Animarium: an open, reproducible pipeline for synthetic populations of Italian cities, from ISTAT sources to open data (Tech Report v1)
- URL: http://arxiv.org/abs/2608.27111v2
- Date: Sat, 29 Aug 2026 08:39:23 GMT
- Title: Animarium: an open, reproducible pipeline for synthetic populations of Italian cities, from ISTAT sources to open data (Tech Report v1)
- Abstract summary: Populations of eleven Italian municipalities generated from published aggregates alone.<n>Report documents the architecture, the sources and their certification, the quality measurements at the release tag, the viewer, and the narrative layer.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Synthetic populations of eleven Italian municipalities (1,814,317 individuals in 887,937 households) generated from published aggregates alone: ISTAT census and register tables, census-section counts, the national civic-address register, public-use survey microdata, and six municipal open-data portals, every source certified in a registry with licence, fingerprint and declared affordances. Four rings give every attribute a declared place: a maximum-entropy joint model of up to nine demographic attributes; whole-vector donation of twenty-three attitudinal and health variables from survey respondents; placement to census section, single year of age and address; and households constrained by the census size distribution per section. Every downstream layer (detailed titles, work, names, biographies) is a declared derivation adding no information. The pipeline is deterministic to the byte: regenerating all eleven municipalities from the tagged commit reproduces every file of every ring bit for bit, in 33 minutes on one workstation. Populations are released in a public regime enforced in the data (no names, no addresses, coordinates randomised within census section), browsable in Animarium, a dependency-free web viewer where every number carries its comparison and every view is a citable URL, and downloadable as an open dataset. The report documents the architecture, the sources and their certification, the reproducibility and quality measurements at the release tag, the viewer, and the narrative layer that renders records into personas for LLM-driven simulation, with the platform's controllability demonstrated in companion experiments, and validation explicitly out of scope.
Related papers
- A Hybrid State-Space Approach for Census-Tract Population Estimation [45.88028371034407]
MambaPop is a method to learn population directly from an administrative unit's own image.<n>It attains a mean absolute error (MAE) of $1141$ persons per tract, matching the strongest convolutional baseline.
arXiv Detail & Related papers (2026-08-30T23:43:06Z) - Benchmarking LLMs for Community Governance Simulation with Life-history Narratives [46.86050402684712]
Large language models (LLMs) are scalable to simulate human attitudes and behaviors at low cost.<n>We present an integrated research framework covering dataset, benchmark, algorithm, and system.<n>System integrates curriculum-LoRA into a closed-loop policy-evaluation pipeline.
arXiv Detail & Related papers (2026-05-22T15:48:49Z) - Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis [0.0]
This paper evaluates whether zero-shot large language model (LLM)-generated health survey data can serve as inputs to a conventional iterative proportional fitting workflow.<n>We generate synthetic survey records for the U.S. states of Colorado and Mississippi with GPT-4.1 and Gemini-2.5-Pro.<n>We use the generated data in an IPF-based synthesis pipeline and evaluate the resulting census tract-level synthetic populations against external benchmarks.
arXiv Detail & Related papers (2026-04-23T21:27:36Z) - A Deep Generative Framework for Joint Households and Individuals Population Synthesis [0.562479170374811]
We propose a deep generative framework to generate a synthetic population with household-individual and individual-individual relationships.
Results for an application in Delaware, USA demonstrate the ability to ensure the realism of generated household-individual records.
arXiv Detail & Related papers (2024-06-30T23:01:58Z) - Least Squares Estimation For Hierarchical Data [0.7366405857677227]
The U.S. Census Bureau's 2020 Disclosure Avoidance System (DAS) bases its output on noisy measurements.<n>This paper describes an algorithm that can leverage a hierarchical structure of the input data in order to compute very high dimensional least squares estimates.
arXiv Detail & Related papers (2024-04-19T20:18:16Z) - Deep Efficient Private Neighbor Generation for Subgraph Federated
Learning [57.39918843245229]
We propose FedDEP to tackle the challenge of incomplete information propagation on local subgraphs due to missing cross-subgraph neighbors.
FedDEP consists of a series of novel technical designs: (1) Deep neighbor generation through leveraging the GNN embeddings of potential missing neighbors; (2) Efficient pseudo-FL for neighbor generation through embedding prototyping; and (3) Privacy protection through noise-less edge-local-differential-privacy.
arXiv Detail & Related papers (2024-01-09T03:29:40Z) - A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census [6.851716728384597]
We show that individual, confidential microdata records from the 2010 U.S. Census can be accurately reconstructed.<n>Ninety-seven million person records (every resident in 70% of all census blocks) are exactly reconstructed with provable certainty using only public information.<n>We show that the methods used for the 2020 Census, based on a differential privacy framework, provide better protection against this type of attack, with better published data accuracy, than feasible alternatives.
arXiv Detail & Related papers (2023-12-18T15:23:12Z) - Fine-grained Population Mapping from Coarse Census Counts and Open
Geodata [19.460864948909936]
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance.
In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts.
arXiv Detail & Related papers (2022-11-08T06:43:52Z) - QuTE: decentralized multiple testing on sensor networks with false discovery rate control [93.1040521878626]
This paper designs methods for decentralized multiple hypothesis testing on graphs equipped with provable guarantees on the false discovery rate (FDR)<n>We consider the setting where distinct agents reside on the nodes of an undirected graph, and each agent possesses p-values corresponding to one or more hypotheses local to its node.<n>Each agent must individually decide whether to reject one or more of its local hypotheses by only communicating with its neighbors, with the joint aim that the global FDR over the entire graph must be controlled at a predefined level.
arXiv Detail & Related papers (2022-10-09T19:48:39Z) - Releasing survey microdata with exact cluster locations and additional
privacy safeguards [77.34726150561087]
We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards.
Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
arXiv Detail & Related papers (2022-05-24T19:37:11Z) - Building a large synthetic population from Australian census data [2.707154152696381]
We present work on creating a synthetic population from census data for Australia, applied to the greater Melbourne region.
We use a sample-free approach to population synthesis that does not rely on a disaggregate sample from the original population.
Our algorithm is efficient in that it can create the synthetic population for Melbourne comprising 4.5 million persons in 1.8 million households within three minutes on a modern computer.
arXiv Detail & Related papers (2020-08-18T05:38:15Z) - Differential Privacy of Hierarchical Census Data: An Optimization
Approach [53.29035917495491]
Census Bureaus are interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual.
Recent events have identified some of the privacy challenges faced by these organizations.
This paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
arXiv Detail & Related papers (2020-06-28T18:19:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.