Geovisualization and Spatial Analysis of Cancer Data

Project Description

The project's goal was to develop, implement, assess, and disseminate the next generation of cross-platform, visually-enabled geospatial analysis methods and tools to support cancer-related public health research and policy. The primary objective of the work was to develop a coordinated visual, statistical, and computational approach that extends current abilities to explore, identify, investigate, and explain spatial patterns of cancer incidence and mortality, and their relationships to population demographics and health policy. Of special note were new mechanisms to assess the potential for errors of omission and commission in that analysis.

The proposed methods and tools facilitated the integration of epidemiological, demographic, and health-policy data, enabling researchers and analysts to take a holistic view of communities, their health with respect to cancer, and relationships to health policy (e.g. screening, accessibility). A series of proof-of-concept case studies were used to demonstrate and assess the methods and tools developed and, at the same time, to address specific cancer research questions relevant to the Appalachia Cancer Network (ACN). Formal usability assessment methods were applied throughout the human-centered process of software design, implementation, and deployment. The goal of these assessments was to ensure that the methods and tools developed were both accessible to and useable by the cancer researchers and analysts whose work they intended to support.

The project took full advantage of outreach efforts within the ACN and the Center for Spatially Integrated Social Science (CSISS), to disseminate software developed and to provide training in its use to the cancer research and policy communities within Appalachia and beyond.

ASU Project Staff

The project was under the direction of Dr. Luc Anselin. Myunghwa Hwang was a research assistant on the project, developing a prototype semantic web service for spatial weights matrix creation and spatial rate smoothing.

Funding

This project was funded by Pennsylvania State University under Subaward Number 3478-ASU-DHHS-5949, from April 2002 to March 2008. The prime sponsor was the National Cancer Institute under Award Number 5 RO1 CA95949.