Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter's interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables -attribute names, values, data types, etc. -are not explicitly stored as table metadata. Consequently, the structure that these tables contain is not accessible to the crawlers that power search engines and thus not accessible to user search queries. We address this lack of structure with a new method for leveraging the principles of table construction in order to extract table schemas. Discovering the schema by which a table is constructed is achieved by harnessing the similarities and differences of nearby table rows through the use of a novel set of features and a feature processing scheme. The schemas of these data tables are determined using a classification technique based on conditional random fields in combination with a novel feature encoding method called logarithmic binning, which is specifically designed for the data table extraction task. Our method provides considerable improvement over the wellknown WebTables schema extraction method. In contrast with previous work that focuses on extracting individual relations, our method excels at correctly interpreting full tables, thereby being capable of handling general tables such as those found in spreadsheets, instead of being restricted to HTML tables as is the case with the WebTables method. We also extract additional schema characteristics, such as row groupings, which are important for supporting information retrieval tasks on tabular data.

Use this map query interface to search the world, even when not sure what information you seek.

No abstract

NewsStand is an example application of a general framework that we are developing to enable searching for information using a map query interface, where the information results from monitoring the output of over 8,000 RSS news sources and is available for retrieval within minutes of publication. The user interface of NewsStand was recently adapted so that NewsStand can execute on mobile and tablet devices with a gesturing touch screen interface such as the iPhone, iPod Touch, and iPad. This action led to a discovery of some shortcomings of current mapping APIs as well as devising some interesting new widgets. These issues are discussed, and the realization can be seen by a demo at http:// newsstand.umiacs.umd.edu on any of the above Apple devices as well as other devices that support gestures such as an Android phone.

The Circular Economy (CE) is expected to accelerate the use of resources with bio-based origin. Cities have an important role in such an economy, not only as main consumers but also because vegetation provides numerous ecosystem services essential for the well-being of urban dwellers. Urban lands are, however, heavily burdened with both past and present activities and ongoing urbanization. Retrofitting obsolete and potentially contaminated brownfields provides an opportunity to engage with bio-based land uses within the city. At the same time, plants are an important part of Gentle Remediation Options (GROs), a more sustainable alternative for managing contamination risks and restoring soil health. This paper (1) provides a tentative selection of Urban Greenspaces (UGSs) relevant for brownfields, and a compilation of ecosystem services provided by the selected UGSs, and (2) presents a framework covering the 14 selected bio-based land uses on brownfields, including GRO interventions over time. This framework provides three practical tools: the conceptualization of linkages between GROs and prospective UGS uses, a scatter diagram for the realization of 14 UGS opportunities on brownfields, and a decision matrix to analyze the requirements for UGS realization on brownfields.

Geographical Information Systems have been increasingly used to aid the prompt detection, tracking, and analysis of disease outbreaks. Web content which is full of healthrelated data also serves as a useful resource for disease outbreak analysis. News posts often report the initial outbreak of diseases and contain valuable information that aids in ascertaining the time and location of the disease outbreak. The locations mentioned in the news posts are specified textually rather than geometrically thereby requiring the use of geotagging methods to detect them and to map the textual specification to the corresponding actual geometric specification. The NewsStand system which aggregates news posts by topic and location while providing a map query interface to them is enhanced to enable disease tracking and analysis by geotagging disease-related web news posts. Besides the powerful functionalities of NewsStand for news exploration, enhancements of NewsStand with respect to the analysis of temporal information are described which include a well-designed time slider, a heatmap-based visualization tool for displaying disease distribution, and intuitive spatiotemporal querying methods. Future improvements to NewsStand are also discussed.

Determining geographic interpretations for place names, or toponyms, involves resolving multiple types of ambiguity. Place names commonly occur within lists and data tables, whose authors frequently omit qualifications (such as city or state containers) for place names because they expect the meaning of individual place names to be obvious from context. We present a novel technique for place name disambiguation (also known as toponym resolution) that uses Bayesian inference to assign categories to lists or tables containing place names, and then interprets individual toponyms based on the most likely category assignments. The categories are defined as nodes in hierarchies along three orthogonal dimensions: place types (e.g., cities, capitals, rivers, etc.), geographic containers, and prominence (e.g., based on population).

The compact city is globally acknowledged as the most adequate urban model to encourage sustainable urban development. Its validity is often assumed, despite the lack of clarity on what such compactness entails. The knowledge gap is even wider regarding how different drivers and pressures influence the development of more compact cities. Therefore, the authors analyse indirect and underlying processes (drivers) and more direct events, actions, and processes (pressures) affecting compact city development. Since compact city driving forces are extensively influenced by local situations, their research focused on district-level case studies within the compact city of Barcelona Municipality. Mixed methods were used, and the authors used both qualitative and quantitative data. The results revealed that drivers and pressures can both support and counteract compact city qualities and therefore any intervention has to be tailored to local conditions. In particular, the results of the in-depth analysis of local pressures and their progression over time foster an understanding of context-related nuances, thereby shifting attention from taken-for-granted compact city qualities to the driving forces that produce beneficial compactness. The authors conclude that the diversity of drivers and pressures requires the involvement of a multiplicity of stakeholders and actors in urban planning, implementation, and management.