OpenDRI brings the philosophies and practices of the global open data movement to the challenges of reducing vulnerability and building resilience to natural hazards and the impacts of climate change across the globe.

news

HOT Supporting the Greater Accra Resilient and Integrated Development Project to Protect Communities from Flooding

This article was originally published by Humanitarian OpenStreetMap Team (HOT). Flooding has plagued the Ghanaian capital of Accra for years, so HOT worked with Mobile Web Ghana and OpenStreetMap Ghana to develop data on local buildings, drainage, and infrastructure that communities and municipal authorities could use to make vulnerable neighborhoods more resilient. Flooding has been a persistent and even deadly problem for the residents of Accra for years. The Ghanaian capital sees floods during the rainy season every year, and 60% of the city’s population lives in the flood-prone Odaw River basin. In April 2019, 5 people were killed by floods, and in 2015 between a hundred and two hundred people were killed either by floods or by fires while flooding confounded rescue efforts. The floods each year also displace many vulnerable residents, destroy properties, and spread waste, leading to outbreaks of diseases such as cholera. The Humanitarian OpenStreetMap Team (HOT) teamed up with Mobile Web Ghana and OpenStreetMap Ghana to execute the Open Cities Accra (OCA Accra) project. This project provided Ghana’s Ministry of Inner-Cities and Zongo Development and the Greater Accra Resilient and Integrated Development (GARID) project with vital data to combat these deadly floods and to build the stakeholder’s capabilities to collect and use spatial data to improve the lives of people in the most vulnerable communities. OCA Accra’s work focused on the under-mapped neighborhoods of Alogboshie, Alajo, Akweteyman, and Nima. These communities lie along the Odaw River and are not only vulnerable to flooding themselves, but are also major sources of waste being swept into the river during floods. These efforts were funded through The World Bank and took place from June 2018 to February 2020. OCA Accra supported the wider GARID project and Open Cities Africa, a World Bank funded effort to use open data to build sustainable and resilient communities across 15 cities in Africa. Starting with Remote Mapping To help the local communities and government tackle the flooding and waste challenges, OCA Accra employed remote mapping, field data collection, and data validation. The project also aimed to build the capacity of the local government and communities to collect and interpret spatial data and contribute to the OpenStreetMap platform even after the project ended. Satellite imagery on the left and drone imagery on the right This remote mapping tapped into both local and global networks. The project was primarily supported by OSM community members in Ghana including YouthMappers. As project stakeholders and community members were also trained in mapping, they joined the local OSM community and provided significant help in mapping the neighborhoods, particularly in Alogboshie, Akweteyman, and Nima. Meanwhile, The World Bank hosted a mapathon in Washington, DC to support the mapping of Alajo. DC mapathon Moving on to Field Mapping The mapping based on the satellite and drone imagery set the stage for in-person mapping and photo-taking in the neighborhoods. Whereas the remote mapping had identified roads and building footprints, the field mapping collected data on the building materials and levels, road widths, waterways, drains, potable water sources, waste dumping sites, and essential services like education and health facilities. For this field mapping, the OCA team recruited local mappers. They were trained in intensive four and five days sessions at Mobile Web Ghana, where they learned how to create and collect geospatial data from imagery and in the field using free and open source software such as JOSM, the Tasking Manager, OpenMapKit, and OpenDataKit. In addition to the data they collected, these teams of mappers found the effort to be a valuable experience to learn and share new skills. Chris Eshun, one of the mappers, said, “Our field team was diverse, with some experts in OSM and data collection and others completely new to the world of mapping.” Pascalina Awelana Abadum, one of the mappers and data cleaners, wrote that she unraveled her career passion and interest area through her participation in this project. Field mappers with Open Cities Accra The field teams mapped and took photos using mobile phones, and the images were then uploaded to Mapillary. These street-level images were an invaluable resource for recording and verifying details about the features they mapped. Engaging community members Validating waste data with geotagged images from the field and JOSM Working with Stakeholders OCA Accra had a multifaceted approach to stakeholders, in both engagement and goals. In planning the project, the team had hosted stakeholders from numerous institutions including the Accra Metropolitan Assembly, Land Use and Spatial Planning Authority, and Forestry Commission, among many others, and assessed what data resources these organizations had and what data they needed from the field. This assessment led to the project prioritizing flood history and physical infrastructure data so that it would be up-to-date and comprehensive for the government and accessible to the public. Open Cities Accra Project team meets with the Ministry of Inner City and Zongo Development Stakeholders were also asked about what data products would be most useful to them, leading to the conclusion that the best way to share the data gathered by the project would be through a web application and wall maps. Web application Stakeholders were also taught how to use and contribute to the OpenStreetMap platform, enabling them to continue generating and using data after the project was over. Impact for the Community The main results of the Open Cities Accra Project were the data products and the geospatial data made available for free on the OSM platform. Across the four communities, over 8 sq km of dense urban development was mapped, including close to 36,000 buildings and over 98 km of roads and paths. To better understand flooding in these communities, the field mappers mapped over 1,500 drain points and over 83 km of drain segments. Hundreds of social services were mapped, including schools, clinics, and financial institutions. In addition, hundreds of solid waste dumping sites, potential sources of disease during floods, were also mapped. This data will enable drainage improvements, flood forecasting, solid waste management, and improved urban planning to support these communities. The OCA Accra team also conducted a public training and exhibition workshop for the stakeholders and community members to showcase the data they had collected. One participant from the Land Use and Spatial Planning Authority reported that his department heavily relies on the OpenStreetMap platform and that data from the project would be essential to the government’s street-naming project. Another participant from the Ministry of Inner-Cities and Zongo Development said the data would be useful for their work to upgrade slum areas to make them resilient to natural hazards. Mapping infrastructure assets would also aid in mobilizing revenue and generating funds within the Ayawaso East Municipal Assembly (AEMA) area, reported one of their representatives. The AEMA intends on also “using the aerial photo extensively in the property addressing exercise we will be embarking on very soon,” reported Jamila Salihu, an Assistant Physical Planning Officer with the AEMA. Engaging stakeholders In addition to the direct impact of the data generated, the OCA Accra project also developed the capacity of local agencies and community members to generate and use spatial data and contribute to OpenStreetMap. Over the course of the project, OCA trained over 110 mappers and 30 staff members of government institutions in contributing to OpenStreetMap, adding to the growing community of Ghanaian OpenStreetMap contributors.

