Journal of Spatial Information Science

Distributed spatial data sharing: a new model for data ownership and access control

Majid Hojati, Rob Feick, Steven Roberts, Carson Farmer, Colin Robertson — Thu, 21 Dec 2023 00:00:00 +0000

With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.

Maximizing the value of a volunteer: A novel method for prioritizing humanitarian VGI activities

Kirsty Watkinson, Jonathan Huck, Angela Harris — Thu, 21 Dec 2023 00:00:00 +0000

As a consequence of their reliance on a scarce volunteer resource, humanitarian mapping organizations must prioritize their mapping activities. For mapping in anticipation of a crisis or mapping in support of long-term crises, the only method available to organizations is an estimation of the "completeness" of the map, with organizations directing volunteers to map areas where data are missing. Whilst this method is suitable for organizations that focus on general map improvement, for those who create data for a specific reason (e.g., drinking water provision) the method is sub-optimal. In this article, we present a new method of humanitarian mapping prioritization, that considers the purpose of map data collection. The method identifies locations where contributions by volunteers are expected to have the biggest impact on the desired use of the map data and therefore maximizes the value gained from volunteer contributions. We explain our method using the example of measuring distance to healthcare and demonstrate its superior ability to consider the context of map data over generic estimations of map "completeness". Our method provides humanitarian mapping organizations with an easily reproducible and low cost method and an opportunity to make better informed decisions about mapping prioritization, when the purpose of map data collection is known. Using our method, organizations will be able to maximize the value gained from a scarce volunteer resource and increase the efficiency of humanitarian map data production.

Procedural metadata for geographic information using an algebra of core concept transformations

Niels Steenbergen, Eric Top, Enkhbold Nyamsuren, Simon Scheider — Thu, 21 Dec 2023 00:00:00 +0000

Transformations are essential for dealing with geographic information. They are involved not only in the conversion between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relation types, and a set of basic transformations described in terms of function signatures that use such types. We enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions and inferring goal concepts across a workflow. In this article, we show how such procedural metadata can be used to retrieve workflows based on task descriptions derived from geo-analytical questions. Transformations can be queried independently from their implementations or data formats.

More is less - Adding zoom levels in multi-scale maps to reduce the need for zooming interactions

Marion Dumont, Guillaume Touya, Cécile Duchêne — Thu, 21 Dec 2023 00:00:00 +0000

When you zoom in or out of current multi-scale cartographic applications, it is common to feel lost and disoriented for a few seconds because dimensions and map symbols have changed. To make the exploration of these multi-scale maps more fluid, one option is to design maps where the transformations due to scale change are more progressive. This paper proposes to use cartographic generalization techniques to design these multi-scale maps with additional intermediate scales to improve progressiveness. These more progressive maps are tested in a user study with a task requiring multiple zooms in and out. The users perform better with the progressive maps, and in particular, the total quantity of required zooming is reduced compared to maps without additional intermediate scales. However, the survey is not fully conclusive on task performance due to the complexity of such a survey with real maps. This difficulty in assessing how well progressive map generalisation reduces disorientation is discussed and guidelines are proposed to design further studies.

Assessing the influence of indoor mapping sources for indoor spatial analysis of physical distancing

Alan Murray, Jiwon Baik, Hannah Malak — Thu, 21 Dec 2023 00:00:00 +0000

GIScience and spatial information contributions to indoor mapping and navigation are many, but there remain significant challenges. Indoor environments are where people spend most of their time, socializing, working, learning, exercising, etc. During times of emergencies, disease outbreaks, and crises, indoor management and planning must be prepared to handle such events, yet doing so is often hindered by a lack of supporting spatial information and appropriate analytics. This paper focuses on COVID-19 mitigation measures to reduce disease transmission through physical distancing in indoor spaces, such as classrooms, offices, dining commons, restaurants, and entertainment venues. Geographical data to support indoor environments is discussed, particularly issues of acquisition, spatial data uncertainty, and implications for spatial analytics. Planning for classroom physical distancing on a university campus highlights capabilities, issues, and challenges, with a comparison made between previous studies relying on architectural data and more precise information obtained using LiDAR.

Reimagining GIScience education for enhanced employability

Hongyu Zhang — Thu, 21 Dec 2023 00:00:00 +0000