Interactive district-level map of Pakistan, visualising indicators from the 2017 and 2023 Population & Housing Censuses, the PSLM 2019-20, the Economic Census 2023, the Labour Force Survey, and the HIES 2024-25 — all published by the Pakistan Bureau of Statistics.
https://hibasameen.github.io/datadarbar/
- 12 indicator groups covering demographics, urban/rural splits, literacy, education attainment, employment status, school attendance, PSLM welfare indicators, Economic Census, Labour Force Survey, and HIES household income/expenditure
- 2017 vs 2023 comparison with a "Change" toggle showing inter-censal differences
- Province filter, district search, and CSV export
- Responsive layout (desktop + mobile)
datadarbar/
├── .github/workflows/ ← GitHub Actions deployment
│ └── deploy.yml
├── app/ ← static site (deployed to GitHub Pages)
│ ├── index.html
│ ├── assets/
│ │ ├── css/styles.css
│ │ ├── js/app.js
│ │ └── img/logo.svg
│ └── data/
│ ├── districts.json
│ └── pakistan_districts_province_boundries.geojson
├── etl/ ← Python data pipeline
│ └── build_dataset.py
├── .gitignore
└── README.md
cd app
python3 -m http.server 8000
# open http://localhost:8000The site deploys automatically to GitHub Pages via GitHub Actions on every push to main. The workflow publishes the app/ folder.
To set up Pages for the first time:
- Push this repo to GitHub
- Go to Settings → Pages
- Set source to GitHub Actions
- The site will be live at https://user.github.io/datadarbar/
If you update source data, regenerate the JSON:
cd etl
python3 build_dataset.pyThe Labour Force Survey (LFS) and Household Integrated Economic Survey (HIES) are designed to be representative at the provincial level, not the district level. To produce district-level estimates from these surveys, the pipeline applies two adjustments:
Minimum sample-size filter. Districts with fewer than 30 survey observations have all derived indicators suppressed (set to null) and are flagged with a low_n marker. On the map, these districts appear with a distinct gold dashed border and a warning in the tooltip. This threshold reflects the standard convention that small samples produce unreliable estimates — particularly for ratio and proportion indicators where a handful of observations can swing values wildly. In the current data, this affects 6 HIES districts (Dera Bugti, Khuzdar, Mastung, Orakzai Agency, Panjgur, and Ziarat), all in remote areas with limited survey coverage.
Post-stratification to 2023 census totals. Survey weights are recalibrated so that weighted district-level totals align with known population counts from Census 2023 (Table 1). For LFS (individual-level microdata), this takes the form of a sex-ratio adjustment: within each district, male and female observations are reweighted so that the weighted sex composition matches the census male/female population shares. For HIES (household-level data), a simpler population calibration factor scales all household weights in a district so that the weighted population total matches the census figure. This corrects for the fact that PBS survey sampling frames may not reflect post-census population shifts across districts, and reduces bias from differential non-response by sex.
These adjustments improve the plausibility of district-level estimates but do not eliminate the fundamental limitation that provincial-level surveys have limited statistical power at finer geographies. Users should interpret district-level survey indicators as approximate and treat cross-district rankings with appropriate caution. The sample size (n) is shown in tooltips for all survey-based indicator groups.
| Source | Years | Coverage |
|---|---|---|
| Population Census | 2017, 2023 | Demographics, literacy, education, employment |
| PSLM | 2019-20 | Water, sanitation, ICT, work participation |
| Economic Census | 2023 | Establishments, workforce by sector |
| Labour Force Survey | 2020-21, 2024-25 | LFPR, unemployment, industry |
| HIES | 2024-25 | Household income & expenditure |
| District boundaries | — | GeoJSON from PBS / geoBoundaries |
Data is from the Pakistan Bureau of Statistics (public domain). This project is licensed under the MIT License — see the LICENSE file for details.