One of the difficulties in processing the results of an empirical quantitative data modeling process is in post-analysis filtering or presenting the information produced. Things are a little easier if the data has a geospatial dimension: one can then use GIS software, specialized statistical modeling software, or produce "mash-ups" for web-based tools. These are all heavyweight solutions. This article presents a lightweight solution which is useful for Grid based or standalone implementation of a problem. Introduction Statistical applications that use social science microdata, whether cross-sectional or longitudinal (panel) studies, suffer from a lack of easy-to-use presentational and exploratory tools. Economic applications, for example, tend to result in a myriad of results tables and/or basic graphics. If such applications are Grid based, in the sense that data hosting and computation are orchestrated via the appropriate middleware, then finishing off the workflow by producing "difficult to digest" hardcopy seems to be a step backwards. However, if the application has a spatial dimension then a natural approach is to use a choropleth map style presentational and exploratory display. Tools of this type are common in the GIS (Geographical Information Systems) domain, but not necessarily easy, or cheap, to deploy. This article presents a lightweight version of one of these tools, which was originally developed for an e-Social Science pilot demonstrator that investigated UK ethnic minority welfare. The original project, entitled Grid Enabled Microeconometric Data Analysis (Peters et al., 2006,2007), Grid enabled two different microdata sources, the British Household Panel Survey (BHPS) and the 1991 Census Sample of Anonymised Records (SARs), and performed calculation of poverty measures and associated statistics using a high performance computing node . The nature of the UK Census at the time meant that the microdata's geography included the UK, its regions, and an artificial local authority area (a SARs area). The project's visualization tool allowed display and investigation of the application's results, by the geography and category of interest (ethnic minority and gender in this case), using a map interface with linked graphics or tables. This used the GeoTools open source GIS Java library (Codehaus, 2006) and was deployed via the project's web interface. Work on the tool has moved on since the aforementioned project's conclusion to address other issues including: 1) data display, 2) tool deployment, and 3) other microdata applications. For issue 1), the original map colour scheme was improved, and the issue of producing output for formal publication was addressed. The nature of the application also suggested enhancements to the visualization process to allow results filtering by sample size and/or statistical precision (p-value for a hypothesis test, standard error for an estimate). Sample size filtering is particularity pertinent as it alludes to the types of data disclosure restrictions imposed upon microdata by governmental and other data owners, a topic which leads into the issue of tool deployment. The original tool was deployed as part of an eResearch project that required authorised and authenticated access to the application's data sources. The tool still retains this characteristic, however, to repeat the application using 2001 UK data (the present Census currency) is not feasible as the data are only available in a secure data enclave. The code, therefore, needs taking to the remote location. The present version can be used standalone and is opensource. The latter point is important, as data owners are unwilling to deploy unvetted black box code on their secure servers. The third and final issue is the ability to use the tool for different applications. This requires both mapping information and microdata, the only requirement being that these are in a format suitable for the GeoTools library. For UK microdata applications that use the Census geographies, the mapping information is available from the Edina UK borders project under Athens authentication. This short paper discusses the present state of the tool, the details associated with the extensions discussed above and developments in progress. Deployment and Implementation Issues The core of the tool is based upon the GeoTools open source Java library that provides methods for the manipulation and viewing of geospatial data. As it is a Java library, associated applets or applications are platform independent. Users do not need to install it on their computers as the necessary parts are downloaded as jar files when the applet is loaded. The present implementation is fixed on GeoTools 2.0 to avoid re-writing the user interface every time GeoTools is updated. The tool itself can be used both as a Java applet embedded in a Grid based application or as a standalone Java application, the latter being particularly useful for special environments such as secure data enclaves. To use it in a Grid based setting the user must have appropriate authentication and authorisation for the service, such as an e-certificate for the NGS (the UK's National Grid Service) or EGEE (The EU's Enabling Grids for E-SciencE). It is designed to sit on top of a statistical analysis that has been deployed on a Grid, and is dissociated from the middleware. This isolates development of the tool from issues (fashion, sustainability) related to the evolution of the Grid. A hypothetical implementation using the P-GRADE Portal (Sipos and Kacsuk, 2006), which can replace certain of the bespoke elements of Peters et al (2006, 2007), is presented in Figure 1 below. Both the Grid and standalone usages may require extra security to permit use of the mapping data. This will depend upon the user's mapping file provider. For example, a user has to agree to the UK Borders license agreement for the embedded maps required for working with UK Census microdata. In practice this requires a user to have an Athens username and password. These maps are downloaded in shape format, the supported format used in the GeoTools library. The correct geographical levels (regional maps and SARs area maps) for the currency of the data sources are readily available, however, some modifications are needed to produce a UK wide map as England, Scotland, and Wales are obtained as separate distinct mapping files. These have to be combined. At the regional level, distinguishing the regions 'South East', 'Outer London', and 'Inner London' also required manipulation of the shape data. Once the final mapping file is available then it can be linked with an appropriately formatted data file. Most data analyses will produce flat result files which can be visualized after some filtering. This requires an application to convert them into the dbf structure required by the shape file. The combination of shape file and dbf file then become the input for the visualisation applet. Figure 1 The Tool as an Add-on to General Middleware User Interface Features In certain social sciences, such as the economics discipline area, viewing the results of any microdata based modelling process in anything other than a table is still a relatively new experience. If the results of the analyses are related to specific geographies then they can be viewed using the aforementioned visualisation tool. Basic interaction (zooming, panning, etc.) within the map is possible, as well as viewing relevant statistical plots linked to a specific area of a map. The user can choose between categories pertinent to the analysis and the geographical level of the map. Our example deals with poverty measures that are produced for an ethnic group and gender category at UK regional or local area (a SARs area) geography. Feedback from a series of peer conferences and workshops about the original visualization applet produced a number of criticisms and constructive proposals for extensions. The three main criticisms concerned: a) the chosen colour map; it was not suitable for the colour blind, b) print journal cost effectiveness; academic articles are still published in journals using greyscale by default1 and c) extra functionality. 1 Publishers will produce colour plates in academic journals by request. The cost for this, however, is somewhat high. P-GRADE Portal 1) stage data extraction job 2) stage compute job 3) stage results processing job HPC computing nodes, on either the NGS or EGEE. Data Host Files for the tool. Session Initiation Visualization Tool Map data
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