Micro Tabulation Block Resolution: Investigating Accuracy in Micro-Area Census Tabulation Blocks
type: CreativeWork
description: Avoiding error in reporting of Census Data is one of the paramount concerns influencing each Decennial Census Operation. Guaranteeing that American citizens are counted accurately and in the right location is the core pillar of the 2020 Decennial Census. In pursuit of this goal, many project initiatives within the Geography Division of the U.S. Census Bureau have been completed alongside 35 core Decennial Operations that have attempted to facilitate the creation and dissemination of the best possible end product. These initiatives, such as the Micro Tabulation Block Resolution initiative on which this thesis reports, have examined micro-scale inconsistencies that have arisen as a result of increased reliance on remote enumeration and automation in place of a more traditional field enumeration methods. This thesis covers the background, methods, and outcome of this initiative, and situates the research in the broader literary context of research concerning Census Data, Big Data, and Geospatial Big Data.
abstract: Avoiding error in reporting of Census Data is one of the paramount concerns influencing each Decennial Census Operation. Guaranteeing that American citizens are counted accurately and in the right location is the core pillar of the 2020 Decennial Census. In pursuit of this goal, many project initiatives within the Geography Division of the U.S. Census Bureau have been completed alongside 35 core Decennial Operations that have attempted to facilitate the creation and dissemination of the best possible end product. These initiatives, such as the Micro Tabulation Block Resolution initiative on which this thesis reports, have examined micro-scale inconsistencies that have arisen as a result of increased reliance on remote enumeration and automation in place of a more traditional field enumeration methods. This thesis covers the background, methods, and outcome of this initiative, and situates the research in the broader literary context of research concerning Census Data, Big Data, and Geospatial Big Data.
author: Twarog, Jacob
language: en
keywords: GIS, Geospatial Science, Census Data, MAF/TIGER
date published: 2020-11-18
date modified: 2026-07-24T02:13:39Z
provider: East Carolina University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: East Carolina University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10342/8837