Field surveys are the workhorse of social and environmental research, but conventional collection through monitors or enumerators are cost prohibitive in many remote or otherwise difficult settings, which can lead to a poor understanding of those environments and an underrepresentation of the people living in them. In such cases, micro-tasking can offer a promising alternative. By activating in-situ data collectors, micro-tasking avoids many of the large expenses related to conventional field survey processes. In addition to relaxing resource constraints, crowd-sourcing can be flexible and employ data quality protocols unheard-of for conventional methods. This study assesses the potential of using micro-tasking to monitor socioeconomic and environmental indicators in remote settings using a new platform called KAZNET. KAZNET leverages the network of people with smartphones, which are becoming ubiquitous even in the remote rural settings, to execute both long-term and short-term data collection activities, with flexibility to adjust or add tasks in real-time. It also allows for multiple projects, requiring different data types, to be rolled out in the same platform simultaneously. For the data-collector, KAZNET is effectively a wrapper for the commonly used and open source, Open Data Kit (ODK) software, which specializes in offline data collection. A web interface allows administrators to calibrate, deploy, and validate tasks performed by contributors. KAZNET has been used in several projects to collect data in remote pastoral regions of East Africa since its inception in 2017. KAZNET has shown to be effective for collecting high frequency and repeated measures from markets, households and rangelands in remote regions at relatively low cost compared to traditional survey methods. While the successes of micro-tasking are promising, there are clear trade-offs and complementarities between micro-tasking and standard surveys methods, which researchers and practitioners need to consider when implementing either approach.
There is an urgent need for improved and timely health and nutrition data. We developed and tested a smartphone application that caregivers from a pastoral population used to measure, record and submit high-frequency and longitudinal health and nutrition information on themselves and their children. The data were assessed by comparing caregiver-submitted measurements of mid-upper arm circumference (MUAC) to several benchmark data sets, including data collected by community health volunteers from the participating caregivers during the project period and data generated by interpreting photographs of MUAC measurements submitted by all participants. We found that the caregivers participated frequently and consistently over the 12-month period of the project; most of them made several measurements and submissions in at least 48 of the 52 weeks of the project. The evaluation of data quality was sensitive to which data set was used as the benchmark, but the results indicate that the errors in the caregivers' submissions were similar to that of enumerators in other studies. We then compare the costs of this alternative approach to data collection through more conventional methods, concluding that conventional methods can be more cost-effective for large socioeconomic surveys that value the breadth of the survey over the frequency of data, while the alternative we tested is favoured for those with objectives that are better met by high-frequency observations of a smaller number of well-defined outcomes.
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