Aleix Alva

@aleixalva.bsky.social

28 Followers 65 Following

Vegan physicist studying, loving, defending, and belonging to nature, both as the physical universe and the living world. Reason & emotion. https://fluents.xyz/

2D UMAP scatter plot of countries based on five statistical metrics. Each point represents a country-year.
The x-axis (umap1) loosely corresponds to income level, with high-income countries on the right.
The y-axis (umap2) loosely corresponds to year, with more recent years toward the bottom.
Point shape represents binned overall score (0–20, 20–40, 40–60, 60–80, 80–100),
point fill represents income category (Not classified, Low, Lower middle, Upper middle, High),
point colour represents year using a black-to-light-green gradient,
and point size encodes population.
Clusters indicate countries with similar profiles.
China (large cyan point) and India (large orange point) are separated,
with India moving toward the high-income region and China moving upward over time.
Legends for all four aesthetics are shown below the plot for clarity.

Scatter plot of countries in a 2D UMAP embedding based on five data metrics.
Each point represents a country-year combination. The x-axis (umap1) roughly aligns with income level,
with high-income countries on the right. The y-axis (umap2) roughly aligns with time, with more recent years toward the bottom.
Point shape and fill encode income category, point size encodes population, and point colour represents year in a yellow-to-red gradient.
Clusters indicate countries with similar metric profiles. China (large cyan point) and India (large orange point) are clearly separated,
with India moving toward the high-income region and China moving upward over time. Mixed-income clusters from earlier years appear in the upper-left region.

Relative contribution of number of published papers by country (architecture and built environment research field). The relative contribution of China notably increases since 2019.