We believe GIS’ role in M&E will also rapidly increase in importance with the availability of the software and data. However it is important to stress that GIS can’t just magically fit into an M&E environment, accordingly we present the following recommendations for use of geospatial tools:
1) Include geographic identifiers in programmatic data – in order to use data in a GIS, it must have a link to geography. This can be something as simple as district or community name or could be coordinates collected using GPS receivers or from a digital globe such as Google Earth.
2) Adhere to data standards for both geographic identifiers and programmatic data. Many countries have standardized unique identifiers and spellings of geographic features in their country. Following these standards will make it easier to link datasets. Programmatic data should follow relevant standards for metadata, indicator selections and other key factors.
3) Be open – Making programmatic data widely available, makes it easier to employ that data in other evaluations. There are confidentiality and security issues that must be considered, however the growth of the open data movement offers promise to M&E.
4) Build organizational capacity to use GIS first: Before asking stakeholders to share data, it is critical they have the necessary skills to use GIS technology, and their own data, within their own organizations. Ensuring the training has a practical use builds ownership and supports effective data-sharing.
5) Develop a strong logic framework: Linking data through GIS is feasible without a logic frame. However, a robust logic frame is critical to ensure a clear linkage between program activities and the output and outcomes indicators associated with these program activities. It is essential that GIS users not only understand GIS technology, applications, and use, but also the need for a sound logic framework to justify the data linkage--as well as how to use linked data to support decision-making
6) Continue to build the evidence base: More research and better data are needed to improve understanding of the drivers of risk for vulnerable populations. For instance, all women aged 15 to 24 are not uniformly at risk for HIV infection, and further research is needed to understand the specific characteristics and risk behaviors to effectively target these women with prevention interventions. Similarly, serodiscordant couples may need different approaches, depending on which partner is infected. In addition, more data are needed on such marginalized groups as men who have sex with men (MSM) to develop appropriate programs and activities and ensure adequate coverage of these populations.