Asher Yair Grinberger

Increasing the realism of agent-based models requires the definition of behavioral mechanisms and rules. Knowledge regarding parameter values and even functional forms for defining this behavior is not always available, leading to arbitrariness in models. Machine learning approaches expose the dynamics underlying empirical data and can assist in overcoming this obstacle. Extending previous work, this article contributes by developing an approach that systematically considers heterogeneity among agents. The procedure replaces hand-crafted utility functions for deriving daily agent routines with reward functions learned using a profile-based training approach. This considers the unique characteristics of agents when generating trajectories. A further contribution is the development of a comprehensive feature matrix and the introduction of a trajectory termination procedure that uses an empirically-derived discount factor to verify that trajectory lengths mimic real-world patterns. We use a global positioning system trajectory dataset from the city of Jerusalem as training data. For implementation, we utilize an epidemiological urban agent-based model for a subsection of the city, calibrated to Covid-19. Results show a clear differentiation between the inverse reinforcement learning-augmented and nonaugmented models. The differences between the various profile-based implementations while more nuanced, also provide significant insights. They emphasize first, the importance of the spatial structure within the model instead of treating the city as unitary system and second, the nonlinearity of spatial utility. Avenues for realizing the full potential of this integration are identified. These include scaling up the simulation model, utilizing geometrical deep learning instead of a linear approach and defining a dynamic learning procedure for understanding mobility-related reactions to morbidity and recreating real-world results.

Informal settlements may appear chaotic, yet often follow internal orders shaped by cultural principles. However, systematic approaches to analyzing and understanding cultural-spatial codes remain lacking. This paper discusses a methodological framework for quantitatively investigating socio-spatial patterns within informal settlements and presents its implementation for Bedouin informal settlements in the Israeli Negev. The framework draws on qualitative data and morphological analysis to examine these settlements’ spatio-cultural order. It generates individual and unified measures of adherence to cultural codes, thereby revealing cultural variation across communities. Our implementation demonstrates how Bedouin communities adhere to the Bedouin Spatial Code, revealing structured spatial patterns that persist despite forced relocation and other external pressures. Distinguishing code components by their relationship to the landscape shows that those less tied to physical geography exhibit greater variation, making them more adaptable for planning interventions. This demonstrates how the framework advances the study of cultural-spatial codes, opening avenues for comparative and longitudinal analyses of socio-spatial order in informal settlements.

Agent behavior in simulation models is often mechanistic and lacking in realism. This chapter shows how machine learning (ML) techniques can be harnessed to increase behavioral realism in agent-based models (ABM). We use an inverse reinforcement learning (IRL) algorithm to derive the decision rules governing agent actions in the context of mobility in cities under COVID-19 restrictions. We combine the trained IRL algorithm with a pre-existing spatial epidemiological ABM that simulates agent behavior for real-world environments at the building level. These feed into the ABM, generating agent mobility trajectories using a Monte Carlo Markov Chain (MCMC) process. This is illustrated using a case study of COVID-19 contagion in Jerusalem city center. Given the level of spatial granularity in this approach, simulations of COVID-19 mitigation measures are feasible for different urban scales (city block, neighborhood, central business district, and so on). The chapter outlines the future challenges for generating behaviorally enhanced agent-based simulations.

The effectiveness and political feasibility of COVID-19 containment measures such as lockdowns, are contentious. This stems in part from an absence of tools for their rigorous evaluation. Common epidemiological models such as the SEIR model generally lack the spatial resolution required for micro-level containment actions, the visualization capabilities for communicating measures such as localized lockdowns and the scenario-testing capabilities for assessing different alternatives. We present an individual-level ABM that generates geo-social networks animated by agent-agent and agent-building interactions. The model simulates real-world contexts and is demonstrated for the city of Jerusalem. Simulation outputs yield much useful information for evaluating the effectiveness of lockdowns. These include network-generated socio-spatial contagion chains for individual agents, dynamic building level contagion processes and neighborhood-level patterns of COVID-19 imports and exports useful in identifying super-spreader neighborhoods. The policy implications afforded by these various outputs are discussed.

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approach.

The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the extent and nature of engagement between the academic research community and the larger communities in OSM. An analysis of OSM-related publications from 2016 to 2019 and seven interviews conducted with members of one research group engaged in OSM-related research are described. The literature analysis seeks to uncover general engagement patterns while the interviews are used to identify possible causal structures explaining how these patterns may emerge within the context of a specific research group. Results indicate that academic papers generally show few signs of engagement and adopt data-oriented perspectives on the OSM project and product. The interviews expose that more complex perspectives and deeper engagement exist within the research group to which the interviewees belong, e.g., engaging in OSM mapping and direct interactions based on specific points-of-contact in the OSM community. Several conclusions and recommendations emerge, most notably: that every engagement with OSM includes an interpretive act which must be acknowledged and that the academic community should act to triangulate its interpretation of the data and OSM community by diversifying their engagement. This could be achieved through channels such as more direct interactions and inviting members of the OSM community to participate in the design and evaluation of research projects and programmes.

Organized mapping activities within OpenStreetMap frequently lead to the production of massive amounts of data over a short period. In this article we utilize a novel procedure to identify such large‐scale data production events in the history of OpenStreetMap and analyze their patterns. We find that events account for a significant share of OpenStreetMap data and that organizational practices have shifted over time towards local knowledge‐based events and well‐organized data imports. However, regions in the “Global South” remain dependent on remote mapping events, pointing to uneven geographies of representation. We also find that events are frequently followed by periods of increased activity, with the exact nature of effects depending on contextual elements such as previous events. These findings portray organized activities as a significant and unique component which requires consideration when using OpenStreetMap data and analyzing their quality.

The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community member performance in a quantitative manner and applied it to the case study of OpenStreetMap. A set of mappers who participated in both field and remote mapping-related happenings was identified. To measure the effects of happenings, we computed attributes characterising the mappers’ contribution behaviour before and after the happenings and tested for significant impacts in relation to a control group. Results showed that newcomers to OpenStreetMap adopted a contribution behaviour similar to the contribution behaviour typical for the respective happening they attended: When contributing after the happening, newcomers who attended a remote mapping event tended to concentrate on creating new data with lower quality but high quantity in places foreign to their home region; newcomers who attended a field mapping event updated and enhanced existing local data with high accuracy. The behaviour of advanced mappers stayed largely unaffected by happenings. Unfortunately, our results did not reveal a positive effect on the community integration of newcomers through happenings.

People share data in different ways. Many of them contribute on a voluntary basis, while others are unaware of their contribution. They have differing intentions, collaborate in different ways, and they contribute data about differing aspects. Shared Data Sources have been explored individually in the literature, in particular OpenStreetMap and Twitter, and some types of Shared Data Sources have widely been studied, such as Volunteered Geographic Information (VGI), Ambient Geographic Information (AGI), and Public Participation Geographic Information Systems (PPGIS). A thorough and systematic discussion of Shared Data Sources in their entirety is, however, still missing. For the purpose of establishing such a discussion, we introduce in this article a schema consisting of a number of dimensions for characterizing socially produced, maintained, and used ‘Shared Data Sources,’ as well as corresponding visualization techniques. Both the schema and the visualization techniques allow for a common characterization in order to set individual data sources into context and to identify clusters of Shared Data Sources with common characteristics. Among others, this makes possible choosing suitable Shared Data Sources for a given task and gaining an understanding of how to interpret them by drawing parallels between several Shared Data Sources.