Spatial video geonarratives and health: case studies in post-disaster recovery, crime, mosquito control and tuberculosis in the homeless

Using spatial video geonarratives to improve nursing care for people who use drugs and experience homelessness: A methodology for nurses

BACKGROUND

People who are insecurely housed and use drugs are disproportionately affected by drug poisonings. Nurses are uniquely positioned to utilize harm reduction strategies to address the needs of the whole person. Needle debris encompasses drug paraphernalia discarded in public spaces. Studying needle debris provides a strategic opportunity to identify where drugs are being used and target public health strategies accordingly.

AIM

Our aim in this article is to illustrate how spatial video geonarratives (SVG) combined GPS technology interviews, and videos of locations with needle debris, can elicit valuable data for nursing research.

METHODS

Using SVG required knowledge of how to collect data wearing cameras and practice sessions were necessary. A Miufly camera worn at waist height on a belt provided the stability to walk while interviewing stakeholders. We wore the cameras and conducted go-along interviews with outreach workers, while filming the built environment. Upon completion of data collection, both the interview and GPS information were analysed using Wordmapper software.

CONCLUSIONS

This methodology resulted in data presented uniquely in both a visual map and narrative. These data were richer than if a single modality had been used. These data highlighted specific contextual factors that were related to the location of needle debris, which created opportunities for nursing interventions to support people experiencing vulnerability.

When Unstructured Big Text Corpus Meets Text Network Analysis: Social Reality Conceptualization and Visualization Graph of Big Interview Data of Heavy Drug Addicts of Skid Row

Relying on user-generated content narrating individual experiences and personalized contextualization of location-specific realities, this study introduced a novel methodological approach and analysis tool that can aid health informatics in understanding the social reality of people with a substance-use disorder in Skid Row, Los Angeles. The study also highlighted analysis possibilities for big unstructured interview text corpus using InfraNodus, a text network analysis tool. InfraNodus, which is a text graph analysis tool, identifies pathways for meaning circulation within unstructured interview data and has the potential to classify topical clusters and generate contextualized analysis results for big narrative textual datasets. Using InfraNodus, we analyzed a 1,103,528-word unstructured interview transcript from 315 interview sessions with people with a substance-use disorder, who narrated their respective social realities. Challenging the overgeneralization of onlookers, the conceptualization process identified topical clusters and pathways for meaning circulation within the narrative data, generating unbiased contextualized meaning for the collective social reality. Our endeavors in this research, along with our methodological setting and selection, might contribute to the methodological efforts of health informatics or the conceptualization and visualization needs of any big text corpus.

Go-along interview assessment of community health priorities for neighborhood renewal

Healthcare systems are increasingly investing in approaches to address social determinants of health and health disparities. Such initiatives dovetail with certain approaches to neighborhood development, such as the EcoDistrict standard for community development, that prioritize both ecologically and socially sustainable neighborhoods. However, healthcare system and community development initiatives can be untethered from the preferences and lived realities of residents in the very neighborhoods upon which they focus. Utilizing the go-along approach to collecting qualitative data in situ, we interviewed 19 adults to delineate residents' community health perspectives and priorities. Findings reveal health priorities distinct from clinical outcomes, with residents emphasizing social connectedness, competing intra- and interneighborhood perceptions that potentially thwart social connectedness, and a neighborhood emplacement of agency, dignity, and self-worth. Priorities of healthcare systems and community members alike must be accounted for to optimize efforts that promote health and social well-being by being valid and meaningful to the community of focus.

