Corporate Editors in the Evolving Landscape of OpenStreetMap

OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community&mdash;and researchers&mdash;might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses. View Full-Text</div

Network and ABM models of SARS-CoV-2

This research investigates the role of road network infrastructure and the socioeconomics of urban space in enabling the spread of airborne disease (SARS-CoV-2). Data from Ireland’s Central Statistics Office was modelled on the national scale, modelling the relationship between road network accessibility and SARS-CoV-2 cases (SARS-CoV-2 14 day incidence rates per 100k of the population) aggregated per Local Electoral Area (LEA). We also modelled time series of SARS-CoV-2 cases in Ireland (August 2020 – November 2021), cases aggregated per 100k for each LEA, to highlight the peak and look for socioeconomic and mobility factors. On the city scale, we modelled Dublin’s road network infrastructure in order to investigate its impact on access to healthcare and the rise in SARS-CoV-2 infections. We used unsupervised learning (PCA analysis) and multivariate analysis to model urban variables and distinguish those that bear a relationship to SARS-CoV-2 cases. On the local neighborhood scale, we presented an agent-based modelling simulation of Dublin’s city center, using standard automata, agents weighted by longest line of sight, and agents weighted Per bin distance. Outcomes of a survey on SARS-CoV-2 cases and the underlying socioeconomic and spatial factors will be presented in order to understand the local dynamics of disease spread on a neighborhood scale, and distinguish socioeconomic and built environment factors that may have contributed to the rise of SARS-CoV-2 cases. These factors may include the size of household, occupation, air pollution, exposure to biohazards, dwelling conditions (age of building, records of mold), and underlying health conditions that are likely to increase vulnerability. ABM models of survey variables will be presented in order to simulate the dynamics of disease spread

Exploring multiple dimensions of conservation success : long-term wildlife trends, anti-poaching efforts and revenue sharing in Kibale National Park, Uganda

Parks are essential for protecting biodiversity and finding ways to improve park effectiveness is an important topic. We contributed to this debate by examining spatial and temporal changes in illegal activities in Kibale National Park, Uganda between 2006 and 2016 and used existing data to evaluate how the changes were correlated with the living conditions of people in neighboring communities, as well as patrolling effort. We explore the effectiveness of conservation strategies implemented in Kibale, by quantifying changes in the abundance of nine animal species over two to five decades. While uncertainty in such animal survey data are inherently large and it is hard to generalize across a 795-km2 area that encompasses diverse habitat types, data suggest an increase in animal abundance in the National Park. An increase in patrolling effort by park guards over the decade was correlated with a decline in the number of traps and snares found, which suggests patrolling helped limit resource extraction from the park. The park’s edge was extensively used for illegal forest product extraction, while the setting of snares occurred more often deeper in the forest. Perhaps counter-intuitively, increased community wealth or park-related employment in a village next to the park were positively correlated with increased illegal forest product extraction. Overall, our results suggest that the portfolio of conservation strategies used over the last two to five decades were effective for protecting the park and its animals, although understanding the impact of these efforts on local human populations and how to mitigate any losses and suffering they sustain remains an important area of research and action. It is evident that complex social, political and economic drivers impact conservation success and more interdisciplinary studies are required to quantify and qualify these dimensions

Research stations as conservation instruments provide long-term community benefits through social connections

The paper considers the benefits accruing from field research stations and how they might promote community-park relationships. In Kibale National Park (Uganda), study findings show that the presence of the research station provides long-term direct employment for 52 people, and indirect, cascading benefits for up to 720 people several kilometers away. While benefits of the research station do not eliminate community-park conflict, the long-term presence of researchers and the gains to local people associated with them is an underappreciated and important means for integrating the goals of biodiversity protection and local community investment. Benefits such as healthcare and education are also linked.Canada Research Chairs Program,Natural Science and Engineering Research Council of Canada,Fonds Québécois de la Recherche sur la Nature et les TechnologiesRathlyn Fieldwork Award,the National Geographic Society

