So I’m going to talk about the following themes in this webinar. I know we have a wide variety of participants on this webinar so I am going to try to make it useful for everyone. First, I’ll briefly go over the OVC DDU workshop that was held in Zanzibar in March of this year. Then, I’m going to talk briefly about some of the main challenges in mapping OVC data; some that we discovered during the workshop, and others that are unique to OVC data and still others that are common to mapping all types of health data. Thirdly, I’ll go over some of the successes that the OVC community at large and GIS folks like me have been able to do successfully in many countries that have large OVC programs and portfolios; in general, these are things we are already doing in the OVC community. Lastly, Ill take a few minutes to show some of the newer ways that OVC data is being mapped and used at different levels.
MEASURE Evaluation facilitated an OVC DDU workshop that was held in Zanzibar in March of this year. We invited three country teams to attend the workshop and asked that these teams consist of a mixture of participants from the Ministries (those that host the OVC portfolio), USAID, Implementing partners and community-based organizations. Two of the main goals of the workshop were to facilitate the broader use of data for OVC programs as well as to increase the demand for more and better data to inform these programs and services.
Prior to the workshop, we did a series of semi-structured interviews to gather information on the types of decisions that these participants had to make at their respective levels, we asked them about data constraints, and we asked them about the types of OVC data that they use for decision-making. This slide shows some of the responses of the types of decisions that the participants need to make. I’ve asterisked some of the decisions that either can be partly helped by using maps, and those that are imperative to make using maps. In the workshop we went over how to interpret maps, we gave all of the country teams many maps of their data to look at and come up with answers, questions, and decisions.
We also talked about the types of OVC program data that the participants use or have access to in their countries. Keep in mind that all countries do not have all of these types of data, but it is an aggregate list.
Now, lets’ talk about some of the challenges we faced in the workshop and face daily when trying to map OVC data for making decisions.
One of probably the most significant challenges to mapping data about children is that we often get a variety of data. Lots of contextual factors play a role in understanding vulnerabilities within households, communities, districts, regions and countries; we often are looking at health data (HIV status, nutrition), social indicators (basic needs, guardian status), economic indicators (HH consumption, wealth indices) and other types of data such as education, livelihoods etc… Getting all of that data in one place to use is sometimes a big challenge.
Another major challenge we find is that the data that we get is coming from all over the place! We have assessments and data from the Ministries of Gender, Justice, Labor, Youth, Health, Education etc.. And then we have data coming in from USAID, data coming from Implementing partners, service providers, national surveys, special surveys… All of this data comes in different shapes and sizes. This creates a challenge of cleaning and formatting data so that it can be imported into a GIS and mapped.
Sometimes we might find really awesome data, but it has no geographical disaggregation, or is not disaggregated to the level that you need., Therefore, it cannot be mapped unless you are mapping regional statistics.
One of the most common challenges that we come across has to do with unclear, undefined, or confusing boundaries of regions or districts. Geographic Boundaries change often, boundaries are not clear or are disputed, regions are spelled differently, or different organizations use different regions to differentiate their programs. In this case we are showing some maps of Uganda. On the left side, this map is from the OVC MIS showing the number of CSOs providing services in each region. The map on the right is from the Uganda DHS report which shows different regions. Based upon this data, you would be unable to link these two datasets to map them together if you wanted to combine DHS data by region with CSO data by region.
Another example, following on the previous slide, shows that there are often different groupings of regions, sometimes different areas are grouped together differently by different organizations. Sometimes some data is outdated and other data is newer and it is impossible to link these types of data together in a map.
Another common challenge to mapping Orphans and Vulnerable Children data is that sometimes its proprietary and sometimes people don’t like to share.
Now, lets’ talk about some of the successes we have had in the world of mapping OVC data.
Due to the increased demand for mapping data, there has been a significant increase in the quality of data that is available. There are now standardized international data schema and structures that allow for better ability to map and link data. People and organizations are now more often collecting geographic identifiers in their data because they need to know where things are happening and want to be able to map and link data together.
We are commonly making maps that show where service providers are located or where interventions are working as well as what types of services they are providing. This can not only help with planning and decision-making, also to eliminate overlaps and inefficiencies, or understanding unmet need.
Here we have a map of Ethiopian DHS data, showing Estimated Number of Orphans as a % of Total under 18 population. Here we can see areas with a higher rate of orphanhood in Ethiopia. These types of maps we can make and we are making all the time.
Here is another example of mapping vulnerabilites; here we are showing two indicators; % of children U5 below severely underweight, and % of children U5 not living with either biological parent. By mapping more than one indicator we can have a better context of the target population.
