This presentation is about how using GIS can improve M&E efforts through its ability to break down data silos.
If you think about the data that ’s out there it can be thought of as many discrete streams of data. Like these pipes data often stays in its own channel. In other words it’s siloed.
It can be a challenge to break down silos due to different issues such as who owns the data and their willingness to share, different reporting systems (say for instance the disease surveillance system is in its own database but doesn ’t link to the routine health system). There’s also issues of data comparability such as data being collected at a different time or different scale than another data set. That being said it can be well worth the effort to link data. I ’ll present an example that illustrates this.
There is great value in linking data, it gives insight into data by providing additional context. It maximizes the use of data and promotes opportunities to leverage efforts and minimized duplication of effort.
In the world of M&E this can be important. No program exists in a vacuum they exist in a world where people may be touched by multiple programs or influenced by the world around them so why not use that fact to help with M&E.
And indeed, there is a recognition that the world is complex and M&E should be cognizant of that complexity. People are starting to realize the limitations of experimental design in M&E and looking for ways to better evaluate programs. There ’s a growing discussion in the M&E about evaluation platforms. ASK CLASS [HAVE ANY OF YOU HEARD ABOUT EVALUATION PLATFORMS OR READ THE LITERATURE ABOUT IT?] Article in the Lancet by Cesar Victora proposing a district based evaluation platform that uses multiple data to inform M&E efforts. Its still something being hotly debated in the M&E world but there is interest in the idea by WHO, World Bank, USAID and others.
GIS can fit nicely into this discussion. There ’s synergy between the evaluation platform idea and the capabilities of GIS. By using the GIS to link data from multiple programs it becomes possible to understand the individual programs better but also better understand the relationship between the programs. Lastly spatial analysis techniques can help with deriving outcomes measures.
In other words, GIS is a tool that can facilitate evaluation through its ability to link data and produce tools for analysis and understanding of data.
PROMPT GROUP : HOW CAN GEOGRAPHIC DATA HELP? EXAMPLES INCLUDE MAPPING POPULATIONS, MAPPING SERVICES, EVEN LINKING DATA
Geographic Tools and M&E: Linking Data to Support M&E
Geographic tools and M&E:Linking data to support M&E
Challenge to break down silos§ Data comparability§ Different reporting systems§ Different data owners But it can be well worth the effort to link data
Value of linking:Gives insight into data byadding additional contextMaximizes use of dataPromotes opportunities toleverage efforts andminimize duplication ofeffort
M&E and GIS§ Programs don’t exist in vacuum§ People may be touched by multiple programs or influenced by the world around them§ Strengthen M&E by using that fact.
M&E is evolving§ Limitations of experimental design§ Growth of district based evaluation platforms
M&E and GIS§ Geography as basis for evaluation§ Multiple programs that can influence outcome§ Link data to better understand program and outcomes§ Spatial analysis techniques can help with deriving outcomes measures
GIS is a tool that can facilitate evaluation § Linking data § Producing tools for analysis and understanding of dataGIS can support evaluation even if no maps are produced
Dist rict HH Served by C by T Integrating 2013 Kuale 1604 data using a Bondo 2000 common data Nairobi Town 2229 model Nakuru 3473(Districtsselected for illustra on purposes) Cash Transfer Database District Orphan Est. 07 District OVC Served Kwale 21821 by PEPFAR Bondo 21804 Kenya Data Kwale 54 Nairobi 471204 Model Bondo 5015 Nakuru 108109 Nairobi 2500 NACCPrevalence Report Nukuru 7074 Integrated Data Table PEPFARKPMS District District Code Orphan Est. 07 OVC Served by PEPFAR HH Served by CT by 2013 Kwale 1001 21821 54 1604 Bondo 1002 21804 5015 2000 Nairobi 1003 471204 2500 2229 Nakuru 1004 108109 7074 3473
Kenya Data Model§ Six elements that allow data to be linked together§ Geography§ Services provided§ Funder§ Implementing organization name§ Timeframe§ Number of beneficiaries
Rural Poverty Es mates2003 and Percent OrphansServed by PEPFAR2009, Nyanza Province Kisumu Siaya Nyando Bondo Rachuonyo Suba Nyamira Homa Bay Cash Transfer District Kisii Central Gucha Migori (South Kisii) KuriaMap produced May 2010Poverty Source: Geographic Dimensions of Well Being in Kenya Report, Kenya Cent. Bur. of Stat., 2003Percent Orphans Served Calculated by dividing 2009 KPMS Number of Orphans Served (table 8.1) byEstimated Number of OVC from NACC Prevalence Report, 2007 (Appx. 3)
§ By linking data from multiple stakeholders it was possible to see patterns that were not observed without linking§ Important to note that there may be legitimate reasons why there were no services in Kuria – map doesn’t provide all the answers just points in a direction for further study.
Key points§ Evaluation is evolving§ Using geography (and GIS) to link data can strengthen M&E§ Many barriers to linking data§ Technical barriers may be easy to overcome, non-technical barriers may be more challenging§ Any questions?