SOVAT: Spatial OLAP Visualization and Analysis Tool

Transcription

1 SOVAT: Spatial OLAP Visualization and Analysis Tool Matthew Scotch a and Bambang Parmanto a,b a Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA b Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA scotch@cbmi.pitt.edu Abstract Community health research is practiced with disparate independently used tools that by themselves do not allow for the type of comprehensive and thorough analysis needed for effective public health evaluation. The Spatial OLAP Visualization and Analysis Tool (SOVAT) is a new type of research application for community health assessments. SOVAT integrates into one system many of the necessary characteristics needed to make comprehensive community health decisions. By combining On-Line Analytical Processing (OLAP) with Geospatial Information System (GIS) capabilities, our system can handle large amounts of data, perform geospatial and statistical calculations, and then display this information in both a numerical and spatial view within the same interface. It is anticipated that this unique system will provide researchers with the ability to perform more comprehensive assessments while enabling for more informed public health decisions. Keywords: community health assessment; OLAP; GIS 1. Introduction Community health assessment research is conducted with independent and disparate tools such as data warehouses, simple analytical software packages, and Geospatial Information Systems (GIS). Currently, a single system that combines all the necessary features of these disparate technologies, such as OLAP and GIS, does not exist in the public health domain. It is anticipated that such a system will allow for more comprehensive and thorough community health analysis leading to more informed public health decisions. We are developing a system called the Spatial OLAP Visualization and Analysis Tool (SOVAT) that can handle large amounts of data, perform geospatial and statistical calculations, and then display this information in both a numerical and spatial view on the same interface. This type of system is comprised of two core technologies: Online Analytical Processing (OLAP) and Geospatial Information System (GIS). OLAP (On-Line Analytical Processing) is a data warehouse technology for the storage and management of multidimensional data. It can facilitate ad-hoc queries across multiple dimensions (views) of data, and produce powerful results and analysis. OLAP is widely used in business areas as a powerful decision-making tool. Its use within the public health field has been limited however, as researchers have preferred the use of perceived simpler technology such as relational database management systems (RDBMS). In contrast to OLAP, Geospatial Information Systems are much more common within public health research; however its use has been primarily limited to simple visualization of spatial objects rather than advanced geospatial analysis. This paper will discuss these two components and detail the development of the SOVAT system for community health assessment research. Illustrations of the system s front-end interface will show the union of OLAP and GIS through a series of user queries. 2. Background 2.1 On-Line Analytical Processing (OLAP) Data sets are increasing in both size and complexity, which makes analytical process more challenging. Community health researchers, who use large and complex public health-related information for research purposes, need technology that will make the analysis of these data sets more comprehensive. On- Line Analytical Processing (OLAP) is one of these technologies that has attracted significant interest for this purpose [1, 2]. OLAP was designed specifically to deal with complex multidimensional data as well as with the statistical summarization over its dimensions. These characteristics fit well with the typical nature of community health-based data[3]. Consider the dataset represented in Figure 1 as a 2-D table. It shows Population by geographic location by age and by disease type. The method of representing multidimensional information in a tabular fashion is very popular based on the limitations of paper to only represent 2-D information. Community health data sets usually have dimensions larger than three. Data representation becomes more difficult as the number of dimensions increase. Data navigation using this type of data set becomes very complex. 1

2 Disease Neoplasm Circulatory State PA County Elk Erie State NH County Carroll Coos State County Figure 1. A tabular (2-D) representation of community health-based data To illustrate how OLAP can be used for analysis and dissemination of community health data sets, we have developed a data warehouse that serves as part of the back-end for our SOVAT system. The data warehouse is populated with information from Census 2000, the Pennsylvania Healthcare Cost Containment Council (PHC4) and the Pennsylvania Department of Health. Unlike a statistical software package or a traditional data warehouse, OLAP stores its data in a multidimensional data cube. This cube contains multiple dimensions with cells that contain multiple measures. Each dimension is a classification hierarchy, containing attributes from detailed to broad levels of aggregation. Each cell contains multiple measures, which may be means, counts, sums, or other simple statistical expressions. For example, the cube in Figure 2 has six dimensions: geography, age, year, diagnosis, sex, and race. Each dimension has attributes that define it. For example, the geography dimension consists of a state, with counties and the municipalities that comprise each county. The diagnosis dimension on the other hand, represents the hierarchical structure of the International Classification of Diseases (ICD), from the broad health categories all the way down to the individual ICD-9 code. The cells of the cube contain measures such as population and incidence rate of disease. Figure 2. Multi-dimensional cube of community health data. 2

