Data Zetu (“Our Data” in English) aims to empower communities to make better, more evidence-based decisions to improve their lives. The program works with communities across Tanzania to discover issues that matter most to them. Then we work with stakeholders to arm them with skills and tools to make sense of data related to those challenges. As a result, we’re helping to foster the use of open data that is relevant, hyper local, and actionable.

OMDTZ participated again as HOT affiliate to support in providing Geo-spatial data and visualizations –that provide evidence to the challenges pointed by community members–ie making maps on time taken by women to reach maternity health care and visualize it on a map.

As Data Zetu consortium we have worked in producing health maps in Dar es Salaam (Temeke district) and  Mbeya ( Mbeya urban and Kyela districts). Mappers worked closely with community members to make sure data collection process capture the clear and accurate information as community members understand well their neighborhood and challenges involved. We conducted household surveys using ODK application and the survey captured the following aspects:


Shina Mapping

This is  the most granular level of administrative boundaries that exist in Tanzania. The discovery of shina boundaries came into place when we were conducting household surveys in Temeke-Makangarawe ward. As we were working closely with wajumbe (Shina leaders) we came to realise that all the survey submitted from a specific mjumbe in the subward fall in a polygon of around 30 to 200 houses when visualized on QGIS- the software that we use to produce maps. 

After discovery of these boundaries the next step was to replicate the process on other subward and produce hyperlocal maps which are very potential when there are disasters or eruption of disease. It will be easier to locate people within a range of households.

Use cases of Shina maps and Health maps produced

Upgrading Amana Hospital's medical record system to enhance patient origin Tracking

Small but mighty tweaks to a major hospital’s electronic health information system (eHIS) are helping clinical staff to more accurately track where patients come from—making it easier to locate disease outbreaks and manage care.

In May 2018, a working relationship between Amana Hospital and Data Zetu was established and

Generating location data to allocate community workers where they are most needed in the fight against HIV.

In the southern highlands of Tanzania, KIHUMBE’s team uses low-cost digital tools to produce maps and manage data to more efficiently allocate their limited resources to reduce HIV/AIDS.

KIHUMBE is a non-profit NGO located in Mbeya, Tanzania—a district that has one of the

highest prevalence rates of HIV/AIDS in the country. KIHUMBE provides HIV-related services

and counselling to at-risk individuals

Hyperlocal Boundary (Shina Mapping)

Community mapping efforts in Makangarawe Ward have surfaced data about hyperlocal “shina” boundaries, which offer local communities and leaders unprecedented information about the most granular level of community administration that exists in Tanzania. This is the first time Dar es Salaam has been mapped to such a detailed level, and the data collected will become invaluable for public health planning, local administration, economic evaluation and disaster prevention.

Dar es Salaam is divided into five municipalities, 92 wards and approximately 450 subwards (a subward is also known as a “mtaa” in Swahili). Through community mapping in November 2017, the HOT team uncovered further divisions within a mtaa known as “shina” (which translates roughly to “branches” in English). These shina are the most hyperlocal decision making structures in urban Tanzania.

Patients tracking system.

In the southern highlands of Tanzania, KYELA District is one the District that is highly affected by seasonal diseases like Cholera due to different reasons. 

Hence, there was the need of  establishing new system at Ward Health Center known as Bujonde Health Center in Bujonde Ward. 

The reason was to identify quickly where these patients are coming from so as to take measures as soon as possible to avoid transferring the disease from one community to other.