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Type of Document

Dissertation

Author

Scotch, Matthew

URN

etd-04112006-095019

Title

An OLAP-GIS System for Numerical-Spatial Problem Solving in Community Health Assessment Analysis

Degree

Doctor of Philosophy

Program

Biomedical Informatics

School

School of Medicine

Advisory Committee

Advisor Name	Title
Bambang Parmanto, PhD	Committee Chair
Cynthia S. Gadd, PhD, MBA, MS	Committee Member
Ravi K. Sharma, PhD	Committee Member
Valerie Monaco, PhD, MHCI	Committee Member
Valerie Watzlaf, PhD	Committee Member

Keywords

GIS
community health assessment
OLAP
numerical-spatial problem solving
decision support systems

Date of Defense

2006-04-05

Availability

unrestricted

Abstract

Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting numerical-spatial problem solving. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. Coupling OLAP with Geospatial Information System (GIS) offers the potential for a very powerful system. For this work, OLAP and GIS were combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT) for numerical-spatial problem solving.
In addition to the development of this system, this dissertation describes three studies in relation to this work: a usability study, a CHA survey, and a summative evaluation.
The purpose of the usability study was to identify human-computer interaction issues. Fifteen participants took part in the study. Three participants per round used the system to complete typical numerical-spatial tasks. Objective and subjective results were analyzed after each round and system modifications were implemented. The result of this study was a novel OLAP-GIS system streamlined for the purposes of numerical-spatial problem solving.
The online CHA survey aimed to identify the information technology currently used for numerical-spatial problem solving. The survey was sent to CHA professionals and allowed for them to record the individual technologies they used during specific steps of a numerical-spatial routine. In total, 27 participants completed the survey. Results favored SPSS for numerical-related steps and GIS for spatial-related steps.
Next, a summative within-subjects crossover design compared SOVAT to the combined use of SPSS and GIS (termed SPSS-GIS) for numerical-spatial problem solving. Twelve individuals from the health sciences at the University of Pittsburgh participated. Half were randomly selected to use SOVAT first, while the other half used SPSS-GIS first. In the second session, they used the alternate application. Objective and subjective results favored SOVAT over SPSS-GIS. Inferential statistics were analyzed using linear mixed model analysis. At the .01 level, SOVAT was statistically significant from SPSS-GIS for satisfaction and time (p < .002).
The results demonstrate the potential for OLAP-GIS in CHA analysis. Future work will explore the impact of an OLAP-GIS system in other areas of public health.

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