Abstract
The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there are few examples of comprehensive data processing and analysis systems that link together these tools for geographic analyses. This paper develops a general approach to the development of spatial information context from unstructured and informal web data sources through the joint analysis of the data’s thematic, spatial, and temporal properties. We explore the utility of this derived contextual information through a case study into maritime surveillance. Extraction and processing techniques such as toponym extraction, disambiguation, and temporal information extraction methods are used to construct a semi-structured maritime context database supporting global scale analysis. Geographic, temporal, and thematic content were analyzed, extracted and processed from a list of information sources. A geoweb interface is developed to allow user visualization of extracted information, as well as to support space-time database queries. Joint keyword clustering and spatial clustering methods are used to demonstrate extraction of documents that relate to real world events in official vessel information data. The quality of contextual geospatial information sources is evaluated in reference to known maritime anomalies obtained from authoritative sources. The feasibility of automated context extraction using the proposed framework and linkage to external data using standard clustering tools is demonstrated.
Highlights
The proliferation of online and streaming spatial information sources has created new opportunities for social and natural sciences, and by extension, geographic information science [1]
For more ephemeral sources of VGI such as geosocial data, analysis has been mostly limited to mapping distributions (e.g., [8,9]), tracking population-level trends (e.g., [10]), identifying points of interest (e.g., [11,12]), and extracting place-related contextual information from geocoded tweets (e.g., [13]), Flickr images (e.g., [14,15,16]), or other sources (e.g., [17])
The spatial analysis of web documents has not been considered fully within this literature, leaving the tools for extraction and data modelling somewhat disjointed from the analytical methods needed to understand these data
Summary
The proliferation of online and streaming spatial information sources has created new opportunities for social and natural sciences, and by extension, geographic information science [1]. In the research on volunteered geographic information (VGI) in recent years, advanced analysis has been mostly limited to platforms with a robust data model such as Open Street Maps [5] and highly focused on issues of data quality [6,7]). For more ephemeral sources of VGI such as geosocial data, analysis has been mostly limited to mapping distributions (e.g., [8,9]), tracking population-level trends (e.g., [10]), identifying points of interest (e.g., [11,12]), and extracting place-related contextual information from geocoded tweets (e.g., [13]), Flickr images (e.g., [14,15,16]), or other sources (e.g., [17]). The spatial analysis of web documents has not been considered fully within this literature, leaving the tools for extraction and data modelling somewhat disjointed from the analytical methods needed to understand these data. We aim to develop an analytical approach for web documents obtained through geographic information extraction methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
