Paper Citation Record · LEDGER

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.12768.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.12768 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:49:43.240677Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact9
verified fuzzy11
unresolved19
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f771e31-9b00-44d9-80be-a7105792ce39 · outbound

This paper cites Axhausen.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Axhausen

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.429924Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4143d52c-6a21-46aa-8e29-5472b6f4e52e · outbound

This paper cites Barthelemy and P.-L.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Barthelemy and P.-L

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:49:44.263896Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d8f0fd5c-4f00-4616-af13-ab42fa8fe064 · outbound

This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:49:44.182354Z

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Observation 2110f070-3a15-4b1c-94a1-9a1de7474230 · outbound

This paper cites Generation of synthetic populations in social simulations: A review of methods and practices.Journal of Artificial Societies and Social Simulation, 25(2):6, 2022.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Generation of synthetic populations in social simulations: A review of methods and practices.Journal of Artificial Societies and Social Simulation, 25(2):6, 2022

Reference 4

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This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation c6f417d5-1e6f-483b-89be-a941a28611c6 · outbound

This paper cites A synthetic population for agent-based modelling in canada.Scientific Data, 10(1):148, 2023.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population A synthetic population for agent-based modelling in canada.Scientific Data, 10(1):148, 2023

Reference 6

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Source-reported events for the cited work

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Observation d1d55332-dbf3-4e34-a73f-52456bbc613f · outbound

This paper cites Pendyala, Bhargava Sana, and Paul Waddell.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Pendyala, Bhargava Sana, and Paul Waddell

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.415361Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c59d923-6309-4d24-972f-2bb64d433a18 · outbound

This paper cites Choupani and A.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Choupani and A

Reference 8

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Observation f4a90c93-dbbc-4d2e-a4a9-9798862beb8c · outbound

This paper cites Lovelace, M.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Lovelace, M

Reference 9

Resolution: verified exact; doi, observed 2026-08-15T23:49:43.497966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea0477fe-965c-4139-b8f5-14720ee0895c · outbound

This paper cites Generating small areal synthetic microdata from public aggre- gated data using an optimization method.The Professional Geographer, 75(6):905–915, 2023.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Generating small areal synthetic microdata from public aggre- gated data using an optimization method.The Professional Geographer, 75(6):905–915, 2023

Reference 10

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:49:44.105063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d17c9ca-d4d8-42a8-b8ab-c3d8b787115f · outbound

This paper cites Simulation based population synthesis.Transportation Research Part B: Methodological, 58:243–263, 2013.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Simulation based population synthesis.Transportation Research Part B: Methodological, 58:243–263, 2013

Reference 11

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Source-reported events for the cited work

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Observation 07acb29d-2a48-49f2-9ac2-6f9dd5a653a8 · outbound

This paper cites Creating spatially-detailed hetero- geneous synthetic populations for agent-based microsimulation.Computers, Environment and Urban Systems, 91:101717, 2022.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Creating spatially-detailed hetero- geneous synthetic populations for agent-based microsimulation.Computers, Environment and Urban Systems, 91:101717, 2022

Reference 12

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Observation fcb45e63-16cd-4aa0-90c7-a57788172a95 · outbound

This paper cites Gareth Polhill.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Gareth Polhill

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.399940Z

Source-reported events for the cited work

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Observation fa5a52ee-4b04-49c4-8714-906a4054a8d9 · outbound

This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 14

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Observation ebf51180-9491-4355-aef7-ff29612a4dd5 · outbound

This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 15

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Observation 03584f99-2d14-4eca-9414-4f85066d7003 · outbound

This paper cites Al-Khasawneh, Zhichao Yang, Javier Bas, Fabian Bastin, and Cinzia Cirillo.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Al-Khasawneh, Zhichao Yang, Javier Bas, Fabian Bastin, and Cinzia Cirillo

Reference 16

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Observation 8cb083a3-7fb9-4cb7-b5a9-23a67d7b1274 · outbound

This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:49:43.124944Z

Source-reported events for the cited work

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Observation 9df7f040-3a7c-4a7b-9f9d-98cc66e73680 · outbound

This paper cites an unresolved cited work.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Unresolved cited work

Reference 18

Resolution: verified exact; doi, observed 2026-08-15T23:49:43.449918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd40a296-4307-4a3b-b3fd-9271b4cb815a · outbound

This paper cites A comprehensive investigation of variational auto-encoders for population synthesis.Journal of Computational Social Science, 8(1):13, 2025.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population A comprehensive investigation of variational auto-encoders for population synthesis.Journal of Computational Social Science, 8(1):13, 2025

Reference 19

Resolution: verified exact; doi, observed 2026-08-15T23:49:43.433274Z

Source-reported events for the cited work

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Observation eeb6a88a-1f4d-4097-a2b8-4f20a4e6abb0 · outbound

