. Đây là cuộc tái phân định
+hành chính lớn nhất từ năm 1975.
+
+🇬🇧 The 2025 reform collapsed Vietnam from **63 first-level units to 34**
+(28 provinces + 6 centrally-administered cities) and re-drew the second
+tier from **705 districts / 10 599 communes** down to **3 321
+communes / wards / special administrative units**. This is the largest
+administrative re-partition since 1975.
+
+🇻🇳 Bộ dữ liệu này lưu lại từng đơn vị còn tồn tại sau sáp nhập, kèm
+thông tin về *gốc gác sáp nhập* (đơn vị tiền nhiệm), diện tích, dân số,
+trụ sở Ủy ban nhân dân, văn bản pháp lý cấp thẩm quyền, và *hình thể
+địa lý* (đa giác hoặc điểm).
+
+🇬🇧 This dataset captures every surviving entity together with its
+*merger lineage* (predecessor units), area, population, People's
+Committee headquarters, the decree of authority, and a *geographic
+geometry* (polygon or point).
+
+## Tổng quan · At a glance
+
+| Chỉ số · Stat | Giá trị · Value |
+|---|---:|
+| Đơn vị hành chính cấp một sau sáp nhập · First-level (post-merger) | **34** |
+| Đơn vị hành chính cấp hai sau sáp nhập · Second-level (post-merger) | **3 321** |
+| Trụ sở UBND · People's-committee HQs    | **3 357** |
+| Dân số toàn quốc 2024 · Total population (2024)  | **113 571 926** |
+| Diện tích đất liền (km²) · Total land area (km²) | **331 325.62** |
+| Đa giác cấp tỉnh (GeoJSON) · Province polygons | **34** |
+| Đa giác cấp xã (GeoJSON) · Commune polygons    | **3 321** |
+| Số đơn vị tiền nhiệm tối đa · Max merger fanout | **16** |
+
+## Hình tiêu biểu · Representative figures
+
+🇻🇳 Một vòng dạo qua bộ dữ liệu, mỗi pack một hình. Mỗi PNG bên dưới đi
+kèm một file `.html` tương tác (Plotly đầy đủ — pan / zoom / hover /
+legend toggle).
+
+🇬🇧 A curated tour, one figure per pack. Every PNG below has a
+matching `.html` next to it — the **fully-interactive Plotly version**
+(pan / zoom / hover tooltips / legend toggle).
+
+### Cartographic pack — population by post-merger province · dân số theo tỉnh
+
+![Vietnam population choropleth, 2024](figures/maps/01_provinces_population.png)
+
+🇻🇳 Choropleth tô bằng thang xanh tuần tự. Hai khung viền nét đứt phía
+phải khai báo hai quần đảo ngoài khơi: **Quần đảo Hoàng Sa** (do Đà Nẵng
+quản lý) và **Quần đảo Trường Sa** (do Khánh Hòa quản lý) — theo quy
+ước của các atlas Việt Nam. Tương tác:
+[`figures/maps/01_provinces_population.html`](figures/maps/01_provinces_population.html).
+
+🇬🇧 A sequential green-scale choropleth. The two dashed boxes on the
+right declare the offshore archipelagos **Quần đảo Hoàng Sa**
+(administered by Đà Nẵng) and **Quần đảo Trường Sa** (administered by
+Khánh Hòa) following standard Vietnamese-atlas convention. Interactive:
+[`figures/maps/01_provinces_population.html`](figures/maps/01_provinces_population.html).
+
+### Cartographic pack — 3 321 communes by macro-region · 3 321 xã theo vùng địa lý
+
+![3,321 communes by centroid, post-merger](figures/maps/04_communes_scatter.png)
+
+🇻🇳 Một dấu chấm cho mỗi xã / phường còn tồn tại; tô màu theo 6 vùng
+địa lý của Tổng cục Thống kê.
+
+🇬🇧 One dot per surviving commune-level admin unit; coloured by GSO
+macro-region.
+Interactive: [`figures/maps/04_communes_scatter.html`](figures/maps/04_communes_scatter.html).
+
+### Analytical pack — 34 provinces by population · 34 tỉnh theo dân số
+
+![34 provinces by population](figures/analysis/03_province_population.png)
+
+🇻🇳 Bar chart sắp xếp giảm dần, tô màu theo vùng địa lý. TPHCM
+(sau sáp nhập: 14.0 triệu) và Hà Nội (8.8 triệu) chiếm ưu thế; đuôi
+dài là các tỉnh miền núi phía Bắc dưới 2 triệu dân.
+
+🇬🇧 Sorted bar chart; colour by macro-region. TP HCM (post-merger:
+14.0 M) and Hà Nội (8.8 M) dominate; the long tail runs into
+Northern-Midlands provinces with under 2 M people.
+Interactive: [`figures/analysis/03_province_population.html`](figures/analysis/03_province_population.html).
+
+### Analytical pack — commune merger fanout · Số đơn vị tiền nhiệm
+
+![Commune merger fanout](figures/analysis/08_merger_fanout_communes.png)
+
+🇻🇳 Mỗi xã / phường mới gộp từ bao nhiêu đơn vị cũ. Mode = 3; max = **16**
+(Phường Văn Miếu - Quốc Tử Giám, Hà Nội).
+
+🇬🇧 How many predecessor wards / xã were absorbed by each surviving
+commune. Modal value 3; max value **16** (Phường Văn Miếu - Quốc Tử
+Giám in Hà Nội).
+Interactive: [`figures/analysis/08_merger_fanout_communes.html`](figures/analysis/08_merger_fanout_communes.html).
+
+### Analytical pack — UMAP × macro-region · UMAP của embedding × vùng địa lý
+
+![UMAP coloured by macro-region](figures/analysis/12_curator_umap_region.png)
+
+🇻🇳 Mỗi đơn vị được mã hoá thành một vector 384-d bằng
+`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` từ mô tả
+gốc gác sáp nhập, sau đó chiếu xuống 2-D bằng UMAP (cosine, 15 hàng
+xóm, `min_dist=0.1`). Các "thuỳ" theo vùng địa lý xuất hiện
+**chỉ từ embedding** — mô hình không hề được cho biết nhãn vùng.
+
+🇬🇧 Each entity's merger-lineage prose is encoded into a 384-d vector
+by `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, then
+projected to 2-D with UMAP (cosine, 15 neighbours, `min_dist=0.1`).
+The macro-region lobes emerge **from the embedding alone** — the
+model has never seen the macro-region label.
+Interactive: [`figures/analysis/12_curator_umap_region.html`](figures/analysis/12_curator_umap_region.html).
+
+### Inventory đầy đủ · Full inventory — 17 figures
+
+| Pack          | Files                                          | Style                                       |
+|---|---|---|
+| `figures/maps/`     | 5 PNG + 5 HTML pairs (1100 × 1100)        | Sans-serif typeface; dual-archipelago declaration |
+| `figures/analysis/` | 12 PNG + 12 HTML pairs (1200 × 900)       | LaTeX-serif typeface; sequential green palette    |
+
+## Cấu trúc kho · What's on the Hub
+
+```
+.
+├── README.md                  (this file)
+├── _stats.json                numbers / tables this card quotes
+├── data/
+│   ├── all.parquet            6 712 rows = provinces + communes + committees
+│   ├── provinces.parquet      34 rows
+│   ├── communes.parquet       3 321 rows
+│   └── committees.parquet     3 357 rows
+├── geo/
+│   ├── provinces.geojson      34 polygons (FeatureCollection)
+│   └── communes.geojson       3 321 polygons (FeatureCollection)
+├── reduced/
+│   └── reduced.parquet        UMAP 2-D coords + HDBSCAN cluster id
+├── figures/
+│   ├── analysis/              12 PNG + 12 HTML pairs (LaTeX-serif theme)
+│   └── maps/                   5 PNG +  5 HTML pairs (sans-serif + archipelagos)
+├── notebooks/DATAANALYSIS.ipynb
+├── docs/{DATAPROCESSING,DATAANALYSIS}.md
+└── raw/{admin_units,committees}.json   (source listings)
+```
+
+🇻🇳 Mọi figure trong thư mục `figures/` đều có **hai bản**: PNG tĩnh để
+nhúng trong card này, và HTML tương tác đầy đủ Plotly. Bấm vào link
+HTML hoặc tải `.html` về để mở trong trình duyệt.
+
+🇬🇧 Every figure under `figures/` ships in **both formats** — the static
+PNG for inline rendering in this card, plus a self-contained
+**interactive HTML** with the full Plotly toolkit. Click any HTML link
+or download the `.html` to open it locally.
+
+## Phân theo vùng địa lý · Per-macro-region inventory
+
+| Vùng địa lý · Macro-region (EN)              | Tỉnh · Provinces | Xã · Communes | UBND · Committees |
+|---|---:|---:|---:|
+| Trung du và miền núi phía Bắc · Northern Midlands & Mountain Areas | 10 | 841 | 842 |
+| Đồng bằng sông Hồng · Red River Delta                              |  5 | 527 | 502 |
+| Bắc Trung Bộ và duyên hải miền Trung · North Central & Coastal     |  8 | 738 | 737 |
+| Tây Nguyên · Central Highlands                                      |  3 | 361 | 380 |
+| Đông Nam Bộ · Southeast                                             |  3 | 359 | 358 |
+| Đồng bằng sông Cửu Long · Mekong River Delta                        |  5 | 495 | 537 |
+
+## Cách dữ liệu được tạo · How the corpus was built
+
+🇻🇳 Hình thể trên đĩa được sinh ra qua một pipeline 5 bước:
+
+🇬🇧 The on-disk shape is produced by a 5-stage curation pipeline:
+
+```
+download → parse → extract → embed → reduce
+```
+
+* **download** — POST đến 4 endpoint của `sapnhap.bando.com.vn`
+  (`p.co_dvhc`, `p.co_uyban`, `p.co_dvhc_id`, `pread_json`); ~6 700 lượt
+  gọi; cache đĩa nên re-run miễn phí. — POST to four endpoints; ~6 700
+  calls; cached to disk so re-runs are free.
+* **parse** — chuẩn hoá số kiểu Việt (`"4.199.824"` → `4 199 824`),
+  tóm tắt GeoJSON thành centroid + bbox + WKT, gắn province cha cho
+  từng xã / UBND. — Normalise Vietnamese-formatted numbers, summarise
+  GeoJSON to centroid + bbox + WKT, attach parent-province for every
+  commune & committee.
+* **extract** — TF-IDF keywords trên mô tả gốc gác sáp nhập; gắn vùng
+  địa lý theo bản ánh xạ chính thức 34 → 6 của Tổng cục Thống kê. —
+  TF-IDF over the merger-lineage prose; macro-region attachment from
+  the curated 34 → 6 GSO mapping.
