Data Analytics PSPP

Data Analytics & GNU PSPP Statistical Suite

High-performance client-side data analytics platform, build-time static KPI engine, W3C CSVW table explorer, GNU PSPP Variable View, and full-width GeoJSON maps.

Published: Aug 24, 2026
Read Time: 3 min

The Hugo-Carbon Data Analytics Suite integrates W3C CSVW static tabular exploration, instant build-time KPI metrics, and the GNU PSPP Statistical Studio into a high-performance, client-side operational environment.

[!NOTE] Static Architecture & Instant Build-Time KPIs: All KPI aggregates are calculated directly during Hugo site compilation, rendering exact values into static HTML with zero browser latency. All analytics, metrics, charts, and maps operate on static, versioned dataset files (.csv, .csv-metadata.json, .geojson). Source datasets can be downloaded directly from the toolbar links.

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┌────────────────────────────────────────────────────────────────────────┐
│             Hugo-Carbon Client-Side Static Data Architecture           │
├────────────────────────────┬───────────────────────────────────────────┤
│ Build Time (Hugo Compiler) │ Background Web Worker (csvw-worker.js)    │
├────────────────────────────┼───────────────────────────────────────────┤
│ • Exact Precomputed KPIs   │ • Asynchronous CSV text tokenizer         │
│ • Static HTML Rendering    │ • Full-text search & column filter engine │
│ • Full-Width Responsive Map│ • Descriptives (Mean, StdDev, Skew, Kurt) │
│ • PSPP Variable View Grid  │ • Contingency Crosstabs & Chi-Square      │
│ • Direct Source Downloads  │ • Pearson / Spearman Correlation Matrices │
└────────────────────────────┴───────────────────────────────────────────┘

1. YAML-Driven Analytics Suite (Static Infrastructure & GeoJSON Distribution)

Below is an interactive suite configured via YAML instructions. It automatically resolves the W3C CSVW schema metadata, computes KPI metrics at build time, renders a full-width interactive map, and generates Totals ($\Sigma$) and Averages ($\mu$) in the table footer:

STATIC DATASET GNU PSPP COMPATIBLE W3C CSVW

Global Node Infrastructure & Performance Dataset

Static cluster fleet matrix with GNU PSPP statistical engine and geospatial coordination.

Mean CPU Load
--%
MEAN • cpu_utilization
Mean Memory Usage
--%
MEAN • memory_usage
Total Fleet Uptime
-- Days
SUM • uptime_days
Monitored Edge Nodes
0 Nodes
COUNT • server_id
VECTOR MAPLIBRE IBM Carbon Style

Global Node Geospatial Distribution

GeoJSON

2. Behavioral Research Demographics & GNU PSPP Variable View

This variation exhibits a static psychological research dataset with Variable View metadata (Scale, Nominal, Ordinal measures), Descriptives, and the GNU PSPP Command Syntax Reference:

STATIC DATASET GNU PSPP COMPATIBLE W3C CSVW

Cognitive Performance & Research Demographics

Multivariate psychological trial dataset with 16 cases and 8 variables.

Mean Cognitive Score
--
MEAN • cognitive_score
Avg Reaction Time
-- ms
MEAN • reaction_time_ms
Mean Stress Index
-- / 10
MEAN • stress_index
Trial Cohort Size
0 Participants
COUNT • participant_id

3. GNU PSPP Command Syntax Generation & Batch Execution

The platform supports and generates standard GNU PSPP command syntax (.sps):

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* GNU PSPP Command Syntax for Static Research Demographics Dataset.
DATA LIST FREE / participant_id age gender education_level cognitive_score reaction_time_ms stress_index treatment_group.
BEGIN DATA.
P-101 24 Female Master 88.5 312 4.2 Experimental
P-102 29 Male Bachelor 76.0 345 6.8 Control
P-103 35 Female PhD 94.2 289 3.1 Experimental
END DATA.

* Run descriptive statistics with standard error and quartiles.
DESCRIPTIVES VARIABLES=age cognitive_score reaction_time_ms stress_index
  /STATISTICS=MEAN STDDEV MIN MAX SEMEAN VARIANCE SKEWNESS KURTOSIS.

* Run frequency distributions and bar charts.
FREQUENCIES VARIABLES=gender education_level treatment_group
  /BARCHART FREQ.

* Run crosstabulation with Pearson Chi-Square test of independence.
CROSSTABS
  /TABLES=treatment_group BY education_level
  /STATISTICS=CHISQ
  /CELLS=COUNT ROW COLUMN TOTAL.

* Run bivariate Pearson correlation matrix.
CORRELATIONS
  /VARIABLES=age WITH cognitive_score reaction_time_ms stress_index.