Complex adaptive blended learning for higher GIS education: A theory-driven pedagogy moving forward after the pandemic

None of the prior studies implementing blended learning (BL) for GIS education has explored pedagogical theories to support BL design, and none coupled blended GIS learning practice with theories. Based on the theoretical framework of complex adaptive blended learning system (CABLS), this article presents a holistic and systematic view of BL for higher GIS education (termed as CABLS-GIS), where its key components are tightly integrated as an integral whole. The framework was implemented for an introductory-level GIS course to empirically evaluate the effectiveness of a BL design from students' perspectives. Most students involved in the implementation agreed that the BL enhanced their learning outcomes. They were mostly positive about the BL and motivated to use the BL materials. Incorporating more BL materials, especially practical ones, and active learning activities were suggested by them. A few negative comments about the BL suggested a thorough consideration of individual learners' characteristics.     

Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience

ABSTRACT

More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors.     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

GIScience research challenges for emergency management in Southeast Asia

Natural Hazards - The study examines the current application of geospatial information technologies and highlights the challenges of and constraints on GIScience research for emergency management...     

Exploratory Spatiotemporal Analysis in Risk Communication during the MERS Outbreak in South Korea

The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law.     

Integrative representation and inference of qualitative locations about points,lines, and polygons

Qualitative knowledge representation of spatial locations and relations is popular in many text-based media, for example, postings on social networks, news reports, and encyclopedia, as representing qualitative spatial locations is indispensable to infer spatial knowledge from them. However, an integrative model capable of handling direction-based locations of various spatial objects is missing. This study presents an integrative representation and inference framework about direction-based qualitative locations for points, lines, and polygons. In the framework, direction partitions of different types of reference objects are first unified to create a partition consisting of cells, segments, and corners. They serve as a frame of reference to locate spatial objects (e.g., points, lines, and polygons). Qualitative relations are then defined to relate spatial objects to the elements in a cell partition, and to form the model of qualitative locations. Last, based on the integrative representation, location-based reasoning mechanism is presented to derive topological relations between objects from their locations, such as point–point, line–line, point–line, point–polygon, line–polygon, and polygon–polygon relations. The presented model can locate any type of spatial objects in a frame of reference composed of points, lines, and polygons, and derive topological relations between any pairs of objects from the locations in a unified method.     

Utilizing fuzzy set theory to assure the quality of volunteered geographic information

This paper presents a fuzzy system to assure the quality of volunteered geographic information (VGI) collected for the purposes of species surveillances. The system uses trust as a proxy of quality. It defines the trust using both the provenance of user expertise and the fitness of geographic context and quantifies it using fuzzy set theory. The system was applied to a specific scenario—VGI-based crop pest surveillance—to demonstrate its usefulness in handling VGI quality. A case study was conducted in Jiangxi province of China, where location-based rice pest surveillance reports generated by the local farmers were collected. A field pest survey was conducted by the local pest management experts to verify the farmer-generated reports, and the survey results were used as ground truth data. The quality of the farmer-generated reports were also assessed through the fuzzy system and compared to the pest survey results. It was observed that the degree to which these two sets of results agreed to each other was satisfactory.     

Comparing English,Mandarin, and Russian hydrographic and terrain categories

The paper compares hydrographic and terrain categories in the geospatial data standards of the United States, Taiwan, and Russian Federation where the dominant languages used are from different language families. It aims to identify structural and semantic differences between similar categories across three geospatial data standards. By formalizing the data standard structures and identifying the properties that differentiate sibling categories in each geospatial data standard using well-known formal relations and quality universals, we develop a common basis on which hydrographic and terrain categories in the three data standards can be compared. The result suggests that all the three data standards structure categories with a mixture of relations even though most of them are well-known relations in top-level ontologies. Similar categories can be found across all the three standards. Cases of categories from different standards carrying identical meaning are rare. Partial overlaps in the meaning of the similar categories can be a direct result of different quality universals at work in defining and distinguishing these categories, or in the case of these categories being ordered by size, the threshold values for distinguishing the categories are ambiguous and language-dependent. Understanding these differences avoids incorrect mappings of categories in multilingual applications. More importantly, it provides a starting point for more effective mapping between hydrographic and terrain categories between English, Mandarin, and Russian.     

Cross-Linguistic Research on Landscape Categories Using GEOnet Names Server Data: A Case Study for Indonesia and Malaysia

This article explores how toponym databases (gazetteers) can be used to examine the ethnophysiography hypothesis, which states that people from different language groups/cultures have different ways of conceptualizing landscape. Toponyms of two classes of eminences, mountain and hill, in Malaysia and Indonesia where the dominant languages are highly cognate were extracted from the National Geospatial-Intelligence Agency (NGA) GEOnet Names Server (GNS) and analyzed using a heuristics-based geomorphometry approach. The result revealed that gunung and bukit, the main terms in Malay or Indonesian languages similar to mountain and hill in English, are used differently in the two countries: Bukit and gunung are used interchangeably for eminence features in Indonesia regardless of size (area extent), but the use of gunung is more strict for Malaysia to refer to larger eminence features. Furthermore, in Indonesia, the NGA GNS can readily be used to identify generic terms in local languages and dialects; we show that in some cases, size of eminence is distinguished with different generic terms and that spatial proximity might affect which term is used. The result suggests that NGA GNS is a promising set of data to explore ethnophysiography research questions, and can be a valuable exploratory tool for guiding detailed ethnophysiography work.