Optimizing Public Passenger Transport in Bucharest and the Metropolitan Area

The paper aims to be a model of analysis on passenger transport management for Bucharest and the metropolitan area, in order to stimulate the economic development of the city by supporting economic activities of local interest, by increasing the mobility of the transport system, economic activities that benefit local communities and that do not adversely affect people's health or the environment. The analysis presented proposes the use of geospatial information systems for urban traffic management and the construction of traffic simulation models.

Landscaper: A Modeling System for 3D Printing Scale Models of Landscapes

Landscape models of geospatial regions provide an intuitive mechanism for exploring complex geospatial information. However, the methods currently used to create these scale models require a large amount of resources, which restricts the availability of these models to a limited number of popular public places, such as museums and airports. In this paper, we have proposed a system for creating these physical models using an affordable 3D printer in order to make the creation of these models more widely accessible. Our system retrieves GIS relevant to creating a physical model of a geospatial region and then addresses the two major limitations of affordable 3D printers, namely the limited number of materials and available printing volume. This is accomplished by separating features into distinct extruded layers and splitting large models into smaller pieces, allowing us to employ different methods for the visualization of different geospatial features, like vegetation and residential areas, in a 3D printing context. We confirm the functionality of our system by printing two large physical models of relatively complex landscape regions.

Application of Geospatial Methods in Evaluating Environmental Interventions and Related Socioeconomic Benefits

Environmental interventions underpin the Sustainable Development Goals (SDGs) and the Rio Conventions. The SDGs are integrated and embody all three aspects of sustainable development—environmental, social, and economic—to capture the interlinkages among the three areas. The Rio Conventions—on biodiversity, climate change, and desertification, also intrinsically linked—operate in the same ecosystems and address interdependent issues, and represent a way of contributing to the SDGs. Assessing the results of environmental interventions and the related socioeconomic benefits is challenging due to their complexity, interlinkages, and often limited data. The COVID-19 crisis has also necessitated creativity to ensure that evaluation’s critical role continues during the crisis. Satellite and other geospatial information, combined with existing survey data, leverage open-source and readily available data to determine the impact of projects. Working with geospatial data helps maintain flexibility and can fill data gaps without designing new and often expensive data tools for every unique evaluation. Using data on interventions implemented by the Global Environment Facility in biodiversity, land degradation, and climate change, we present the application of geospatial approaches to evaluate the relevance, efficiency, and effectiveness of interventions in terms of their environmental outcomes and observable socioeconomic and health co-benefits.

The Role of the Geospatial Information System (GIS) in Achieving the Sustainable Development Goals (SDGs)

The Sustainable Development Goals (SDGs) represent an innovative strategy to transform the socio-economic and environmental aspects of communities. Sustainable development provides the communities with a set of substantial challenges that are totally geospatial in concept and practice. Most of these challenges can be identified, examined, and visualized within a spatial framework. Despite of noteworthy progress in geospatial information system and science, the lack of comprehensive impressions in planning necessitates the integrative role of geospatial information. This study aims to investigate this role in contributing to SDGs by describing each single goal and following objectives. Furthermore, spatial and non-spatial issues regarding every specific SDG will be accurately discussed to determine the spatial aspects in practice. In this way, the communities will be empowered by unique opportunities to integrate and represent geospatial information into the global agenda in a specific manner, specifically in contributing data resources toward measuring and monitoring the 17 SDGs.

Integration of Different Mobility Behaviors and Intermodal Trips in MATSim

MATSim is an open-source simulation framework for mesoscopic traffic simulations that has gained popularity in recent years. In this paper, we present a MATSim model for the city of Vienna, with a particular emphasis on the intermodal routing framework used to create agent trips, and the development of a utility function to specify different agents’ mode preferences. To create agent activity chains, we use mobility diaries from the national transportation survey in Austria and disaggregate the available geospatial information to best fit the reported travel times. The novelty of the intermodal framework is the ability to create trips that do not consist of only one mode of transportation, but to also include bicycle, car, and demand-responsive transport (e.g., cab, car sharing) trips in combination with public transportation. To represent the different mobility behaviors of agents, we divide the population into groups and assign them different utility functions for transportation modes according to their socio-demographic characteristics. After presenting the validation of the model, we discuss ways to improve the model.

An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.

The Grouting Process as an Innovative Tool for the Assessment of the State of Preservation and Internal Features of the Holy Aedicule of the Holy Sepulchre

Grouting of historic structures is a common procedure in many restoration projects, as the masonry in many cases requires additional strengthening. However, grouting of complex historic structures can also provide important information regarding the construction phases and the state of preservation of the internal structure of a monument, which may not be visible by the naked eye. This requires an innovative approach in order to reveal these aspects. In the current research, the data recorded from the grouting of the Holy Aedicule are implemented and analyzed, in order to obtain information regarding the construction phases of the complex Holy Aedicule structure, as well as information regarding the state of preservation of the internal structure behind the marble cladding that encloses it. The correlation of detailed grouting data with geospatial information allows for a more detailed analysis, which, coupled with ground-penetrating radar prospections, can provide critical information regarding the features of the internal structure. The results highlight the importance of this correlation to reveal information that may not be obtained through a typical approach. Thus, this study allowed for the development of an evolved interdisciplinary approach for the management of grouting data in a 2.5D environment, which can be applied in other historic structures and buildings.

Neural factoid geospatial question answering

Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.

Is it the “outstanding universal value" of heritage really global? : Measuring reputation using social media

Web 2.0 and social media play an important role nowadays in our society, not only from a user perspective, but also on an academic perspective. The data and information production based on the user-generated content is an important source to conduct scientific studies, specially the new geospatial information that exists due to the widespread of technological devices that capture the geospatial data. The main objective of this research is to assess if we can measure the brand awareness, with a focus in the reputation component, using geospatial user-generated content with an approach as a geographic problem. In this paper is identified the main research question and objectives, the methodological approach and the expected results regarding this Doctorate Thesis in Information Management.<br><div>Keywords: geographic information, social media, web 2.0, citizen sensing, ambient information systems, GIS, world heritage, brand awareness, reputation<br></div>