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      VisualCommunity: a platform for archiving and studying communities

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.

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          A systematic review of the application and utility of geographical information systems for exploring disease-disease relationships in paediatric global health research: the case of anaemia and malaria

          Ashley Aimone, Nandita Perumal, Donald C. Cole (2013)
          Malaria and anaemia are important health problems among children globally. Iron deficiency anaemia may offer protection against malaria infection and iron supplementation may increase the risk of malaria-related hospitalization and mortality. The nature and mechanism of these relationships, however, remain largely unresolved, resulting in concern and uncertainty around policies for non-selective iron supplementation in malaria endemic areas. Use of geographical information systems (GIS) to investigate this disease-disease interaction could contribute important new information for developing safe and effective anaemia and malaria interventions. To assess the current state of knowledge we conducted a systematic review of peer-reviewed and grey literature. Our primary objective was to qualitatively assess the application and utility of geographical concepts or spatial analyses in paediatric global health research. The secondary objective was to identify geographical factors that may be associated with anaemia and malaria prevalence or incidence among children 0–5 years of age living in low- and middle-income countries. Evaluation tools for assessing the quality of geographical data could not be found in the peer-reviewed or grey literature, and thus adapted versions of the STROBE (Strengthening The Reporting of Observational Studies in Epidemiology) and GRADE (Grades of Recommendation, Assessment, Development and Evaluation) methods were used to create reporting, and overall evidence quality scoring systems. Among the 20 included studies, we found that both malaria and anaemia were more prevalent in rural communities compared to urban areas. Geographical factors associated with malaria prevalence included regional transmission stability, and proximity to a mosquito breeding area. The prevalence of anaemia tended to vary inversely with greater or poorer access to community services such as piped water. Techniques for investigating geographic relationships ranged from simple descriptive mapping of spatial distribution patterns, to more complex statistical models that incorporated environmental factors such as seasonal temperature and rain fall. Including GIS in paediatric global health research may be an effective approach to explore relationships between childhood diseases and contribute key evidence for safe implementation of anaemia control programs in malaria endemic areas. Further, GIS presentation of ecological health data could provide an efficient means of translating this knowledge to lay audiences.
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            Visual analytics of movement: An overview of methods, tools and procedures

            Gennady Andrienko, Natalia Andrienko (2012)
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              StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views.

              Qiaomu Shen, Wei Zeng, Yu Ye … (2018)
              Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.
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                Author and article information

                Contributors
                Suphanut Jamonnak:
                ORCID: http://orcid.org/0000-0003-4707-8529
                sjamonna@kent.edu
                Journal
                Journal ID (nlm-ta): J Comput Soc Sci
                Journal ID (iso-abbrev): J Comput Soc Sci
                Title: Journal of Computational Social Science
                Publisher: Springer Nature Singapore (Singapore )
                ISSN (Print): 2432-2717
                ISSN (Electronic): 2432-2725
                Publication date (Electronic): 16 May 2022
                Pages: 1-23
                Affiliations
                [1 ]GRID grid.258518.3, ISNI 0000 0001 0656 9343, Department of Computer Science, , Kent State University, ; Kent, OH USA
                [2 ]GRID grid.268132.c, ISNI 0000 0001 0701 2416, Department of Computer Science, , West Chester University, ; West Chester, PA USA
                [3 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Department of Landscape Architecture and Urban Planning, , Texas A&M University, ; College Station, TX USA
                [4 ]GRID grid.67105.35, ISNI 0000 0001 2164 3847, Department of Population and Quantitative Health Sciences, , Case Western Reserve University, ; Cleveland, OH USA
                Author information
                http://orcid.org/0000-0003-4707-8529
                Article
                Publisher ID: 170
                DOI: 10.1007/s42001-022-00170-y
                PMC ID: 9109455
                PubMed ID: 35602668
                SO-VID: 7418a11e-c94d-4cb9-9382-f7f5c79aa4c7
                Copyright © © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 20 September 2021
                Date accepted : 21 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1739491
                Award Recipient :
                Categories
                Subject: Research Article

                Keywords: ai processing,community study,geo-narrative,spatial video,visualization system
                Data availability:
                Keywords: ai processing, community study, geo-narrative, spatial video, visualization system

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