Blogs

Celebrating women in mapping: María Lucía Rodríguez

This month we are celebrating some of the many women who support our work as volunteer mappers, dotted all over the world. Our volunteers make up a global cohort that is collaborating virtually to map rural Tanzania, and help end FGM. They are from all sorts of backgrounds, hold different careers and live in many locations. We will be releasing a new post each week throughout March:

María Lucía Rodríguez

Location: Bogotá, Colombia

Mapping since: October 2020

What are you doing currently: I am an Architect and recently I finished my Master’s Degree in Landscape Architecture in Barcelona, ​​Spain. Currently I work as a freelance developing Masterplans for a few projects.

Maria Lucia Rodriguez

Why this cause: I guess because when I first heard that female genital mutilation existed, I was shocked. I thought it was incredible that something like this still happened today and that being so far away I didn’t know how I could help. When I discovered that I could use my skills and spare time to help women on the other side of the world, I knew I had to contribute.

Why mapping: As an architect, I understand the power of drawing / mapping where and how things are and how they relate to each other. Maps have always been a key tool both in understanding a land and its people and in managing it. You can learn so much from maps and satellite images! I also wanted to learn more about GIS and its applications, and about African geography, as we often don’t know much about it in Latin America.

Find out more and get involved here

Detecting pests in Maize and Cassava with the PlantNuru app

Agriculture is the backbone of the rural economy in Tanzania and the families of girls at risk of FGM and GBV are farmers.

Although in theory there are networks of agriculture extension officers to help them, often in practice they are too far away to be of any use. Therefore, we were very pleased to learn of the PlantVillage Nuru app which seeks to help farmers improve their practice.  In February 2021 TDT  had an online training session for people interested in how to use this free app to detect Fall Army Worm (a pest for maize) and Cassava diseases which was attended by our volunteer and GIS specialist Herry Kasunga. 

Since then he has been out training our Digital Champions to use the app. As maize and cassava are the main staple crops grown in their areas this is particularly important.

Here you can see the Digital Champion for Burunga village, Agness Marinya checking her crops with the app.  She says, “It is an easy way to monitor crops and give you feedbacks on how crops grow, and I will provide training to other farmers in my village.

“With better agriculture people are less likely to need to cut their daughters and sell them for cows.  I have 3 children all girls. I am so proud of my work as a Digital Champion in Burunga, because there have been so much changes in my village.

“Now the number of girls who are cut is reduced. We all need to raise our voices to say no so our children can live free from FGM.”

The slides from our training session are here, and the recording here.  You can also view and download the slides Herry used for training the digital champions below.

Please watch this space for further updates on how this helpful app is being used in Tanzania. 

How mapping is helping Tanzanian villages source water

 

At the Humanitarian OpenStreetMap Team global conference on Friday December 4 2020, Herry Kasunga talked about the Water Source mapping project that he has been coordinating with the Hope for Girls and Women digital champions.

Herry Kasunga

This is an extremely important project as the majority of people in Mara, as in the rest of Tanzania, are dependent on rainwater for household water, sanitation and to grow their food. It is also estimated that 40% of village water sources are degraded or non-functional. The shots below show some of the water points used by the digital champions:

In addition, climate change further threatens water access and means droughts and average temperature rises are likely, coupled with intense flooding events with significant damage to infrastructure and livelihoods, meaning mapping will become even more important.

Herry’s presentation slides are available here.

 

Missing villages In Tanzania II

Scene 2 – Find the village

Since the first part of our “Missing villages” project is already in the finish line, we have to explain the second part, which called “Find the village”.

In the first phase, we tried to name the village, now we have to create the village boundaries, if possible. As we said in the previous article, the delimitation of human habitats is not easy, the structure of the settlement is often region dependent. In the Ruvuma region (southern Tanzania) the settlements are well separated on the map. In contrast, in agricultural areas of the Shinyanga region, delimitation sometimes seems an impossible task.

Sources

According to the data created by Digital Globe and funded by the Gates Foundation, approximately 17 million buildings are in Tanzania. By the time that this study was made, the OSM community has mapped circa 11 million building so far, so using buildings data from OpenStreetMap is not enough, we had to use a different source to identify the settlement pattern. To map populations, we used the previously mentioned data from the Gates Foundation and Facebook’s High-Resolution Population Density data for validation.