Spatial social networks: exploring theoretical and methodological challenges

Social Network Analysis (SNA) relies on a network structure composed of nodes connected via edges to represent entities (e.g., people as nodes) and relationships (e.g., friendships as edges) between them. The nodes and edges can be augmented with multiple attribute information (e.g. age, sex, relationship type) to enhance the insights available from SNA. Even though societies are embedded in geographic space which impact the formation, maintenance, and dissolving of social ties, hitherto adding spatial considerations in SNA has received relatively less critical attention because geographic embedding reshapes the structure of network and its processes, and thus cannot be treated akin to other attribute information. This dissertation looks at the challenges and opportunities of incorporating spatial information in SNA and introduces new methodological approaches to leverage socio-spatial properties of such networks, hitherto termed Spatial Social Networks (SSNs). Introducing spatial information in SNA comes with its challenges. The first is deciding on the sophistication of the incorporation of spatial information in the social network. In response, we create a typology of existing research focused on the integration of geography and SNA. Additionally, although there is a long tradition of network analysis in Geography, SSNs require a new perspective for understanding social networks in the context of GIScience. For example, distance, community, and scale are three concepts that resonate in both fields and offers potential opportunities for understanding the socio-spatial properties that are modelled through SSNs. In SNA, networks are abstract representations of a system which model conceptual relationships (e.g. friendship, collaborations) between entities (e.g. people, organisations). Thus, unlike road networks, where both the nodes and edges have explicit spatiality, a SSN can incorporate spatial information in different ways in its node and edge structure. Thus, SSNs are not constrained to its most common manifestation of incorporating spatial information into only the nodes in the form of nominal location or (x, y) co-ordinates. We create three conceptualizations of SSNs from a single National Geographic grants dataset that incorporate spatial information differently to highlight the different ways in which spatial information can be incorporated in the node-and-edge network structure. The three-different SSN highlight varied spatial relationships latent in the dataset, and analysing them provides new insights into global and regional trends of research collaborations. Further, SNA relies on metrics to extract meaningful information about network structure from underlying topological node-and-edge structure. In SSNs with geolocated nodes, non-spatial metrics provide limited insight into the socio-spatial structure of the networks. We introduce a new set of metrics which can be used to identify important nodes in a socio-spatial context. We prove the efficacy of the new metrics on two simulated networks as well as on a real-world network of economic benefits. Finally, while SSNs are a unique way of understanding society, it provides a single dimensional view of a multi-faceted social system as it over-privileges connections above other social dimensions. Thus, SNA should be complimented with qualitative and quantitative analysis to provide complete understanding of the system under study. While use of the new metrics on the network of economic relationships originating from the research field station located at Kibale National Park helps understand the network structure and identify important individuals responsible for spreading the economic benefits through the community, additional analysis helps understand the role of the research field station in shaping the community-park relationship across space that could not be captured by only modelling the flow of economic benefits through social connections.L'analyse de Réseau Sociale (ARS) compte sur une structure de réseau de nœuds et de liens pour représenter des entités (par ex., les gens) et des relations (par ex., des amitiés). Les nœuds et les bords peuvent être enrichis avec des informations complémentaires (par ex., l'âge, le sexe, le type de relation). Bien que les sociétés se développent dans un contexte géographique qui influence la formation, le maintien et la dissolution de liens sociaux, l'ajout de considérations spatiales à ARS a reçu peu d'attention parce que l'ancrage géographique réorganise la structure du réseau et de ses processus et doit donc être traité différemment. Cette dissertation examine les défis et les opportunités d'incorporer des informations spatiales dans l'ARS et présente des nouvelles méthodologie afin de profiter des propriétés socio-spatiales d'un tel réseau, dès lors nommé Réseaux Sociaux Spatiaux (RSSs). Inclure des informations spatiales dans l'ARS présente un ensemble unique de défis. Le premier défi est associé au choix du niveau de sophistication d'informations spatiales à incorporer. En réponse, nous avons créé une typologie de la recherche existante qui porte sur l'intégration de géographie et de l'ARS selon le niveau de sophistication. Malgré une longue tradition d'analyse de réseau en géographie des phénomènes humains et physiques, la réapparition d'ARS dans des champs divers exige une nouvelle perspective pour comprendre les réseaux sociaux dans le contexte de la Science de l'Information Géographique. Par exemple, la distance, la communauté et l'échelle sont trois concepts pertinents aux deux disciplines et offre l'opportunité de comprendre les propriétés socio-spatiales qui sont modelées par des RSS. Les réseaux sont des représentations abstraites d'un système qui modélisent des relations conceptuelles entre les entités. Ainsi, un réseau social peut incorporer des informations spatiales de différentes façons dans ses nœuds et la structure des liens. Les RSSs peuvent aller au delà de l'intégrer de l'information spatiale dans nœuds sous forme de localisation nominale ou (x, y) ou de coordonnées. En utilisant une grande base de données de subventions de National Géographic, nous créons trois conceptualisations de RSS qui incorporent des informations spatiales différemments. Ces trois RSS mettent en évidence diverses relations spatiales latentes dans l'ensemble de données et leur analyse fournit de nouveaux aperçus des tendances mondiales et régionales dans la collaboration en recherche. En outre, l'ARS compte sur des métriques pour extraire des informations significatives du graphique relationnelle sous-jacent. Dans l'ARS, la métrique non-spatiale fournit un aperçu limité de la structure socio-spatiale du réseau. Nous fournissons de nouveaux métriques pour les réseaux sociaux spatiaux qui fournissent une compréhension des propriétés socio-spatiales du réseau et l'identification des nœuds importants dans un contexte socio-spatial. Nous prouvons l'efficacité des nouveaux métriques sur deux réseaux simulés et un réseau réel. Finalement, quoique unidimensionnel, les RSS offrent une façon unique de mieux comprendre un système social, en privilégiant les liens géographiques. Pour une vision globale du système à l'étude, l'ARS devrait être complété par une analyse qualitative et qualitative. L'utilisation de nouveaux métriques sur le réseau social de la station de recherche du Parc nationale Kibale facilite la compréhension de la structure du réseau et identifie les individus importants responsables du partage des avantages économiques au sein de la communauté. De plus, les analyses permettent de mieux comprendre comment la station de recherche, en fournissant une gamme de services supplémentaires, influence la relation entre le parc et la communauté. L'effet des ses services ne peut pas être perçu par une simple modélisation des flux d'avantages économiques permis par les connections sociales

OpenStreetMap as Space

Sarkar and Kay (2019). OpenStreetMap as Space In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 19-20. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.338769