From water point data to rural water services

Please tell us how water point data has been used in this very brief survey: https://www.surveymonkey.com/r/SVDGHTR

Overcoming water point amnesia

Where and how people access water and the location of the water sources, for example, a borehole or well, is useful information for those interested in ensuring safe water services for everyone. Naturally, local communities do know the location of these water points as well as the people who maintained them. However, until recently, the installers, district governments, donors, and NGOs typically lost track of the water points and would have a very limited understanding of the status of specific water services from a water point after the installation. This is a significant barrier to ensuring sustainability of basic water services. A lack of data could be a barrier to achieving the Sustainable Development Goal 6.

From new technology to national inventories

In the last 15 years, GPS and inexpensive mobile phones have improved access to accurate water point data. Since then, several national water point atlases are publicly available online, for example, Ghana, Liberia, Sierra Leone, Uganda, and Tanzania. The Water Point Data Exchange website now provides public access to more than 350,000 such data points. But is this information being used? And, if so, is it, in fact, leading to improved rural water services? There is anecdotal evidence that it might be helping.

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Modelling for Change

This blog has originally been posted on ircwash.org.

Can Agent-Based simulation models help us to improve services in complex WASH systems?

Practitioners in the water, sanitation and hygiene (WASH) sector use a variety of modelling tools to guide them in understanding and improving service delivery. Examples include financial modelling in spreadsheet models, graphic information system-based (GIS) modelling for geographic mapping of infrastructure and conceptual flow modelling in a sanitation system. These tools are powerful in their respective area of interest. However, in this blog, I advocate for the use of a complementary modelling tool that will help us to understand and analyse complex social interactions in WASH: an Agent-Based Modelling (ABM) tool. ABM can help practitioners to:

  1. diagnose the system;
  2. explore the effects of policy interventions; and
  3. discuss with partners and clients how the theory of complex systems affects them.

Policy Interventions in WASH

In a previous IRC blog, I described WASH as a Complex Adaptive System (CAS). This perspective taught us that a system concerned with the delivery of water or sanitation services is complex. Complexity theory investigates how relations between parts in a system result in a collective, observable behaviour. We can translate this to interactions and relationships between donor organisations, governments, service providers, technical infrastructure and water resources – the parts of the system – that result in a certain level of service delivery – the collective, observable behaviour.

How stakeholders and people involved in service delivery react to each other and to a policy intervention is very difficult to predict. (Policy) interventions can result in different outcomes due to unforeseen and unexpected reactions and interactions among the people, organisations and governments involved. This creates a major challenge for improving service levels.

In an ideal situation we are able to test any intervention we plan beforehand, turn back the time if the intervention does not go according to plan, and try a new intervention. Time and time again, until we get it right. Unfortunately, this is not possible. The best we can do is make a well-educated guess of the effect of a policy. We can support this guess with calculations, by consulting stakeholders and by piloting the policy in an isolated environment. ABM is a method that investigates and anticipates the interactions between people and organisations. Furthermore, ABM can be used to test an intervention, turn back time, and test another intervention. The premise of ABM can be summarised as follows:

1. ABM is a tool for diagnosis

In an ABM a modeller determines relations between agents. These agents form the key entities in an ABM and can be anything: a person, a hand pump, an organisation or a country. The agents are given a set of decision rules. Based on the defined relations and decision rules the agents interact with each other, resulting in some form of observable behaviour.

The core of an ABM is nothing more than lines of code.

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