Workflow

A building aggregation tool was prepared in ArcGIS to aggregate the building footprints (Polygon layer) to produce settlement layers.

  1. At first, buffer building footprints by 50m was created then merged
  2. Secondly, the merged multipolygons were divided into single polygons.
  3. Each settlement patterns area was measured, also the covered buildings were counted for each “building cluster”. These two data can help us to calculate:
    1. Building density (number of buildings / total area)
    1. Number of buildings, which data can help to predict the number of the population
    1. The building density and building number can help in the classification of settlements
      1. Urban or rural area
      1. Settlement type: hamlet, village, or town
  4. During the post-process, sharp angles in polygon were outlined and made smoother to improve aesthetic or cartographic quality. Finally, the unwanted “holes” from polygons were eliminated.

Using this data, we can identify the settlement pattern which can help us creating settlement boundaries for bigger villages, – especially with high building density – and adding to OSM.

In the final steps, first we have to remove those from the resulting settlement boundaries that are already in the OSM database. Secondly – using the validated settlement POIs, we select those settlement boundaries features that contain the village’s point features.

In the end, the selected village boundaries will be uploaded to OSM and will be waiting for phase 3.

Missing villages In Tanzania

Knowing the vast, rural area of Tanzania is crucial to provide timely and effective help for girls during Female Genital Mutilation (FGM) ‘cutting seasons’. In recent years, we have managed to map millions of buildings which can help us determine the distribution of the population. Although low population density areas in Tanzania are not sufficiently mapped yet, the initial steps have already been taken.

Goals of mapping

Crowd2Map Tanzania is a “crowdsourced mapping project aiming to put rural Tanzania on the map”. A primary goal is to help fight against FGM. Girls are rescued and taken to safe houses by local volunteers and police. However, for this they need maps. But maps can do more than just show these rescue teams the way to remote villages. The existence of spatial information can help with development and to increase commercial efficiency and economic growth opportunities for businesses and entrepreneurs, giving them the opportunity to make better-informed decisions. Growing wealth improves the quality of life, gives a chance for more opportunities and a better quality of education.

Find the village

So, we now know where to find traces of human settlements, but how do we delineate each settlement and, more importantly, how do we know what the name of the settlement is?

The delimitation of human habitats is not easy, the structure of the settlement is often region dependent. What does it mean? In the Ruvuma region (southern Tanzania) the settlements are well separated on the map. In contrast, in agricultural areas of the Shinyanga region, delimitation sometimes seems an impossible task.

And what about the names of the settlements? Local volunteers can help us identify all the names of circa 10,000 – 12,000 settlements in Tanzania, OR we can try to find some open source data which contains this information. Recruiting hundreds of volunteers from all over the country is beyond our power, so we need to focus on the second SOLUTION in most places. Fortunately, we have some open source data from The United Republic of Tanzania – Government Basic Statistics Portal, like health facilities or schools, or waterpoints located all over Tanzania.  

Our project objective is to add the missing village names in Tanzania, using open source government data about water sources in Tanzania. 

Water Points Location in Rural Water Supply – 2015-2016

Method for the estimation of village position

The shared database contains about 87,000 water sources, which can be lakes, rivers, machine drilled boreholes or springs. The database also contains the physical condition (quality, quantity) of the water sources as well as their spatial location, indicating, for example, the village name where the water source is, or the nearest village to it. This data helps us determine the name of the village in OSM.

Workflow

For data validation the best possible application is JOSM, which can prepare our data to upload to OSM after data validation. During validation, the next datasets and imagery were used: 