When community mapping meets artificial intelligence

By Vivien Deparday, Dave Luo, Robert Soden, Nick Jones, and Grace Doherty This article was originally published on Medium: World of Opportunity. The Open Cities community mapping team in Ngaoundéré, Cameroon prepares ground control points for a drone flight over an area known by residents for landslides. The images captured are now used to improve local urban planning activities and international machine learning methods via the Open Cities AI Challenge. Credit: Michel Tchotsoua, ACAGER Disaster risk specialists have an oft-repeated phrase: There is nothing “natural” in natural disasters. That is, the hazards are natural, but disasters can be reduced or averted with planning and investment. Disaster risk starts when people — and the things they use and own — are in situations that expose them to hazards. For example, the location and construction material of someone’s home can tell us how likely it would be damaged by a flood or landslide. We need detailed geographic data about populations and their built environment to understand this exposure and to inform disaster reduction investments like early warning systems, risk financing mechanisms, and public services management. This becomes even more important as the gendered and compounding risks associated with COVID-19 are projected to fall on the most marginalized communities. For example, take Ghana — a country with whom the World Bank works closely to reduce its disaster risk. In 2015, flooding from heavy rains displaced 50 thousand people living in the Odaw river basin of Greater Accra, Ghana’s capital. Today, Accra’s leaders reflect on their past as they invest in actions that will build a better future. What would the aftermath of the 2015 rains have been if government and civil society had the data to pinpoint who and what was most vulnerable? Three years after the devastating floods of 2015, Open Cities mappers in Accra collect information about vulnerable neighborhoods of the city to improve disaster response and urban planning. Credit: Gabriel Joe Amuzu, Amuzujoe Photography. Artificial intelligence scales up local knowledge Getting those data is easier said than done. Part of the challenge is knowing what data is needed. Many features pertinent to urban service delivery and urban risk profiles — such as the status of waste management, health and education services, condition of drainage networks, stability of slopes and embankments — are well-known to ward level leaders and communities, but often difficult for the city government to monitor. Collecting data accurately and to scale can also be trying. Field-based community mapping efforts like Open Cities Africa and the Resilience Academy coordinate with local leadership to create rich, locally validated details about these vulnerable places. Through Open Cities Africa, Accra’s government has worked with residents to map thousands of features including 35,000 buildings into OpenStreetMap. Even though on-the-ground data collection is affordable and sorely needed, field methods by themselves cannot always keep up with urban growth’s increasing density and sprawl. Advances in machine learning (ML), an application of artificial intelligence (AI), can help scale up data collection efforts. Where community mapping meets ML, we are seeing local data collection and validation efforts across a much larger geographic area than what field methods could do alone. But we’re also learning that AI without ethical safeguards can have damaging consequences. The Labs team at the Global Facility for Disaster Reduction and Recovery (GFDRR) explored what that means for vulnerable populations in the recently completed Open Cities AI Challenge in partnership with DrivenData and Azavea. The Challenge featured locally collected high-resolution drone imagery and manually labeled geographic data from more than a dozen African cities. This extensive and geographically diverse dataset enabled experts to develop ML models that can automatically classify every pixel representing building footprints (aka “building footprint segmentation”) from drone imagery. Sample outputs reflecting predictions from the winning building footprint segmentation model from the Open Cities AI Challenge (red) compared with ground truth labels (black). Left image from Lusaka, Zambia, right image from Zanzibar, Tanzania. Machine learning done responsibly The Challenge offered an unprecedented opportunity to use locally-produced training data to map African cities with AI assistance. It also shed light on ways that bias and error in AI techniques can misrepresent marginalized communities. In addition to the algorithmic challenge of improving building footprint segmentation, Challenge participants engaged in a Responsible AI for Disaster Risk Management (Responsible AI for DRM) track to explore ethical considerations and consequences of bias in data collection and ML applications for mapping. The three winning Responsible AI entries draw common threads in recognizing biases and downstream consequences in ML systems, maintaining ethical oversight and safeguards, and promoting data stewardship, especially where the most vulnerable members of society are impacted. “It’s commonly said that algorithms learn the inherent bias in the data that they are trained on: ‘bias in, bias out’. It’s clear how this could work for case studies such as loan acceptance or fraud detection, where models are often trained on individual personal data, past outcomes or decision-making records from previous cases. But in the case of machine learning on aerial imagery for mapping, we’re ultimately just working with pixels. How can a model learn a prejudice purely from pixels?”Catherine Inness, a Responsible AI Challenge track winner Energy and climate data consultant Chris Arderne illustrated how seemingly benign technical decisions, such as the choice of accuracy metric used to evaluate a model, can lead to unintended real-world consequences like ML systems that under-recognize small and informally constructed buildings. Catherine Inness, a master’s student in data science at UCL London, discusses the tendency for ML model improvements to optimize for the majority population — leaving the burden of error to disproportionately affect often already vulnerable groups — and reviews possible safeguard measures across the ML development pipeline. Lastly, data scientists Thomas Kavanagh and Alex Weston propose an ethical framework for the use of contributed geographic information and highlight the responsibility of ML developers as data stewards: identifying, acknowledging, and reducing biases and potential abuses that come with any data collection and decision-making system. Read more from all three entries on OpenDRI.org. Chris Arderne demonstrates how bias can arise from the metric used to evaluate models and result in over- or under-prediction of specific kinds of buildings. A model that maps fewer, larger buildings could score better than one that maps more, smaller buildings on a pixel-based evaluation metric. Pictured: Gray represents the true shape and location of buildings. Red represents a model’s attempt to map them in four different places. What did the model miss? If these were homes, who would be left off the map? Source is here. What does ethical data stewardship look like? “We take community participation very seriously. If the people are engaged, the impact is felt at the end of the day, because they were involved from Day 1. After all, the project is for them.”Jamila Salihu, Assistant Development Planning Officer, Ayawaso East Municipal Assembly, Accra, Ghana. Guided by Responsible AI principles, local data stewardship must happen at many stages: Community leadership should have a voice when determining the data model, i.e. the types of information to collect.Residents should also be empowered in data collection and in validation, providing recommendations for tough labelling situations where an ML system could misinterpret the image.Communities deserve to access, use, and take ownership over the data collected about them. Open Cities projects include a consultation phase with local residents and community stakeholders to refine the data model. Left: Men from Ngaoundéré, Cameroon identify flood and rockslide risk areas on a map. Credit: Michel Tchotsoua, ACAGER; Right: Neighborhood leader Mrs. Pay Pay speaks on behalf of her flood- and erosion-prone district of Delapaix at an Open Cities civil society workshop in Kinshasa, Democratic Republic of Congo. Photo credit: OSFAC We need advances in geospatial data and ML systems to scale our efforts to protect populations. But we need these advances to be grounded by the communities they aim to protect. Responsible AI for disaster risk and other sectors of development is a growing area of research, and the World Bank and GFDRR continue to engage with these ethical considerations through community mapping and youth digital employment and learning programs in Africa and around the globe. Reach out to the Responsible AI for DRM working group to learn more about activities around this emerging body of work. Read more World Bank blogs and stories.