+* **embed** — `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`
+  trên CPU (384-d) trên các mô tả tiếng Việt chuẩn hoá. — A 384-d
+  multilingual sentence encoder over the canonical Vietnamese descriptors.
+* **reduce** — UMAP 2-D + HDBSCAN cụm theo mật độ. — UMAP → 2-D
+  coordinates + density-based HDBSCAN clusters.
+
+🇻🇳 Xem [`docs/DATAPROCESSING.md`](docs/DATAPROCESSING.md) để biết toàn
+bộ giao thức crawl, [`docs/DATAANALYSIS.md`](docs/DATAANALYSIS.md) cho
+tour phân tích, và sổ tay
+[`notebooks/DATAANALYSIS.ipynb`](notebooks/DATAANALYSIS.ipynb) cho các
+ô Plotly đã thực thi.
+
+🇬🇧 See [`docs/DATAPROCESSING.md`](docs/DATAPROCESSING.md) for the full
+crawl protocol, [`docs/DATAANALYSIS.md`](docs/DATAANALYSIS.md) for the
+analytical walkthrough, and the
+[`notebooks/DATAANALYSIS.ipynb`](notebooks/DATAANALYSIS.ipynb) notebook
+for the executed Plotly outputs.
+
+## Cách dùng · Usage
+
+```python
+from datasets import load_dataset
+
+# Default config — toàn bộ 6 712 dòng (34 tỉnh + 3 321 xã + 3 357 UBND)
+ds = load_dataset("tmquan/sapnhap-bando-vn")["train"]
+print(ds.column_names)
+# -> ['id', 'kind', 'ma', 'ten', 'type', 'ten_short', 'area_km2',
+#     'population', 'density', 'capital', 'address', 'phone', 'decree',
+#     'decree_url', 'predecessors', 'parent_ma', 'parent_ten',
+#     'centroid_lon', 'centroid_lat', 'bbox', 'geom_type', 'wkt',
+#     'predecessors_list', 'n_predecessors', 'macro_region',
+#     'embed_text', 'keywords']
+
+# Chỉ 34 đơn vị cấp tỉnh
+provinces = load_dataset("tmquan/sapnhap-bando-vn", "provinces")["train"]
+
+# Chỉ 3 321 xã / phường
+communes = load_dataset("tmquan/sapnhap-bando-vn", "communes")["train"]
+
+# Chỉ 3 357 trụ sở Ủy ban nhân dân
+committees = load_dataset("tmquan/sapnhap-bando-vn", "committees")["train"]
+
+# Tải GeoJSON riêng (datasets không tự nạp .geojson)
+import huggingface_hub as hf, json
+path = hf.hf_hub_download(
+    "tmquan/sapnhap-bando-vn", "geo/provinces.geojson", repo_type="dataset",
+)
+fc = json.load(open(path, "r", encoding="utf-8"))
+print(len(fc["features"]))   # 34
+```
+
+## Giấy phép · License & terms
+
+🇻🇳 Dữ liệu gốc thuộc về **Bộ Nông nghiệp và Môi trường** và **Nhà Xuất
+Bản Tài Nguyên - Môi Trường và Bản Đồ Việt Nam**. Bản phân phối lại
+này dùng giấy phép **CC-BY-NC 4.0** — *vui lòng kiểm tra điều khoản
+sử dụng của trang nguồn trước khi tái phân phối thương mại*.
+
+🇬🇧 The underlying data belongs to the **Ministry of Agriculture and
+Environment** and the **Vietnam Cartographic Publishing House**
+(Nhà Xuất Bản Tài Nguyên - Môi Trường và Bản Đồ Việt Nam). This
+redistribution is shared under **CC-BY-NC 4.0** — *please check the
+source-website terms before commercial redistribution*.
+
+| Thông tin xuất bản · Publication info | |
+|---|---|
+| ISBN                | 978-632-622-303-3 |
+| Publication ID      | 1027-2026/CXBIPH/03-129/BĐ |
+| Quyết định / Decision | 30/QĐ-NXBTNMT, 16 April 2026 |
+| Source              | <https://sapnhap.bando.com.vn/> · <https://bando.com.vn> |
+
+Cơ sở pháp lý của cuộc sáp nhập · Authoritative legal sources:
+
+* National Assembly Resolution **202/2025/QH15** (12 June 2025)
+* Standing Committee resolutions of **16 June 2025** (×34)
+* Government decrees published at <https://vanban.chinhphu.vn>
+
+## Trích dẫn · Citation
+
+```bibtex
+@misc{sapnhap_2026,
+  title        = {Sáp Nhập — Vietnam Administrative Merger Atlas (HF mirror)},
+  author       = {TMQuan},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/tmquan/sapnhap-bando-vn}},
+  note         = {Mirror of the post-merger administrative-units atlas published at \url{https://sapnhap.bando.com.vn/}.}
+}
+
+@misc{sapnhap_atlas_2026,
+  title        = {Atlas Sáp Nhập Hành Chính Việt Nam},
+  author       = {{Ministry of Agriculture and Environment of Vietnam} and {Vietnam Cartographic Publishing House}},
+  year         = {2026},
+  publisher    = {Nhà Xuất Bản Tài Nguyên - Môi Trường và Bản Đồ Việt Nam},
+  howpublished = {\url{https://sapnhap.bando.com.vn/}},
+  note         = {ISBN 978-632-622-303-3, Publication ID 1027-2026/CXBIPH/03-129/BĐ. Quyết định số 30/QĐ-NXBTNMT, 16 April 2026. Implements National Assembly Resolution 202/2025/QH15 and the 16 June 2025 Standing Committee resolutions.}
+}
+```

_stats.json

@@ -0,0 +1,56 @@
+{
+  "n_total": 6712,
+  "by_kind": {
+    "committee": 3357,
+    "commune": 3321,
+    "province": 34
+  },
+  "by_macro_region": {
+    "central_coast": {
+      "committee": 737,
+      "commune": 738,
+      "province": 8
+    },
+    "central_highlands": {
+      "committee": 380,
+      "commune": 361,
+      "province": 3
+    },
+    "mekong_delta": {
+      "committee": 537,
+      "commune": 495,
+      "province": 5
+    },
+    "northern_midlands": {
+      "committee": 842,
+      "commune": 841,
+      "province": 10
+    },
+    "red_river_delta": {
+      "committee": 502,
+      "commune": 527,
+      "province": 5
+    },
+    "southeast": {
+      "committee": 358,
+      "commune": 359,
+      "province": 3
+    },
+    "unknown": {
+      "committee": 1,
+      "commune": 0,
+      "province": 0
+    }
+  },
+  "n_provinces": 34,
+  "n_communes": 3321,
+  "n_committees": 3357,
+  "n_reduced": 6712,
+  "n_geo_polygons": {
+    "provinces": 34,
+    "communes": 3321
+  },
+  "total_population": 113571926,
+  "total_area_km2": 331325.62,
+  "merger_fanout_max": 16
+}

data/all.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4823f6008802e46070bc6d059e903a83e85c92fba56d8e34b33c94c283f3909d
+size 833971

data/committees.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbfce972dfc4f71b9a49e15f6fc2f27c7a047f2f7644c91bc64c6ed664b3f2e8
+size 268162

data/communes.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b66f129f32d4167dd4e831fd830978e34384f1bbaa7aaa421d7a13741c287d30
+size 614092

data/provinces.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e90c31e6a71d007da0772705d9cb45bbaaabc9cf40d12c8904f963659a5b1a42
+size 26182

docs/DATAANALYSIS.md

@@ -0,0 +1,344 @@
+# DATAANALYSIS — what the curated data actually says
+
+This document is the analytical companion to the curated parquet bundle
+produced by `geography-vn curate`. It walks through the 14-figure pack
+under [`docs/figures/analysis/`](docs/figures/analysis/) (rendered by
+`scripts/analyze.py` and embedded inline in
+[`DATAANALYSIS.ipynb`](DATAANALYSIS.ipynb)) and tells the story of
+**Vietnam after Resolution 202/2025/QH15** through the data alone.
+
+> **Source:** every number below comes from
+> `data/sapnhap-bando-vn/extracted/extracted.parquet` (6,712 rows = 34
+> provinces + 3,321 communes + 3,357 commune people's-committee
+> headquarters). The parquet was produced by the five-stage NeMo Curator
+> pipeline described in [`DATAPROCESSING.md`](DATAPROCESSING.md).
+
+## TL;DR
+
+| Stat                                  | Value             |
+| ------------------------------------- | ----------------- |
+| First-level units (post-merger)       | **34**            |
+| Second-level units (post-merger)      | **3,321**         |
+| People's-committee headquarters       | **3,357**         |
+| Total Vietnam population (2024)       | **113,571,926**   |
+| Total Vietnam land area (km²)         | **331,325.62**    |
+| Largest province by population        | TPHCM — **14,002,598**   |
+| Largest province by area              | Lâm Đồng — **24,233 km²** |
+| Densest province                      | Hà Nội — **2,621 / km²**  |
+| Most-merged commune                   | Phường Văn Miếu - Quốc Tử Giám — **16 predecessor wards** |
+| Distinct authorising decrees          | **37**            |
+| Provinces that kept their old borders | **11** out of 34  |
+
+## 1 — Inventory ([fig 01](docs/figures/analysis/01_admin_kind_donut.png))
+
+The atlas publishes three entity kinds, in roughly equal sub-counts:
+
+| Kind        | Count   | What it is                                              |
+| ----------- | ------- | ------------------------------------------------------- |
+| `province`   | 34      | First-level admin units (28 provinces + 6 cities)        |
+| `commune`    | 3,321   | Second-level units (phường / xã / đặc khu after the merger) |
+| `committee`  | 3,357   | Commune people's-committee headquarters (point markers) |
+
+The committee count slightly exceeds the commune count (3,357 > 3,321)
+because some communes have multiple registered committee buildings (the
+seat plus a satellite office) and a handful of pre-merger committees
+remain marked on the map even after their parent commune dissolved.
+
+## 2 — Macro-region balance ([fig 02](docs/figures/analysis/02_macro_region_breakdown.png))
+
+The 34 surviving provinces redistribute across the six GSO macro-regions
+unevenly. The north (where the 2025 merger was the most aggressive) keeps
+**10 + 5 = 15 provinces** under two macro-regions; the centre keeps
+**8 + 3 = 11**; the south keeps **3 + 5 = 8**. Including the
+people's-committee tier (which inherits its parent province's region):
+
+| Macro-region (EN)                          | Provinces | Communes | Committees | Total |
+| ------------------------------------------ | --------: | -------: | ---------: | ----: |
+| Northern Midlands and Mountain Areas       | **10**    |   841    |    842     | 1,693 |
+| Red River Delta                            | **5**     |   527    |    502     | 1,034 |
+| North Central and Central Coastal Areas    | **8**     |   738    |    737     | 1,483 |
+| Central Highlands                          | **3**     |   361    |    380     |   744 |
+| Southeast                                  | **3**     |   359    |    358     |   720 |
+| Mekong River Delta                         | **5**     |   495    |    537     | 1,037 |
+| (unknown — disputed-zone duplicates)       |    0      |     0    |      1     |     1 |
+
+Note that the **Northern Midlands** retains the highest *count* of
+first-level units (10) despite being the region the merger consolidated
+hardest — pre-merger it had 14 provinces. The **Southeast** ends up the
+smallest by province count (3) because TPHCM swallowed both Bình Dương
+and Bà Rịa-Vũng Tàu into a single mega-city.
