The geographic map has been designed as an intuitive visualization of spatial datasets, for example the distribution of news articles or social media postings. Triggered by a search query, the system analyzes the set of matching documents to determine all referenced locations.
While the notion of a Geospatial Web  has inspired our work for the better part of the last decade, the March 2017 release Mangrove Monitor takes webLyzard’s geospatial analytics capabilities to a new level. The geographic map has been rebuilt from scratch for optimized performance and a more effective information design, using custom base layers and adaptive tooltips to adapt the map to specific tasks, or export it for reuse in external applications.
Try out the dashboard at asap.weblyzard.com
Layout and Data Representation
Dynamic updates triggered by user interactions help analysts understand the geographic context of their queries without interrupting their workflow. Layout and color scheme of the geographic map depend on the chosen base layer, which enriches the display and adds geographic details. Single documents or document clusters are shown as circles of different size and color:
- The position of circles is determined by the coordinates – i.e., longitude and latitude – of locations (cities, countries, landmarks, etc.) that were identified in the documents.
- The size of the circles is proportional to the number of documents referring to a specific position.
- The color represents either one of the selected topics, or metadata attributes such as sentiment. In the case of sentiment, for example, the color ranges from red (negative) to grey (neutral) and green (positive). Colors vary in saturation, depending on the degree of polarity – vivid colors indicate emotionally charged issues, less saturated shades a more neutral coverage.
- Optional Arcs connect referenced locations with the origin of the documents (i.e., the locations of authors or publishers). Accessible via the menu in the lower left corner, this feature allows analysts to explore which locations are frequently mentioned by a particular country’s news media channels.
The full potential of the geographic map unfolds in conjunction with the drill-down capabilities of adaptive tooltips. Based on the user’s current context (country shape, point of interest, etc.), the tooltip displays the most relevant information in a local context, and the option to restrict or extend the search.
- Hovering over a circle activates a document preview and a tooltip with a line chart and top associations with this particular location. Additionally, all arcs that point to this location are displayed with higher opacity, emphasizing in which countries this location is being discussed.
- Clicking on a circle or country displays an extended tooltip with context-specific actions: (i) Focus on this Point and show results within a 100 km radius, automatically extended to 1000 km in case of sparse coverage, (ii) Replace the current search with a search for mentions of this location, and (iii) Restrict or Extend the search via Boolean operators (AND, OR).
- Zooming is available via the mouse wheel or double click, while clicking and dragging enables a seamless panning of the entire display.
Details on Demand
Advancing the State of the Art
Developed as part of the ASAP and PHEME research projects, the geographic map is capable of processing very large datasets and has been tested with search queries returning more than 100 million documents. The screenshot above shows the maximized version zoomed to street level, including an adaptive tooltip for on-the-fly query refinements. It presents anonymized cell tower activity data for the City of Rome (blue markers), and overlays this information with geotagged Twitter postings (green = positive; red = negative); e.g., to identify communication hotspots during an event. This shows how semantic technologies in conjunction with advanced visual tools can transform statistical data into valuable repositories of actionable knowledge .
- Scharl, A. and Tochtermann, K., Eds. (2007). The Geospatial Web – How Geo-Browsers, Social Software and the Web 2.0 are Shaping the Network Society. London: Springer.
- Bostock, M., Ogievetsky, V. and Heer, J. (2011). “D3: Data-Driven Documents”, IEEE Transactions on Visualization and Computer Graphics, 17(12): 2301-2309.
- Brasoveanu, A.M.P., Sabou, M., Scharl, A., Hubmann-Haidvogel, A. and Fischl, D. (2017). “Visualizing Statistical Linked Knowledge for Decision Support”, Semantic Web Journal, 8(1): 113-137.