Household Change & Relationship Reasoning

Connect household formation, membership, housing and finance to prototype reasoning about household change.

The record concepts include household composition, membership changes, household lifecycle, age / life stage, household relationships, dated demographic events and housing tenure. Inspect who belonged to the household at the cutoff. Review transitions in membership and housing context. Distinguish documented relationships from unsupported links.

This dataset is prepared to order. Choose the city, date range and required scope; the accepted order defines the population, final fields, formats and delivery.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

African Major Cities · Additional cities require availability, scope and quote confirmation.

Data type

Synthetic

Connected domains

Households, Demographics, Housing, Finance

Catalogue structure

Longitudinal benchmark

Key features

Record concepts for your order-specific extract.

Who it is for

• Agent researchers
• LLM evaluators
• social-computing teams
• universities

Entities represented

Resident + linked events

This identifies the entities described by the catalogue. The analytical row grain, eligibility, keys and dates are agreed for your extract.

Explore household membership and recorded relationship changes at a specified date.

Record concepts for your extract

• household composition
• membership changes
• household lifecycle
• age / life stage
• household relationships
• dated demographic events
• housing tenure

The agreed specification defines the final columns, keys, relationships and observation period for your order.

Evaluate membership and role answers against dated links; test overlapping households, unavailable relationship evidence and household versus individual event dates.

Proposed questions for your custom order

1. Can an AI reconstruct who belonged to a household at a given date, explain recorded membership changes and distinguish documented relationships from unknown ones?
2. Can AI identify who belonged to a household at a selected date?
3. Can an AI assistant explain joins, departures and changes in household composition?
4. Does an AI assistant keep recorded household roles and membership changes distinct?
5. Can an AI assistant flag an explanation that conflicts with recorded household membership?

Proposed research and analytical applications

• Task prototyping
• Evidence tracing
• Error analysis
• Controlled evaluation

Study and modeling guidance

Information available at prediction or evaluation time

Use only evidence valid and available at the agreed anchor; do not use later outcomes as features.

Comparison and evaluation

Specify comparable cohorts or held-out evaluation before reporting results.

Incomplete observation

Agree follow-up and retain incomplete observation as censored or unknown.

Data generation

FCTE synthetic city engine

Data classification

Synthetic

FCTE generation methods and published city/domain checks provide source context. The order defines the final extract and the release-specific checks, documentation and limitations supplied with it.

Known limitations

• Synthetic records describe generated scenarios; they do not establish observed population prevalence or real-world causal effects.

Catalogue base price

$495 USD

Price basis

Base price per selected city

Availability

Prepared after order

The displayed catalogue amount is a base price per selected city. Your requested dates, population, fields, formats and usage rights determine the agreed scope and final quote. Applicable published discounts and taxes are confirmed with that quote.

Your prepared package

Your order confirms the data files, documentation, evidence, examples and release controls included in the prepared package.

• Agreed data extract
• Schema and field documentation
• Release-specific evidence and limitations
• Agreed loading or analysis examples
• Licence, manifest, checksums and delivery note

SD_RESEARCH_DATASET/
├── data/
│   ├── household_history.parquet
│   ├── citizens.parquet
│   ├── housing_records.parquet
│   └── finance_records.parquet
├── tasks/
│   ├── task_definition.yaml
│   ├── questions.jsonl
│   ├── reference_answers.jsonl
│   └── evidence_paths.jsonl
├── evaluation/
│   ├── baseline_model.py
│   ├── metrics.json
│   └── example_notebook.ipynb
├── documentation/
│   ├── README.md
│   ├── DATA_DICTIONARY.csv
│   ├── SCHEMA.md
│   └── METHODOLOGY.md
├── evidence/
│   ├── CERTIFICATION.json
│   ├── VALIDATION_REPORT.pdf
│   └── KNOWN_LIMITATIONS.md
├── MANIFEST.json
├── CHECKSUMS.sha256
└── LICENSE.pdf

Delivery process

Confirm the cities, date range, records, formats, rights and price in writing. The order-specific extract is then prepared and supplied under the agreed payment and delivery conditions.

One secure download package for the agreed release and licensed entity.

Delivery timing, access expiry, formats and prerequisites are confirmed in the accepted order.

Support, refreshes, future releases, hosting and APIs are included only when expressly ordered.

Data Licensing Agreement · 2026-10

A non-exclusive, non-transferable licence for the named customer within the accepted scope and duration.

Permitted use

Internal research, development, testing and analytics within the accepted order.

Model training

Model training, external applications, commercial distribution and access by other legal entities must be expressly addressed in the order.

Redistribution

Raw or substantially reconstructable data cannot be resold, published or redistributed without separate written authorization.

Attribution

Preserve release provenance and applicable third-party notices. Published outputs must identify the data as synthetic.