+
+## 3 — Province population ([fig 03](docs/figures/analysis/03_province_population.png))
+
+The top-five most populous provinces, after the merger:
+
+| Rank | Province                | Population   | Area (km²) | Density (/km²) | Predecessors |
+| ---- | ----------------------- | -----------: | ---------: | -------------: | -----------: |
+| 1    | Thành Phố Hồ Chí Minh    | 14,002,598   |  6,772.59  | 2,067.5        | 3            |
+| 2    | Thủ Đô Hà Nội           |  8,807,523   |  3,359.84  | 2,621.4        | 1            |
+| 3    | Tỉnh An Giang            |  4,952,238   |  9,888.91  |   500.8        | 2            |
+| 4    | Thành Phố Hải Phòng      |  4,664,124   |  3,194.72  | 1,460.0        | 2            |
+| 5    | Thành phố Đồng Nai      |  4,491,408   | 12,737.18  |   352.6        | 4            |
+
+TPHCM's merger absorbed **Bình Dương** (industrial belt) and **Bà Rịa
+- Vũng Tàu** (the southern coast & Côn Đảo) into one super-city, doubling
+its population from ~7 M to **14 M** — now larger than New York's metro.
+
+Đồng Nai is the only province in the country that absorbed **four**
+predecessor units (the original Đồng Nai + Bình Phước + parts of two
+neighbours), making it the largest-area populated unit in the south.
+
+## 4 — Province area ([fig 04](docs/figures/analysis/04_province_area.png))
+
+The top-five largest provinces by km², all in the central highlands or
+upper-central coast:
+
+| Rank | Province          | Area (km²)  | Population | Density (/km²) | Predecessors |
+| ---- | ----------------- | ----------: | ---------: | -------------: | -----------: |
+| 1    | Tỉnh Lâm Đồng    | 24,233.07   |  3,872,999 | 159.8           | 3            |
+| 2    | Tỉnh Gia Lai      | 21,576.53   |  3,583,693 | 166.1           | 2            |
+| 3    | Tỉnh Đắk Lắk      | 18,096.40   |  3,346,853 | 184.9           | 2            |
+| 4    | Tỉnh Nghệ An      | 16,486.50   |  3,831,694 | 232.4           | 1            |
+| 5    | Tỉnh Quảng Ngãi   | 14,832.55   |  2,161,755 | 145.7           | 2            |
+
+Lâm Đồng is the merger's largest single creation: the old Lâm Đồng +
+**Bình Thuận** (south-central coast) + **Đắk Nông** (Central Highlands)
+fused into a 24,233 km² province that now stretches from the central
+plateau to the South China Sea coast.
+
+## 5 — Province density ([fig 05](docs/figures/analysis/05_province_density.png))
+
+| Rank | Province                | Density (/km²) | Population | Area (km²) |
+| ---- | ----------------------- | -------------: | ---------: | ---------: |
+| 1    | Thủ Đô Hà Nội           | 2,621.4        |  8,807,523 |  3,359.84  |
+| 2    | Thành Phố Hồ Chí Minh    | 2,067.5        | 14,002,598 |  6,772.59  |
+| 3    | Thành Phố Hải Phòng      | 1,460.0        |  4,664,124 |  3,194.72  |
+| 4    | Tỉnh Hưng Yên            | 1,418.8        |  3,567,943 |  2,514.81  |
+| 5    | Tỉnh Ninh Bình           | 1,119.1        |  4,412,264 |  3,942.62  |
+
+Hà Nội remains the densest province even though it kept its pre-merger
+borders unchanged (its `n_predecessors == 1`). Hưng Yên, **post-merger**
+absorbing Thái Bình, jumps into the top 4 — the merged Red River Delta
+provinces concentrate density there.
+
+## 6 — Communes per province ([fig 06](docs/figures/analysis/06_communes_per_province.png))
+
+The lower-tier consolidation is more uneven:
+
+* **Min**: 38 communes (Tỉnh Lai Châu — sparsely populated mountains)
+* **Max**: 168 communes (Thành phố Hồ Chí Minh — mega-city)
+* **Median**: 99 communes per province
+* **Mean**: 97.7 communes per province
+
+Bottom five (sparsest):
+
+| Province           | Communes |
+| ------------------ | -------: |
+| Tỉnh Lai Châu      |    38    |
+| Thành phố Huế      |    40    |
+| Tỉnh Điện Biên     |    45    |
+| Tỉnh Quảng Ninh    |    54    |
+| Tỉnh Cao Bằng      |    56    |
+
+Top five (densest):
+
+| Province                   | Communes |
+| -------------------------- | -------: |
+| Thành phố Hồ Chí Minh      |   168    |
+| Tỉnh Thanh Hóa             |   166    |
+| Tỉnh Phú Thọ               |   148    |
+| Tỉnh Gia Lai               |   135    |
+| Tỉnh Nghệ An               |   130    |
+
+## 7 — Province merger fanout ([fig 07](docs/figures/analysis/07_merger_fanout_provinces.png))
+
+How many predecessor provinces fed each surviving first-level unit:
+
+| n_predecessors | Provinces | Names                                                                                                    |
+| -------------- | --------: | -------------------------------------------------------------------------------------------------------- |
+| **1**           | **11**    | Hà Nội, Huế, Cao Bằng, Điện Biên, Hà Tĩnh, Lai Châu, Lạng Sơn, Nghệ An, Quảng Ninh, Sơn La, Thanh Hóa     |
+| **2**           | **16**    | Đà Nẵng, Hải Phòng, An Giang, Bắc Ninh, Cà Mau, Đắk Lắk, Đồng Tháp, Gia Lai, Hưng Yên, Khánh Hòa, … |
+| **3**           | **6**     | Cần Thơ, Hồ Chí Minh, Lâm Đồng, Ninh Bình, Phú Thọ, Vĩnh Long                                          |
+| **4**           | **1**     | Đồng Nai                                                                                                  |
+
+Eleven provinces kept their old borders — they were already large enough
+or geographically isolated enough that the merger left them alone. The
+modal merger absorbed exactly one neighbour. Đồng Nai is the singular
+four-way merger.
+
+## 8 — Commune merger fanout ([fig 08](docs/figures/analysis/08_merger_fanout_communes.png))
+
+The lower tier was consolidated much more aggressively. Of the 3,321
+surviving communes:
+
+| n_predecessors | Communes | %     |
+| -------------- | -------: | ----: |
+|  **1**          |    139   |  4.2% |
+|  **2**          |    758   | 22.8% |
+|  **3**          |  1,498   | 45.1% |
+|  **4**          |    559   | 16.8% |
+|  **5**          |    179   |  5.4% |
+|  **6**          |     78   |  2.3% |
+|  **7+**         |    110   |  3.4% |
+| **max**         |     16   |       |
+
+The modal commune absorbed **3 predecessor wards/xã**. The mean fanout
+is 3.4 (compared to ~1.6 for the first-level tier).
+
+The **most-merged commune is Phường Văn Miếu - Quốc Tử Giám** in Hà
+Nội, which fused **16 predecessor wards** into a single 4-character
+neighbourhood. Hà Nội dominates the high-fanout extreme — 8 of the top
+15 communes by fanout are in Hà Nội, reflecting the post-merger desire
+to flatten the dense old ward grid into larger, more administratively
+manageable units. The other notable mega-mergers are coastal special
+zones: **Đặc khu Cát Hải** (Hải Phòng, 12 preds), **Đặc khu Vân Đồn**
+(Quảng Ninh, 12 preds).
+
+## 9 — Commune size distribution ([fig 09](docs/figures/analysis/09_commune_size_distribution.png))
+
+A log-log scatter of commune area vs population reveals three regimes:
+
+* **Special administrative zones (Đặc khu)** — sit in the bottom-left:
+  tiny populations on tiny islands. Hoàng Sa (the Paracels — 0
+  registered population, 350 km²), Trường Sa (Spratly — 153 people,
+  496 km²), Cồn Cỏ (139 people, 2.3 km²), Bạch Long Vĩ (686 people,
+  3.07 km²). All have ambiguous-to-disputed sovereignty status and
+  serve mainly as markers of national territory.
+* **Highland xã** — the upper-left band: tens of thousands of
+  inhabitants spread across 200-1100 km² of mountain terrain. Buôn Đôn
+  (Đắk Lắk, 1,114 km², 6.6K people) is the largest by area in the entire
+  set. The Trường Sơn cordillera communes in Quảng Trị (Thượng Trạch,
+  Trường Sơn, Kim Ngân) all exceed 800 km² each.
+* **Urban phường** — the lower-right cluster: 50-130K people on a few
+  km² of city. Phường Hải Châu (Đà Nẵng, 7.58 km², 131K people) is the
+  densest by far.
+
+## 10 — Decree corpus ([fig 10](docs/figures/analysis/10_decree_map.png))
+
+The merger is authorised by **37 distinct decrees**: 1 from the National
+Assembly itself (`Nghị quyết số 202/2025/QH15`) and 34 follow-up
+Standing Committee resolutions (`NQ-UBTVQH15`) of 16 June 2025 that flesh
+out the lower-tier merger commune-by-commune. The top decrees by number
+of units cited:
+
+| Decree                            | Units cited |
+| --------------------------------- | ----------: |
+| `Nghị quyết số 1685/NQ-UBTVQH15`   | 168          |
+| `Nghị quyết số 1686/NQ-UBTVQH15`   | 166          |
+| `Nghị quyết số 1676/NQ-UBTVQH15`   | 148          |
+| `Nghị quyết số 1664/NQ-UBTVQH15`   | 135          |
+| `Nghị quyết số 1678/NQ-UBTVQH15`   | 130          |
+| `Nghị quyết số 1674/NQ-UBTVQH15`   | 129          |
+| `Nghị quyết số 1656/NQ-UBTVQH15`   | 126          |
+| … (30 more)                       | …            |
+
+Each `NQ-1656…1690` decree corresponds to one of the 34 surviving
+provinces. The top decree (NQ-1685) authorises all 168 wards of TPHCM;
+NQ-1686 covers the 166 communes of Thanh Hóa; etc. Every commune row in
+the parquet has a `decree_url` column pointing to the official text at
+`vanban.chinhphu.vn`.
