arrow
Return

Spatial data uncertainty for location modeling: Ghost blocks and their implications

delete2024-05-01
delete0
delete
OA
AI
T
Tony H. Grubesic *
R
Ran Wei
E
Edward Helderop
DOI:10.1016/j.apgeog.2024.103266delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Census blocks are administrative units that serve as statistical areas for the decennial Census in the United States. Visible and nonvisible features bound blocks, including roads, railroads, streams, property lines, and city boundaries. The Census Bureau builds blocks using the Master Address File (MAF), which includes field-verified geographic information about the location of housing unit addresses. Unfortunately, there are substantial errors in the counts of housing units at the block level, even with the purported quality checks by the Census Bureau. This paper aims to detail a method of identifying problematic blocks (i.e., ghost blocks) that report the presence of housing units, but no such units exist. Further, we identify the implications of using ghost blocks in location models using the maximal covering location problem (MCLP) in a case study for sensor locations in Los Angeles, California. We discuss policy implications and strategies to address these errors for developing higher-fidelity location models.
Keywords:
Census
Master address file
Uncertainty
Housing units
Location modeling
Spatial analysis
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Applied Geography cover
Applied Geography
IF:
5.4
Papers:
3.9K
Citations:
1.2W

Organization

University of California System cover
University of California System
Scholars:
37.5W
Papers: 33.7W
Citations: 6.6K