UN-Habitat Arena - Transforming informal settlements: Data, standards, and practice (WUF13)

Hello.
Hello.
Hello, colleagues.
Good afternoon.
Good afternoon to all of you.
We are going to start in a minute.
I just kindly ask all of you to take your seats so that we can start.
It's okay? One.
Colleagues? Everybody okay? Can we start? Yes.
Yes.
Good.
Okay.
Good afternoon, everyone, and thanks for creating time to join us today for our session on transforming informal settlements, data standards and practice.
My name is Ain Nima.
I'm the head of UTAC, that's the United Nations Innovation Technology Accelerator for cities.
I'm also the overseeing the data and innovation section, and I'm the coordinator and the head of the Geneva Office that's based in Switzerland.
In this session, we are focusing on one of the most pressing urban challenges.
Of our time, Islams and informal settlements, which represent a critical housing segment for many of our cities.
UN Habitat data shows that the proportion of people living in slums and informal settlements has been on a slow but steady decline since 2000, with a total decline of just 5.9% during the 2020, 2024 period.
The absolute numbers of people living in formal settlements and slums has recorded as much faster increased with a record increase in Sorry, colleagues.
In total population of 31.5% 2000-2024.
In the last decade alone, data period for last decade was 2015 and 2024 for your reference, we have recorded an additional 142 million people living in slumps and informal settlements, which not only limits the right to decent shelter and urban living for millions of urban populations, but directly challenges our collective ability to meet the 2030 agenda.
In today's event, we ask difficult questions such as, could the reason we are not making progress be that we do not have the right data or are not developing the right policies, implementation plans? Could it be that we are leaving behind key stakeholders and partners as we try to create solutions aimed at transforming formal settlements? Are the financing modalities we are promoting fit to purpose and what options should we consider? Are the roles of different actors take into account and what should we change collectively to help us move forward closer to a shared version? This session will have two parts just to give you some guidance on housekeeping.
In the first part, in a few minutes, we will get to appreciate some of the key global trends in slums and informal settlements, as well as to see some of the key innovations around mapping informal settlements using a scalable approach.
For this, we will get very short presentations from Robert Ngua from Chief of the Data and Statistics Unit at UN HaptT, Professor doctor Monica Kofer from the University of Ten, and doctor Sophie Nu from UTAC and Happen City University.
After the presentations, we will have an open conversation with our distinguished panel that includes representatives from government, civil society, and community organizations, development financing institutions, and the space sector.
Without any further delay, I would like to welcome Robert to kick us off with his presentation.
Thank you, Ali.
Good afternoon to you all or good evening to you all.
It's a firestarters so I'm just going to run through very quickly, so I hope you can make use of your phones to capture a few images where you can.
The topic is very much about transforming informal settlements, but at the back end of transformation here, we're looking at the data and the statistics that at least has been around to help us understand and piece together the story.
So here we do have what we call the informal settlements paradox.
What we see from the data that we've collected very much recently for the time period 2000 all the way to 2024, it shows us two clear messages.
One, the share of the urban population that lives in slums and informal settlements in general, we are seeing that declining and that is very true across almost all the regions, but that's just the proportional share.
In terms of the absolute numbers of people who live in slums and informal settlements, we see the reverse.
Those numbers are increasing and it's also very true for quite a number of reasons.
Here, perhaps the message is that while the proportion is actually declining, it doesn't translate necessarily into absolute numbers declining, we see an increase in those absolute numbers.
The story behind that statistics that I've just provided for you is that we do have today from that approach and methodology, about 1.16 billion people who live in slums and informal settlements.
Again, that is very much based on a household based definition, which basically looks at the prevention measures.
We look at water and sanitation, crowding.
We do look at security of tenure, but also the structure durability of the housing unit itself.
This methodology has been around since the time of the MDGs.
We are now in the period of SDGs, where we do have, of course, technological advantages, whether it's the geospatial data that has come in.
The transition we're looking for here is very much moving from a deprivation based measure where we look at a household deprived of certain elements and then qualify that as a household that is qualified to be slams and informant settlements, vis-à-vis, the possibility of at least today using remote sensing and earth observation technologies to help us understand where are these people, which settlements are they living in, and where are those types of settlements? It's basically moving and transitioning from household to a very spial based technology.
