Data Sovereign Humans and the Information Economy: Towards Design Principles for Human Centric B2C Data Ecosystems

The ever-growing amounts of data offer companies many opportunities for data-driven-value generation which, in turn, can be multiplied by leveraging data across company boundaries in evolving data ecosystems. However, while such systems increasingly emerge in B2B environments enabling systematic sharing and utilization of “industrial data”, comparable concepts in B2C ambits have not yet prevailed. Despite the rising importance of personal data in the information economy, B2C data ecosystems represent a widely unexplored research area. To remedy this gap, the study generates design principles for human centric B2C data ecosystems to aid in their development. For this purpose, a qualitative interview study with experts of interdisciplinary domains and a structured literature review are conducted both embedded into a methodology for generating design principles. On this basis, derived design principles help to understand peculiarities of data ecosystems in B2C ambits and provide solutions to overcome their obstacles identified in the empirical investigation

ChatGPT is not a pocket calculator -- Problems of AI-chatbots for teaching Geography

The recent success of large language models and AI chatbots such as ChatGPT in various knowledge domains has a severe impact on teaching and learning Geography and GIScience. The underlying revolution is often compared to the introduction of pocket calculators, suggesting analogous adaptations that prioritize higher-level skills over other learning content. However, using ChatGPT can be fraudulent because it threatens the validity of assessments. The success of such a strategy therefore rests on the assumption that lower-level learning goals are substitutable by AI, and supervision and assessments can be refocused on higher-level goals. Based on a preliminary survey on ChatGPT's quality in answering questions in Geography and GIScience, we demonstrate that this assumption might be fairly naive, and effective control in assessments and supervision is required.Comment: 8 pages, 2 figure

Assemble geo-analytical questions through a Blockly-based natural language interface

Natural language Interfaces (NLIs) have the ability to make Geographic Information Systems more accessible for interdisciplinary researchers or any inexperienced users. However, the majority of research on NLIs for GIS explored NLIs for visualization or spatial data retrieval. Research on NLIs for geo-analytical questions is still lacking. Google Blockly, an open-source JavaScript library, is frequently used for developing visual programming editors for young students learning programming languages. Students set up program functions by selecting and assembling the programming blocks and the Blockly system automatically translates the block stacks into different programming languages. Similarly, we present a Blockly-based interface that generates a question depending on the blocks the user has assembled. It can be seen that a Blockly-based interface not only naturally represents syntactic structures in geo-analytical questions but also well assists users in familiarizing the blocks and generating clear and complete questions. A comprehensive usability study is still necessary to better evaluate the interface’s performance

Ontology of core concept data types for answering geo-analytical questions

In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources

A Reference System Architecture with Data Sovereignty for Human-Centric Data Ecosystems

Since the European information economy faces insufficient access to and joint utilization of data, data ecosystems increasingly emerge as economical solutions in B2B environments. Contrarily, in B2C ambits, concepts for sharing and monetizing personal data have not yet prevailed, impeding growth and innovation. Their major pitfall is European data protection law that merely ascribes human data subjects a need for data privacy while widely neglecting their economic participatory claims to data. The study reports on a design science research (DSR) approach addressing this gap and proposes an abstract reference system architecture for an ecosystem centered on humans with personal data. In this DSR approach, multiple methods are embedded to iteratively build and evaluate the artifact, i.e., structured literature reviews, design recovery, prototyping, and expert interviews. Managerial contributions embody novel design knowledge about the conceptual development of human-centric B2C data ecosystems, considering their legal, ethical, economic, and technical constraints

The observational roots of reference of the semantic web

Shared reference is an essential aspect of meaning. It is also indispensable for the semantic web, since it enables to weave the global graph, i.e., it allows different users to contribute to an identical referent. For example, an essential kind of referent is a geographic place, to which users may contribute observations. We argue for a human-centric, operational approach towards reference, based on respective human competences. These competences encompass perceptual, cognitive as well as technical ones, and together they allow humans to inter-subjectively refer to a phenomenon in their environment. The technology stack of the semantic web should be extended by such operations. This would allow establishing new kinds of observation-based reference systems that help constrain and integrate the semantic web bottom-up

The Rebirth of Urban Subcenters: How Subway Expansion Impacts the Spatial Structure and Mix of Amenities in European Cities

Why do some neighborhoods thrive, and others do not? While the importance of the local amenity mix has been established as a key determinant of local livability, its link to urban transport infrastructure remains understudied, partially due to a lack of data. Using spatiotemporal social media data from Foursquare, we analyze the impact of metro stations which opened between 2014 and 2017 on the amenity mix of surrounding neighborhoods in nine European cities: Rome, Milan, Barcelona, Budapest, Warsaw, Sofia, Vienna, Helsinki, and Stuttgart. Thereby, we study three properties of the local amenity mix: its density, multifunctionality, and the heterogeneity between amenity types. For this purpose, we propose a new measurement of multifunctionality, which calculates the entropy of the locally present amenity set incorporating the degree of similarity between amenity types. For causal inference, we use Difference-in-Difference Regression based on Propensity Score Matching and Entropy Balancing. Our findings show that in most cities, subway expansion had a significant positive impact on the local amenity density and multifunctionality and that especially the social amenities—Arts & Entertainment, Restaurants and Nightlife—responded strongly. Moreover, considerable agglomeration forces seem to prevail, causing existing subcenters to benefit most from new metro stations

ChatGPT is not a pocket calculator: Problems of AI-chatbots for teaching Geography

The recent success of large language models and AI chatbots such as ChatGPT in various knowledge domains has a severe impact on teaching and learning Geography and GIScience. The underlying revolution is often compared to the introduction of pocket calculators, suggesting analogous adaptations that prioritize higher-level skills over other learning content. However, using ChatGPT can be fraudulent because it threatens the validity of assessments. The success of such a strategy therefore rests on the assumption that lower-level learning goals are substitutable by AI, and supervision and assessments can be refocused on higher-level goals. Based on a preliminary survey on ChatGPT's quality in answering questions in Geography and GIScience, we demonstrate that this assumption might be fairly naive, and effective control in assessments and supervision is required