Conditioned Choropleth Maps and Hypothesis Generation

Designing a web-based learning portal for geographic visualization and analysis in public health

Interactive mapping and spatial analysis tools are under-utilized by health researchers and decision-makers as a result of scarce training materials, few examples demonstrating the successful use of geographic visualization, and poor mechanisms for sharing results generated by geovisualization. Here, we report on the development of the Geovisual EXplication(G-EX) Portal, a web-based application designed to connect researchers in geovisualization and related mapping sciences, to users who are working in public health and epidemiology. This paper focuses on the design and development of the G-EX Portal Learn module, a set of tools intended to disseminate learning artifacts. Initial design and development of the G-EX Portal has been guided by our past research on the use and usability of geovisualization in public health. As part of the iterative design and development process, we conducted a needs assessment survey with targeted end-users, which we report on here. The survey focused on users' current learning habits, their preferred kind of learning artifacts and issues they may have with contributing learning artifacts to web portals. Survey results showed that users desire a diverse set of learning artifacts in terms of both formats and topics covered. Results also revealed a willingness of users to contribute both learning artifacts and personal information that would help other users to evaluate the credibility of the learning artifact source. We include a detailed description of the G-EX Portal Learn module and focus on modifications to the design of the Learn module as a result from feedback we received from our survey.

Geographic Information Systems

This chapter presents an overview of the development, capabilities, and utilization of geographic information systems (GIS). There are nearly an unlimited number of applications that are relevant to GIS because virtually all human interactions, natural and man-made features, resources, and populations have a geographic component. Everything happens somewhere and the location often has a role that affects what occurs. This role is often called spatial dependence or spatial autocorrelation, which exists when a phenomenon is not randomly geographically distributed. GIS has a number of key capabilities that are needed to conduct a spatial analysis to assess this spatial dependence. This chapter presents these capabilities (e.g., georeferencing, adjacency/distance measures, overlays) and provides a case study to illustrate how GIS can be used for both research and planning. Although GIS has developed into a relatively mature application for basic functions, development is needed to more seamlessly integrate spatial statistics and models.The issue of location, especially the geography of human activities, interactions between humanity and nature, and the distribution and location of natural resources and features, is one of the most basic elements of scientific inquiry. Conceptualizations and physical maps of geographic space have existed since the beginning of time because all human activity takes place in a geographic context. Representing objects in space, basically where things are located, is a critical aspect of the natural, social, and applied sciences. Throughout history there have been many methods of characterizing geographic space, especially maps created by artists, mariners, and others eventually leading to the development of the field of cartography. It is no surprise that the digital age has launched a major effort to utilize geographic data, but not just as maps. A geographic information system (GIS) facilitates the collection, analysis, and reporting of spatial data and related phenomena. The capabilities of GIS are much more than just mapping, although map production is one of the most utilized features. GIS applications are relevant in a tremendous number of areas ranging from basic geographic inventories to simulation models.This chapter presents a general overview of geographic information system topics. The purpose is to provide the reader with a basic understanding of a GIS, the types of data that are needed, the basic functionality of these systems, the role of spatial analysis, and an example in the form of a case study. The chapter is designed to provide advanced students and experts outside of the field of GIS sufficient information to begin to utilize GIS and spatial analytic concepts, but it is not designed to be the sole basis for becoming a GIS expert. There is a tremendous level of sophistication related to the digital cartographic databases and manipulation of those databases underlying the display and use of GIS that is more appropriately a part of geographic information science (i.e., basic research issues associated with geographic data including technical as well as theoretical aspects such as the impact on society [1]) rather than being relevant to this chapter. The utilization of GIS for conducting spatial analysis is the guiding theme for the chapter.

Informing geospatial toolset design: understanding the process of cancer data exploration and analysis

There is an increasing need for new methods and tools that support knowledge construction from complex geospatial datasets related to public health. This study is part of a larger effort to develop, implement, and test such methods and tools. To be successful, the design of methods and tools must be grounded in a solid understanding of the work practices within the domain of use; the research reported here focuses on developing that understanding. We adopted a user-centered approach to toolset design where we investigated the work of cancer researchers and used the results of that investigation as inputs into the development of design guidelines for new geovisualization and spatial analysis tools. Specifically, we conducted key informant interviews focused on use, or potential use, of geographic information, methods, and tools and complemented this with a systematic analysis of published, peer-reviewed articles on geospatial cancer research. Results were used to characterize the typical process of analysis, to identify fundamental differences between intensive users of geospatial methods and infrequent users, and to outline key stages in analysis and tasks within the stages that methods and tools must support. Our findings inform design and implementation decisions for visual and analytic tools that support cancer prevention and control research and they provide insight into the processes used by cancer researchers for addressing the challenges of geographic factors in public health research and policy.

Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis

We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impement these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's dataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales.

A design framework for exploratory geovisualization in epidemiology

This paper presents a design framework for geographic visualization based on iterative evaluations of a toolkit designed to support cancer epidemiology. The Exploratory Spatio-Temporal Analysis Toolkit (ESTAT), is intended to support visual exploration through multivariate health data. Its purpose is to provide epidemiologists with the ability to generate new hypotheses or further refine those they may already have. Through an iterative user-centered design process, ESTAT has been evaluated by epidemiologists at the National Cancer Institute (NCI). Results of these evaluations are discussed, and a design framework based on evaluation evidence is presented. The framework provides specific recommendations and considerations for the design and development of a geovisualization toolkit for epidemiology. Its basic structure provides a model for future design and evaluation efforts in information visualization.

A visualization system for space-time and multivariate patterns (VIS-STAMP)

The research reported here integrates computational, visual, and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display, and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and, through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 Contest data set, which contains time-varying, geographically referenced, and multivariate data for technology companies in the US.