+
+## 11 — Cartographic pack with dual-archipelago declaration
+
+Geographic visualisations live in their own folder under
+[`docs/figures/maps/`](docs/figures/maps/) — rendered standalone by
+`python -m scripts.render_maps` because kaleido + Mapbox-GL crashes
+inside a Jupyter kernel. Five figures total, each with the **actual
+scraped province polygons** (not bubbles) and the standard
+Vietnamese-atlas declaration of both offshore archipelagos:
+* **Quần đảo Hoàng Sa** (Paracel Islands) — dashed bounding outline
+  ~15.45°N–17.20°N × 110.85°E–113.10°E with markers for Phú Lâm, Tri Tôn,
+  Linh Côn, Quang Hòa. Administered by **Thành phố Đà Nẵng** under the
+  post-2025 geography (Đặc khu Hoàng Sa).
+* **Quần đảo Trường Sa** (Spratly Islands) — dashed bounding outline
+  ~7.40°N–11.85°N × 111.80°E–115.30°E with markers for Trường Sa Lớn,
+  Song Tử Tây, Sinh Tồn, Phan Vinh, An Bang, Nam Yết, Cô Lin, Sơn Ca.
+  Administered by **Tỉnh Khánh Hòa** (Đặc khu Trường Sa).
+
+Each map additionally carries: ★ Thủ đô Hà Nội (capital), 7 secondary
+cities (TP. Hồ Chí Minh / Đà Nẵng / Hải Phòng / Huế / Vinh / Cần Thơ /
+Nha Trang) with leader-line labels into open sea, and 5 island callouts
+(Đảo Phú Quốc / Cát Bà / Bạch Long Vĩ / Lý Sơn + Côn Đảo).
+
+| File                                            | Visualisation                                                |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| `maps/01_provinces_population.png`               | population choropleth (sequential NVIDIA-green)              |
+| `maps/02_provinces_density.png`                  | people / km²                                                 |
+| `maps/03_provinces_area.png`                     | land area (km²)                                              |
+| `maps/04_communes_scatter.png`                   | 3,321 commune centroids by macro-region                      |
+| `maps/05_committees_scatter.png`                 | 3,357 commune people's-committee headquarters by lat/lon     |
+
+The cartographic pack uses NVIDIA Sans typography on a square 1100 × 1100
+canvas; the analytical pack above uses the LaTeX-serif `nvidia_latex`
+template at 1200 × 900. Maps follow brand convention, charts follow
+academic-paper convention.
+
+## 12 — Curator UMAP ([fig 11](docs/figures/analysis/11_curator_umap_kind.png), [fig 12](docs/figures/analysis/12_curator_umap_region.png))
+
+A 2-D UMAP projection of the 6,712-point sentence-transformers
+embedding of every entity's `embed_text` descriptor. Three observations:
+
+1. **Kind separation is partial.** Provinces and committees occupy
+   compact, well-separated tail regions of the map (provinces
+   because their descriptors include rich predecessor-province prose
+   that other entities do not; committees because every committee
+   description follows the standard "Ủy ban nhân dân …" template).
+   Communes fill the middle and overlap with both extremes.
+2. **Macro-region structure emerges.** Even though the embed text
+   does not explicitly mention macro-region, the UMAP × macro-region
+   colouring shows clean lobes for the Mekong Delta, the Northern
+   Midlands, and the Central Highlands — the model picks up on
+   regional naming conventions (`Đặc khu Phú Quốc` clusters with the
+   southern coast, `Xã Mường Khương` with the Northwest, etc.).
+3. **HDBSCAN finds ~30-50 dense subclusters** within the broader
+   regional structure. Most are sub-province-level: a single
+   province's communes cluster together because their descriptors
+   share a common `parent_ten` tag, the same parent decree, and
+   similar predecessor-name patterns ("Phường … và Phường … sau khi
+   sắp xếp"). The `cluster` column in `reduced.parquet` carries the
+   integer label per row, with `-1` reserved for low-density noise
+   points.
+
+## Caveats & known limits
+
+* **`Đặc khu Hoàng Sa` has zero registered population.** The Paracel
+  Islands are administered by the People's Republic of China and have
+  not been physically Vietnamese-controlled since 1974. The Vietnamese
+  atlas marks them as a special administrative zone of Đà Nẵng with a
+  populations of zero — a sovereignty assertion, not a usable
+  demographic figure.
+* **Population numbers are 2024 mid-year estimates** as published with
+  the merger decrees (rounded thousands at the commune level). They
+  predate the merger by 6–12 months and may not yet reflect intra-merger
+  population migration.
+* **Two parent-province strings come back inconsistent:** "Thủ đô Hà Nội"
+  vs "Thủ Đô Hà Nội" (different `Đ`/`đ` capitalisation depending on the
+  endpoint). The `parent_ma` column normalises this — join on `parent_ma`
+  not on `parent_ten` for analysis.
+* **3 communes carry the name "Xã Hoàng Hoa Thám"** (each in a different
+  province) and 1 carries "Đặc khu Phú Quốc". Use `id` (== feature id)
+  not `ten` as the primary key.
+* **The committee macro-region** is derived from the GeoJSON
+  `properties.a04_tentinh` field, which can be missing for ~20 of the
+  3,357 committees (mostly defunct pre-merger seats). Those rows show
+  `macro_region == "unknown"` in the parquet.
+
+## How to reproduce
+
+```bash
+# 1) Download + curate (~35 min on first run, then cached)
+geography-vn curate
+
+# 2) Render the figure pack (~2 min)
+python -m scripts.analyze
+
+# 3) Re-execute the notebook (optional)
+jupyter nbconvert --to notebook --execute --inplace DATAANALYSIS.ipynb
+
+# 4) Stage and push the HF dataset
+python -m scripts.upload_to_hf --no-upload
+python -m scripts.upload_to_hf --repo <your-org>/sapnhap-bando-vn
+```
+
+See [`DATAPROCESSING.md`](DATAPROCESSING.md) for the curator-pipeline
+internals.

docs/DATAPROCESSING.md

@@ -0,0 +1,227 @@
+# DATAPROCESSING — five-stage curator pipeline
+
+This document walks through the five-stage NeMo-Curator-compatible pipeline
+that turns the raw web responses from <https://sapnhap.bando.com.vn/> into
+the typed, joinable, embedding-augmented parquet bundle that ships to
+HuggingFace and feeds [`DATAANALYSIS.md`](DATAANALYSIS.md).
+
+The five stages mirror the same layout used by ViLA's
+[`packages/datasites/anle/`](https://github.com/tmquan/ViLA/tree/main/packages/datasites/anle)
+and `personas-vn`'s
+[`packages/curator/`](https://github.com/tmquan/personas-vn/tree/main/packages/curator):
+
+```
+download → parse → extract → embed → reduce
+```
+
+Each stage reads from the previous stage's output directory and writes to its
+own. A failure inside one stage never destroys earlier work, and re-running a
+single stage in isolation (`--only embed`, `--skip download`, …) is the
+intended development loop.
+
+```
+data/sapnhap-bando-vn/
+├── raw/         ← stage 1
+│   ├── admin_units.json          (one POST: 3,355 listings)
+│   ├── committees.json           (one POST: 3,357 committee markers)
+│   ├── details/<malk>.json       (~3,355 POSTs to p.co_dvhc_id)
+│   ├── geom/<id>.geojson         (~6,712 POSTs to pread_json)
+│   └── _cache/                   (per-URL HTTP cache; re-runs are free)
+├── parsed/      ← stage 2: parsed.jsonl + parsed.parquet
+├── extracted/   ← stage 3: extracted.jsonl + extracted.parquet
+├── embedded/    ← stage 4: embedded.parquet (n × 384-d vectors)
+└── reduced/     ← stage 5: reduced.parquet (UMAP 2-D + cluster id)
+```
+
+## Source surface
+
+The site at `sapnhap.bando.com.vn/` is a thin PHP front-end
+(`D:\map34tinh\s.index.php`) over a QGIS Server WMS/WFS deployment. It
+exposes four POST endpoints we care about — every one returns JSON, even
+when the server lies in its `Content-Type` header:
+
+| Endpoint                | Form data                       | Returns                                              |
+| ----------------------- | ------------------------------- | ---------------------------------------------------- |
+| `POST /p.co_dvhc`        | `ma=0`                           | List of every admin unit (34 prov + 3,321 communes)  |
+| `POST /p.co_uyban`       | `ma=0`                           | List of 3,357 commune people's-committee headquarters |
+| `POST /p.co_dvhc_id`     | `malk=<feature_id>`              | Full attribute row (area, population, decree, …)     |
+| `POST /pread_json`       | `id=<feature_id>`                | GeoJSON FeatureCollection (Polygon / MultiPolygon / Point) |
+
+Feature-id conventions:
+
+* `diaphanhanhchinhcaptinh_sn.<n>`   — province polygons (only 34 of the 132
+  pre-merger ids survive).
+* `diaphanhanhchinhcapxa_2025.<n>`  — commune polygons; 3,321 alive, the
+  rest dissolved into neighbours.
+* `uybannhandancapxa_2025.<n>`     — point markers for every commune
+  people's committee (n = 1 … 3,357).
+
+The PHP front-end occasionally injects an HTML warning preamble before the
+JSON body when QGIS Server is mid-restart — `packages.common.http` peels
+that off transparently.
+
+## Stage 1 — download
+
+```python
+DownloadStage(config.download, raw_dir).run()
+```
+
+* **Two listing POSTs** capture the complete inventory in one shot each
+  (`/p.co_dvhc` and `/p.co_uyban`).
+* **Per-unit detail walk** (~3,355 POSTs to `/p.co_dvhc_id`) pulls the rich
+  attribute row for every admin unit: area in km², population, capital,
+  predecessors prose, decree of authority, link to the official decree at
+  `vanban.chinhphu.vn`.
+* **Per-feature geometry walk** (~6,712 POSTs to `/pread_json`) pulls the
+  polygon for every admin unit and the point marker for every committee.
+* Every URL is cached on disk under `raw/_cache/`; re-runs hit the cache and
+  finish in seconds.
+* `delay_between_requests_s: 0.10` keeps the crawl polite — ~10 req/s, no
+  hint of rate limiting from the server.
+* Wall time on first run: ~12 minutes for the listings + details, plus
+  ~22 minutes for the geometries; ~35 minutes end-to-end on a home
+  broadband line.
+
+The full crawl materialises **roughly 6,700 small JSON / GeoJSON files**
+totaling ~120 MB.
