Using Crowdsourcing to Improve Accessibility of Geographic Maps on Mobile Devices

The continuous growth of the use of technology and mobile applications means that more people have access to information published on the web, including geographic information. However, for visually impaired people interaction is difficult if maps are not accessible. For this reason, in this paper we analyze accessibility barriers of webpages with geographic content presented on mobile devices. With the purpose of showing an alternative to improve accessibility in these pages, this study proposes the use of a technique called crowdsourcing, i.e., a group of people that voluntarily access to webpages and provide information about physical accessibility and a general description in each map element (point, line or polygon). This description is written into the Scalable Vector Graphics Tiny (SVG Tiny) code. SVG Tiny is used to represent geographic maps with HTML. In this way, screen readers can interpret the descriptions to visually impaired people, thus making maps more accessible.This work has been partially supported by the research group "Ingeniería Web, Aplicaciones y Desarrollos (IWAD)" of the Universtiy of Alicante

A flexible framework for assessing the quality of crowdsourced data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Crowdsourcing as a means of data collection has produced previously unavailable data assets and enriched existing ones, but its quality can be highly variable. This presents several challenges to potential end users that are concerned with the validation and quality assurance of the data collected. Being able to quantify the uncertainty, define and measure the different quality elements associated with crowdsourced data, and introduce means for dynamically assessing and improving it is the focus of this paper. We argue that the required quality assurance and quality control is dependent on the studied domain, the style of crowdsourcing and the goals of the study. We describe a framework for qualifying geolocated data collected from non-authoritative sources that enables assessment for specific case studies by creating a workflow supported by an ontological description of a range of choices. The top levels of this ontology describe seven pillars of quality checks and assessments that present a range of techniques to qualify, improve or reject data. Our generic operational framework allows for extension of this ontology to specific applied domains. This will facilitate quality assurance in real-time or for post-processing to validate data and produce quality metadata. It enables a system that dynamically optimises the usability value of the data captured. A case study illustrates this framework

Topographic analysis supported by a knowledge graph: A case of ridge landscape recognition

The intrinsic connections between geographical elements are important for uncovering hidden geo-scientific laws. However, current research on terrain and landform analysis mainly focuses on the landscapes themselves, with insufficient attention to the connections between them. Therefore, this study proposes a knowledge graph approach based on geographical units (TUKG). Specifically, fi-negrained geographical units are extracted based on three types of data: remote sensing images, DEM, and contour lines. These units serve as entity nodes in the TUKG and are described by their slope and aspect. Additionally, point-based and line-based connections between geographical units are proposed based on spatial topological relationships, serving as connections between entity nodes in the TUKG. Finally, inference rules for ridge landscape problems are extracted from typical cases of ridge land-scapes to support reasoning in the TUKG. Experimental results conducted in the Yarlung Zangbo Grand Canyon in southwest China demonstrate that the TUKG can accurately infer ridge landscapes and has the potential to identify more complex terrain landscapes

Geospatial Anarchy: Managing datasets the Open Source way

OpenStreetMap (OSM) is the largest and best-known example of geospatial data creation using Volunteered Geographic Information (VGI). A large group of non-specialists joins their efforts online to create an open, worldwide map of the world. The project differs from traditional management of geospatial data on several accounts: both the underlying technology (Open Source components) and the mindset (schema-less structures using tags and changesets). We review how traditional organizations are currently using the OSM technology to meet their needs and how the mindset of OSM could be employed to traditional management of spatial datasets as well

Retrieval and interpretation of textual geolocalized information based on semantic geolocalized relations

This paper describes a method for geolocalized information retrieval from natural language text and its interpretation by assigning them geographic coordinates. A proof-of-concept implementation is discussed, along with geolocalized dictionary stored in PostGIS/PostgreSQL spatial relational database. Discussed research focuses on strongly inflectional Polish language, hence additional complexity had to be taken into account. Presented method has been evaluated with the use of diverse metrics