Population census estimation for Spatial Microsimulation

Néstor DeLaPaz-Ruíz

doi:10.5281/ZENODO.5558638

Population census estimation for Spatial Microsimulation

Néstor DeLaPaz-Ruíz

Research output: Non-textual form › Software › Academic

Abstract

Problem and motivation. Data privacy policies protect inhabitants' sensitive information and make population census data difficult to use in research activities. Especially, privacy policies make data partly inaccessible at small spatial units such as neighborhoods blocks with low-population density. Estimations of inaccessible data not only increase the accuracy of qualitative research but also facilitates applying methods at low-density places that otherwise become impractical to study. Spatial microsimulation (SMS) refers to "the creation, analysis, and modelling of individual-level data allocated to geographic zones" (Lovelace, 2018). Estimates at small spatial units are required for SMS to increase the certainty of behavioral dynamics in complex phenomena as security, pollution, or health at low-density localities.

Objective. Develop a method to estimate inaccessible population census data to enable more complex applications such as SMS.

Original language	English
Publisher	Zenodo
Media of output	Online
DOIs	https://doi.org/10.5281/ZENODO.5558638
Publication status	Published - 8 Oct 2021
Event	International Conference on Geospatial Information Sciences, IGISc 2021 - Mérida, Mexico Duration: 3 Nov 2021 → 5 Nov 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

Census
Spatial microsimulation

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10.5281/ZENODO.5558638Licence: CC BY

Cite this

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title = "Population census estimation for Spatial Microsimulation",

abstract = "Problem and motivation. Data privacy policies protect inhabitants' sensitive information and make population census data difficult to use in research activities. Especially, privacy policies make data partly inaccessible at small spatial units such as neighborhoods blocks with low-population density. Estimations of inaccessible data not only increase the accuracy of qualitative research but also facilitates applying methods at low-density places that otherwise become impractical to study. Spatial microsimulation (SMS) refers to {"}the creation, analysis, and modelling of individual-level data allocated to geographic zones{"} (Lovelace, 2018). Estimates at small spatial units are required for SMS to increase the certainty of behavioral dynamics in complex phenomena as security, pollution, or health at low-density localities. Objective. Develop a method to estimate inaccessible population census data to enable more complex applications such as SMS.",

keywords = "Census, Spatial microsimulation",

author = "N{\'e}stor DeLaPaz-Ru{\'i}z",

year = "2021",

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day = "8",

doi = "10.5281/ZENODO.5558638",

language = "English",

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note = "International Conference on Geospatial Information Sciences, IGISc 2021 , IGISc ; Conference date: 03-11-2021 Through 05-11-2021",

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T1 - Population census estimation for Spatial Microsimulation

AU - DeLaPaz-Ruíz, Néstor

PY - 2021/10/8

Y1 - 2021/10/8

N2 - Problem and motivation. Data privacy policies protect inhabitants' sensitive information and make population census data difficult to use in research activities. Especially, privacy policies make data partly inaccessible at small spatial units such as neighborhoods blocks with low-population density. Estimations of inaccessible data not only increase the accuracy of qualitative research but also facilitates applying methods at low-density places that otherwise become impractical to study. Spatial microsimulation (SMS) refers to "the creation, analysis, and modelling of individual-level data allocated to geographic zones" (Lovelace, 2018). Estimates at small spatial units are required for SMS to increase the certainty of behavioral dynamics in complex phenomena as security, pollution, or health at low-density localities. Objective. Develop a method to estimate inaccessible population census data to enable more complex applications such as SMS.

AB - Problem and motivation. Data privacy policies protect inhabitants' sensitive information and make population census data difficult to use in research activities. Especially, privacy policies make data partly inaccessible at small spatial units such as neighborhoods blocks with low-population density. Estimations of inaccessible data not only increase the accuracy of qualitative research but also facilitates applying methods at low-density places that otherwise become impractical to study. Spatial microsimulation (SMS) refers to "the creation, analysis, and modelling of individual-level data allocated to geographic zones" (Lovelace, 2018). Estimates at small spatial units are required for SMS to increase the certainty of behavioral dynamics in complex phenomena as security, pollution, or health at low-density localities. Objective. Develop a method to estimate inaccessible population census data to enable more complex applications such as SMS.

KW - Census

KW - Spatial microsimulation

UR - https://zenodo.org/record/5558638

U2 - 10.5281/ZENODO.5558638

DO - 10.5281/ZENODO.5558638

M3 - Software

PB - Zenodo

T2 - International Conference on Geospatial Information Sciences, IGISc 2021

Y2 - 3 November 2021 through 5 November 2021

ER -

Population census estimation for Spatial Microsimulation

Abstract

UN SDGs

Keywords

Access to Document

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