The Open Cities AI Challenge

Post by Dave Luo, Grace Doherty, and Nicholas Jones, GFDRR Labs/World Bank This article was originally published on Towards Data Science. Takeaways The Global Facility for Disaster Reduction and Recovery (GFDRR) is partnering with Azavea and DrivenData to introduce a new dataset and machine learning (ML) competition ($15,000 in total prizes) to improve mapping for resilient urban planning. Better ML-supported mapping for disaster risk management means addressing barriers to applying ML in African urban environments and adopting best practices in geospatial data preparation to enable easier ML usage. The competition dataset — over 400 square kilometers of high-resolution drone imagery and 790K building footprints — is sourced from locally validated, open source community mapping efforts from 10+ urban areas across Africa. Prize-winning solutions will be published as open-source tools for continued ML development and benchmarking. The Open Cities AI Challenge has two participation tracks: $12,000 in prizes for best open-source semantic segmentation of building footprints from drone imagery that can generalize across a diverse range of African urban environments, spatial resolutions, and imaging conditions.$3,000 in prizes for thoughtful explorations of Responsible AI development and application for disaster risk management. How might we improve the creation and use of ML systems to mitigate biases, promote fair and ethical use, inform decision-making with clarity, and make safeguards to protect users and end-beneficiaries? The competition is ongoing and ends March 16th, 2020. Join today! Open Data for Resilient Urban Planning Cities around the world are growing rapidly, especially in Africa — by 2030, half of Sub-Saharan Africa’s population will live in urban areas. As urban populations grow, their exposure to flooding, erosion, earthquakes, coastal storms, and other hazards becomes a complex challenge for urban planning. Understanding how assets and people are vulnerable to these risks requires detailed, up-to-date geographic data of the built environment. For example, a building’s particular location, shape, and construction style can tell us whether it will be more exposed to earthquake or wind damage than nearby buildings. Roads, buildings, and critical infrastructure need to be mapped frequently, accurately, and in detail if we are to understand and manage risk effectively. But in countries with less developed data infrastructure, traditional urban data collection methods can’t keep up with increasing density and sprawl. A field mapper from Open Cities Accra observes standing water and refuse in a flood-prone neighborhood of Accra, Ghana. Photo courtesy of Gabriel Joe Amuzu, Amuzujoe Photography. Thankfully, collaborative and open data collection practices are reshaping the way we map cities. Today, local mapping communities are improving maps for some of the world’s most vulnerable neighborhoods — bringing highly accurate and detailed geographic data up-to-date and to scale. GFDRR at the World Bank supports programs like Open Cities Africa and Dar Ramani Huria to map buildings, roads, drainage networks and more in over a dozen African cities, and Zanzibar Mapping Initiative was the world’s largest aerial mapping exercise using consumer drones and local mappers to produce open spatial data for conservation and development in the archipelago. To-date, OpenStreetMap contributors have mapped more than 70 million ways and 600 million nodes onto the African continent. Data collected in these community mapping programs are used to design tools and products that support government decision-making. Digitized maps are published to OpenStreetMap and aerial imagery to OpenAerialMap where they serve as data public goods that can be used and improved by all. The open source philosophy behind the movement and an emphasis on local skill-building has fostered local networks of talent in digital cartography, robotics, software development, and data science. Potential of Machine Learning for Mapping Advances in ML for visual tasks could further improve mapping quality, speed, and cost. Recent examples of ML applications for mapping include Facebook’s AI-assisted mapping tool for OpenStreetMap and Microsoft’s country-scale automated building footprint extraction (in USA, Canada, Tanzania and Uganda). Competitions like SpaceNet and xView2 advance ML practices for automated mapping of roads, buildings, and building damage assessment after disasters. Obstacles, however, stand in the way of effectively applying current ML mapping solutions to the African disaster risk management context. Africa’s urban environments differ significantly in make-up and appearance from European, American, or Asian cities which have more abundant data that ML models are often trained on. Buildings that are more densely situated and diverse in shape, construction style, and size may be less recognizable to ML models that saw few or no such examples in their training. Comparing urban built environments of Las Vegas, USA (left) to Monrovia, Liberia (right) at the same visual scale. Imagery courtesy of Microsoft Bing Maps and Maxar (DigitalGlobe) Imagery is collected by commercial drones at much higher resolution under diverse environmental conditions, requiring adaptation of models usually trained on lower-resolution, more consistently collected and preprocessed satellite imagery. Comparing urban details at typical satellite image resolution (>30cm/pixel, top) to drone/aerial image resolution (3–20cm/pixel, bottom) in Dar es Salaam, Tanzania. Imagery courtesy of Maxar and OpenAerialMap. Crowdsourced and community-driven data labeling may differ greatly in what base imagery layers are used, workflow, data schema, and quality control, requiring models that are robust to more label noise. Quality of hand-drawn building footprint labels (alignment and completeness) can vary across or within image scenes. Examples from Challenge training dataset for Kampala, Uganda (left) and Kinshasa, DRC (right). Geospatial data comes in a diversity of file formats, sizes, and schemas that create high adoption and knowledge barriers that hamper their use in machine learning. There is now a growing abundance of locally-validated open map data and high resolution drone imagery in diverse built environments. How might we best address these obstacles and enhance the state of practice in machine learning to support mapping for urban development and risk reduction for Africa’s cities? Introducing the Open Cities AI Challenge Dataset Working with partners Azavea and DrivenData, the Labs team at GFDRR combined the excellent work of many participatory mapping communities across Africa, applied best practices in cloud-native geospatial data processing (i.e. using Cloud-Optimized GeoTIFFs [COG] and SpatioTemporal Asset Catalogs [STAC]), and standardized wherever possible to make data more readily usable for machine learning. The result is a novel, extensive, open dataset of over 790K building footprints and 400 square kilometers of drone imagery representing 10 diverse African urban areas