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Abstract by Spencer Galbraith

Personal Infomation


Presenter's Name

Spencer Galbraith

Degree Level

Masters

Abstract Infomation


Department

Statistics

Faculty Advisor

Matthew Heaton

Title

Realignment of Areal Data using Spatial Point Processes

Abstract

Census data contain a rich store of information useful to many fields of study. Unfortunately, the use of census data is often a challenge because the census regions don't align with those used by other sources of data. In statistics, this is the so called ``change of support" problem. Many researchers attempt to realign multiple data sources to a common scale; however, this process implies several inherent complexities. This paper proposes a method to realign areal data using discrete kernel convolutions and a spatial point process. The proposed methodology is applied to census data from the city of Houston, Texas. We show that areal data can be successfully realigned using spatially relevant information.