Data underpinning research article 'Sand dam contributions to year-round water security monitored through remotely sensed handpump data'

Date published

2022-12-01 15:20

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Cranfield University

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Citation

Ritchie, Hannah Nicola Grace; Holman, Ian; Parker, Alison; Chan, Joanna (2022). Data underpinning research article "Sand dam contributions to year-round water security monitored through remotely sensed handpump data". Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.21435690.v1

Abstract

The repository contains an hourly abstraction data set collected from 26 sand dam hand pumps in Makueni and Machakos, Kenya via remote sensing, using Water Point Data Transmitters (WDT). The WDT were attached to the handles of the hand pumps in April 2019, and transmit abstraction via an SMS message, generated according to the number of times that the pump is used in an hour. The data set includes date, time, pump location, population size, and variables related to each sand dam (including, size, average distance to reach pump, and livestock usage). The variables are present in their raw form and also in their grouped form, which were used for the modelling in this study. The data was provided by Sand Dams Worldwide and has been approved for publication. The repository also contains an interview data set collected by Joanna Chan (MSc) in 2019 in Makueni and Machakos, Kenya. The interview data was collected at 30 sand dam sites. The data contains questions centred on water use behiours. This data was grouped to provide the variable data present in the abstraction data set. The repository contains an hourly abstraction data set collected from 26 sand dam hand pumps in Makueni and Machakos, Kenya via remote sensing, using Water Point Data Transmitters (WDT). The WDT were attached to the handles of the hand pumps in April 2019, and transmit abstraction via an SMS message, generated according to the number of times that the pump is used in an hour. The data set includes date, time, pump location, population size, and variables related to each sand dam (including, size, average distance to reach pump, and livestock usage). The variables are present in their raw form and also in their grouped form, which were used for the modelling in this study. The data was provided by Sand Dams Worldwide and has been approved for publication. The repository also contains an interview data set collected by Joanna Chan (MSc) in 2019 in Makueni and Machakos, Kenya. The interview data was collected at 30 sand dam sites. The data contains questions centred on water use behiours. This data was grouped to provide the variable data present in the abstraction data set.

Description

Software Description

Software Language

Github

Keywords

'Water', 'Abstraction', 'Remote sensing'

DOI

10.17862/cranfield.rd.21435690.v1

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CC0

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Funder/s

EPSRC Centre for Doctoral Training in Water and Waste Infrastructure Systems Engineered for Resilience (Water-WISER)