in ML-ready form. Comparing hand-labeled building footprints overlaid on drone imagery for 10 African urban areas included in the Challenge training dataset. Using COG and STAC for geospatial data provides us with bandwidth-efficient, rapid, and query-able access to our imagery and labels in a standardized format. Ease of access to files and indexing of data catalogs is particularly important for geospatial data which can quickly grow to 100s of gigabytes. It also enables us to tap into the growing ecosystem of COG and STAC tools, like STAC Browser to rapidly visualize and access any training data asset in a web browser, despite individual image files being up to several GBs and the entire dataset totaling over 70 GBs in size: Animated demo of using STAC Browser to visualize Challenge training data collections and assets . PySTAC, a new Python library by Azavea, enables STAC users to load, traverse, access, and manipulate data within catalogs programmatically. For example, reading a STAC catalog: train1_cat = Catalog.from_file('https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/catalog.json') train1_cat.describe()* <Catalog id=train_tier_1> * <Collection id=acc> * <Item id=665946> * <LabelItem id=665946-labels> * <Item id=a42435> * <LabelItem id=a42435-labels> * <Item id=ca041a> * <LabelItem id=ca041a-labels> * <Item id=d41d81> * <LabelItem id=d41d81-labels> * <Collection id=mon> * <Item id=401175> ... Inspecting an item’s metadata: one_item = train1_cat.get_child(id='acc').get_item(id='ca041a')one_item.to_dict(){ "assets": { "image": { "href": "https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/acc/ca041a/ca041a.tif", "title": "GeoTIFF", "type": "image/tiff; application=geotiff; profile=cloud-optimized" } }, "bbox": [ -0.22707525357332697, 5.585527399115482, -0.20581415249279408, 5.610742610987594 ], "collection": "acc", "geometry": { "coordinates": [ [ [ -0.2260939759101167, 5.607821019807083 ], ... [ -0.2260939759101167, 5.607821019807083 ] ] ], "type": "Polygon" }, "id": "ca041a", "links": [ { "href": "../collection.json", "rel": "collection", "type": "application/json" }, { "href": "https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/acc/ca041a/ca041a.json", "rel": "self", "type": "application/json" }, { "href": "../../catalog.json", "rel": "root", "type": "application/json" }, { "href": "../collection.json", "rel": "parent", "type": "application/json" } ], "properties": { "area": "acc", "datetime": "2018-11-12 00:00:00Z", "license": "CC BY 4.0" }, "stac_version": "0.8.1", "type": "Feature"} Learn more about the dataset and STAC resources. Competition Accompanying the dataset is a competitive machine learning challenge with $15,000 in total prizes to encourage ML experts globally to develop more accurate, relevant, and readily usable open-source solutions to support mapping in African cities. There are 2 participation tracks: Semantic Segmentation track: $12,000 in prizes for the best open-source semantic segmentation models to map building footprints from aerial imagery. The machine learning objective is to segment (classify) every pixel in every image as building or no-building with model performance being evaluated with the Intersection-over-Union metric (aka Jaccard Index): Semantic segmentation is useful for mapping because its pixel-level outputs are relatively easy to visually interpret, verify, and use as-is (e.g. in the calculation of built-up surface area) or as inputs to downstream steps (e.g. first segment buildings and then classify attributes about each segmented building like its construction status or roof material). Segmentation track participants must also submit at least once to the Responsible AI track to qualify for $12,000 in segmentation track prizes. Example image chip (left) and segmentation (right) from the Challenge dataset. Responsible AI track: $3,000 in prizes will be awarded for best ideas applying an ethical lens to the design and use of ML systems for disaster risk management. ML can improve data applications in disaster risk management, especially when coupled with computer vision and geospatial technologies, by providing more accurate, faster, or lower-cost approaches to assessing risk. At the same time, we urgently need to develop a better understanding of the potential for negative or unintended consequences of their use. With growing attention given to questions of appropriate and ethical ML use for facial recognition, criminal justice, healthcare, and other domains, we have an immediate responsibility to elevate these questions for disaster risk. Examples of potential harm that ML technologies present in this space include, but are not limited to: Perpetuating and aggravating societal inequalities through the presence of biases throughout the machine learning development pipeline.Aggravating privacy and security concerns in Fragility, Conflict and Violence settings through combination of previously distinct datasets.Limiting opportunities for public participation in disaster risk management due to increased complexity of data products.Reducing the role of expert judgement in data and modeling tasks and in turn increasing probability of error or misuse.Inadequately communicating methods, results, or degrees of uncertainty, which increases the chance of misuse. ML practitioners and data scientists are uniquely positioned to examine and influence the ethical implications of our work. We ask challenge participants to consider the applied ethical issues that arise in designing and using ML systems for disaster risk management. How might we improve the creation and application of ML to mitigate biases, promote fair and ethical use, inform decision-making with clarity, and make safeguards to protect users and end-beneficiaries? This track’s submission format is flexible: participants can submit Jupyter notebooks, slides, blogs, essays, demos, product mockups, speculative fiction, art work, synthesis of research papers or original research, or whatever other format best suits you. Submissions will be evaluated by a panel of judges on thoughtfulness, relevance, innovation, and clarity. What Comes Next This challenge will produce new public goods that advance our state of practice in applying ML for understanding risk in urban Africa; this includes new ML performance benchmarks for building segmentation from aerial imagery in relevant geographies, top-performing solutions for mapping in African cities, and in-depth explorations of how we responsibly create and deploy AI systems for disaster risk management. Prize-winning solutions will be published as open-source tools and knowledge and the challenge dataset will remain an open data resource for continued ML development and benchmarking. GFDRR will use lessons learned to inform policies and procurement strategies for using ML for urban mapping and planning. Join the Challenge! The competition is currently running until March 16, 2020. With one month to go, there is plenty of time to explore the data and participate in either tracks but don’t delay, join today at: drivendata.org/competitions/60/building-segmentation-disaster-resilience