3 OLAP is designed to support intensive, complex, and ad-hoc queries in order to assist in decision modeling and analysis of multidimensional data. OLAP supports operations such as rollup and drill down, slice and dice, and pivot that significantly facilitate data navigation. For example, Figure 3 exhibits a query performed with SOVAT using simple drag and drop operations on the user interface. Here, the population for Pennsylvania is shown for all years ( ) and for all ages. This type of operation can be done with SOVAT and it will take a split second to get the results. This is different from a regular database that will take minutes to hours (for large data sets) to get one result depending on the size of the data sets and the types of joins performed. Figure 3. OLAP as a tool for exploration and discovery, as well as a dissemination tool. Here, our SOVAT system shows numerical data displayed as a bar chart. The queries are performed via drag and drop operations onto the graphs and the OLAP engine returns the results in seconds. Community health data sets need to be disseminated to a vast array of users such as researchers or policy makers. Different user groups have different needs for data granularity (whether detailed or summarized), as well as different levels of complexity in statistical analysis. OLAP lends itself to these varying requests since users can choose and explore the specific data set that is best tailored towards their needs[2, 4]. 2.2 Geographic Information Systems (GIS) The majority of community health researchers do not utilize the full potential of Geographic Information Systems (GIS). This is a concern since research suggests that GIS alone is not an effective tool for data analysis[5]. Researchers are becoming inundated with massive amounts of geo-referenced data sets, creating a data-rich environment that is considerably different from the data-poor environment when GIS was first developed. Today s researchers and policy makers need more sophisticated analysis beyond simple the querying and visualization of spatial objects. A combination or integration of OLAP and GIS will open a new ground for potentially rich areas of community health research. The importance of this convergence is evident as far back as John Snow s methodology for discovering the source of the cholera outbreak in London during the 19 th century [6]. Using both spatial and numerical display to map death counts to spatial objects, Snow was able to quickly determine the source; a contaminated water pump near a popular brewery. While the combination of numerical and spatial data in the area of community health research offers great potential for providing experts with the ability to discover new methods of analyzing data, an OLAP-GIS system is extremely rare in this domain. This is greatly influenced how researchers view the individual technologies of OLAP and GIS. In relation to the use of GIS, a survey of 30 community health researchers indicated that 70% were aware of GIS and felt that it could aid them in decision making, however most felt that it was not being utilized to its full potential, using it mostly for just presentation and visualization of spatial data [7]. Advances in GIS technology have allowed for elaborate spatial analysis 3

4 such as buffering, networking (shortest path), and clipping that goes far beyond simple data representation and display. A positive from this study is that 93% of the participants indicated that they would like to use geospatial technology in a more elaborate fashion to aid them in their research[7]. 2.3 Combining OLAP and GIS Since GIS is built on a transactional architecture, it is not sufficient for knowledge discovery and data mining[8]. Thus GIS must be aligned with Online Analytical Processing in order to have a significant impact in community health research. Bedard[8] has shown the potential of combining OLAP with GIS for geographic knowledge discovery in the area of environmental health research. Efforts to combine GIS with relational databases have been established by such groups as the Human Computer Interaction Lab at the University of Maryland with the Dynamaps Project [9]. The main focus on this project is to facilitate population-based data retrieval and analysis through elaborate front-end user interface solutions. Dynamap uses a small relational database (Microsoft Access) to store the data sets. SOVAT focuses on the integration of On-Line Analytical Processing and Geospatial Information Systems to enhance the data retrieval and knowledge discovery process for the user. The capability of our OLAP-GIS system as a powerful community health research tool is evident when considering the two fundamental technologies we are combining. OLAP is built to handle large and complex data sets while presenting a multidimensional view of the data through visual and descriptive charts, while Geospatial Information Systems provide spatial representation and spatial analysis of data. A system like ours that is able to successfully merge these two technologies together inherits both of their characteristics and produces one potentially powerful research tool that has rarely been seen in community health research (Table1) Large, Complex Data Sets Multidimensional View Statistical Analysis Spatial Presentation Visual Charts Spatial Analysis Statistical Tools (e.g. SPSS, SAS) GIS (e.g. ArcView) Relational DB OLAP SOVAT Table1. Some of the technologies used during the process of community health research and the characteristics they provide. SOVAT will be able to offer these capabilities by itself providing the potential for a powerful single community health research tool. 3. Case Study We performed a case study of a community health assessment for rural Pennsylvania using SOVAT. The goal of the assessment was to identify community health factors by analyzing and integrating disparate information from various sources such as the US Census Bureau, the Pennsylvania Health Care Cost Containment Council (PHC4), and the Pennsylvania Department of Health.. The specific data sets chosen include: 1. Population-based Data (from the US Census Bureau) 2. Socioeconomic Data (from the US Census Bureau) 3. Inpatient Utilization (from PHC4) 4. Outpatient Utilization (from PHC4) 5. Birth Statistics (from the PA DOH) 6. Death Statistics (from the PA DOH) 7. Cancer Incidence (from the PA DOH) OLAP was chosen as the analysis software and Microsoft SQL Server 2000 as the server for the data warehouse architecture. The dimensions (or views of the data) chosen for the data warehouse were: Age, Sex, Race, Education Level, Region (i.e. live in an urban or rural location), Diagnosis, Birth weight, Geography, and Year. The specific attributes, which characterize the dimensions, were then determined. For example, male and female were logical attribute choices for the Sex dimension. The developmental focus of the interface consisted of two main components: Provide an easy-to-use Graphical User Interface (GUI) drag and drop environment that hides the underlying query details from the user. Seamlessly integrate the OLAP and spatial components (GIS) together to create a viewer that provides the researcher with different visual representations of the data. 4