In this map we are looking at a map of OVC service providers and catchment areas. The rings around the point (the service provider) indicate 10, 20 and 50 km buffers. We often use the buffer function to create catchment areas; often for a health facility we use 5 km. There are a lot of things to take into account such as roads, rivers, and other geographic elements that may prohibit this catchment area from being precise, but it’ s a quick and easy way to understand service provision.
Here are the service providers and here are the catchment areas.
Here we have a map from an activity that was done by MEASURE Evaluation mapping OVC data in Nigeria. Many different maps were produced, but this one in particular shows something surprising that we may not have thought about. Here the map is showing that were the percent of orphans per state were higher, poverty was actually lower. By linking different data sets and putting them together on a map we have a better understanding of our data and of our target population.
Now, I’d like to introduce you to a few things that I think are kind of interesting and useful in the mapping world for use in decision-making and support for Orphans and Vulnerable Children programs.
Network analysis is a useful tool for understanding relationships both formal and informal between different actors. Recently there has been a lot of buzz about social network analysis which takes individuals and uses quantitative methods to understand how people are connected. Here we are talking about organizational network analysis in which the actors are organizations. Understanding relationships, or the lack of relationships can be helpful for a variety of reasons; we can understand the where and how referrals for different services are being given, we can better understand and strengthen case management if we know what organization work together, which ones don’t and which ones should, ONA can also help us to strengthen the continuum of care for OVC services, and in knowing what the network looks like and how actors relate and work with one another, we can help to strengthen the network thereby increasing postive outcomes for OVC.
The population council in conjunction with the South Africa Department of Social Development, HIV911/HIVAN, Westat and Trialogue contributed to this PEPFAR funding activity which includes a full directory of OVC service providers in South Africa. The OVC and Directory Project includes the directory in hard and soft copies, and SMS service in various South African languages in which one can SMS a request for a service provider in a specific area and they will get an SMS response, as well as an online interactive map which shows the location of the service providers as well as contextual data about each district in the country.
We can do stronger and more data analysis with some software; this one is called Open GeoDA (and its free and open-source) in which you can create maps and do data analysis at the same time. Here we can see a map showing the age a first marriage were the darker colors are show a higher age at first marriage and the lighter colors indicate early marriage. The Scatter plot at the right shows two variables plotted against each other showing age at first marriage on the x-axis and % of HHs in the lowest wealth quintile on the y-axis. The scatter plot is showing that the older age at first marriage, the fewer HHs in that district in the lowest wealth quintile. This is useful to answer questions on the fly, like “why are there more orphans in these districts than in these districts”
This is the same program and the same data; this software allows you to have all the windows that are open attached to the map. I’ve highlighted the districts in Bangladesh with the oldest average age a first marriages, you can see proablby only by the arrows, that the districts are highlighted on the scatterplot as well as in the table. This can help us understand the data behind our maps and make on the fly analysis of this data.
This may not be a new technology, but it is a type of mapping that can be done to better understand vulnerabilities at a community level. I believe there are a few countries currently undertaking this activity. Not only does this get community members involved, help provide services better and more effectively, it can help create and enhance dialogue about vulnerable children, and it can also help communities feel more empowered to solve their problems with community led solutions. In community mapping, also called social mapping, participatory mapping, stakeholders within a community will get together to map the community including resources, service providers and vulnerable households.
Crowdsourcing data is a somewhat new technology. For those not familiar with crowdsourcing data, what it means is involving the public to outsource a data collection task. Problems are generally presented to a non-defined, or defined public who provide solutions or data regarding the problem. There are many uses for crowdsourcing data; some of them that you may have heard of more recently have been to get real-time data about damages and injuries immediately after the earthquake in Haiti in 2010. Another more recent example of crowdsourcing data occurred during the “ arab spring ’ to gather photos, videos and reports about what was happening. This is a short video, about 2 minutes that shows how crowdsourcing data is being used for the prevention and response of violence against children.
Here is another example of the crowdsourcing of OVC data using the same platforms as Plan in Benin for violence against children. Here we can see the crowdsourced reports that are mapped on to the Ushahidi platform (open-source software for crowdsourcing). Since this is a screen shot, you can only see the static map that I pulled up but I’ll show you what the rest of the platform looks like.
Here what I’ve done is click on the list of coding options on the right hand side of the page and clicked on ‘urgent’ by zooming in you can see where these reports are coming from.
In this particular slide, I’ve clicked on one report of a male double orphan in Emergency need of Food Assistance. You can see that this report is verified. So when the crowdsourced reports come in they need to be verfied and entered into the system. These ‘alarm’ reports are sent to the proper organization responsible for providing these services in this region. Also monthly these reports are complied and sent to the organizations for their programming and M&E.