We are living in a time where We are also very lucky that member states have recognized the aspect and the breadth of how much the problem looks like.
1.16 billion people is quite a massive number to deal with.
We do have quite a number of resolutions coming in from member states, both from the UNESCO Commission but also from our intergovernmental governing bodies and they've given us matching orders.
Let's try and harmonize data where we can.
Let's try and harmonize standards where we can, but also let's try and piece together new methodologies that can help us to even understand these populations better in many different facets.
Today, we do have a global Urban data coalition that is also very much looking at one thematic area on slums and informal settlements, which means improving data, improving standards, and harmonizing where we can.
At the same time, we also have at the UN Statistical Commission level, a special expert group that has been constituted, again, to look at a similar aspects of understanding the populations living in slums and informal settlements, but from a standard setting point of view, as well as a methodology point of view.
At the same time, We do have an open ended intergovernmental working group that member states put together that is also looking again on the aspect of methodologies.
So here we have an opportunity that basically in all different facets and directions, we are converging in terms of thoughts, particularly around harmonizing data collection, but also the methodologies, per se.
And for those involved in the population and housing censuses, we've already mainstreamed some of those early thoughts.
So the new generation of 20, 30 rounds of censuses, at least are going to integrate a lot of the ideas that we are currently developing.
Essentially what we are looking at here is From the household best definitions, we've learned a lot, but today we are more less interested in knowing where are those slums in terms of locations and the extent of those specific slums and informal settlements.
We're interested in knowing the number of those settlements in a given country or location or a city.
We're also interested in knowing the levels of severity.
Basically, the deprivations that we've seen through households could also be understood through different aspects.
We're interested in the temporal changes, basically year after year, month after month.
If demolitions do happen, we want to capture all that, but essential, of course, keep a tab on the shares and populations that are living in slums and informal settlements.
Of course, the idea here is to appreciate that slums and informal settlements in one city can look very different from another city and of course, even in national or across countries.
We do require different approaches to study and understand all these different locations.
From our side of UN habitat, we've been coordinating with a lot of partners.
We have an early idea of a strategy that we can deploy.
One, of course, is very much at the upstream level.
We're looking at setting baseline layers based on low resolution images and analysis that can help us understand at a lower cost, but at least giving us insights.
That of course, involves much, much higher levels of harmonizing all our different parameters and measurements.
At the local level, we're looking mostly at the national.
Again, picking up the facets and nuances that might be at play at the national level and then also looking at the neighborhood and local level where citizens, for example, can help us map um, some of the spaces and informal settments, understanding them, of course, in layers that might require also very high resolution datasets.
So these three layers where we think that at the global level, we can work with many global partners, but at the national level, we can also set up structures that help us understand and piece together that evidence.
But at the local level, we can also have opportunities to engage citizens.
So in nutshell, that is the structure that we are working with, and we'll be able to hear from a lot of partners present here today who will give us some of those insights at the global, national, but also the local level.
So Monica, why not hand over to you or Arlene? Thanks a lot, Robert.
It's a nice transition.
Robert was already talking about the need for acknowledging informal settlements, and working with more low resolution data to capture them on a global scale.
What you see here is a wonderful scene, um, a drone image from the city of Mumbai where you see very much the difference in the north of a plant urban area in the south of an informal settlement.
And these places are very different.
If you go to another city, they will have a very different characteristics, but they share often very specific morphological characteristics, meaning they are densely built up.
They have small building structures, which are very close together.
They have irregular outlines, which of course is also not always true at all.
But we can capture them in images, in satellite images, and I will talk very briefly about an approach we developed to map them on a global scale.
We started with a couple of cities, together with our colleagues.
It was an ASA funded project to start with a more balanced dataset of recognizing the diversity and developing advanced AI models to extract the location, but also the dynamics of the settlement from moderate resolution, ten meter resolution satellite images.
Um, and we need to do this in a diversity and having sufficient high quality reference data.
So what we developed is an approach, building on free cost data, sentinel images, investing in data quality because that's a big crucial element in any AI model.
You need high data quality to train AI models.