How participatory mapping can make Brazzaville’s poor neighborhoods safer

Author: Dina Ranarifidy, World Bank Christ Mboungou, local cartographer from Moukoundzi-Ngouaka, says he wants to use the skills he gained to map climate risks in his neighborhood and develop better infrastructure. As you walk around Brazzaville, Republic of Congo, chances are that you will notice dapper-looking gentlemen and ladies in stylish and colorful attire. Striding through the streets of the capital city with pride, they are known as the Sapeurs. The presence of these Sapeurs showcasing their style might seem stark in contrast to their environment, which is often of infrastructure with low-quality social services in comparison. Putting looks and prestige before other needs may raise eyebrows, but the Sapeurs’ passion to distinguish themselves only clearly represents the lively and colorful aspirations of many people in the city, despite their unfortunate living conditions. Community participation is key to sustainably upgrading informal settlements To improve the urban environment for aspiring communities living in informal settlements in Brazzaville, the World Bank-financed Congo Urban Development and Poor Neighborhood Upgrading project (DURQuaP) has put the communities themselves at the forefront of decision making. To give communities a sense of worth and belonging, the project set up local development committees in target neighborhoods. Over 300 people—half of them women— were selected on a voluntary basis and received trainings on dynamics and leadership change to help them voice their needs, participate in community planning and implementation, and help strengthen community ownership. A neighborhood committee meeting in Moukoundzi Ngouaka in Brazzaville Upgrading informal settings through community mapping To mitigate adverse effects of flood risk – a daily threat in Brazzaville’s poor urban settlements –  engaging communities and valuing the knowledge of their own space is critical. Brazzaville, together with 11 other African cities, is taking part in the Africa Open Cities Initiative to engage local government, civil society, and the private sector to develop data needed to meet urban resilience challenges. This initiative feeds into the participatory approach that shapes the project’s design and complements DURQuaP efforts by helping communities take the lead in making their neighborhoods safer. The Open Cities teams have been working hand and hand with communities and technical leaders to get a better sense of their perception of risks, collect, analyze and map risk-related data to update the Open Street Map (OSM) database.  In Brazzaville, the methodology includes exploratory walks around neighborhoods and focus groups with residents who share their perception of the risks in their neighborhoods and living spaces. Interestingly, the exercise not only values and formalizes the knowledge of the community in their own territory, but also allows to differentiate the perception of risks by gender and generation, offering a wider span of data. The qualitative information gathered is then combined with quantitative data that is collected, also in a participatory manner. The project has also teamed up with the city of Brazzaville to integrate existing geospatial data on the neighborhoods and to make them available in Open Street Map. The data from community maps will directly inform the neighborhood upgrading plans that the DURQuaP project will finance, bolstering physical investments. Building human capital and bringing innovation to poor urban neighborhoods through data collection Community mapping activities have been critical to creating new skills for neighborhood residents. In this light, students from the Université Marien Ngouabi receive targeted training, technical support, and mentorship to compile open spatial data related to natural hazards and develop tools for stakeholders to utilize risk information. This empowerment of youth is expected to foster new vocations in cartography and urban planning – and promote the emergence of a more environmentally conscious generation of young Congolese. Additionally, innovative tools such as satellite and drone imagery provided up-to-date geographic data on the environment. Bintou Moussoyi, student at the University Marien Ngouabi, explains how the Open Cities field work exposed her to erosion, flood damages and houses buried under sand and made her better understand their impact on populations’ lives, in Open Cities workshop in Brazzaville in May 2019. Less climate risk, more resources to improve lives Through the neighborhood-focused approach, the project will impact the lives of people at a granular level by mitigating the climate risk they face today and – by extension, at a macroeconomic level – will contribute to the goal of reducing poverty and increasing the welfare of the poorest in society. The aim must be fewer losses and therefore more opportunities and resources the residents of Brazzaville can mobilize for their own benefit. Less avoidable risk to worry about will help everybody, not only the Sapeurs, focus on the more enterprising aspects of life. Yet, one thing is certain: the Sapeurs will use this opportunity the most creatively… Open Cities Africa is financed by the EU-funded ACP-EU Africa Disaster Risk Financing Program, and the implementation of the resilience-related activities within the DURQuaP is supported by the ACP-EU Natural Disaster Risk Reduction Program, both managed by the Global Facility for Disaster Reduction and Recovery. More information on the ACP-EU NDRR Program and the support it provides to the Republic of Congo can be found here. This piece was originally published on the World Bank Nasikiliza blog.