5 SOVAT is a unique system that is capable of providing comprehensive exploratory data analysis on very large data sets for community health research. The two illustrations below show some of the capabilities of our system for community health research (Figures 4, 5). In Figure 4, a simple drag and drop query quickly displays the results in both descriptive and spatial display. Here, the death rate for elderly persons age in 2000 for circulatory system-related deaths is shown. In Figure 5, a drill-down operation on the geography dimension is easily processed and quickly shows results at the municipality, rather than the county level. With SOVAT, the OLAP and GIS components are connected so that the same results will be represented in different fashions (both descriptive and spatial) allowing the researcher to link the numerical data shown in the visual charts with the spatial context shown in the map. The illustrations show the potential of the system to provide the six essential characteristics previously discussed that we feel will benefit the health assessment process. Our system currently contains four of these six characteristics: handling of large data sets, multidimensional view of the data, spatial presentation, and descriptive presentation through visual charts. The other two components, statistical analysis and spatial analysis, will be added soon. The OLAP technology within our system allows the handling of large disparate data sets. Currently, the system contains over 6.5 million records and more data sets are expected to be added soon. It also supports a multidimensional view of the data and allows the user to drill down through the dimensions either via the tabs on the upper left section of the interface, or by drilling up or down directly on the chart or graph. These actions will allow the researcher to instantly view data from a different granularity. In addition, while it is currently not one of its characteristics, our OLAP component has the potential of being linked with statistical software packages that will then provide the necessary data transformations. We have already added certain statistical measurements such as age adjustments and spatial smoothing to our system. 5

6 Figure 4. The OLAP engine in SOVAT can present numerical data by slicing across multiple dimensions. Here, a death rate is shown for a specific disease, for a specific age-group, during a specific year, for selected geographic locations. Figure 5. An example of integrating OLAP and GIS technology within our system. By drilling down on the geography dimension, data can be explored at a lower level of granularity, in this example from county to municipalities (of Allegheny County). This type of operation would be very difficult to perform using only GIS. Combing OLAP-GIS has the potential of providing more powerful analysis for community health researchers 6

7 4. Conclusion OLAP and GIS are still underutilized technologies in the community health field. As data sets for community health assessments become larger and the demand for efficient ad-hoc results become greater, the inclusion of powerful multidimensional data warehouses such as OLAP should be considered. In addition, descriptive data in the form of bar and line graphs is not adequate when attempting to identify spatial relationships of public health concerns. A more powerful and useful tool that contains many of the characteristics needed for comprehensive community health research is needed. This paper showed the potential of combining OLAP and GIS in the realm of community health assessments. We outlined six important characteristics a system should have for performing this type of research. Our Spatial OLAP Visualization and Analysis Tool (SOVAT) contains: the ability to: handle large data sets, perform statistical analysis, allow for detailed exploration of data, display data visually with charts or spatial objects, and perform spatial analysis. Work is now being done to provide spatial analysis and data mining capabilities that will enhance the capabilities of the system even further. Future studies will aim at measuring the Perceived Ease of Use (PEU) and Perceived Usefulness (PU)[10] of SOVAT versus existing community health technologies. Acknowledgments The project is supported in part by the National Library of Medicine (NLM) Training Grant 5 TI5 LM 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. Systems, PODS May 1997 Tucson, AZ, USA. 1997: ACM, New York, NY, USA. 4. Westlake A, Multiway table for storage of summary data, in RSS statistical computing section meeting on statistical environments for the 21st century Shan Y, Lin H, and F. W. Integration of webbased GIS and online analytical processing. in 21st Asian Conference on Remote Sensing Taipei, Taiwan. 6. Tufte, E.R., Visual and Statistical Thinking: Displays of Evidence for Making Decisions, in Envisioning Information. 1990, Graphics Press: Cheshire, CT. 7. Mowat DL, et al. Improving Health Surveillance in Canada - What are the Needs? in ITCH 2000, An International Conference Addressing Information Technology In Community Health Victoria, BC, Canada. 8. Bedard, Y., et al., Integrating GIS components with knowledge discovery technology for environmental health decision support. Int J Med Inf, (1): p Dang G, North C, and Schneiderman B. Dynamic Queries and Brushing on Choropleth Maps. in Proceedings of the Fifth International Conference on Information Visualisation (IV'01) London, England. 10. Davis, F., Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, (3): p References 1. Yeboah DA. Processing and Dissemination of Social Statistics in the Carribean: The User and Producer Perspective. in 26th Meeting of the Standing Committee of Caribbean Statisticians (SCCS) Nasau, Bahamas. 2. Backlund S. The role of IT in disseminating statistics: Focusing user needs and expectation. in Symposium on Global Review of 2000 Round of Population and Housing Censuses: Md-Decade Assessment and Future Prospects New York, NY. 3. Shoshani, A. OLAP and statistical databases: similarities and differences. in Proceedings of the Sixteenth ACM SIGACT SIGMOD SIGART Symposium on Principles of Database 7