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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.

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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.

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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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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.

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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.

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Diffusion of cholera and other diarrheal diseases in an informal settlement is a product of multiple behavioral, environmental and spatial risk factors. One of the most important components is the spatial interconnections among water points, drainage ditches, toilets and the intervening environment. This risk is also longitudinal and variable as water points fluctuate in relation to bacterial contamination. In this paper we consider part of this micro space complexity for three informal settlements in Port au Prince, Haiti. We expand on more typical epidemiological analysis of fecal coliforms at water points, drainage ditches and ocean sites by considering the importance of single point location fluctuation coupled with recording micro-space environmental conditions around each sample site. Results show that spatial variation in enteric disease risk occurs within neighborhoods, and that while certain trends are evident, the degree of individual site fluctuation should question the utility of both cross-sectional and more aggregate analysis. Various factors increase the counts of fecal coliform present, including the type of water point, how water was stored at that water point, and the proximity of the water point to local drainage. Some locations fluctuated considerably between being safe and unsafe on a monthly basis. Next steps to form a more comprehensive contextualized understanding of enteric disease risk in these environments should include the addition of behavioral factors and local insight.

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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.

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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.

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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.

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The cartographic challenge in many developing world environments suffering a high disease burden is a lack of granular environmental covariates suitable for modeling disease outcomes. As a result, epidemiological questions, such as how disease diffuses at intra urban scales are extremely difficult to answer. This paper presents a novel geospatial methodology, spatial video, which can be used to collect and map environmental covariates, while also supporting field epidemiology. An example of epidemic cholera in a coastal town of Haiti is used to illustrate the potential of this new method. Water risks from a 2012 spatial video collection are used to guide a 2014 survey, which concurrently included the collection of water samples, two of which resulted in positive lab results "of interest" (bacteriophage specific for clinical cholera strains) to the current cholera situation. By overlaying sample sites on 2012 water risk maps, a further fifteen proposed water sample locations are suggested. These resulted in a third spatial video survey and an additional "of interest" positive water sample. A potential spatial connection between the "of interest" water samples is suggested. The paper concludes with how spatial video can be an integral part of future fine-scale epidemiological investigations for different pathogens.

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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.

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OBJECTIVE: Innovatively apply geographic information system (GIS) software and spatial video cameras to examine hotspots of smoking and secondhand smoke (SHS) exposure on a college campus. PARTICIPANTS: Pedestrians on a large public university in the United States. METHODS: Spatial video cameras mounted on bicycles and GIS software captured and analyzed smoking on campus. Data were collected during times of heavy pedestrian traffic. Hotspot locations with high concentrations of observed smoking were identified. RESULTS: Using this innovative methodology, several hotspots were identified on campus where SHS exposure occurs frequently. These hotspots were in areas of high pedestrian traffic, thus indicating a potential for SHS exposure to many students. CONCLUSIONS: This paper demonstrates an innovative application of GIS and spatial video technology to understand outdoor smoking patterns on campus. These techniques can aid in program planning, decision-making, policy development, and enforcement.

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