projects

Open Cities Africa

Carried out in 11 cities in Sub-Saharan Africa to engage local government, civil society, and the private sector to develop the information infrastructures necessary to meet 21st century urban resilience challenges. The project is implemented through a unique partnership between GFDRR and the World Bank, city governments across the continent, and a partner community comprised of regional scientific and technology organizations, development partners, and technology companies. WEBSITE COUNTERPARTSCITIES opencitiesproject.org National and Provincial Ministries, Municipal Offices and Local Development Committees ACCRA, Ghana ANTANANARIVO, Madagascar BRAZZAVILLE, Republic of Congo KAMPALA, Uganda KINSHASA, Democratic Republic of Congo MONROVIA, Liberia NGAOUNDÉRÉ, Cameroon NIAMEY, Niger POINTE-NOIRE, Republic of Congo SAINT-LOUIS, Senegal SEYCHELLES ZANZIBAR CITY, Tanzania Overview As urban populations and vulnerability grow, managing urban growth in a way that fosters cities’ resilience to natural hazards and the impacts of climate change becomes a greater challenge that requires detailed, up-to-date geographic data of the built environment. Addressing this challenge requires innovative, open, and dynamic data collection and mapping processes that support management of urban growth and disaster risk. Success is often contingent on local capacities and networks to maintain and utilize risk information, enabling policy environments to support effective data management and sharing, and targeted tools that can help translate data into meaningful action. Building on the success of the global Open Data for Resilience Initiative, its work on Open Cities projects in South Asia, and GFDRR’s Code for Resilience, Open Cities Africa is carried out in 11 cities in Sub-Saharan Africa to engage local government, civil society, and the private sector to develop the information infrastructures necessary to meet 21st century urban resilience challenges. Following an application process, a small team of mappers, technologists, designers, and risk experts in each of the selected cities receive funding, targeted training, technical support, and mentorship throughout the year of work to: i) create and/or compile open spatial data on the built environment, critical infrastructure, and natural hazards; ii) develop targeted systems and tools to assist key stakeholders to utilize risk information; and iii) support local capacity-building and institutional development necessary for designing and implementing evidence-driven urban resilience interventions. Phases of Implementation 1. Plan and Assess In the first phase, Open Cities teams establish what data already exists and its openness, relevance and value. Project target area and data to collect are finalized. This phase is also when teams identify project partners and stakeholders to ensure that efforts are a participatory process. At the Open Cities Kick Off Meeting, teams meet with Open Cities leadership and the other Open Cities teams in their cohort and receive training on project components. 2. Map In this second phase, teams roll out the findings and data capture strategy developed in the first phase to address critical data gaps relevant to their specific Problem Statements. On the ground, teams coordinate field data collection according to the approach developed and agreed upon in consultation with project stakeholders. Depending on needs, tools for data collection may include smartphones or tablets, drones for the collection of high resolution imagery, or handheld GPS. As the project team is training team members to collect data for the project, efforts are made to develop, and/or strengthen the local OpenStreetMap community within the selected city working in partnership with local stakeholders. Project teams may hold trainings, mapathons, or community town halls in coordination with a local university, NGO or government counterparts. 3. Design In this third phase of the project, teams use the data collected in the Map Phase to design a tool or product to communicate the data to their stakeholders to support decision-making. Products vary widely depending on city context and may include a database and visualization tool, an atlas, a map series, or a mobile application. 4. Develop and Present In the final phase of the project, teams develop their tools/products and share results with targeted end user populations and other relevant stakeholders. Once final products are shared, teams work with project mentors and Open Cities Africa leadership to establish a sustainability plan and to explore opportunities for expansion or extension. This could include convening meetings with the World Bank, government counterparts, or the nongovernmental organization and donor communities. It may also include the development of concept notes, proposals or additional user research. Learn More More information about the project and team activities can be found on the Open Cities Africa site.

