The lack of data on the distribution of the water resources, possess a great challenge for the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. This paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making.
Access to clean and safe drinking water is critical to public health and socioeconomic prosperity, yet an estimated quarter of the world’s population lacks such. This was evidenced by the unprecedented outbreak of the COVID-19 pandemic, which left communities extremely vulnerable to fatal illnesses due to the limited access to water for handwashing or lack of knowledge of the existence of the utility. Subsequently, the lack of data on the distribution of the water resources poses a great challenge to the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. Influencing the frequency of water point data collection through crowdsourcing and volunteered geographic information, would greatly improve the availability of water point data, and contribute to the extended roles of water resource distribution, monitoring, and management especially in rural communities. Therefore, this paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making.
The recent technological advances including the web 2.0, cameras, smartphones and sensor networks continue to empower the development of empirical methods as well as the generation of big data and analytical platforms that provide predictive performance on the various socioeconomic needs for sustainable development. OpenStreetMap (OSM) is a crowdsourcing platform which offers a collaborative experience through its database, community, and wiki platforms, to create and update data relevant to support or transform various data deficiencies whether humanitarian or planning. However, the project’s data quality shortcomings often hinder simultaneous data integration with other analytical platforms such as the Water Point Data Exchange (WPdx) that would explicitly maximize the usage and application of these crowdsourced data. Through a project dubbed ‘Water Infrastructure Mapping Uganda’, a data model based upon open mapping methods and survey tools was developed to facilitate the mapping of water infrastructure data points and simultaneous updates of both the WPdx and OSM databases.
The project engaged a comprehensive review of the OSM water tag, rural water infrastructure data standards and the WPdx database to generate a survey data form that supported one-time collection of a water point for both OSM and WPdx databases. Underlying the development of the data model/schema in the overall project, a design criterion was established which guided and justified the overall selection of the most relevant factors to include in the process that would eventually become detailed to communicate water infrastructure and functionality. The criteria were followed by an assessment of the; compliance [agreement of the tag], consistency [temporal and spatial representation of the tag], completeness [attribute description of the tag], and granularity [quality of the event information] of the OSM tag to support the development of the. osm language in the Kobo toolbox.
Gulu district, located in the North of Uganda, was identified as a potential pilot area for improving the approach created by the project based on its rich WPdx footprint as well as a well-established OSM community of YouthMappers. Up to date satellite imagery of up to 50cm spatial resolution was acquired through the USAID GeoCentre to facilitate any visual detection of water points, and the digitization of base map data including, buildings, roads and waterways, to be employed in the field mapping exercise. A field mapping workflow was designed to facilitate the field-data collection employing the developed water infrastructure data model and Kobo toolbox. An API link was developed that simultaneously tapped the open-source field collected data into the WPdx database.
Through the project, more than 15000 buildings, 1400square kilometres of roads and over 500 water data points were added to OSM as well as the WPdx database for the later data. Also, from the project, several observations were made regarding the improvement of such processes and the extension of the data model beyond one geographical area. The developed workflows characterized and provided a general improvement in the water infrastructure data quality especially for OSM based on WASH indicators used to officially report on the sustainable development agenda. The workflow development waivered the interoperability gap in geospatial data sharing platforms which often results from unharmonized data structures. It was established that the designed methodology cannot be applied to water data updates but rather to freshwater point data collection. This would lead to exponential water point data increase, however, the workflow may be revised to include the framework for data updates without having to engage the full field mapping process. As well, the data model design was mainly based on the African water infrastructure and open mapping reviews, hence, the transfer of the data model from one continent to another may require a review of some data factors to create better insights of the water indicators in a place of that given continent.