This paper cites How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data

Reference 20

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Observation 823ec1e3-a603-4817-a691-73cb8f4d2f32 · outbound

This paper cites Generative Adversarial Networks.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Generative Adversarial Networks

Reference 21

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Observation fdd0ddd7-16d7-452a-a2dc-395b911b7136 · outbound

This paper cites Auto-Encoding Variational Bayes.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Auto-Encoding Variational Bayes

Reference 22

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Observation cbb50591-b96b-40d3-aef0-3dfdabfd1379 · outbound

This paper cites Modeling tabular data using conditional gan.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Modeling tabular data using conditional gan

Reference 23

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Observation ef002952-3dd6-4da3-81dd-9dab68dbbdb7 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Denoising Diffusion Probabilistic Models

Reference 24

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Observation c89acf21-4c4b-4534-9edf-2b85c6b2b4d3 · outbound

This paper cites TabDDPM: Modelling Tabular Data with Diffusion Models.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population TabDDPM: Modelling Tabular Data with Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T23:49:43.166489Z

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Observation 984c5c43-103c-4a78-bc86-e72fdb09af77 · outbound

This paper cites Generating Feasible and Diverse Synthetic Populations Using Diffusion Models.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Generating Feasible and Diverse Synthetic Populations Using Diffusion Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:49:43.172105Z

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Observation 61240ed2-233d-4ed0-82a6-9ff3e1ff858a · outbound

This paper cites Gen- erating population synthesis using a diffusion model.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Gen- erating population synthesis using a diffusion model

Reference 27

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Observation 91094a51-89da-4726-82ba-7a0a1cd2f67b · outbound

This paper cites Gen*: A generic toolkit to generate spatially explicit synthetic populations.International Journal of Geograph- ical Information Science, 32(6):1194–1210, 2018.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Gen*: A generic toolkit to generate spatially explicit synthetic populations.International Journal of Geograph- ical Information Science, 32(6):1194–1210, 2018

Reference 28

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Observation 98576e2f-eea0-422b-b68d-bb08e1d55eda · outbound

This paper cites Crooks, and Li Yin.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Crooks, and Li Yin

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:49:43.188731Z

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Observation 48707322-64d8-4bd0-9296-6e50303a9280 · outbound

This paper cites Crooks, Dieter Pfoser, Carola Wenk, and Andreas Z¨ ufle.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Crooks, Dieter Pfoser, Carola Wenk, and Andreas Z¨ ufle

Reference 30

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Observation d5c69ba6-99a7-4456-9450-5b38a9856c29 · outbound

This paper cites Public use microdata areas (PUMAs), 2026.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Public use microdata areas (PUMAs), 2026

Reference 31

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Observation fb494912-b615-444a-9edb-b5532d220e72 · outbound

This paper cites Understanding and using the American Community Survey public use microdata sample files: What data users need to know.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Understanding and using the American Community Survey public use microdata sample files: What data users need to know

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.369884Z

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Observation 28a6b2c1-362d-43e6-ab5e-abfaba4f0c63 · outbound

This paper cites American community survey (acs), 2026.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population American community survey (acs), 2026

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.355483Z

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Observation 91f807bc-0002-4682-9349-2f88cc9e6e95 · outbound

This paper cites 2023 ACS 5-year detailed tables: Geographies, 2023.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population 2023 ACS 5-year detailed tables: Geographies, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.340273Z

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Observation ce3464ba-8488-4073-b090-40d4008569bf · outbound

This paper cites Openstreetmap, 2026.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Openstreetmap, 2026

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.325432Z

Source-reported events for the cited work

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Observation b3555bcd-b41b-4215-93a0-f234af8bd8ba · outbound

This paper cites Dataplor global point of interest (POI) data, 2026.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Dataplor global point of interest (POI) data, 2026

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.310253Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1b350796-a5dc-473e-a52d-ce29b7027eb3 · outbound

This paper cites Lehd origin-destination employment statistics (lodes) version 7.5 technical documentation, 2021.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Lehd origin-destination employment statistics (lodes) version 7.5 technical documentation, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.294910Z

Source-reported events for the cited work

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Observation bb461f40-8949-4a13-aa09-2d0c070a7954 · outbound

This paper cites Public use microdata sample (pums), 2026.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Public use microdata sample (pums), 2026

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:49:44.279651Z

Source-reported events for the cited work

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Observation 04396ad3-a4ed-4ee1-ad39-c3344650a282 · outbound

This paper cites Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:49:43.302763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5e23d7a-a8f1-4305-b54d-1b75b6fdb2f8 · outbound

This paper cites Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D.

A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T23:49:43.240677Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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