+
+## Stage 2 — parse
+
+```python
+ParseStage(config.parse, raw_dir, parsed_dir).run()
+```
+
+Three jobs:
+
+1. **Normalise Vietnamese-formatted numbers.** The `p.co_dvhc_id` endpoint
+   uses Vietnamese locale (`"6.360,83"` = 6,360.83 km²); the GeoJSON
+   `properties` block uses English (`"575.29"` = 575.29 km²,
+   `"157629"` = 157,629 people). `parse_vi_decimal` and `parse_vi_int` in
+   `packages/scraper/sapnhap.py` cover both idioms.
+2. **Summarise GeoJSON.** Each FeatureCollection collapses to a single row
+   with `centroid_lon`, `centroid_lat`, `bbox`, `geom_type`, `n_vertices`,
+   and (when `parse.flatten_geojson=true`) a shapely-emitted WKT string.
+   We use shapely's true centroid for polygons; for the rare environments
+   without shapely, a ring-walk arithmetic-mean centroid is good enough for
+   plotting.
+3. **Stamp parent-province for every commune & committee.** The
+   `tentinh` attribute on `p.co_dvhc_id` and the `a04_tentinh` attribute on
+   the GeoJSON committee features carry the parent-province name; we
+   resolve it against the 34-row province list to attach a stable
+   `parent_ma` (NSO 2-digit province code).
+
+Output is a single canonical row per entity (province, commune, or
+committee), written as both `parsed.jsonl` and `parsed.parquet`. Schema:
+
+| column          | type     | notes                                                         |
+| --------------- | -------- | ------------------------------------------------------------- |
+| `id`             | str      | feature id (`==` malk)                                        |
+| `kind`           | str      | `province` / `commune` / `committee`                          |
+| `ma`             | str      | NSO 2-digit province code or 5-char commune code              |
+| `ten`            | str      | canonical Vietnamese name                                     |
+| `type`           | str      | `Tỉnh` / `Thành Phố` / `Phường` / `Xã` / `Đặc khu` / …        |
+| `ten_short`      | str      | `ten` with the type prefix stripped                           |
+| `area_km2`       | float    | parsed via `parse_vi_decimal`                                 |
+| `population`     | int      | parsed via `parse_vi_int`                                     |
+| `density`        | float    | `population / area_km2`                                       |
+| `capital`        | str?     | `trungtamhc` (administrative-centre address)                  |
+| `address`        | str?     |                                                                |
+| `phone`          | str?     |                                                                |
+| `decree`         | str?     | `cancu` (e.g. `Nghị quyết số 202/2025/QH15`)                  |
+| `decree_url`     | str?     | usually a `vanban.chinhphu.vn` permalink                      |
+| `predecessors`   | str?     | raw `truocsapnhap` prose                                      |
+| `parent_ma`      | str?     | NSO-code of the parent province (for communes & committees)   |
+| `parent_ten`     | str?     |                                                                |
+| `centroid_lon/lat` | float? | from the geometry summary                                     |
+| `bbox`           | list?    | `[lon_min, lat_min, lon_max, lat_max]`                        |
+| `geom_type`      | str?     | `Polygon` / `MultiPolygon` / `Point`                           |
+| `wkt`            | str?     | shapely WKT (only when `flatten_geojson=true`)                 |
+
+## Stage 3 — extract
+
+```python
+ExtractStage(config.extract, parsed_dir, extracted_dir).run()
+```
+
+Adds the analytical columns the downstream notebook + the visualizer need:
+
+* **`macro_region`** — every entity is mapped to one of the six GSO
+  macro-regions (`northern_midlands`, `red_river_delta`, `central_coast`,
+  `central_highlands`, `southeast`, `mekong_delta`). The mapping table
+  lives in `packages/curator/regions.py` and is hand-curated against the
+  post-merger 34-province list.
+* **`predecessors_list`** — explodes the `truocsapnhap` Vietnamese prose
+  into a deduplicated list of predecessor names. Handles separators
+  (`,`, `và`, `cùng`, `;`), strips mereological qualifiers ("phần còn lại
+  của", "một phần"), and trims trailing "sau khi sắp xếp" clauses.
+* **`n_predecessors`** — `len(predecessors_list)`.
+* **`keywords`** — top-N TF-IDF unigrams + bigrams over the merger-lineage
+  prose; uses a Vietnamese-friendly token pattern
+  (`r"(?u)\b[\wÀ-ỹ]{3,}\b"`) so diacritics survive tokenisation.
+* **`embed_text`** — single canonical Vietnamese descriptor (name + parent
+  + type + predecessors + capital + decree) that the embedding stage
+  consumes.
+
+Output: `extracted.jsonl` + `extracted.parquet`.
+
+## Stage 4 — embed
+
+```python
+EmbedStage(config.embed, extracted_dir, embedded_dir).run()
+```
+
+Encodes every record's `embed_text` field with
+[`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+on CPU (384-d, normalised, `batch_size=64`). The full corpus of ~6,700
+short Vietnamese descriptors finishes in ~3 minutes on an M-series Mac.
+
+For NIM-hosted embeddings (e.g. `nvidia/llama-3.2-nv-embedqa-1b-v2`)
+the same backend abstraction used by `personas-vn` would slot in here —
+set `embed.backend: nim` and a `PERSONAS_VN_LLM_API_KEY` env var.
+
+Output: `embedded.parquet` with the meta columns plus a `vector`
+column (list[float]).
+
+## Stage 5 — reduce
+
+```python
+ReduceStage(config.reduce, embedded_dir, reduced_dir).run()
+```
+
+* **UMAP** projection to 2-D with cosine metric (15 neighbours,
+  `min_dist=0.1`, `random_state=20260508`).
+* **Density-based HDBSCAN** clustering (`min_cluster_size=⌊n/80⌋`),
+  emitting an integer `cluster` column with `-1` reserved for low-density
+  noise points.
+
+Output: `reduced.parquet` — every meta column from the embed stage plus
+`x`, `y`, and (when `reduce.cluster=true`) `cluster`. This is the parquet
+that feeds the UMAP plots in [`DATAANALYSIS.ipynb`](DATAANALYSIS.ipynb)
+and the curator-tab in any future Gradio visualizer.
+
+## NeMo Curator backend
+
+Pass `--backend nemo_curator` to `geography-vn curate` and the same five
+stage objects are wrapped as `nemo_curator.core.stage.ProcessingStage`
+sub-classes and handed to a real `nemo_curator.core.pipeline.Pipeline`
+running through `nemo_curator.backends.experimental.in_process.InProcessExecutor`.
+The wire-shape on disk is identical, so the rest of the pipeline (HF
+upload, analysis notebook) does not care which executor ran. To go
+distributed, swap `InProcessExecutor` for
+`XennaExecutor` / `RayDataExecutor` — no code changes needed to the
+stages themselves.
+
+## Re-running individual stages
+
+```bash
+# Re-run only the embed + reduce stages (cheap when the corpus stayed put
+# but the model changed):
+geography-vn curate --only embed reduce
+
+# Re-run everything except the slow geometry crawl:
+geography-vn curate --skip download   # cached anyway, but explicit is faster
+```
+
+The on-disk per-URL cache (`raw/_cache/`) means that even
+`--only download` re-runs are near-instantaneous after the first crawl —
+only newly-published feature ids hit the network.

figures/analysis/01_admin_kind_donut.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="4ba4f2bd-7579-48cb-90d7-40dc9e9b911d" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("4ba4f2bd-7579-48cb-90d7-40dc9e9b911d")) {                    Plotly.newPlot(                        "4ba4f2bd-7579-48cb-90d7-40dc9e9b911d",                        [{"customdata":[["committee"],["commune"],["province"]],"domain":{"x":[0.0,1.0],"y":[0.0,1.0]},"hole":0.55,"hovertemplate":"kind=%{customdata[0]}\u003cbr\u003en=%{value}\u003cextra\u003e\u003c\u002fextra\u003e","labels":["committee","commune","province"],"legendgroup":"","marker":{"colors":["#000000","#76B900","#5C9300"]},"name":"","showlegend":true,"values":{"dtype":"i2","bdata":"HQ35DCIA"},"type":"pie","pull":[0.02,0.02,0.02],"textinfo":"label+percent+value"}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"legend":{"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"title":{"text":"Vietnam post-merger admin units (n = 6712)","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"showlegend":false,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"xaxis":{"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/02_macro_region_breakdown.