The Use of Spatial Video to Map Dynamic and Challenging Environments: A Case Study of Cholera Risk in the Mujoga Relief Camp, D.R.C

In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method’s flexibility, we consider the life cycle of the Mujoga relief camp set up after the Nyiragongo volcanic eruption in the Democratic Republic of Congo on 22 May 2021. More specifically we investigate how these methods have captured the deteriorating conditions in a camp which is also experiencing lab-confirmed cholera cases. Spatial video data are collected every month from June 2021 to March 2022. These coordinate-tagged images are used to make monthly camp maps, which are then returned to the field teams for added contextual insights. At the same time, a zoom-based geonarrative is used to discuss the camp’s changes, including the cessation of free water supplies and the visible deterioration of toilet facilities. The paper concludes by highlighting the next data science advances to be made with SV mapping, including machine learning to automatically identify and map risks, and how these are already being applied in Mujoga.

Spatial Video and EpiExplorer: A Field Strategy to Contextualize Enteric Disease Risk in Slum Environments

Disease risk associated with contaminated water, poor sanitation, and hygiene in informal settlement environments is conceptually well understood. From an analytical perspective, collecting data at a suitably fine scale spatial and temporal granularity is challenging. Novel mobile methodologies, such as spatial video (SV), can complement more traditional epidemiological field work to address this gap. However, this work then poses additional challenges in terms of analytical visualizations that can be used to both understand sub-neighborhood patterns of risk, and even provide an early warning system. In this paper, we use bespoke spatial programming to create a framework for flexible, fine-scale exploratory investigations of simultaneously-collected water quality and environmental surveys in three different informal settlements of Port-au-Prince, Haiti. We dynamically mine these spatio-temporal epidemiological and environmental data to provide insights not easily achievable using more traditional spatial software, such as Geographic Information System (GIS). The results include sub-neighborhood maps of localized risk that vary monthly. Most interestingly, some of these epidemiological variations might have previously been erroneously explained because of proximate environmental factors and/or meteorological conditions.

VisualCommunity: a platform for archiving and studying communities

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.

An Exploration of Narcan as a Harm Reduction Strategy and User's Attitudes toward Law Enforcement Involvement in Overdose Cases

The street homeless, those who spend their nights either in shelters or unofficial camps, whether in tents on a street or in society’s hidden spaces such as beneath an overpass, face multiple challenges beyond finding a safe place to sleep. Of further concern is how official actions can worsen these situations, through day-to-day activities or planned intervention strategies. In this paper we explore how a planned intervention may be negatively perceived—even as a form of “structural violence”—and may prevent Narcan (naloxone) use to stop an overdose related death in the Skid Row of Los Angeles. Data for this study consisted of a combination of Spatial Video Geonarratives (SVGs) and 325 incident reports from the Homeless Health Care Los Angeles Center for Harm Reduction (HHCLA-HRC) between November 2014 and December 2015. Chi-square and simple logistic regression models were used to examine the association between fear-of-arrest and other covariates of interest. Mapping results are presented with different sets of shapefiles created for (1) all Narcan uses, (2) all homeless, (3) all homeless with a worry about being arrested, (4) all Narcan uses where an ambulance attended, (5) and the same as 4 but also with police attendance. In the multivariable model, the estimated adjusted odds of fear-of-arrest is over three times higher among Narcan users ages 30–39 when compared to users under the age of 30. Analyzing the association of calling 9-1-1 on Narcan user demographics, socio-contextual characteristics, and overdose victim demographics, the crude estimated probability of calling 9-1-1 for Narcan users aged 50 and older is nearly three times higher when compared to Narcan users aged 19–29. Conclusion: Results suggest that the fear-of-arrest and calling 9-1-1 during an overdose is still a concern among Narcan users despite protective legislation and access to harm reduction resources.

