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

Curtis, Andrew; Bempah, Sandra; Ajayakumar, Jayakrishnan; Mofleh, Dania; Odhiambo, Lorriane

doi:10.3390/ijerph16010033

Cited by 11 publications

(17 citation statements)

References 44 publications

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“…Starting in February 2017, in addition to the water samples, micro environmental surveys were also collected by the field team around each sample site using Contour +2 and Patrol Eye Body cameras. These spatial video (SV) cameras have an internal global positioning system (GPS) receiver, which means the videoed environment around each testing site can be subsequently viewed for risks using CameraPlayer, software the team has developed to simultaneously locate each image frame on a map [ 30 ]. After each monthly collection, video and GPS paths were downloaded and meta data sheets completed, which recorded the technical performance of each camera.…”

Section: Methodsmentioning

confidence: 99%

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Curtis

Squires

Rouzier

et al. 2019

IJERPH

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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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Section: Methodsmentioning

confidence: 99%

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Curtis

Squires

Rouzier

et al. 2019

IJERPH

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“…A novel GPS‐based system on jerry cans, developed by Pearson (), yields an estimate of minutes per roundtrip, distance traveled, and validates data for self‐reported water journeys. Spatial video is another tool to collect accurate water acquisition data—such as time, path, and hazards—to assess the risk and burden to household members in urban and peri‐urban environments (Curtis et al, ; Smiley, Curtis, & Kiwango, ). Such methods combine video with embedded GPS coordinates, which are then digitized to a geographic information system.…”

Section: Estimating Household Water Insecurity Statusmentioning

confidence: 99%

Measuring Human Water Needs

Wutich

Rosinger

Stoler

et al. 2019

American J Hum Biol

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Water connects the environment, culture, and biology, yet only recently has it emerged as a major focus for research in human biology. To facilitate such research, we describe methods to measure biological, environmental, and perceptual indicators of human water needs. This toolkit provides an overview of methods for assessing different dimensions of human water need, both well-established and newly-developed. These include:(a) markers of hydration (eg, urine specific gravity, doubly labeled water) important for measuring the impacts of water need on human biological functioning; (b) methods for measuring water quality (eg, digital colorimeter, membrane filtration) essential for understanding the health risks associated with exposure to microbiological, organic, metal, inorganic nonmental, and other contaminants; and (c) assessments of household water insecurity status that track aspects of unmet water needs (eg, inadequate water service, unaffordability, and experiences of water insecurity) that are directly relevant to human health and biology. Together, these methods can advance new research about the role of water in human biology and health, including the ways that insufficient, unsafe, or insecure water produces negative biological and health outcomes.

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“…To use machine learning at this scale, to capture factors that often occur beneath the overlapping building canopy and therefore beyond normal remotely sensed imagery [ 45 ], a new image library is required. These data also need to be longitudinal given the dynamic nature of these spaces, with significant changes occurring at different cadences, both seasonally and then from year to year [ 9 , 13 ]. To be able to create a sustainable way to identify and map health risks could prove vital for health intervention initiatives.…”

Section: Discussionmentioning

confidence: 99%

“…Adding further complexity is that these environments are dynamic in nature; the dramatic difference encountered between wet and dry seasons being one obvious example [ 46 ]. Further dynamism occurs with critical infrastructure, for example water points (W.Point) or toilets [ 26 ], require frequent updating, not only in terms of shifting locations [ 4 , 9 , 9 , 13 , 13 ] but also on how their quality and risks vary temporally [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

et al. 2021

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Background The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models. Results We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance. Conclusion Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

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

Cited by 11 publications

References 44 publications

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Measuring Human Water Needs

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

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