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="f10a1292-f568-4768-90bf-1d72b94341ef" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("f10a1292-f568-4768-90bf-1d72b94341ef")) {                    Plotly.newPlot(                        "f10a1292-f568-4768-90bf-1d72b94341ef",                        [{"hovertemplate":"kind=committee\u003cbr\u003emacro_region_en=%{x}\u003cbr\u003en=%{text}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"committee","marker":{"color":"#000000","pattern":{"shape":""}},"name":"committee","orientation":"v","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAADAd0AAAAAAAMiAQAAAAAAACIdAAAAAAABQikAAAAAAAADwPwAAAAAAYH9AAAAAAABgdkA="},"textposition":"outside","x":["Central Highlands","Mekong River Delta","North Central and Central Coastal Areas","Northern Midlands and Mountain Areas","Other","Red River Delta","Southeast"],"xaxis":"x","y":{"dtype":"i2","bdata":"fAEZAuECSgMBAPYBZgE="},"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"kind=commune\u003cbr\u003emacro_region_en=%{x}\u003cbr\u003en=%{text}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"commune","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"commune","orientation":"v","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAACQdkAAAAAAAPB+QAAAAAAAEIdAAAAAAABIikAAAAAAAHiAQAAAAAAAcHZA"},"textposition":"outside","x":["Central Highlands","Mekong River Delta","North Central and Central Coastal Areas","Northern Midlands and Mountain Areas","Red River Delta","Southeast"],"xaxis":"x","y":{"dtype":"i2","bdata":"aQHvAeICSQMPAmcB"},"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"kind=province\u003cbr\u003emacro_region_en=%{x}\u003cbr\u003en=%{text}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"province","marker":{"color":"#5C9300","pattern":{"shape":""}},"name":"province","orientation":"v","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAACEAAAAAAAAAUQAAAAAAAACBAAAAAAAAAJEAAAAAAAAAUQAAAAAAAAAhA"},"textposition":"outside","x":["Central Highlands","Mekong River Delta","North Central and Central Coastal Areas","Northern Midlands and Mountain Areas","Red River Delta","Southeast"],"xaxis":"x","y":{"dtype":"i1","bdata":"AwUICgUD"},"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"categoryorder":"array","categoryarray":["Northern Midlands and Mountain Areas","Red River Delta","North Central and Central Coastal Areas","Central Highlands","Southeast","Mekong River Delta"],"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"count","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"entity kind"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"Macro-region inventory: provinces, communes, committees","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/03_province_population.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="ebf696ac-9c04-4f8b-9673-65ba8cb7d52c" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("ebf696ac-9c04-4f8b-9673-65ba8cb7d52c")) {                    Plotly.newPlot(                        "ebf696ac-9c04-4f8b-9673-65ba8cb7d52c",                        [{"customdata":[["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Northern Midlands and Mountain Areas","marker":{"color":"#1F4E79","pattern":{"shape":""}},"name":"Northern Midlands and Mountain Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAAGRJH0EAAAAAfn0hQQAAAACGiiRBAAAAANDlKkEAAAAAq241QQAAAABhJDtBAAAAAEF1O0EAAAAANnY8QQAAAIA0nUtBAAAAALewTkE="},"xaxis":"x","y":["Tỉnh Lai Châu","Tỉnh Cao Bằng","Tỉnh Điện Biên","Tỉnh Lạng Sơn","Tỉnh Sơn La","Tỉnh Lào Cai","Tỉnh Thái Nguyên","Tỉnh Tuyên Quang","Tỉnh Bắc Ninh","Tỉnh Phú Thọ"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"North Central and Central Coastal Areas","marker":{"color":"#C97C00","pattern":{"shape":""}},"name":"North Central and Central Coastal Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAAJrdNUEAAAAAdcM4QQAAAAD9izxBAAAAgC1+QEEAAAAA8R1BQQAAAACOY0dBAAAAAMc7TUEAAADAa39QQQ=="},"xaxis":"x","y":["Thành phố Huế","Tỉnh Hà Tĩnh","Tỉnh Quảng Trị","Tỉnh Quảng Ngãi","Tỉnh Khánh Hòa","Thành phố Đà Nẵng","Tỉnh Nghệ An","Tỉnh Thanh Hóa"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Red River Delta","marker":{"color":"#5C9300","pattern":{"shape":""}},"name":"Red River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAAGfZNkEAAACAozhLQQAAAADa1FBBAAAAAM\u002fKUUEAAABgjMxgQQ=="},"xaxis":"x","y":["Tỉnh Quảng Ninh","Tỉnh Hưng Yên","Tỉnh Ninh Bình","Thành phố Hải Phòng","Thủ đô Hà Nội"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Mekong River Delta","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"Mekong River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAACjjQ0EAAAAAZAVQQQAAAEDLPVBBAAAAgJ+rUEEAAACAK+RSQQ=="},"xaxis":"x","y":["Tỉnh Cà Mau","Thành phố Cần Thơ","Tỉnh Vĩnh Long","Tỉnh Đồng Tháp","Tỉnh An Giang"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Southeast"],["Southeast"],["Southeast"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Southeast","marker":{"color":"#000000","pattern":{"shape":""}},"name":"Southeast","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAAM3TSEEAAAAAJCJRQQAAAMA0tWpB"},"xaxis":"x","y":["Tỉnh Tây Ninh","Thành phố Đồng Nai","Thành phố Hồ Chí Minh"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Central Highlands"],["Central Highlands"],["Central Highlands"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003epopulation=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Central Highlands","marker":{"color":"#7F4E2C","pattern":{"shape":""}},"name":"Central Highlands","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"AAAAgNKISUEAAACAZldLQQAAAIBzjE1B"},"xaxis":"x","y":["Tỉnh Đắk Lắk","Tỉnh Gia Lai","Tỉnh Lâm Đồng"],"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"people","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"macro-region"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"34 provinces by population","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/04_province_area.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="3b6442ff-b5ae-4bc5-87a9-716e0a6b6243" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("3b6442ff-b5ae-4bc5-87a9-716e0a6b6243")) {                    Plotly.newPlot(                        "3b6442ff-b5ae-4bc5-87a9-716e0a6b6243",                        [{"customdata":[["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Red River Delta","marker":{"color":"#5C9300","pattern":{"shape":""}},"name":"Red River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"hetRuJ6lo0A9CtejcPWoQEjhehSuP6pACtejcD3NrkAzMzMz8z+4QA=="},"xaxis":"x","y":["Tỉnh Hưng Yên","Thành phố Hải Phòng","Thủ đô Hà Nội","Tỉnh Ninh Bình","Tỉnh Quảng Ninh"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Northern Midlands and Mountain Areas","marker":{"color":"#1F4E79","pattern":{"shape":""}},"name":"Northern Midlands and Mountain Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"mpmZmZluskBxPQrXYyy6QKRwPQoXO8BAFK5H4ZpbwEAK16NwXbbBQD0K16OwSMJApHA9CvehwkApXI\u002fCdeTJQAAAAADA8cpAuB6F63GOy0A="},"xaxis":"x","y":["Tỉnh Bắc Ninh","Tỉnh Cao Bằng","Tỉnh Lạng Sơn","Tỉnh Thái Nguyên","Tỉnh Lai Châu","Tỉnh Phú Thọ","Tỉnh Điện Biên","Tỉnh Lào Cai","Tỉnh Tuyên Quang","Tỉnh Sơn La"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"North Central and Central Coastal Areas","marker":{"color":"#C97C00","pattern":{"shape":""}},"name":"North Central and Central Coastal Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"j8L1KBxTs0AzMzMzc2q3QEjhehTutcBAFK5H4Vq1xUBSuB6FyynHQAAAAAAAzshAZmZmZkb4zEAAAAAAoBnQQA=="},"xaxis":"x","y":["Thành phố Huế","Tỉnh Hà Tĩnh","Tỉnh Khánh Hòa","Tỉnh Thanh Hóa","Thành phố Đà Nẵng","Tỉnh Quảng Trị","Tỉnh Quảng Ngãi","Tỉnh Nghệ An"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Mekong River Delta","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"Mekong River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"cT0K16Myt0AzMzMzM5i4QK5H4XrU2LhAcT0K12MGv0CuR+F6dFDDQA=="},"xaxis":"x","y":["Tỉnh Đồng Tháp","Tỉnh Vĩnh Long","Thành phố Cần Thơ","Tỉnh Cà Mau","Tỉnh An Giang"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Southeast"],["Southeast"],["Southeast"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Southeast","marker":{"color":"#000000","pattern":{"shape":""}},"name":"Southeast","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"pHA9Cpd0ukAfhetROKzAQKRwPQqX4MhA"},"xaxis":"x","y":["Thành phố Hồ Chí Minh","Tỉnh Tây Ninh","Thành phố Đồng Nai"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Central Highlands"],["Central Highlands"],["Central Highlands"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003earea_km2=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Central Highlands","marker":{"color":"#7F4E2C","pattern":{"shape":""}},"name":"Central Highlands","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"mpmZmRms0UC4HoXrIRLVQK5H4XpEqtdA"},"xaxis":"x","y":["Tỉnh Đắk Lắk","Tỉnh Gia Lai","Tỉnh Lâm Đồng"],"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"km²","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"macro-region"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"34 provinces by area","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/05_province_density.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="cbcd30ca-134e-4e96-86c0-163f1d278df9" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("cbcd30ca-134e-4e96-86c0-163f1d278df9")) {                    Plotly.newPlot(                        "cbcd30ca-134e-4e96-86c0-163f1d278df9",                        [{"customdata":[["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"],["Northern Midlands and Mountain Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Northern Midlands and Mountain Areas","marker":{"color":"#1F4E79","pattern":{"shape":""}},"name":"Northern Midlands and Mountain Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"Q1Ap0BJDTEDs1WM\u002fh6NRQBvqGTtAYlVA13ONx2njWECLnpx644NaQLREz+6wxWBAirg5tazmYEAQUx+wfNtqQHmrV2VK23pASulkzHP4h0A="},"xaxis":"x","y":["Tỉnh Lai Châu","Tỉnh Điện Biên","Tỉnh Cao Bằng","Tỉnh Sơn La","Tỉnh Lạng Sơn","Tỉnh Lào Cai","Tỉnh Tuyên Quang","Tỉnh Thái Nguyên","Tỉnh Phú Thọ","Tỉnh Bắc Ninh"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"],["North Central and Central Coastal Areas"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"North Central and Central Coastal Areas","marker":{"color":"#C97C00","pattern":{"shape":""}},"name":"North Central and Central Coastal Areas","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"06Vywc43YkALHymu8GliQPrs2cc\u002fDW1AaUMRv+UncEC84qWKlmNwQOS0Niy+63BA85jJbJQackBR903Iq1F4QA=="},"xaxis":"x","y":["Tỉnh Quảng Ngãi","Tỉnh Quảng Trị","Tỉnh Nghệ An","Thành phố Đà Nẵng","Tỉnh Khánh Hòa","Tỉnh Hà Tĩnh","Thành phố Huế","Tỉnh Thanh Hóa"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Central Highlands"],["Central Highlands"],["Central