Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estim...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | SSM: Population Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352827323001763 |
| _version_ | 1797429990915047424 |
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| author | Ben R. Spoer Alexander S. Chen Taylor M. Lampe Isabel S. Nelson Anne Vierse Noah V. Zazanis Byoungjun Kim Lorna E. Thorpe S.V. Subramanian Marc N. Gourevitch |
| author_facet | Ben R. Spoer Alexander S. Chen Taylor M. Lampe Isabel S. Nelson Anne Vierse Noah V. Zazanis Byoungjun Kim Lorna E. Thorpe S.V. Subramanian Marc N. Gourevitch |
| author_sort | Ben R. Spoer |
| collection | DOAJ |
| description | Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap. |
| first_indexed | 2024-03-09T09:21:16Z |
| format | Article |
| id | doaj.art-83355fd976754baeaaba21de2b796f63 |
| institution | Directory Open Access Journal |
| issn | 2352-8273 |
| language | English |
| last_indexed | 2024-03-09T09:21:16Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SSM: Population Health |
| spelling | doaj.art-83355fd976754baeaaba21de2b796f632023-12-02T07:00:38ZengElsevierSSM: Population Health2352-82732023-12-0124101511Validation of a geospatial aggregation method for congressional districts and other US administrative geographiesBen R. Spoer0Alexander S. Chen1Taylor M. Lampe2Isabel S. Nelson3Anne Vierse4Noah V. Zazanis5Byoungjun Kim6Lorna E. Thorpe7S.V. Subramanian8Marc N. Gourevitch9New York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USA; Corresponding author. 180 Madison Ave, M-18, New York, NY, 10016, USA.New York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USANew York University Grossman School of Medicine, Department of Population Health, Division of Epidemiology, New York, NY, USAHarvard T.H. Chan School of Public Health, Department of Social and Behavioral Sciences, Boston, MA, USA; Center for Geographic Analysis, Harvard University, Cambridge MA, USANew York University Grossman School of Medicine, Department of Population Health, New York, NY, USAStakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.http://www.sciencedirect.com/science/article/pii/S2352827323001763Geospatial analysisSpatial data aggregationCongressional districtsUS administrative geographies |
| spellingShingle | Ben R. Spoer Alexander S. Chen Taylor M. Lampe Isabel S. Nelson Anne Vierse Noah V. Zazanis Byoungjun Kim Lorna E. Thorpe S.V. Subramanian Marc N. Gourevitch Validation of a geospatial aggregation method for congressional districts and other US administrative geographies SSM: Population Health Geospatial analysis Spatial data aggregation Congressional districts US administrative geographies |
| title | Validation of a geospatial aggregation method for congressional districts and other US administrative geographies |
| title_full | Validation of a geospatial aggregation method for congressional districts and other US administrative geographies |
| title_fullStr | Validation of a geospatial aggregation method for congressional districts and other US administrative geographies |
| title_full_unstemmed | Validation of a geospatial aggregation method for congressional districts and other US administrative geographies |
| title_short | Validation of a geospatial aggregation method for congressional districts and other US administrative geographies |
| title_sort | validation of a geospatial aggregation method for congressional districts and other us administrative geographies |
| topic | Geospatial analysis Spatial data aggregation Congressional districts US administrative geographies |
| url | http://www.sciencedirect.com/science/article/pii/S2352827323001763 |
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