Abstract
BackgroundThe 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.ResultsWe 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.ConclusionMachine 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.
Highlights
IntroductionInformal settlements remain one of the greatest public health challenges due to the nexus of a variety of disease causing systems (such as extreme poverty, overcrowding, lack of local services and health care), and generally poor data to help guide solutions
Informal settlements remain one of the greatest public health challenges due to the nexus of a variety of disease causing systems, and generally poor data to help guide solutions
While different solutions have been utilized to improve on-the-ground spatial detail, such as participatory mapping approaches [16, 35], or through crowd sourcing platforms such as Map Kibera [8], these tend to be cross sectional in nature because of the logistical problems faced during data collection
Summary
Informal settlements remain one of the greatest public health challenges due to the nexus of a variety of disease causing systems (such as extreme poverty, overcrowding, lack of local services and health care), and generally poor data to help guide solutions. While different solutions have been utilized to improve on-the-ground spatial detail, such as participatory mapping approaches [16, 35], or through crowd sourcing platforms such as Map Kibera [8], these tend to be cross sectional in nature because of the logistical problems faced during data collection. The data deficiencies found in such environments are well documented, and even when on-the-ground technological advances are utilized, meaning solutions designed to collect the required risk data for localized mapping, they tend to lack the sustainability and granularity required for analysis and intervention [19]. 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. 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
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