Niger

In Niger, the World Bank is supporting the Government reduce the vulnerability of populations at risk of flooding, while taking into account the requirements of community development and capacity building of national structures both at central and local level. DATA SHARING PLATFORM http://risques-niger.org   COUNTERPART PGCR-DU (Projet de Gestion des Risques de Catastrophes et de Développement Urbain – Disaster Risk Management and Urban Development Project) NUMBER OF GEONODE LAYERS39 Understanding Niger’s Risks Despite its semi-arid climate, Niger is regularly stricken by floods that destroy housing, infrastructure and croplands everywhere in the country. While flood damages usually occur in the vicinity of permanent water bodies such as the Niger and Komadougou rivers, more and more damages and casualties have been reported as linked to intense precipitations and runoff in urban areas. Despite the recurrent losses, little is known about the number of people who are living in flood-prone areas or the value of properties at risk. Furthermore, the vast majority of stations in the meteorological and hydrological collection network does not have the ability to transmit data in real-time and therefore cannot be fully exploited in emergency situations. Collecting Data With the support of the World Bank, the PGRC-DU is supporting the Nigerien Ministry of water and sanitation to retrofit the hydrometric station network with new water level gauges with real-time data transmission capability. The new gauges will make hydrometric data collection more efficient and more reliable while allowing for a faster detection of flood risk. At the same time, the PGRC-DU is funding the collection of critical socio-economic information and building characteristics in all areas of Niamey (the capital of Niger) that are deemed vulnerable to floods. UAVs are being used to acquire high-resolution images of potentially flooded areas that would help better identify buildings characteristics and develop a Digital Terrain Model (DTM) with 10cm vertical resolution, which will help better predict water movement in the area. Sharing Data The collected hydrometric data will be available to selected users in an online portal, along with various other data sets from regional and global sources. Part of the data collected in Niamey is expected to contribute to the OpenStreetMap project. The rest of the data will be analyzed and converted into vulnerability maps and reports available to the public. Using Data It is expected that the network of real-time hydrometric stations will be used to feed a flood warning system that will provide authorities a better estimate of flood risk at any given time. The acquired DTM is being used to develop computer models that can simulate flood propagation in the city of Niamey and evaluate the effects of existing of planned flood protection infrastructures. Finally, the collected socio-economic data combined with flood simulations will provide decision-makers an accurate estimation of flood risk in terms of exposed populations and expected economic damages.

Uganda

In Uganda, the World Bank is supporting the Government to develop improved access to drought risk related information and quicken the decision of scaling up disaster risk financing (DRF) mechanisms COUNTERPART National Emergency Coordination and Operations Center (NECOC) Project Overview In the context of the third Northern Uganda Social Action Fund Project (NUSAF III), the World Bank is supporting the Government of Uganda to develop improved access to drought risk related information and quicken the decision of scaling up disaster risk financing (DRF) mechanisms. The OpenDRI team is providing technical assistance to Uganda’s National Emergency Coordination and Operations Center (NECOC) in determining requirements for collecting, storing and analyzing satellite data used for monitoring drought conditions. Understanding Uganda’s Risk In recent years Uganda has been impacted by drought, with more than 10% of the population being at risk. The northern sub-region of Karamoja is one of the most severely hit, with a consequent increase in food insecurity. Currently the Government of Uganda (GoU) faces challenges in the collection and analysis of information upon which they can base a decision to respond and mitigate such risk. Without transparent, objective and timely data, times in mobilizing and financing responses can be delayed. Collecting Data The World Bank is supporting GoU to strengthen its disaster risk management strategy and response mechanisms. The current engagement looks to develop a more systematic, robust system for collecting, storing and analyzing drought risk related information to enable GoU to make more timely decisions. By retrieving satellite data systematically, NECOC will be able to analyze current crop and vegetation conditions with historic information, and quickly detect early warning signs of drought. Uganda has a vibrant OpenStreetMap community, which has been mapping the country since 2010. A pilot community mapping project funded by GFDRR with support from the Government of Belgium, is being conducted in the city of Kampala. Sharing Data The OpenDRI team provides support and advice to GoU in developing best practices for sharing and managing risk related information. Interoperability of data sources produced by various ministries and non-government organizations is critical to ensure timely access to data by NECOC and conduct effective drought risk analysis. A geospatial data sharing platform will be deployed by GoU to facilitate exchange of such critical information and adoption of data standards. Using Data A technical committee, composed of experts from the government and partner organizations, has agreed to use a satellite derived indicator known as Normalized Difference Vegetation Index (NDVI) as the primary dataset to inform decisions for triggering the disaster risk financing mechanism. Initially the system will be exclusively dedicated to monitoring drought risk in the northern sub-region of Karamoja. In the following years, it is expected to expand operations and cover other regions exposed to drought risk, integrating additional data sources which will become accessible thanks to improved data collection strategies and sharing mechanisms.