This slide is just showing the diagram of how the technology in namibia works. The form is brought up on the mobile phone and filled out, the SMS with the information is sent and received by Frontline SMS and saved in the server. Alarm and urgent responses are sent instantly to the constitutents. Reports are then automatically sent into the Ushahidi platform which is interactive online and searchable. Follow-up emails are sent to the messengers (who collected the data) to see if the OVC that were labeled ‘urgent’ have received their services. If the problems haven’t been solved then a second line of respondents are notified.
Using Maps in Decision Making to Strengthen Programs for Orphans and Vulnerable Children
Using maps in Decision-Making toStrengthen Programs for Orphans and Vulnerable Children Jen Curran MEASURE Evaluation September 2012
• OVC Data Demand and Use Workshop, March 2012• Challenges to mapping data related to OVC and OVC programs• Successes in mapping data related to OVC and OVC programs• Where do we go now? Future in mapping OVC related data.
OVC Workshop in March Three country teams; Ethiopia, Uganda & Tanzania Participants were a mix of Ministry, USAID, IPs, and CBOs. Goals were to help facilitate the broader use of data for evidence-based programming and to increase demand for an increase in data availability and quality.
Types of decisions OVC programs need to make Allocating resources to sub-partners * Determining resource allocations for specific activities within the OVC program (health, nutrition, education, etc…) * OVC program quality improvements Determining location of OVC service delivery ** Determining unmet OVC needs * OVC program expansion * OVC program monitoring Allocating resources to Ips ** Maps can be used to help make these decisions** Maps are imperative for making these decisions
Types of program data use/have OVC Situational Analysis National HIV Prevalence Data OVC MIS DHS/AIS/MIS APR Reports Impact Evaluations National Strategic Plans IP Quarterly Reports OGAC Priority reports Census data COP Planning Education statistics Human Resources data ILO statistics LQAs & DQAs Household expenditures data Costed-plans of action FEWS NET Anecdotal information
Mapping Orphans and Vulnerable Children Data CHALLENGES
Challenges to Mapping OVC data Lots of differenttype of indicators
Challenges to Mapping OVC dataData comes infrom differentsources and indifferent sizesand shapes:
Challenges to Mapping OVC data:Lack of disaggregation
Challenges to Mapping OVC data• Unclear geographic boundaries • Different names or spelling
Challenges to Mapping OVC dataUnlinkable datasets due to difference in regions.
Challenges to Mapping OVC data Some of its proprietary and some people don’t like sharing…Source: http://www.ulsterbusiness.com/articles/2012/04/16/cim-ireland-data-protection-event
Mapping Orphans and Vulnerable Children Data SUCCESSES
Successes in mapping OVC data Linking datais easier due tostandardized codes
Successes in mapping OVC data:Here is where we are working…
Successes in mapping OVC data: Showing Service Providerslocation and catchment areas
Successes in mapping OVC dataShowing ServiceProviderlocations andcatchment areasusingtransportationnetworks
Successes in mapping OVC dataWe’ve created a deeperunderstanding of our dataSource: MEASURE Evaluation
Mapping Orphans and Vulnerable Children Data What’s next?
Where can we go from here? Network analysis for understanding formal and informal relationships: •Referrals •Case Management •Continuum of Care •Strengthening the networkSource:http://designmonitoringevaluation.blogspot.com/2010/03/network-analysis-visualization-of.html
Where do we go from here OVC Mapping and Directory Project Children Services Directory: 1.Hard Copy 2.Websites of service providers 3.CD-ROM 4.SMS provider directory 5.Map providing services and contextual dataSource: http://pdf.usaid.gov/pdf_docs/PNADS350.pdf
Where can we go from here? Stronger Data Analysis:On the fly analysis of correlating variables
Where can we go from here? Robust geographic analysis of our data
Where can we go from here? Community/Social mappingSource: http://go.worldbank.org/6AV491AUX0
Where can we go from here? Crowdsourcing data http://www.youtube.com/watch?feature=player_embeSource: http://www.frontlinesms.com/frontlinesms-in-action/video-audio/
Where can we go from here?Source: https://smsconnectnamibia.crowdmap.com/?
MEASURE Evaluation is funded by the U.S. Agency forInternational Development (USAID) and implemented by theCarolina Population Center at the University of North Carolinaat Chapel Hill in partnership with Futures Group International,ICF International, John Snow, Inc., Management Sciences forHealth, and Tulane University. Views expressed in thispresentation do not necessarily reflect the views of USAID or theU.S. government.MEASURE Evaluation is the USAID Global Health Bureausprimary vehicle for supporting improvements in monitoring andevaluation in population, health and nutrition worldwide.
MEASURE Evaluation is funded by the U.S. Agency forInternational Development (USAID) and implemented by theCarolina Population Center at the University of NorthCarolina at Chapel Hill in partnership with Futures GroupInternational, ICF International, John Snow, Inc.,Management Sciences for Health, and Tulane University.The views expressed in this presentation do not necessarilyreflect the views of USAID or the United States government.