And we do this not as an end.
Often what you see research outputs, they train a model and they have an output, and that's it.
For us, an output is a start where we go back to the local user community and ask them, how good is our model? What is wrong in our model? We go create local reference data that allow us step by step to improve our models.
And we do this efficient cloud computation, and we want also to stimulate the community because it's a big problem that every research lab shares the reference data, so we are not moving forward, building a global set of reference data.
And that's what we started doing releasing our first set of reference data.
What we map is the location and the dynamics, and what you see in an example of the city of Nairobi is settlements are often very dynamic, in very dramatic conditions dynamic also, like in this case, an eviction which happened in an area in Nairobi, where large part of our settlement has been demolished.
Um.
We also not map only the location dynamic, but also the variation of settlements, the severity of settlements, different conditions underground.
And we wrap this up into city level statistics to support SDG 1111 monitoring.
We also respond to local user needs saying, Hey, particularly in the very urban areas, the settlements are growing very rapidly.
We need relatively rapid updated information.
What we have been developing are two major things we have been developing an AI model which is very lightweighted, that works with moderate resolution data at a very computation efficient way, and we have been developing a user portal which allows us to deploy, we push our model outputs into the user portal and user can react to our models and say, Hey, that's correct, that's wrong.
With this information, we can step by step improve our model outputs to further develop balanced reference data set towards a global model of informal settlements.
What you see on the right is our deprivation index, which allows us to see the diversity of the settlements, acknowledging that a settlement in even in one city, they have different issues.
Some lack hazard prone, they are flat hazard prone, others lack access to health, et cetera.
And how dynamic of the settlements are is an example of Median in Colombia, where you see a settlement which have been going into actually a park which was developed for the settlement, which was a beautiful urban garden, where basically the massive expansion went very rapidly into the settlement.
What is our major vision is to make this AI model open and open it up to local municipalities to be able to run this model, not to keep this model in research labs and empower municipalities to use this model and run the models themselves.
We are in the moment with our colleagues from the Inter American Development Bank, developing a user step by step user guide to run these models for a local municipality.
I think that's the end of the story, thanks and handing over to my colleague, Sophine Thank.
Great.
Thank you, Monica and welcome also from my side, pineaa, I'm working for UNTk as a senior researcher and project manager, and I will quickly give you a glimpse of what we have developed so far at UNITAk related to informal settlement mapping.
I'm happy to be here with colleagues and friends from the global mapping community, and I think it fits quite well to speak after Monica because our data is a little bit more granular.
Monica looked at the settlement level, and here at UNTC we developed a tool which is called Beam the Building and Establishment automated Mapper.
Um, it's a tool that uses machine learning to identify rooftops of informal settlements.
We're looking at the building level in informal settlements, to understand how informal settlements are growing, and to understand the dynamics of informal settlements.
We developed this tool mainly to quickly generate shape files of building footprints in diverse informal settlement contexts, be using aerial or satellite imagery, high resolutional, um, not publicly available imagery to have the granular data on informal settlements.
Um, we started with a deployment in Iiki in South Africa, we scaled to Cape Town in South Africa, and we also use this tool in Central America mapping all the eight Sica capitals in Central America and currently working in Namibia.
The main idea here was to understand how informal settlements do grow.
Um, they grow in a very different manner in different contexts and to understand also how informal settlements are densifying over time.
The idea was here to develop this tool that reduces time and effort for automating such processes.
In ItiQwi we were able to map the whole city and all informal settlements in ItiQini within 72 hours.
So that's the work that usually manually or doing surveys and mapping manually would have taken more than a few months and a very big project team also.
We created a training dataset on imagery from 2020, in a very dense area in ItiQei so that we had a variety of different building typologies, um, different sizes of typologies, and we could at least include more than 1.5, million building footprints into the local dataset of the city of ItiQini.
We handed over this tool to the city, so the Human Settlement Unit and also the corporate GIS branch are using it and they can deploy it annually or whenever they have new images available themselves.
Yeah, as I mentioned before, we deployed also in Cape Town.
The use case was a little bit different.
Here, we looked into enumeration of informal backyarding structures in Cape Town.
