GIScience Driven R&D: Interdisciplinary GIST Group at Oak Ridge National Laboratory

Oak Ridge National Laboratory (ORNL) is the largest DOE multi-research facility in the US and is located in Oak Ridge, TN. One of the signature strengths of ORNL is Computational Science and Engineering and the Geographic Information Science and Technology (GIST) group contributes to that strength as part of the Computer Sciences and Engineering Division (CSED) within the Computer Sciences Directorate. The GIST group is at the forefront of High Resolution Population and Social Dynamics research and development resulting in innovative products such as LandScan Global (population distribution at 30 arc seconds) and now LandScan HD (population distribution at 3 arc seconds). Other research capabilities within the group include Critical Infrastructure Modeling, Energy Assurance, High Performance Geocomputation and Visualization, Emergency Preparedness and Response, Earth Science Informatics, and Climate Change Impacts. The GIST group is an interdisciplinary group ranging of approximately 50 researchers (staff and students) and over the summer, the number of students increases anywhere from 15 to 25. As for Purdue graduates within the group, there are three staff and two interns at this time and Purdue students regularly participate in our summer internships programs

Multiphase procedure for landscape reconstruction and their evolution analysis. GIS modelling for areas exposed to high volcanic risk

This paper – focussed on the province of Naples, where many municipalities with a huge demographic and building density are subject to high volcanic risk owing to the presence of the Campi Flegrei (Phlegrean Fields) caldera and the Somma-Vesuvius complex – highlights the methodological-applicative steps leading to the setting up of a multiphase procedure for landscape reconstruction and their evolution analysis. From the operational point of view, the research led to the: (1) digitalisation, georeferencing and comparison of cartographies of different periods of time and recent satellite images; (2) elaboration and publication of a multilayer Story Map; (3) accurate vectorisation of the data of the buildings, for each period of time considered, and the use of kernel density in 2D and 3D; (4) application of the extrusion techniques to the physical aspects and anthropic structures; (5) production of 4D animations and film clips for each period of time considered. A procedure is thus tested made up of preparatory sequences, leading to a GIS modelling aimed at highlighting and quantifying significant problem areas and high exposure situations and at reconstructing the phases which in time have brought about an intense and widespread growth process of the artificial surfaces, considerably altering the features of the landscape and noticeably showing up the risk values. In a context characterised by land use conflicts and anomalous conditions of anthropic congestion, a diagnostic approach through images in 2D, 3D and 4D is used, with the aim to support the prevention and planning of emergencies, process damage scenarios and identify the main intervention orders, raise awareness and educate to risk, making an impact on the collective imagination through the enhancement of specific geotechnological functionalities of great didactic interest

Spatial Analytics -- A Missing Key to Ending Homelessness

Homelessness is a complex social phenomenon which requires a nuanced understanding of the development and implementation of lasting solutions. Such an endeavor requires inter-disciplinary research and collaboration. Yet the body of scholarly work on this phenomenon is invariably compartmentalized by distinct disciplines, often being studied separately in urban studies, sociology, public policy, health, and economic literature. It is no surprise then that there is a dearth of scholarly work on homelessness focused on the importance of understanding its spatial dimension. The studies that do include spatial data on homelessness tend to limit their analysis efforts to basic map visualizations. This paper contributes to Geographic Information Science by surveying the peer-reviewed research literature to assess current understanding of the spatial dimension of homelessness, and how this understanding informs efforts to address homelessness, to uncover the gaps that remain

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes

The Reorganization of a Psychiatric Unit During COVID-19:A Reflection for Psychiatric Hospital Design

OBJECTIVE: The COVID-19 pandemic has impacted healthcare systems worldwide. Although this disease has primarily impacted general medicine intensive care units, other areas of healthcare including psychiatry were modified in response to corona measures to decrease the transmission of the disease. Reflecting on the modifications to the environment provides an opportunity to design psychiatric environments for future pandemics or other demands for healthcare. BACKGROUND: The therapeutic environment of psychiatric wards was modified in Friesland, the Netherlands, in response to COVID-19. During this time, an interdisciplinary team met consistently to contribute to the preliminary design of a new psychiatric hospital. METHODS: During the first 18 months of the pandemic, clinical reflections were made to describe the impact of COVID-19 on the psychiatric care environment. Architects have created a preliminary design of a new psychiatric hospital based on these reflections, monthly collaborative design discussions based on virtual mock-ups and evidence-based design based on theoretical concepts and research. RESULTS AND CONCLUSIONS: This theoretical and reflective study describes how an inpatient psychiatric environment was restructured to manage infection during COVID-19. The therapeutic environment of the psychiatric ward and patient care changed drastically during COVID-19. The number of patients accessing care decreased, patient autonomy was restricted, and the function of designated behavioral support spaces changed to manage the risk of infection. However, these challenging times have provided an opportunity to reflect on theories and consider the design of new hospital environments that can be adapted in response to future pandemics or be restructured for different care functions

Introduction: reimagining futures

What might reimagining development futures look like and involve for development students, educators, researchers, and practitioners? In this final section of the Handbook, contributors offer a range of practices, orientations and methodologies that current and future people working in this vast and changing field might do well to consider and take on as part of re-imagining development futures beyond what we have come to know. A strong thread working through all of the chapters is the importance of attending more deeply to the peoples, knowledges, and non-human kin relations that have for far too long been relegated to development’s margins. Each chapter makes a case for why development, in the diverse contexts within which the authors are writing, needs to change and what this change might encompass leading to more equitable, creative, and nourishing human/more-than-human futures (see McGregor and Alam in this volume). Reflecting authors’ geographical location in the Asia-Pacific region, the chapters focus on specific examples of development futures from settler-colonial-Indigenous Australia, South Pacific island countries, Cambodia, Sri Lanka, and the Philippines

An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities

Coping with COVID-19: The sociomaterial dimensions of living with pre-existing mental illness during the early stages of the coronavirus crisis

In this article, we use the case study method to detail the experiences of five participants who reported living with pre-existing mental illness during COVID-19. We adopted a sociomaterial analytical approach, seeking to identify how human and nonhuman agents came together to generate states of wellbeing or distress during this challenging period. As the case studies show, feelings of anxiety, fear and risk were generated from the following sociomaterial conditions: loss of face-to-face contact with friends and family members; concerns about hygiene and infecting others; financial stress; loss of regular paid employment or volunteering work; public spaces; and the behaviour of unknown others in public spaces. The agents and practices that emerged as most important for opening capacities for coping and maintaining wellness during lockdown included: the space of the home; contact with a small number of intimate others; online therapeutic care; practising self-care skills learnt from previous difficult times; helping and supporting others; engaging in leisure activities; and the companionship of pets. Contributing to an affirmative approach to more-than-human assemblages of health, distress and recovery, these findings demonstrate what bodies can do in times of crisis and the agents and practices that can generate capacities for coping