  1. Thyessen polygons were calculated from the water points layer, to get the influence zone of each water point. Then, the polygons were merged by attribute, where the village name is the same. The resulting polygons can help to determine the area where the village has to be.
  2. In the same time, Mean center was calculated for the points inside a polygon → potential position of the village. (Since in a few cases the name of a village occurs more than once in the country, a “village+district” combined data was used to help us to find the real mean center.) This is our village data POI which need to be implemented to OSM.
  3. OpenStreetMap imagery was used to identify the trace of human activity if the area was well mapped. We were also able to get an answer as to whether the name of the settlement has already been given to OSM. 
  4. Maxar satellite imagery was used for those areas that weren’t mapped yet. 
  5. Other useful datasets for validation
    • Waterpoints: can be really useful, if the position of the village’s POI is unusually far from any populated area. In this case, it is worth looking at how each water point is located in the area. Another example, when the village consists of two sub-villages, then the “SUBVILLAGE” attribute of the water database can help determine where the center of the village can be.
    • Health facilities data: The government data contains more than 7,000 health facilities like hospitals or clinics. The names of these facilities are usually, but not exclusively, the same as the name of the municipality where it is located. 
    • Education data: The government data contains almost 7,000 schools. The village names are available in this data. 

In summary

The Voronoi polygon assigns the area where the village is located (or has to be). The village POI assigns the potential location of the settlement, BUT its accuracy depends on the number of water abstraction points and their location in/around the given settlement.

In a well-mapped area - where, moreover, the settlements can be easily separated from each other - we did not have a difficult time with validation (mean centers before validation).
In a well-mapped area – where, moreover, the settlements can be easily separated from each other – we did not have a difficult time with validation (mean centers before validation).
The mean center of the Waterpoints sometimes clearly shows the center of the settlement if these water points are evenly distributed within and around the settlement.

Provisional results

By the end of September, more than 143 districts were validated (88% of all districts), and 5505 villages POIs were added which is 52% of the total village POIs in Tanzania.

User nameTotal edits
Bgabor1802
SHABANI MAGAWILA2255
Kasunga884
Stuart Ward49
Number of edits by users – which was added with “TNZ_missing_villages” hashtag

Crowd2map volunteers in the lead

The OSM database currently contains 10483 Tanzanian village points, a significant part was added by the volunteers of the Crowd2map team. The following pie chat shows how this 10483 POIs is divided between the TOP5 volunteers and the rest of mapper community:

Updated results – 31/10/2020

By the end of October, more than 157 districts were validated (97% of all districts), and 6759 villages POIs were added.

Against my will: A collaborative effort to end gender inequality

United Nations Population Fund (UNFPA) has published its State of the World Population 2020 paper. Against my will: Defying the practices that harm women and girls and undermine equality, has contributions from many important figures focused on improving female prospects globally through a combination of determination and ongoing action.

State of the world population 2020 cover

Rhobi Samwelly tells her harrowing story of experiencing near fatal female genital mutilation and seeing it kill her friend, and how this galvanised the founding of our partner organisation, Hope for Girls and Women. Rhobi is featured from page 67 of the report.
There is a wealth of important information about gender inequality within the document.  We were therefore keen to share it as a wider reading resource for those campaigning for an end to FGM and those interested in learning more about this and other practices that aim to prohibit the rights of women.

The full report can be downloaded via the UNFPA site from the button below:

Improving the impact of data from our partner, Hope for Girls and Women

‘Female Genital Mutilation’ and ‘data visualisation’ might not be two terms that you would immediately put together. However on June 1st, the Viz5 team and makeovermonday.co.uk did just that. Their global community of data enthusiasts were challenged to help communicate some of Hope for Girls and Women’s critical stats through a range of different visualisation techniques. 

Created byPriya Padham

Data can, at times, be quite impenetrable and dry. Being able to identify a logical flow and narrative using data visualisation techniques on a webpage, presentation or report, can help the information become more digestible and intuitive for the audience. According to t-sciences.com,  ‘the human brain processes images 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual.’ 

Created byLiam Spencer

As part of the monthly #Viz5 data visualisation challenge, the team featured data from Hope in an effort to support our advocacy work and raise awareness of the fight to end FGM. There were so many great data visualisations produced! These were reviewed by Eva Murray, Technology Evangelist & Tableau Zen Master at Exasol and Seth Cochran, Founder & CEO at OpFistula.org.

  • You can see and hear the feedback they provided here.
  • The shortlisted visualisations are also available to view here.

Hope has a relationship with the Viz5 team through our association with the Tanzania Development Trust and Crowd2map. They have supported with our data collection and mapping of Tanzania, and were keen to use their platform to help us drive awareness around the challenges we face with FGM and the support we provide through the safe houses. Their passion comes across in the feedback session – we look forward to collaborating again soon!

