The effect of pre-aggregation scale on spatially adaptive filters

David Haynes; Kelly D. Hughes; Austin Rau; Anne M. Joseph

doi:10.1016/j.sste.2021.100476

The effect of pre-aggregation scale on spatially adaptive filters

David Haynes, Kelly D. Hughes, Austin Rau, Anne M. Joseph

Research output: Contribution to journal › Article › peer-review

Abstract

Choropleth mapping continues to be a dominant mapping technique despite suffering from the Modifiable Areal Unit Problem (MAUP), which may distort disease risk patterns when different administrative units are used. Spatially adaptive filters (SAF) are one mapping technique that can address the MAUP, but the limitations and accuracy of spatially adaptive filters are not well tested. Our work examines these limitations by using varying levels of data aggregation using a case study of geocoded breast cancer screening data and a synthetic georeferenced population dataset that allows us to calculate SAFs at the individual-level. Data were grouped into four administrative boundaries (i.e., county, Zip Code Tabulated Areas, census tracts, and census blocks) and compared to individual-level data (control). Correlation assessed the similarity of SAFs, and map algebra calculated error maps compared to control. This work describes how pre-aggregation affects the level of spatial detail, map patterns, and over and under-prediction.

Original language	English (US)
Article number	100476
Journal	Spatial and Spatio-temporal Epidemiology
Volume	40
DOIs	https://doi.org/10.1016/j.sste.2021.100476
State	Published - Feb 2022

Bibliographical note

Funding Information:
Anne M. Joseph is employed by the University of Minnesota. She is supported in part by funding from the National Cancer Institute.

Funding Information:
David Haynes’ time was supported by NIH grant 5T32CA163184 while working on this project and while employed at the University of Minnesota. David Haynes is an unpaid contractor in relation to the State of Minnesota. David Haynes’ sponsors had no input into the project.

Funding Information:
Kelly D. Hughes is employed by the State of Minnesota and the University of Minnesota. Her effort on this project was supported by the State of Minnesota. Materials for this project were supplied by the State of Minnesota. Representatives of the Sage Program of the State of Minnesota were key stakeholders study design and data collection for the project. The State of Minnesota has approved this manuscript.

Publisher Copyright:
© 2021

Keywords

Aggregation
Breast cancer
Health programs
Modifiable areal unit problem
Spatial smoothing techniques

PubMed: MeSH publication types

Journal Article
Research Support, N.I.H., Extramural

UN SDGs

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

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10.1016/j.sste.2021.100476

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abstract = "Choropleth mapping continues to be a dominant mapping technique despite suffering from the Modifiable Areal Unit Problem (MAUP), which may distort disease risk patterns when different administrative units are used. Spatially adaptive filters (SAF) are one mapping technique that can address the MAUP, but the limitations and accuracy of spatially adaptive filters are not well tested. Our work examines these limitations by using varying levels of data aggregation using a case study of geocoded breast cancer screening data and a synthetic georeferenced population dataset that allows us to calculate SAFs at the individual-level. Data were grouped into four administrative boundaries (i.e., county, Zip Code Tabulated Areas, census tracts, and census blocks) and compared to individual-level data (control). Correlation assessed the similarity of SAFs, and map algebra calculated error maps compared to control. This work describes how pre-aggregation affects the level of spatial detail, map patterns, and over and under-prediction.",

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N2 - Choropleth mapping continues to be a dominant mapping technique despite suffering from the Modifiable Areal Unit Problem (MAUP), which may distort disease risk patterns when different administrative units are used. Spatially adaptive filters (SAF) are one mapping technique that can address the MAUP, but the limitations and accuracy of spatially adaptive filters are not well tested. Our work examines these limitations by using varying levels of data aggregation using a case study of geocoded breast cancer screening data and a synthetic georeferenced population dataset that allows us to calculate SAFs at the individual-level. Data were grouped into four administrative boundaries (i.e., county, Zip Code Tabulated Areas, census tracts, and census blocks) and compared to individual-level data (control). Correlation assessed the similarity of SAFs, and map algebra calculated error maps compared to control. This work describes how pre-aggregation affects the level of spatial detail, map patterns, and over and under-prediction.

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The effect of pre-aggregation scale on spatially adaptive filters

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

UN SDGs

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