Trail Conditions and Community Use: Utilizing Geospatial Video to Guide the Adoption of a Spatial-Temporal Trail Audit Tool (STAT)

Physical activity (PA), associated with all-cause mortality, morbidity, and healthcare costs, improves vitamin D absorption, immune response, and stress when completed outdoors. Rural communities, which experience PA inequities, rely on trails to meet PA guidelines. However, current trail audit methods could be more efficient and accurate, which geospatial video may support. Therefore, the study purpose was (1) to identify and adopt validated instruments for trail audit evaluations using geospatial video and a composite score and (2) to determine if geospatial video and a composite score motivate (influence the decision to use) specific trail selection among current trail users. Phase 1 used a mixed-method exploratory sequential core design using qualitative data, then quantitative data for the development of the Spatial-temporal Trail Audit Tool (STAT). Geospatial videos of two Northeast Ohio trails were collected using a bicycle-mounted spatial video camera and video analysis software. The creation of STAT was integrated from Neighborhood Environment Walkability Scale (NEWS), Walk Score, and Path Environment Audit Tool (PEAT) audit tools based on four constructs: trail accessibility, conditions, amenities, and safety. Scoring was determined by three independent reviewers. Phase 2 included a mixed-method convergent core design to test the applicability of STAT for trail participant motivation. STAT has 20 items in 4 content areas computing a composite score and was found to increase trail quality and motivation for use. STAT can evaluate trails for PA using geospatial video and a composite score which may spur PA through increased motivation to select and use trails.

Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana

Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques include a survey, spatial video geonarrative (SVG) in the field with study participants, and the extraction of moments of stress (MOS) from biosensing wristbands. This study enrolled 46 participants who completed geonarratives and MOS detection. A subset of 10 of these geonarratives are presented here. Each participant was driven in a car equipped with audio recording and spatial video along a predefined route while wearing the Empatica E4 wristbands to measure three physiological variables, all of them linked by timestamp. The results show differences in the participants' sentiments (positive or negative) and MOS in the field based on gender. These mixed-methods are encouraging for finding relationships between actual crime occurrences and the community perceived fear of crime in urban areas.

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

The complex interrelationship between the built environment and social problems is often described but frequently lacks the data and analytical framework to explore the potential of such a relationship in different applications. We address this gap using a machine learning (ML) approach to study whether street-level built environment visuals can be used to classify locations with high-crime and lower-crime activities. For training the ML model, spatialized expert narratives are used to label different locations. Semantic categories (e.g., road, sky, greenery, etc.) are extracted from Google Street View (GSV) images of those locations through a deep learning image segmentation algorithm. From these, local visual representatives are generated and used to train the classification model. The model is applied to two cities in the U.S. to predict the locations as being linked to high crime. Results show our model can predict high- and lower-crime areas with high accuracies (above 98% and 95% in first and second test cities, accordingly).

The Use of Geonarratives to Add Context to Fine Scale Geospatial Research

There has been a move towards using mixed method approaches in geospatial research to gain context in understanding health related social patterns and processes. The central premise is that official data is often too reductionist and misses’ nuances that can help explain causality. One example is the geonarrative, a spatially relevant commentary or interview that can be mapped by content and/or location. While there have been several examples of geonarratives being used by researchers, there is no commonly available software that can easily transfer the associated text into spatial data. Having a standardized software platform is vital if these methods are to be used across different disciplines. This paper presents an overview of a solution, Wordmapper (WM), which is a standalone software developed to process geonarratives from a transcription and associated global positioning system (GPS) path. Apart from querying textual narrative data, Wordmapper facilitates qualitative coding which could be used to extract latent contextual information from the narratives. In order to improve interoperability, Wordmapper provides spatialized narrative data in formats, such as ESRI shape files, Keyhole Markup Language (KML), and Comma Separated Values (CSV). A case study based on five different spatial video geonarratives (SVG) collected to assess the human impacts following the 2011 Joplin, Missouri are used for illustration.

Spatial Video Health Risk Mapping in Informal Settlements: Correcting GPS Error

Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error.

Environmental and social-demographic predictors of the southern house mosquito Culex quinquefasciatus in New Orleans, Louisiana

BACKGROUND

Understanding the major predictors of disease vectors such as mosquitoes can guide the development of effective and timely strategies for mitigating vector-borne disease outbreaks. This study examined the influence of selected environmental, weather and sociodemographic factors on the spatial and temporal distribution of the southern house mosquito Culex quinquefasciatus Say in New Orleans, Louisiana, USA.

