Designed and Developed a customizeable thematic map web viewer
CMAP required a map module in its MetroPulse web application framework to help visualize over 100 different regional indicators including economic, environmental, social and cultural data over time. These indicators needed to be rendered thematically at multiple geographic ranges including county, municipality, community, census tract, block group, and blocks.
Great Arc provided a Flex-based mapping application that would be configured at runtime to provide maps based on the user’s chosen criteria. The application would also allow users to change their search criteria and update the map based on the new criteria. By working closely with CMAP on the design of the project, we defined the methods, parameters, and procedures to be incorporated into the application.
Using this application, CMAP is able to provide data to the public in thousands of different user-selected configurations which are dynamically loaded and configured at run time. The map module dynamically loads and thematically renders any of a large number of geographies and related data fields – spanning seven counties, five decades, and many public policy areas. The module integrates with a Flex host application developed by another consultant. Additionally, we designed and developed .Net web services which deliver XML data directly to the Flex map module. For performance benefits, we developed a custom vector service that directly accesses spatial data stored in SQL Server 2008 geometry type for rendering within the ESRI Flex map control. As a result, we were able to compress 17 MB of serialized spatial data delivered by ArcGIS Server to less than 1 MB, resulting in significantly improved map rendering performance.
Large spatial datasets such as those involved in this project can often contribute to the poor performance of applications. With this knowledge, Great Arc replicated the CMAP SQL Server WebData database as well as the SDE database in order to prototype map services and fully test map performance against the existing CMAP data model. During this process, we developed new spatial technologies to leverage that CMAP’s data architecture and provide significant performance gains.