We worked with the city's geomatics branch in Cape Town together, to understand this quite dense neighborhoods in the backyards of formal housing.
Um, areas, and we applied this tool on openly available eight centimeter resolutional imagery.
This was actually a dream this situation because we had this great data available from the city of Cape Town and could apply the tool and adding more than 800,000 additional building footprints to their local datasets.
Um Yeah.
Also, as I mentioned before, we scaled this tool to Central America as part of the UN habitat mandate or doing an inventory of informal settlements in the Ska region.
Here we enhanced our data scientists at Hamburg enhanced the logic on satellite imagery, so different resolution, different accuracy, and also a little bit different logic from the model to map on satellite image instead of aerial imagery.
Um, we deployed the tool in eight different cities from Guatemala to Santo Domingo and added more than 500,000 additional buildings to the datasets and had more than 60,000 hand labeled buildings.
That's a lot of labeling that still needs to be done manually beside the AI and machine learning algorithms.
Yeah.
Currently, with Robert and Dennis and his team from the Data Analytics unit in our project just in Transition Finance by the BM set, we are deploying the Beam tool in Namibia.
We're deploying the tool in Namibia together with the Namibian Statistics Agency.
The Namibian Statistics Agency, they developed the National Housing Information System.
It's a system where they want to understand the growth of informal settlements and incorporating census data, and we are deploying this tool in six areas of interest at the moment, um Um, our data scientists are still working on accuracy because you have different densities in Namibia, so that you can see in the images, you have a few areas where the model performed already very well, which are not so dense areas, denser areas in Wintuk where we're still training the model and then integrating this into the system.
It's a great step for UNTk to do so because it's coming from a pilot integrating into a national system, looking forward to contribute on this also in the future.
And my last slide is just, um, UTC, or the tool that we developed was able to map informal settlements across different regions, different contexts, different informal settlements.
As Monica mentioned in the beginning, all those settlements do differ quite a lot, and this is also a challenge for such tools.
Um, It challenges also that we need always new training data, so there is a lot of hand labeling involved.
There is high images involved, and also when we hand over such tools to local governments, municipalities, then there is a requirement of computational power so that such tools also run on their capacity.
I would say starting with the data or creating the data is only the first step, and then it depends on the analytics and how this data is used.
Thank you.
Thank you.
Thank you.
Thank you so much, Robert.
Thank you, Monica.
Thank you, Sophie, for setting the scene to us.
So I will ask you to stay around because we are going to have a round of discussions with the participants after the panel, and it can be an opportunity also for colleagues to raise you some questions about your presentations.
So now, I would like to welcome our diverse team of panelists that are joining us today to take the stage.
So please join me in welcoming miss Mercy Dub representing the Ministry of Lands, Housing and Urban Development from Malawi.
Please.
We have Felippe Mendez Kronnberg representing the Brazilian Institute of Geography and Statistics.
We have miss Joan Nissimo representing AC Together and Islams Was International from Uganda.
Welcome.
We have Pete Masers from the Humanitarian Open Street Map.
And we have Francesca Lisa Loneli from the European Space Agency.
And we have Washington Mens Pagaru from the Inter American Development Bank.
A round of applause star panelists.
Welcome.
Okay.
So as the panelists settle down, I welcome you all also to put together your questions and any comments that you may have.
After the panel, we are going to have a quick round of discussion with all of you.
So welcome again.
So I will ask each of you to briefly 1 minute, introduce yourself and also what you do and how your work is connected to the discussions that we have today.
So maybe we could start with the Minister of Lands Housing and Urban Development from Malawi.
So let me give you Good afternoon, everyone.
My name, as she has already said, is Messi Dube.
I work as Director for Urban Development in the Minister of Lands Housing and Urban Development in Malawi.
And one of the responsibilities of the department is to upgrade informal settlements.
We look at urban development planning and management, and this is one of our responsibilities.
So being one of our duties, I think that's why I was invited to share what we're doing in Malawi, the challenges that we're facing and maybe what we think can be done for us to do our work better.
Thank you.
Thank you so much, Philippe.
Hi, I'm Philippe Kronnbgm.
I'm the head of the Geography coordination in the Brazilian Institute of Geography and Statistics.
