Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

Hybrid cities and new working spaces – The case of Oslo

Recent decades have seen the emergence of hybrid models of living and working associated typologies. These developments have been analysed from the perspective of different disciplines, each with their own interpretation of this phenomenon. Planning and architecture have addressed hybridization as a specific form of interaction between spatio-functional features (such as mixed use, multi-functionality and flexibility) and social features (such as formal and informal interactions and the spontaneous appropriation of spaces) or have sometimes simply focused on the spatio-functional dimension in urban spaces. Studies from other disciplines (e.g. mobility networks, transportation, sociology and information technology) have shown that hybrid spaces cannot exist without access to digitalization technologies. Such technologies are accelerating hybridization processes. This study examines the complex and layered phenomenon of hybridization as a possible combination of (or interaction between) spatio-functional, social and digital features within the planning debate and related fields. Most of the case studies explored by scholars so far have focused on interactions occurring between residential, social and recreational functions, but working functions are playing an increasingly important role. Furthermore, the COVID-19 pandemic has accelerated the development of new forms of hybridity in cities. As a consequence, the rising use of hybrid (on-site and on-line) working practices, planners, policy makers and stakeholders, as well as scholars, have increasingly discussed the concept of hybridization. In this context, various hybrid typologies of urban spaces have materialized in forms such as new working spaces (NWS) which include co-working spaces, incubators, as well as some cafés and multi-functional public libraries, which have recently provided working spaces. This paper focuses on the evolving concept of hybridity from the planning perspective. Based on five hybrid NWS including their surrounding neighbourhoods in Oslo, it provides empirical evidence for an understanding of the phenomenon that may support the development of hybrid spaces and buildings and develops suggestions for planning strategies. © 2022 The Author

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis