Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

A sketch planning methodology for determining interventions for bicycle and pedestrian crashes: an ecological approach

Bicycle and pedestrian safety planning have recently been gaining increased attention. With this focus, however, comes increased responsibilities for planning agencies and organizations tasked with evaluating and selecting safety interventions, a potentially arduous task given limited staff and resources. This study presents a sketch planning framework based on ecological factors that attempts to provide an efficient and effective method of selecting appropriate intervention measures. A Chicago case study is used to demonstrate how such a method may be applied

Review on the Application of Geographical Information Systems (GIS) in Veterinary Medicine

Geographical Information System (GIS) is a computer system that displays stored digital data developed over the last decade. GIS is a platform consisting of hardware, software, data and people and encompasses a fundamental and universally applicable set of value-added tools for input, transforming, data management and storage, analyzing, and output information that are geographically referenced. GIS can be applied to different veterinary activities. It can help to understand and explain the dynamics and spreading pattern of a disease and increase the speed of response in the case of a disease emergency. In an outbreak of a disease it could make the management of the situation easier, and it could also provide a tool to evaluate different strategies to prevent the spread of infectious diseases. The following areas in which GIS and special GIS-functions could be incorporated are presented: surveillance and monitoring of animal diseases, recording and reporting information, epidemic emergency, modeling disease spread, and planning control strategies. The technology has many features which make it ideal for use in animal disease control, including the ability to store information relating to demographic and causal factors and disease incidence on a geographical background, and a variety of spatial analysis functions

Geospatial Artificial Intelligence (GeoAI): Applications in Health Care

GeoAI is a new emerging research area that refers to set of technologies that integrate AI technology with a diversity of GIS (Geographic Information System) techniques. The present study observed that GeoAI goes beyond current GIS expectations and into the domain of possibility in the not-too-distant future. This emerging interdisciplinary science will lead us to sustainable decisions and explore the most suitable solutions to the existing problems. GeoAI has the potential to transform current geography and geomatics programs by incorporating a GeoAI dimension into modern GIS curricula. In this review, we have studied the application GeoAI in various healthcare fields. GeoAI has the potential to revolutionize healthcare, public health, infectious disease control, disaster aid, and the achievements of Sustainable Development Goals (SDG). in healthcare, GeoAI can help with disease diagnosis, treatment planning, and resource allocation. In public health, it can aid in disease surveillance, emergency response planning, and identifying health disparities. In infectious disease control, GeoAI can help predict and track disease outbreaks and support vaccination campaigns. In disaster aid, GeoAI can provide real time data on environmental hazards and their impact on public health. In achieving Sustainable Development Goals, it can support in land use planning, urban development, and resource allocation to promote health and environmental sustainability. Overall GeoAI has the potential to transform multiple sectors and improve the well-being of populations worldwide

Mapping environmental injustices: pitfalls and potential of geographic information systems in assessing environmental health and equity.

Geographic Information Systems (GIS) have been used increasingly to map instances of environmental injustice, the disproportionate exposure of certain populations to environmental hazards. Some of the technical and analytic difficulties of mapping environmental injustice are outlined in this article, along with suggestions for using GIS to better assess and predict environmental health and equity. I examine 13 GIS-based environmental equity studies conducted within the past decade and use a study of noxious land use locations in the Bronx, New York, to illustrate and evaluate the differences in two common methods of determining exposure extent and the characteristics of proximate populations. Unresolved issues in mapping environmental equity and health include lack of comprehensive hazards databases; the inadequacy of current exposure indices; the need to develop realistic methodologies for determining the geographic extent of exposure and the characteristics of the affected populations; and the paucity and insufficiency of health assessment data. GIS have great potential to help us understand the spatial relationship between pollution and health. Refinements in exposure indices; the use of dispersion modeling and advanced proximity analysis; the application of neighborhood-scale analysis; and the consideration of other factors such as zoning and planning policies will enable more conclusive findings. The environmental equity studies reviewed in this article found a disproportionate environmental burden based on race and/or income. It is critical now to demonstrate correspondence between environmental burdens and adverse health impacts--to show the disproportionate effects of pollution rather than just the disproportionate distribution of pollution sources

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

On epidemiology and geographic information systems: a review and discussion of future directions

Geographic information systems are powerful automated systems for the capture, storage, retrieval, analysis, and display of spatial data. While the systems have been in development for more than 20 years, recent software has made them substantially easier to use for those outside the field. The systems offer new and expanding opportunities for epidemiology because they allow an informed user to choose between options when geographic distributions are part of the problem. Even when used minimally, these systems allow a spatial perspective on disease. Used to their optimum level, as tools for analysis and decision making, they are indeed a new information management vehicle with a rich potential for public health and epidemiology