KudzaiNovae/techdev-zimbabwe

TECHDEV-ZIMBABWE ITU Hackathon # TECHDEV-ZIMBABWE: Mapping Zimbabwe's Digital Deserts **ITU Data Hackathon 2026 — Stage 2 Submission** An open, reproducible data pipeline that identifies, measures, and visualises coverage, quality, and affordability gaps in connectivity across Zimbabwe using ITU DataHub indicators, POTRAZ sector data, and World Bank socio-economic indicators. The core output is a **Digital Desert Index (DDI)** — a composite score per district that makes digital exclusion visible and measurable. --- ## Prerequisites - **Python 3.10+** with pip - ~500 MB disk space for data and outputs - Internet access (for World Bank API and GADM download) Install dependencies: ```bash pip install wbdata pandas matplotlib geopandas ``` --- ## Reproduction steps (start to finish) Follow these steps in order. Every command is run from the repository root. At the end you will have all figures, CSVs, and the district-level GeoPackage used in the submission. ### Step 1: Pull World Bank data (automated, ~1 minute) ```bash python src/extract_worldbank.py ``` **What it does:** Connects to the World Bank API via the `wbdata` library and pulls 25 indicators for 6 countries (Zimbabwe, Zambia, Mozambique, Malawi, Botswana, South Africa), 2010–latest. Pulls one indicator at a time so a single failure doesn't kill the run. **Indicators pulled:** | Tier | Indicator | WB Code | |---|---|---| | Core | GNI per capita (USD, PPP) | NY.GNP.PCAP.CD, NY.GNP.PCAP.PP.CD | | Core | GDP per capita | NY.GDP.PCAP.CD | | Core | Poverty headcount (national, $2.15/day) | SI.POV.NAHC, SI.POV.DDAY | | Core | Gini index | SI.POV.GINI | | Core | Population, rural/urban split | SP.POP.TOTL, SP.RUR.TOTL.ZS, SP.URB.TOTL.IN.ZS | | Core | Electricity access (total, rural, urban) | EG.ELC.ACCS.ZS, .RU.ZS, .UR.ZS | | Tier 2 | Literacy, secondary enrolment, education spending | SE.ADT.LITR.ZS, SE.SEC.ENRR, SE.XPD.TOTL.GD.ZS | | Tier 2 | Physicians, health spending | SH.MED.PHYS.ZS, SH.XPD.CHEX.GD.ZS | | Tier 2 | Account owne …