Developing open source materials for foundation phase: Diverse spaces for collaboration

Determining a space for collaborative activities involves the creation of a unique space which encourages reciprocal learning and the creation of products that can benefit all participants. The aim of this article was to explore the experiences of group members in this collaborative space. The group consisted of lecturers from different Higher Education Institutions (HEIs) tasked to create quality open-source materials for teaching and learning Literacy in Foundation Phase at HEIs (coming together and work in one space) in the endeavour to connect and share collaboratively. Selected components of the theories of space suggested by Lefebvre and Soja were employed to explain the data which were gleaned through interviews with all participants in the group. This paper makes two important contributions: firstly, it substantively presents a deeper understanding of the experiences of the group in collaborative work and secondly, through the use of a theoretical framework on space, offers insights into the nature of collaboration within diverse spaces

Geospatial Semantics

Geospatial semantics is a broad field that involves a variety of research areas. The term semantics refers to the meaning of things, and is in contrast with the term syntactics. Accordingly, studies on geospatial semantics usually focus on understanding the meaning of geographic entities as well as their counterparts in the cognitive and digital world, such as cognitive geographic concepts and digital gazetteers. Geospatial semantics can also facilitate the design of geographic information systems (GIS) by enhancing the interoperability of distributed systems and developing more intelligent interfaces for user interactions. During the past years, a lot of research has been conducted, approaching geospatial semantics from different perspectives, using a variety of methods, and targeting different problems. Meanwhile, the arrival of big geo data, especially the large amount of unstructured text data on the Web, and the fast development of natural language processing methods enable new research directions in geospatial semantics. This chapter, therefore, provides a systematic review on the existing geospatial semantic research. Six major research areas are identified and discussed, including semantic interoperability, digital gazetteers, geographic information retrieval, geospatial Semantic Web, place semantics, and cognitive geographic concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova, and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information Systems, Elsevier. Oxford, U

Using Geographic Relevance (GR) to contextualize structured and unstructured spatial data

Geographic relevance is a concept that has been used to improve spatial information retrieval on mobile devices, but the idea of geographic relevance has several potential applications outside of mobile computing. Geographic relevance is used measure how related two spatial entities are using a set of criteria such as distance between features, the semantic similarity of feature names or clustering pattern of features. This thesis examines the use of geographic relevance to organize and filter web based spatial data such as framework data from open data portals and unstructured volunteer geographic information generated from social media or map-based surveys. There are many new users and producers of geographic information and it is unclear to new users which data sets they should use to solve a given problem. Governments and organizations also have access to a growing volume of volunteer geographic information but current models for matching citizen generated information to locations of concern to support filtering and reporting are inadequate. For both problems, there is an opportunity to develop semi-automated solutions using geographic relevance metrics such as topicality, spatial proximity, cluster and co-location. In this thesis, two geographic relevance models were developed using Python and PostgreSQL to measure relevance and identify relationships between structured framework data and unstructured VGI in order to support data organization, retrieval and filtering. This idea was explored through two related case studies and prototype applications. The first study developed a prototype application to retrieve spatial data from open data portals using four geographic relevance criteria which included topicality, proximity, co-location and cluster co-location. The second study developed a prototype application that matches VGI data to authoritative framework data to dynamically summarize and organize unstructured VGI data. This thesis demonstrates two possible approaches for using GR metrics to evaluate spatial relevance between large data sets and individual features. This thesis evaluates the effectiveness of GR metrics for performing spatial relevance analysis and it demonstrates two potential use cases for GR