To read more about the outstanding efforts and this important collaboration, please find the Viz5 article here.

Meet our mapping volunteers who are helping to end FGM in Tanzania

Female Genital Mutilation/Cutting (FGM/C) is a traditional practice prevalent in many parts of Africa and across the world. It is rooted in gender inequality and attempts to control a woman’s body. FGM in Tanzania secures a higher dowry for the parents of the girl who has undergone the procedure.  FGM was criminalised in 1998 in Tanzania, so it is frequently undertaken in secret and unhygienic, dangerous conditions. 

How can I help stop FGM?

Crowd2Map Tanzania is an entirely volunteer-based mapping project putting rural Tanzania on the map.  Having better open-source maps helps activists protect girls from FGM and supports navigation and community development.

Since 2015, over 13,000 remote volunteers worldwide, based in countries ranging from Poland to China, Brazil to the United States, to name a few, have been adding roads and buildings to digital maps and supporting the cause.

This year with the many challenges resulting from COVID-19, more than ever, we need all the support we can get to continue our mission to end FGM and get help to vulnerable girls faster.  You can volunteer from home, contributing to the first stage of mapping, as long as you have an internet connection, the team on the ground in Tanzania then completes the process using their local knowledge. Get started and find out more here.

Will you become a remote online volunteer?

Find out more about some of our volunteers here:

Katerina - Crowd2Map volunteer of the month January 2021
Crowd2Map volunteer Si Wilde
Crowd2Map volunteer Maria Cielecka
Image of Crowd 2 Map volunteer MaryAnn Obidike
Image of Crowd 2 Map volunteer, Pietro Fasciolo
Anshul Vohra - C2M volunteer

Emmanuel Uwinima - C2M volunteer
Maja Turkalj - C2M volunteer


Landscape mapping and monitoring with GeoSurvey

Dr. Markus Walsh is a Senior Research Scientist in Ecosystems and Landscape Ecology with the Earth Institute at Columbia University. He will show how a software application called GeoSurvey (try it @ https://geosurvey.qed.ai/) can be used to rapidly map and monitor land cover and use characteristics of countries and/or other large regions of interest. He will also demonstrate how the Africa Soil Information Service (AfSIS), based in Arusha, Tanzania, is applying the GeoSurvey approach for predictive soil mapping.

This free seminar is aimed at anyone with an interest in how remote sensing, machine learning and predictive mapping can help monitor progress towards the SDGs, particularly in a country like Tanzania. It assumes no prior technical knowledge. There will be a chance to ask questions and the webinar will be recorded. Everyone is welcome to attend!

Dr. Walsh was raised & schooled in Kenya and has over 35 years of professional experience in land degradation, ecosystems & landscape ecology research in Africa. He was a senior research scientist at the World Agroforestry Center (ICRAF) where he led landscape restoration research in Africa, based out of Kisumu, Kenya. Since 2008 he has been based at the Selian Agricultural Research Institute (SARI) in Arusha, Tanzania.

He has developed numerous practical spectrometry and remote sensing applications for mapping and monitoring biological, chemical and physical characteristics of soils and ecosystems across Africa. After joining the Earth Institute in 2007, his research has focused on developing operational tools for mapping and monitoring of the ecological condition of African landscapes with an emphasis on the application of IT and data science in agriculture. He is passionate about the application of open source information and reproducible research in solving Africa’s natural resource management problems.

 

How Open Data can Help Tanzania

Come and hear about orgs that produce and use Open Data to improve Public Health, Flood Resilience and reduce GBV, FGM, etc. in Tanzania

Join us at this free event to hear from a range of organisations that produce and use Open Data in Tanzania with use cases to improve Public Health and Flood Resilience in the cities also reduction of Gender Based Violence (GBV), Female Genital Mutilation (FGM) etc. No prior knowledge is needed. There will be a keynote on what is Open Data and the advantages of using Open Data in your work, followed by a review of the Open Data that is available to help you. Then there will be an overview of the free tools you can use to collect, analyse and map Open Data. We will then hear from a number of experts on how they use Open Data to plan and evaluate their work, with an opportunity to ask questions.