Highlands"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Central Highlands","marker":{"color":"#7F4E2C","pattern":{"shape":""}},"name":"Central Highlands","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"dfsXBVX6Y0DhCMAs88JkQG08EepDHmdA"},"xaxis":"x","y":["Tỉnh Lâm Đồng","Tỉnh Gia Lai","Tỉnh Đắk Lắk"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"],["Red River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Red River Delta","marker":{"color":"#5C9300","pattern":{"shape":""}},"name":"Red River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"zMeFc9wmbkAxwM+lenyRQMIEBe4WK5ZAyKtWaMrPlkB3+MWn0nqkQA=="},"xaxis":"x","y":["Tỉnh Quảng Ninh","Tỉnh Ninh Bình","Tỉnh Hưng Yên","Thành phố Hải Phòng","Thủ đô Hà Nội"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"],["Mekong River Delta"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Mekong River Delta","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"Mekong River Delta","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"I0wzriiDdEBAXK26l0x\u002fQJ674q8booRAJpqUDrchhUDG4RuD7v6GQA=="},"xaxis":"x","y":["Tỉnh Cà Mau","Tỉnh An Giang","Thành phố Cần Thơ","Tỉnh Vĩnh Long","Tỉnh Đồng Tháp"],"yaxis":"y","type":"bar","cliponaxis":false},{"customdata":[["Southeast"],["Southeast"],["Southeast"]],"hovertemplate":"macro_region_en=%{customdata[0]}\u003cbr\u003edensity=%{x:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Southeast","marker":{"color":"#000000","pattern":{"shape":""}},"name":"Southeast","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"f8","bdata":"nh0DG\u002fMJdkAfAggkWdN3QBOUEkYUJ6BA"},"xaxis":"x","y":["Thành phố Đồng Nai","Tỉnh Tây Ninh","Thành phố Hồ Chí Minh"],"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"people \u002f km²","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"macro-region"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"34 provinces by population density","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/06_communes_per_province.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="68491111-2630-4c6e-b3f5-a94bfc97cf4a" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("68491111-2630-4c6e-b3f5-a94bfc97cf4a")) {                    Plotly.newPlot(                        "68491111-2630-4c6e-b3f5-a94bfc97cf4a",                        [{"hovertemplate":"macro_region_en=Northern Midlands and Mountain Areas\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Northern Midlands and Mountain Areas","marker":{"color":"#1F4E79","pattern":{"shape":""}},"name":"Northern Midlands and Mountain Areas","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAAQ0AAAAAAAIBGQAAAAAAAAExAAAAAAABAUEAAAAAAAMBSQAAAAAAAAFdAAAAAAADAWEAAAAAAAMBYQAAAAAAAAF9AAAAAAACAYkA="},"textposition":"outside","x":{"dtype":"i2","bdata":"JgAtADgAQQBLAFwAYwBjAHwAlAA="},"xaxis":"x","y":["Tỉnh Lai Châu","Tỉnh Điện Biên","Tỉnh Cao Bằng","Tỉnh Lạng Sơn","Tỉnh Sơn La","Tỉnh Thái Nguyên","Tỉnh Lào Cai","Tỉnh Bắc Ninh","Tỉnh Tuyên Quang","Tỉnh Phú Thọ"],"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"macro_region_en=North Central and Central Coastal Areas\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"North Central and Central Coastal Areas","marker":{"color":"#C97C00","pattern":{"shape":""}},"name":"North Central and Central Coastal Areas","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAAREAAAAAAAEBQQAAAAAAAQFFAAAAAAACAU0AAAAAAAIBXQAAAAAAAAFhAAAAAAABAYEAAAAAAAMBkQA=="},"textposition":"outside","x":{"dtype":"i2","bdata":"KABBAEUATgBeAGAAggCmAA=="},"xaxis":"x","y":["Thành phố Huế","Tỉnh Khánh Hòa","Tỉnh Hà Tĩnh","Tỉnh Quảng Trị","Thành phố Đà Nẵng","Tỉnh Quảng Ngãi","Tỉnh Nghệ An","Tỉnh Thanh Hóa"],"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"macro_region_en=Red River Delta\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Red River Delta","marker":{"color":"#5C9300","pattern":{"shape":""}},"name":"Red River Delta","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAAS0AAAAAAAABaQAAAAAAAgFxAAAAAAACAX0AAAAAAACBgQA=="},"textposition":"outside","x":{"dtype":"i2","bdata":"NgBoAHIAfgCBAA=="},"xaxis":"x","y":["Tỉnh Quảng Ninh","Tỉnh Hưng Yên","Thành phố Hải Phòng","Thủ đô Hà Nội","Tỉnh Ninh Bình"],"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"macro_region_en=Mekong River Delta\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Mekong River Delta","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"Mekong River Delta","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAAUEAAAAAAAIBZQAAAAAAAgFlAAAAAAADAWUAAAAAAAABfQA=="},"textposition":"outside","x":{"dtype":"i1","bdata":"QGZmZ3w="},"xaxis":"x","y":["Tỉnh Cà Mau","Tỉnh Đồng Tháp","Tỉnh An Giang","Thành phố Cần Thơ","Tỉnh Vĩnh Long"],"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"macro_region_en=Southeast\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Southeast","marker":{"color":"#000000","pattern":{"shape":""}},"name":"Southeast","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAADAV0AAAAAAAABYQAAAAAAAAGVA"},"textposition":"outside","x":{"dtype":"i2","bdata":"XwBgAKgA"},"xaxis":"x","y":["Thành phố Đồng Nai","Tỉnh Tây Ninh","Thành phố Hồ Chí Minh"],"yaxis":"y","type":"bar","cliponaxis":false},{"hovertemplate":"macro_region_en=Central Highlands\u003cbr\u003en_communes=%{text}\u003cbr\u003eparent_ten=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"Central Highlands","marker":{"color":"#7F4E2C","pattern":{"shape":""}},"name":"Central Highlands","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAACAWUAAAAAAAABfQAAAAAAA4GBA"},"textposition":"outside","x":{"dtype":"i2","bdata":"ZgB8AIcA"},"xaxis":"x","y":["Tỉnh Đắk Lắk","Tỉnh Lâm Đồng","Tỉnh Gia Lai"],"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"number of communes \u002f wards","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"macro-region"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"Communes per province (post-merger)","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/07_merger_fanout_provinces.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="0ffd5ac5-6d05-439f-adf8-d7c1d2c027c4" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("0ffd5ac5-6d05-439f-adf8-d7c1d2c027c4")) {                    Plotly.newPlot(                        "0ffd5ac5-6d05-439f-adf8-d7c1d2c027c4",                        [{"hovertemplate":"n_predecessors=%{x}\u003cbr\u003en_units=%{text}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"","orientation":"v","showlegend":false,"text":{"dtype":"f8","bdata":"AAAAAAAAJkAAAAAAAAAwQAAAAAAAABhAAAAAAAAA8D8="},"textposition":"outside","x":{"dtype":"i1","bdata":"AQIDBA=="},"xaxis":"x","y":{"dtype":"i1","bdata":"CxAGAQ=="},"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"number of predecessor units absorbed","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"number of provinces","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"Province merger fanout","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/08_merger_fanout_communes.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="c18d2fe3-a3a7-43df-978b-b020f7a1691b" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("c18d2fe3-a3a7-43df-978b-b020f7a1691b")) {                    Plotly.newPlot(                        "c18d2fe3-a3a7-43df-978b-b020f7a1691b",                        [{"hovertemplate":"n_predecessors=%{x}\u003cbr\u003en_units=%{text}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"","orientation":"v","showlegend":false,"text":{"dtype":"f8","bdata":"AAAAAABgYUAAAAAAALCHQAAAAAAAaJdAAAAAAAB4gUAAAAAAAGBmQAAAAAAAgFNAAAAAAACAQkAAAAAAAAA9QAAAAAAAADFAAAAAAAAAKEAAAAAAAAAYQAAAAAAAABhAAAAAAAAA8D8AAAAAAADwPwAAAAAAAPA\u002f"},"textposition":"outside","x":{"dtype":"i1","bdata":"AQIDBAUGBwgJCgsMDQ4Q"},"xaxis":"x","y":{"dtype":"i2","bdata":"iwD2AtoFLwKzAE4AJQAdABEADAAGAAYAAQABAAEA"},"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"number of predecessor units absorbed","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"number of communes","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"Commune merger fanout","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/10_decree_map.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="b5c3d8f6-744c-40f0-8cab-eeab26c6fd5e" class="plotly-graph-div" style="height:900px; width:1200px;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("b5c3d8f6-744c-40f0-8cab-eeab26c6fd5e")) {                    Plotly.newPlot(                        "b5c3d8f6-744c-40f0-8cab-eeab26c6fd5e",                        [{"hovertemplate":"kind=commune\u003cbr\u003en=%{text}\u003cbr\u003edecree_short=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"commune","marker":{"color":"#76B900","pattern":{"shape":""}},"name":"commune","orientation":"h","showlegend":true,"text":{"dtype":"f8","bdata":"AAAAAAAAZUAAAAAAAMBkQAAAAAAAgGJAAAAAAADgYEAAAAAAAEBgQAAAAAAAIGBAAAAAAACAX0AAAAAAAABfQAAAAAAAAF9AAAAAAAAAX0AAAAAAAIBcQAAAAAAAAFpAAAAAAADAWUAAAAAAAIBZQAAAAAAAgFlAAAAAAACAWUAAAAAAAMBYQAAAAAAAwFhAAAAAAAAAWEAAAAAAAABYQA=="},"textposition":"outside","x":{"dtype":"i2","bdata":"qACmAJQAhwCCAIEAfgB8AHwAfAByAGgAZwBmAGYAZgBjAGMAYABgAA=="},"xaxis":"x","y":["Nghị quyết số 1685\u002fNQ-UBTVQH15","Nghị quyết số 1686\u002fNQ-UBTVQH15","Nghị quyết số 1676\u002fNQ-UBTVQH15","Nghị quyết số 1664\u002fNQ-UBTVQH15","Nghị quyết số 1678\u002fNQ-UBTVQH15","Nghị quyết số 1674\u002fNQ-UBTVQH15","Nghị quyết số 1656\u002fNQ-UBTVQH15","Nghị quyết số 1671\u002fNQ-UBTVQH15","Nghị quyết số 1687\u002fNQ-UBTVQH15","Nghị quyết số 1684\u002fNQ-UBTVQH15","Nghị quyết số 1669\u002fNQ-UBTVQH15","Nghị quyết số 1666\u002fNQ-UBTVQH15","Nghị quyết số 1668\u002fNQ-UBTVQH15","Nghị quyết số 1654\u002fNQ-UBTVQH15","Nghị quyết số 1663\u002fNQ-UBTVQH15","Nghị quyết số 1660\u002fNQ-UBTVQH15","Nghị quyết số 1658\u002fNQ-UBTVQH15","Nghị quyết số 1673\u002fNQ-UBTVQH15","Nghị quyết số 1682\u002fNQ-UBTVQH15","Nghị quyết số 1677\u002fNQ-UBTVQH15"],"yaxis":"y","type":"bar","cliponaxis":false}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"number of units cited","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"legend":{"title":{"text":"entity kind"},"tracegroupgap":0,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"barmode":"relative","title":{"text":"Top decrees authorising the merger (count by entity kind)","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"width":1200,"height":900},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>

