Place descriptions are used in everyday communication as a common way to convey spatial information. Processing the information from place descriptions poses multiple significant challenges because these descriptions are written in natural language. In particular, corpora of place descriptions provide a plethora of human spatial knowledge beyond geographical information system, even if these descriptions refer to the same places in various ways. This article focuses on resolving ambiguous or synonymous place names from place descriptions by exploring the given relationships with other spatial features. It matches place names from multiple descriptions by developing a novel labelled graph matching process that relies solely on the comparison of string, linguistic and spatial similarities between identified places. This process uses unstructured place descriptions as an input, and produces a composite place graph with qualitative spatial relations from the descriptions. The performance of this novel process exceeds current toponym resolution by coping with non-gazetteered places.

Similarity matching for integrating spatial information extracted from place descriptions

Distributed and Parallel Computing, Monir H. Sharker and Hassan A. Karimi GEOSS Clearinghouse: Integrating Geospatial Resources to Support the Global Earth Observation System of Systems, Chaowei Yang, Kai Liu, Zhenlong Li, Wenwen Li, Huayi Wu, Jizhe Xia, Qunying Huang, Jing Li, Min Sun, Lizhi Miao, Nanyin Zhou, and Doug Nebert Using a Cloud Computing Environment to Process Large 3D Spatial Datasets, Ramanathan Sugumaran, Jeffrey Burnett, and Marc P. Armstrong Building Open Environments to Meet Big Data Challenges in Earth Sciences, Meixia Deng and Liping Di Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era, Zhong Liu, Dana Ostrenga, William Teng, and Steven Kempler Algorithmic Design Considerations for Geospatial and/or Temporal Big Data, Terence van Zyl Machine Learning on Geospatial Big Data, Terence van Zyl Spatial Big Data: Case Studies on Volume, Velocity, and Variety, Michael R. Evans, Dev Oliver, Xun Zhou, and Shashi Shekhar Exploiting Big VGI to Improve Routing and Navigation Services, Mohamed Bakillah, Johannes Lauer, Steve H.L. Liang, Alexander Zipf, Jamal Jokar Arsanjani, Amin Mobasheri, and Lukas Loos Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts, Jianting Zhang Geoinformatics and Social Media: New Big Data Challenge, Arie Croitoru, Andrew Crooks, Jacek Radzikowski, Anthony Stefanidis, Ranga R. Vatsavai, and Nicole Wayant Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern, Roland Assam and Thomas Seidl Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web, Steve H.L. Liang and Chih-Yuan Huang OGC Standards and Geospatial Big Data, Carl Reed Index

Big Data: Techniques and Technologies in Geoinformatics

Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects’ historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area.

Measure of Landmark Semantic Salience through Geosocial Data Streams

Landmarks provide the most predominant navigation cue for pedestrian navigation. Very few navigation data models in the geographical information science and transportation communities support modeling of landmarks and use of landmark-based route instructions for pedestrian navigation services. This article proposes a landmark-based pedestrian navigation data model to fill this gap. This data model can model landmarks in several pedestrian navigation scenarios buildings, open spaces, multimodal transportation systems, and urban streets. This article implements the proposed model in the ArcGIS software environment and demonstrates two typical pedestrian navigation scenarios: 1 a multimodal pedestrian navigation environment involving bus lines, parks, and indoor spaces and 2 a subway system in a metropolitan environment. These two scenarios illustrate the feasibility of the proposed data model in real-world environments. Further improvements of this model could lead to more intuitive and user-friendly landmark-based pedestrian navigation services than the functions supported by current map-based navigation systems.

A GIS data model for landmark-based pedestrian navigation

With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) contains a vast amount of geospatial information that can be tapped into for identifying these landmark features. This paper presents a prototype navigation service that extracts landmarks suitable for navigation instructions from the OSM dataset based on several metrics. This is coupled with a short comparison of landmark availability within OSM, differences in routes between locations with different levels of OSM completeness and a short evaluation of the suitability of the landmarks provided by the prototype. Landmark extraction is performed on a server-side service, with the instructions being delivered to a pedestrian navigation application running on an Android mobile device.

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

This article addresses the problem of incorporating cognitively salient landmarks in computer-generated navigation instructions. On the basis of a review of the existing literature in the domain of navigation with landmarks, the article develops algorithms for generating routing instructions that include references to landmarks. The most basic algorithm uses a new weighting model to annotate simple routes with references to landmarks. A key novel feature of this algorithm is that it depends only on commonly available data and generic capabilities of existing web mapping environments. A suite of extensions are also proposed for improving the cognitive ergonomics of the basic landmark instructions. A case study, implemented within a national online routing system, demonstrates practicality of the approach. The article then concludes by reviewing a range of further issues for future work.

Michelle Robinson

Papers

Including landmarks in routing instructions