METHODS

Adult mosquitoes were collected over a 4-year period (2006, 2008, 2009 and 2010) using CDC gravid traps. Socio-demographic predictors were obtained from the United States Census Bureau, 2005-2009 American Community Survey and the City of New Orleans Department of Code Enforcement. Linear mixed effects models and ERDAS image processing software were used for statistical analysis and image processing.

RESULTS

Only two of the 22 predictors examined were significant predictors of Cx. quinquefasciatus abundance. Mean temperature during the week of mosquito collection was positively associated with Cx. quinquefasciatus abundance while developed high intensity areas were negatively associated with Cx. quinquefasciatus abundance.

CONCLUSION

The findings of this study illustrate the power and utility of integrating biophysical and sociodemographic data using GIS analysis to identify the biophysical and sociodemographic processes that increase the risk of vector mosquito abundance. This knowledge can inform development of accurate predictive models that ensure timely implementation of mosquito control interventions.

Community context and sub-neighborhood scale detail to explain dengue, chikungunya and Zika patterns in Cali, Colombia

Background

Cali, Colombia has experienced chikungunya and Zika outbreaks and hypoendemic dengue. Studies have explained Cali’s dengue patterns but lack the sub-neighborhood-scale detail investigated here.

Methods

Spatial-video geonarratives (SVG) with Ministry of Health officials and Community Health Workers were collected in hotspots, providing perspective on perceptions of why dengue, chikungunya and Zika hotspots exist, impediments to control, and social outcomes. Using spatial video and Google Street View, sub-neighborhood features possibly contributing to incidence were mapped to create risk surfaces, later compared with dengue, chikungunya and Zika case data.

Results

SVG captured insights in 24 neighborhoods. Trash and water risks in Calipso were mapped using SVG results. Perceived risk factors included proximity to standing water, canals, poverty, invasions, localized violence and military migration. These risks overlapped case density maps and identified areas that are suitable for transmission but are possibly underreporting to the surveillance system.

Conclusion

Resulting risk maps with local context could be leveraged to increase vector-control efficiency- targeting key areas of environmental risk.

Using Spatial Video to Analyze and Map the Water-Fetching Path in Challenging Environments: A Case Study of Dar es Salaam, Tanzania

Access to clean drinking water remains a significant health problem in the developing world. Traditional definitions of water access oversimplify the geographic context of water availability, the burden of water collection, and challenges faced along the path, mainly due to a lack of fine scale spatial data. This paper demonstrates how spatial video collected in three informal areas of Dar es Salaam, Tanzania, can be used to quantify aspects of the walk to water. These include impediments encountered along the path such as changes in elevation and proximity to traffic. All are mapped along with classic health-related environmental and social information, such as standing water, drains, and trash. The issue of GPS error was encountered due to the built environment that is typical of informal settlements. The spatial video allowed for the correction of the path to gain a more accurate estimate of time and distance for each walk. The resulting mapped health risks at this fine scale of detail reveal micro-geographies of concern. Spatial video is a useful tool for visualizing and analyzing the challenges of water collection. It also allows for data generated along the walk to become part of both a household and local area risk assessment.

Breaking Out of Surveillance Silos: Integrative Geospatial Data Collection for Child Injury Risk and Active School Transport

The preponderance of active school transport (AST) and child injury research has occurred independently, yet they are inherently related. This is particularly true in urban areas where the environmental context of AST may pose risks to safety. However, it can be difficult to make these connections due to the often segregated nature in which these veins of research operate. Spatial video presents a geospatial approach for simultaneous data collection related to both issues. This article reports on a multi-sector pilot project among researchers, a children's hospital, and a police department, using spatial video to map child AST behaviors; a geographic information system (GIS) is used to analyze these data in the environmental context of child pedestrian injury and community violence.