Mohamed AliApplied AI Engineer
PROJECT / 05Visual analytics · Geographic data

Mapping where new coders live

A map becomes analytically useful when data cleaning, sample size, missingness, and the comparison layer remain visible.

Role
Geographic data integration and visualization
Context
Four-person JKU Visual Analytics project
Period
2022
INTERACTIVE EVIDENCE

Compare both maps.

A geographic comparison of human development and where new coders live within each country’s urban system.

Countries with n ≥ 20
Human Development Index by countrySelect a country to compare its 2018 HDI, large-city respondent share, and usable sample size.
lowerhigher
GUIDED STOPS
THE QUESTION

Join country names across a global developer survey, HDI data, and geographic boundaries, then compare development level with the share of respondents living in cities above one million.

HOW I APPROACHED IT
  1. 01

    Normalized inconsistent country names and generated standard country identifiers.

  2. 02

    Aggregated 28,397 survey responses by country and city-population category.

  3. 03

    Merged the resulting proportions with 2018 Human Development Index values and geographic data.

Survey28,397 responses
Public view91 country aggregates
Display thresholdn ≥ 20 per country
BOUNDARY / LIMITATION

The maps are descriptive rather than causal. Country samples differ substantially, and the portfolio view excludes countries with fewer than 20 usable responses.

TOOLS & METHODS
  • Python
  • Pandas
  • GeoPandas
  • Data cleaning
  • Choropleths