TY - JOUR
T1 - Development of SOVAT
T2 - A numerical-spatial decision support system for community health assessment research
AU - Scotch, Matthew
AU - Parmanto, Bambang
N1 - Funding Information:
The project is supported in part by The National Library of Medicine (NLM) Training Grant 5 TI5 LM007059 to Matthew Scotch, and by the National Institute on Disability and Rehabilitation Research (NIDRR) and National Telecommunication and Information Administration (NTIA) to Bambang Parmanto. The authors would like to thank Dr. Ravi Sharma for input and feedback during the development of the system as well as I. Wayan Sugiantara for his programming work for SOVAT development. In addition, the authors would like to thank Mike Meit and the University of Pittsburgh's Center for Rural Health Practice for funding support during the development process.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2006/10
Y1 - 2006/10
N2 - Introduction: The development of numerical-spatial routines is frequently required to solve complex community health problems. Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting the development of numerical-spatial routines. Background: Currently, there is no decision support system (DSS) that is effectively able to accomplish this task as the majority of public health geospatial information systems (GIS) are based on traditional (relational) database architecture. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. OLAP alone is not sufficient for solving numerical-spatial problems that frequently occur in CHA research. Coupling it with GIS technology offers the potential for a very powerful and useful system. Methodology: A community health OLAP cube was created by integrating health and population data from various sources. OLAP and GIS technologies were then combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT). Results: The synergy of numerical and spatial environments within SOVAT is shown through an elaborate and easy-to-use drag and drop and direct manipulation graphical user interface (GUI). Community health problem-solving examples (routines) using SOVAT are shown through a series of screen shots. Discussion: The impact of the difference between SOVAT and existing GIS public health applications can be seen by considering the numerical-spatial problem-solving examples. These examples are facilitated using OLAP-GIS functions. These functions can be mimicked in existing GIS public applications, but their performance and system response would be significantly worse since GIS is based on traditional (relational) backend. Conclusion: OLAP-GIS system offer great potential for powerful numerical-spatial decision support in community health analysis. The functionality of an OLAP-GIS system has been shown through a series of example community health numerical-spatial problems. Efforts are now focused on determining its usability during human-computer interaction (HCI). Later work will focus on performing summative evaluations comparing SOVAT to existing decision support tools used during community health assessment research.
AB - Introduction: The development of numerical-spatial routines is frequently required to solve complex community health problems. Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting the development of numerical-spatial routines. Background: Currently, there is no decision support system (DSS) that is effectively able to accomplish this task as the majority of public health geospatial information systems (GIS) are based on traditional (relational) database architecture. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. OLAP alone is not sufficient for solving numerical-spatial problems that frequently occur in CHA research. Coupling it with GIS technology offers the potential for a very powerful and useful system. Methodology: A community health OLAP cube was created by integrating health and population data from various sources. OLAP and GIS technologies were then combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT). Results: The synergy of numerical and spatial environments within SOVAT is shown through an elaborate and easy-to-use drag and drop and direct manipulation graphical user interface (GUI). Community health problem-solving examples (routines) using SOVAT are shown through a series of screen shots. Discussion: The impact of the difference between SOVAT and existing GIS public health applications can be seen by considering the numerical-spatial problem-solving examples. These examples are facilitated using OLAP-GIS functions. These functions can be mimicked in existing GIS public applications, but their performance and system response would be significantly worse since GIS is based on traditional (relational) backend. Conclusion: OLAP-GIS system offer great potential for powerful numerical-spatial decision support in community health analysis. The functionality of an OLAP-GIS system has been shown through a series of example community health numerical-spatial problems. Efforts are now focused on determining its usability during human-computer interaction (HCI). Later work will focus on performing summative evaluations comparing SOVAT to existing decision support tools used during community health assessment research.
KW - Community health assessment
KW - Geographic information systems
KW - Numerical-spatial problem solving
KW - On-Line Analytical Processing
UR - http://www.scopus.com/inward/record.url?scp=33749176723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749176723&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2005.10.008
DO - 10.1016/j.ijmedinf.2005.10.008
M3 - Article
C2 - 16359916
AN - SCOPUS:33749176723
SN - 1386-5056
VL - 75
SP - 771
EP - 784
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
IS - 10-11
ER -