Zanzibar

The Revolutionary Government of Zanzibar (RGoZ) with the support of the World Bank has been developing evidence-based and innovative solutions to better plan, mitigate, and prepare for natural disasters. Zanzibar is part of the Southwest Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) which seeks to address high vulnerability of the Southwest Indian Ocean Island States to disaster losses from catastrophes such as cyclones, floods, earthquakes and tsunamis. These threats are exacerbated by the effects of climate change, a growing population and increased economic impacts. DATA SHARING PLATFORM PROJECT PAGE ZAN SEA FACEBOOK PAGE http://zansea-geonode.org www.zanzibarmapping.org https://www.facebook.com/zansea/   Understanding Zanzibar’s Risk Zanzibar’s disaster events are mainly related to rainfall, and both severe flooding and droughts have been experienced. Sharing Data Island Map: OpenStreetMap Data collected through SWIO RAFI activities will be shared on a GeoNode. The ZanSea GeoNode currently contains 42 maps and 102 layers of geospatial data for Zanzibar. Collecting Data The Zanzibar mapping initiative is creating a high resolution map of the islands of Zanzibar and Pemba, over 2300 square km, using low-cost drones instead of satellite images or manned planes. The Zanzibar Commission for Lands will use the maps for better planning, land tenure and environmental monitoring. Data is being collected in collaboration with the RGoZ. Using Data Data collected can be used for risk assessment and planning activities.

Pacific Islands: Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu, Vanuatu

Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) is a joint initiative of SOPAC/SPC, World Bank, and the Asian Development Bank with the financial support of the Government of Japan, the Global Facility for Disaster Reduction and Recovery (GFDRR) and the ACP-EU Natural Disaster Risk Reduction Programme, and technical support from AIR Worldwide, New Zealand GNS Science, Geoscience Australia, Pacific Disaster Center (PDC), OpenGeo and GFDRR Labs. DATA SHARING PLATFORM http://pcrafi.spc.int/beta/ NUMBER OF LAYERS 522 Understanding Risks in Pacific Island Countries The Pacific Island Countries are highly exposed to the adverse effects of climate change and natural hazards, which can result in disasters affecting their economic, human, and physical environment and impacting their long-term development agenda. Since 1950, natural disasters have affected approximately 9.2 million people in the Pacific Region, causing 9,811 reported deaths. Sharing Data throughout the Pacific Islands Launched in December 2011, the Pacific Risk Information System enhances management and sharing of geospatial data within the Pacific community. The system enables the creation of a dynamic online community around risk data by piloting the integration of social web features with geospatial data management. Exposure, hazard, and risk maps for 15 Pacific Countries were produced as part of the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) 2 and are accessible through this platform as powerful visual tools for informing decision-makers, facilitating communication and education on disaster risk management. Thumbnail Image by Samoa Department of Foreign Affairs and Trade licensed under CC BY 2.0

Sri Lanka

The Disaster Management Centre of Sri Lanka (DMC) with the support of the World Bank has been developing the Open Data for Resilience Initiative (OpenDRI) to support evidence-based methods to better plan for, mitigate, and respond to natural disasters. COUNTERPART Disaster Management Centre, Ministry of Disaster Management NUMBER OF BUILDINGS MAPPED 130,564 with 8 attributes each ROADS MAPPED >1000 km   Understanding Sri Lanka’s Risks Since 2000, flood and drought events have cumulatively affected more than 13 million people across Sri Lanka. Regular flooding, drought, and landslides are natural hazards that threaten the long-term growth and development of the country. In Sri Lanka, nearly $500 million in unplanned expenditures resulting from flooding in 2010 and 2011 has strained government budgets and required reallocation from other planned development priorities. The impacts of these events are growing due to increased development and climate change, both of which put more assets at risk. Sharing Data To enable better disaster risk modeling, the Government of Sri Lanka partnered with GFDRR, UNDP and OCHA on the development of an OpenDRI program in November 2012. This branch of the initiative focused on the South Asia Region and was dubbed the Open Cities project. A component of the OpenDRI Open Cities mission in Sri Lanka was to collate data around hazards and exposure and prepare them to be uploaded into a GeoNode which serves as a disaster risk information platform. Working with the DMC, the National Survey Department, Department of the Census and Statistics, Nation Building Research Organization, Information and Communication Technology Agency, Department of Irrigation, several universities and the international partners, the OpenDRI team supported DMC with the aggregation of data that had been stored in static PDFs, old paper maps and several databases onto the GeoNode. The data on the GeoNode is currently available to authorized users in the OpenDRI network, in preparation for launch. This transitional state is typical for open data projects, as the partnership reviews data with the parties and affirms that it is ready for release to the open public. Some layers may restrict access only to authorized users. Collecting Data The project has also built technical capacity and awareness in Sri Lanka through training sessions on open data and crowdsourced mapping in Batticaloa city and Gampaha District. As a result of the Open Data for Resilience Initiative, government and academic volunteers have mapped over 130,000 buildings and 1000 kilometers of roadways on the crowdsourced OpenStreetMap database. This enables the country to plan ahead and be prepared for future disaster and climate risks. It also helps planning during disaster responses: the data was used to assess flooding impacts in real time and direct government resources during the May 2016 floods in Gampaha district.

resources

At OpenDRI we are committed to increasing information that can empower individuals and their governments to reduce risk to natural hazards and climate change in their communities. We’ve compiled a database of relevant resources to share what we have learned through our own projects and from the work of others.

view all resources