Proceedings of the Academic Track at State of the Map 2020

Proceedings of the Academic Track at State of the Map 202

Proceedings of the Academic Track at the State of the Map 2020

Proceedings of the Academic Track at the State of the Map 2020 - Online (originally planned in Cape Town, South Africa) July 4-6, 2020

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Not at Home on the Range: Peer Production and the Urban/Rural Divide

ABSTRACT Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions

Not at Home on the Range: Peer Production and the Urban/Rural Divide

ABSTRACT Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Understanding Geographic Bias in Crowd Systems

University of Minnesota Ph.D. dissertation. December 2017. Major: Computer Science. Advisors: Loren Terveen, Brent Hecht. 1 computer file (PDF); ix, 159 pages.Crowd platforms are increasingly geographic, from the sharing economy to peer production systems like OpenStreetMap. Unfortunately, this means that existing geographic advantages or disadvantages (e.g. by income, urbanness, or race) may also impact these crowd systems. This thesis focuses on two primary themes: (1) how these geographic advantages and disadvantages interact with crowd platform services, and (2) how people’s geographic behavior within these platforms may lead to these biases being reflected. The first chapter in my thesis finds that sharing economy services fare less well in low-income, non-white, and more suburban areas. This chapter introduces the spatial Durbin model to the field of HCI, and shows that geographic factors like distance, socioeconomic status and demographics inform where sharing economy workers provide service. The second chapter in my thesis provides focuses on people in peer production communities contribute geographic content. By considering peer production as a spatial interaction process, this study finds that some kinds of content tend to be produced much more locally than others. Finally, my third contribution focuses on individual contributor behavior, and shows geographic “born, not made” trends. People tend to be consistent in the places, and kinds of places (urban, and non-high poverty counties) they contribute. The findings of this third study help identify mechanisms for how geographic biases may come about. Looking forward, my work helps inform an exciting agenda of future work, including building systems that provide individual crowd members sufficient geographic context to counteract these worrying geographic biases