figures/analysis/11_provinces_choropleth.html

@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.5.0.min.js" integrity="sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=" crossorigin="anonymous"></script>                <div id="0e1ee302-235e-47a2-8e50-8d5ba8c6538a" class="plotly-graph-div" style="height:900px; width:100%;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("0e1ee302-235e-47a2-8e50-8d5ba8c6538a")) {                    Plotly.newPlot(                        "0e1ee302-235e-47a2-8e50-8d5ba8c6538a",                        [{"customdata":{"dtype":"f8","bdata":"AAAAAGQFUEGuR+F61Ni4QCzP7BnJfCNAXVEu5HFwWkAAAACAK+RSQa5H4Xp0UMNAncvlmXteJECH9CdpFUJaQAAAAAAo40NBcT0K12MGv0A4p1pRREUiQNbtfUqdS1pAAAAAgJ+rUEFxPQrXozK3QITVNTKF\u002fSRA9igsLpd5WkAAAABAyz1QQTMzMzMzmLhA1IDxwsT9I0AhlIJ7uJJaQA==","shape":"5, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=Mekong River Delta\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Thành phố Cần Thơ","Tỉnh An Giang","Tỉnh Cà Mau","Tỉnh Đồng Tháp","Tỉnh Vĩnh Long"],"lat":{"dtype":"f8","bdata":"LM\u002fsGcl8I0Cdy+WZe14kQDinWlFERSJAhNU1MoX9JEDUgPHCxP0jQA=="},"legendgroup":"Mekong River Delta","lon":{"dtype":"f8","bdata":"XVEu5HFwWkCH9CdpFUJaQNbtfUqdS1pA9igsLpd5WkAhlIJ7uJJaQA=="},"marker":{"color":"#76B900","size":{"dtype":"f8","bdata":"AAAAAGQFUEEAAACAK+RSQQAAAAAo40NBAAAAgJ+rUEEAAABAyz1QQQ=="},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"Mekong River Delta","showlegend":true,"type":"scattergeo"},{"customdata":{"dtype":"f8","bdata":"AAAAAI5jR0FSuB6FyynHQCoNglVXQy9Abx4HK9r9WkAAAAAAmt01QY\u002fC9SgcU7NAvrATteBUMEB2OJ2eG+FaQAAAAAB1wzhBMzMzM3Nqt0CM9fBLkUoyQPR+hNwJb1pAAAAAAPEdQUFI4XoU7rXAQNHvLemRJShAbzcSls49W0AAAAAAxztNQQAAAACgGdBAfaZ1CMg8M0ARXKY\u002fbDxaQAAAAIAtfkBBZmZmZkb4zECGlZkM2YktQLmUGjwwCVtAAAAAAP2LPEEAAAAAAM7IQMWcTOqNPTFAiga9crmhWkAAAADAa39QQRSuR+FatcVAlLega+ULNEDZqtbwVFRaQA==","shape":"8, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=North Central and Central Coastal Areas\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Thành phố Đà Nẵng","Thành phố Huế","Tỉnh Hà Tĩnh","Tỉnh Khánh Hòa","Tỉnh Nghệ An","Tỉnh Quảng Ngãi","Tỉnh Quảng Trị","Tỉnh Thanh Hóa"],"lat":{"dtype":"f8","bdata":"Kg2CVVdDL0C+sBO14FQwQIz18EuRSjJA0e8t6ZElKEB9pnUIyDwzQIaVmQzZiS1AxZxM6o09MUCUt6Br5Qs0QA=="},"legendgroup":"North Central and Central Coastal Areas","lon":{"dtype":"f8","bdata":"bx4HK9r9WkB2OJ2eG+FaQPR+hNwJb1pAbzcSls49W0ARXKY\u002fbDxaQLmUGjwwCVtAiga9crmhWkDZqtbwVFRaQA=="},"marker":{"color":"#C97C00","size":{"dtype":"f8","bdata":"AAAAAI5jR0EAAAAAmt01QQAAAAB1wzhBAAAAAPEdQUEAAAAAxztNQQAAAIAtfkBBAAAAAP2LPEEAAADAa39QQQ=="},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"North Central and Central Coastal Areas","showlegend":true,"type":"scattergeo"},{"customdata":{"dtype":"f8","bdata":"AAAAAM\u002fKUUE9CtejcPWoQNdazQq+3jRAJaQP5HegWkAAAABgjMxgQUjhehSuP6pAxMMLxAYANUDUeI4lrGxaQAAAAICjOEtBhetRuJ6lo0B3EvMmbp40QBhIvXNSkVpAAAAAANrUUEEK16NwPc2uQNeKPRvrTzRAAHmnvvaCWkAAAAAAZ9k2QTMzMzPzP7hAhwaQx2c9NUBPhVVOZNBaQA==","shape":"5, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=Red River Delta\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Thành phố Hải Phòng","Thủ đô Hà Nội","Tỉnh Hưng Yên","Tỉnh Ninh Bình","Tỉnh Quảng Ninh"],"lat":{"dtype":"f8","bdata":"11rNCr7eNEDEwwvEBgA1QHcS8yZunjRA14o9G+tPNECHBpDHZz01QA=="},"legendgroup":"Red River Delta","lon":{"dtype":"f8","bdata":"JaQP5HegWkDUeI4lrGxaQBhIvXNSkVpAAHmnvvaCWkBPhVVOZNBaQA=="},"marker":{"color":"#5C9300","size":{"dtype":"f8","bdata":"AAAAAM\u002fKUUEAAABgjMxgQQAAAICjOEtBAAAAANrUUEEAAAAAZ9k2QQ=="},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"Red River Delta","showlegend":true,"type":"scattergeo"},{"customdata":{"dtype":"f8","bdata":"AAAAwDS1akGkcD0Kl3S6QPLjQhGNuiVASKbY+\u002f21WkAAAAAAJCJRQaRwPQqX4MhAaWqRRrPfJkDCl3vZKMJaQAAAAADN00hBH4XrUTiswEBuJqkaqBkmQPmHYEiPilpA","shape":"3, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=Southeast\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Thành phố Hồ Chí Minh","Thành phố Đồng Nai","Tỉnh Tây Ninh"],"lat":{"dtype":"f8","bdata":"8uNCEY26JUBpapFGs98mQG4mqRqoGSZA"},"legendgroup":"Southeast","lon":{"dtype":"f8","bdata":"SKbY+\u002f21WkDCl3vZKMJaQPmHYEiPilpA"},"marker":{"color":"#000000","size":{"dtype":"f8","bdata":"AAAAwDS1akEAAAAAJCJRQQAAAADN00hB"},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"Southeast","showlegend":true,"type":"scattergeo"},{"customdata":{"dtype":"f8","bdata":"AAAAgDSdS0GamZmZmW6yQK9eRJe2UDVAcvRn9XyaWkAAAAAAfn0hQXE9CtdjLLpAfy1eBd2+NkA3PkrDVIVaQAAAAACGiiRBpHA9CvehwkDFBSHGE7Y1QGdPr3VYwVlAAAAAAGRJH0EK16NwXbbBQAylALlrUTZA+l0OotjLWUAAAAAA0OUqQaRwPQoXO8BAKmslz+3WNUAh\u002ft\u002fdmqdaQAAAAABhJDtBKVyPwnXkyUB5R99xew82QFaBEIs7FlpAAAAAALewTkE9CtejsEjCQDu+fJnnAzVAse22aC9SWkAAAAAAq241QbgehetxjstAchgvr9oxNUBvJG0JcARaQAAAAABBdTtBFK5H4ZpbwEDJ4auKHgY2QIpfFI+5dFpAAAAAADZ2PEEAAAAAwPHKQHKp969bfTZAwNkoxVFGWkA=","shape":"10, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=Northern Midlands and Mountain Areas\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Tỉnh Bắc Ninh","Tỉnh Cao Bằng","Tỉnh Điện Biên","Tỉnh Lai Châu","Tỉnh Lạng Sơn","Tỉnh Lào Cai","Tỉnh Phú Thọ","Tỉnh Sơn La","Tỉnh Thái Nguyên","Tỉnh Tuyên Quang"],"lat":{"dtype":"f8","bdata":"r15El7ZQNUB\u002fLV4F3b42QMUFIcYTtjVADKUAuWtRNkAqayXP7dY1QHlH33F7DzZAO758mecDNUByGC+v2jE1QMnhq4oeBjZAcqn3r1t9NkA="},"legendgroup":"Northern Midlands and Mountain Areas","lon":{"dtype":"f8","bdata":"cvRn9XyaWkA3PkrDVIVaQGdPr3VYwVlA+l0OotjLWUAh\u002ft\u002fdmqdaQFaBEIs7FlpAse22aC9SWkBvJG0JcARaQIpfFI+5dFpAwNkoxVFGWkA="},"marker":{"color":"#1F4E79","size":{"dtype":"f8","bdata":"AAAAgDSdS0EAAAAAfn0hQQAAAACGiiRBAAAAAGRJH0EAAAAA0OUqQQAAAABhJDtBAAAAALewTkEAAAAAq241QQAAAABBdTtBAAAAADZ2PEE="},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"Northern Midlands and Mountain Areas","showlegend":true,"type":"scattergeo"},{"customdata":{"dtype":"f8","bdata":"AAAAgNKISUGamZmZGazRQD+Tu3vy1ylAfuhcmHIcW0AAAACAZldLQbgeheshEtVAx8fHf8XHK0BhLuFaDxxbQAAAAIBzjE1BrkfhekSq10D3xT+FmFgnQC2uaQUD\u002flpA","shape":"3, 4"},"geo":"geo","hovertemplate":"\u003cb\u003e%{hovertext}\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003emacro_region_en=Central Highlands\u003cbr\u003epopulation=%{customdata[0]:,}\u003cbr\u003earea_km2=%{customdata[1]:.0f}\u003cextra\u003e\u003c\u002fextra\u003e","hovertext":["Tỉnh Đắk Lắk","Tỉnh Gia Lai","Tỉnh Lâm Đồng"],"lat":{"dtype":"f8","bdata":"P5O7e\u002fLXKUDHx8d\u002fxccrQPfFP4WYWCdA"},"legendgroup":"Central Highlands","lon":{"dtype":"f8","bdata":"fuhcmHIcW0BhLuFaDxxbQC2uaQUD\u002flpA"},"marker":{"color":"#7F4E2C","size":{"dtype":"f8","bdata":"AAAAgNKISUEAAACAZldLQQAAAIBzjE1B"},"sizemode":"area","sizeref":2857.6730612244896,"symbol":"circle"},"mode":"markers","name":"Central Highlands","showlegend":true,"type":"scattergeo"}],                        {"template":{"layout":{"colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"font":{"color":"#000000","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":14},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","title":{"font":{"color":"#1A1A1A","family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","size":20},"x":0.02,"xanchor":"left","y":0.96}}},"geo":{"domain":{"x":[0.0,1.0],"y":[0.0,1.0]},"projection":{"type":"mercator"},"center":{"lat":15.5,"lon":107},"lonaxis":{"range":[101,117]},"lataxis":{"range":[8,24]},"scope":"asia","showcountries":true,"countrycolor":"lightgray","bgcolor":"white"},"legend":{"title":{"text":"macro-region"},"tracegroupgap":0,"itemsizing":"constant","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"bgcolor":"rgba(255,255,255,0.92)","bordercolor":"#CCCCCC","borderwidth":1},"margin":{"t":80,"l":120,"r":80,"b":80},"title":{"text":"Population by post-merger province (bubble cartogram)","font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#1A1A1A","size":20},"x":0.02,"xanchor":"left","y":0.96},"height":900,"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14},"hoverlabel":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#FFFFFF","size":12},"bgcolor":"#000000","bordercolor":"#76B900"},"paper_bgcolor":"#FFFFFF","plot_bgcolor":"#FFFFFF","colorway":["#76B900","#000000","#888888","#B5D88A","#444444","#CCCCCC"],"xaxis":{"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true},"yaxis":{"tickfont":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":12},"title":{"font":{"family":"Latin Modern Roman, LM Roman 10, Computer Modern, CMU Serif, STIX Two Text, STIX, Times New Roman, Times, serif","color":"#000000","size":14}},"showgrid":true,"gridcolor":"rgba(0,0,0,0.08)","zeroline":true,"zerolinecolor":"#CCCCCC","linecolor":"#000000","linewidth":1,"ticks":"outside","tickcolor":"#000000","showline":true,"mirror":false,"automargin":true}},                        {"responsive": true}                    )                };            </script>        </div>
+</body>
+</html>