We do the mapping areas of slums and the survey for statistical data for economic and social data for the whole Brazil but e Islams tool.
Joan.
Hi, I'm Pete Masters from the Humanitarian Open Street Map team or HOT.
So we support partners, communities, governments to develop the geospatial data they need, the capability to develop it themselves, and we support a lot of technical tools and methodologies.
My name is Simo Joan.
I work with Act Together Uganda in Kampala, Uganda, and basically we're working with people in the informal settlements, the unprivileged ones, and we say, we are the voices of the voiceless.
Hi everyone.
I'm Francesca from the European Space Agency and we fund projects that use our satellite data to provide useful information.
This is the case also for informal settlements applications that Monica presented before.
Thank you, Ai, Hubert, and Dennis for having me.
My name is Washington Fajardo.
I'm the coordinator of the City's Lab, which is the innovation unit within the Housing Urban Development Division, the Inter American Development Bank, and we provide open source solutions for mapping informal settlements in whole Latin American and also offering training and offering capacity building for local and national governments.
I'm here with my colleague António Vasquez Brett.
Thank you.
Thank you.
Thank you all for your brief introduction.
Now we are going to have two rounds of questions.
Each of you are going to have 2 minutes to respond.
We're going to start with the Ministry of Lands.
We have other mics here also if you need to use, just to facilitate for you.
We're starting with the Minister of Lands Housing and Urban Development from Malawi.
A part of your work is developing policies and designing solutions around housing and informal settlements upgrading as you're mentioning.
What critical data do you need but currently lack to guide informal settlements interventions and who is responsible for producing this data? Thank you so much.
When you're working, you need to know what you're dealing with.
So we need to know where are these informal settlements? How big are they? And who is living there in terms of, are they women, children? How old are they? Do they need schools? So one of the biggest data that we need and we lack is up to dates disaggregated data on informal settlements both at city and settlement level.
If you go to a council, they will not be able to tell you they may tell you the number of informal settlements in their city, but they will not be able to tell you the size of those informal settlements and the data of population.
What we need is disaggregated data which they do not have.
That is one of the things that we lack.
We also need to know the tenure security of the area where these informal settlements are, and also, is it an area where we can actually do an upgrading? Because most of the informal settlements are in areas which are risk or prone to disasters and as such, those cannot be upgraded.
So the critical data gaps, we actually need accurate settlement level mapping and enumeration of informal settlements.
We would also like to know land ownership, tenure security, and customer land transitions.
Some of the informal settlements are in areas which are still under what we call customer land, not government land, and then we need to know whether the land is already under government because if it is customer land, if you want to do an upgrading, now, there will be need for compensation because that is not government land.
We would also like to know the socioeconomic and demographic profiles especially on youth, women, and vulnerable households, and also the dynamic urban growth and migration trends to support prevention of new informal settlement.
Because what we want is we don't just want to upgrade the existing settlement, but we want to make sure that as we are upgrading, we are not creating room for growth of new, informal settlement.
So the The responsibility of this provision of data in my country, it rests with the National Statistical Office, but also my Minister of Lands, Housing and Urban Development, as well as the local government authorities.
But then I feel we also need to collaborate with the universities, the civil society organizations because as a government official, if I go and talk to the communities, I will not get the same information that a civil society organization can get because they have the tools that they use which I do not have.
So And the other thing is, we need to do community led enumeration.
We have to work with the communities.
If we do that, we're going to get more information because they are the owners of those settlements, and they know exactly what the what they are lacking.
Thank you.
Thank you.
Thank you so much.
Colleagues, I will ask you to help me keep the time so we can have some time for discussions with participants.
Now we're moving to Brazil.
Brazil has a long experience with Ave upgrading, so informal Sums and slums.
What lessons can be applied globally, particularly around data driving planning, including how you ensure that data reflects realities and supports inclusive policymaking.
I would like first to thanks Robert and Denise for the invitation.
It's a pleasure to be here.
I'd like to thanks to you and HabDAT for all the support it gives all along these years.
I would start pointing that B has always tried a participatory data collection.
We emphathize involving community directly in census survey and processes.
We need these for two reasons.
First, community engagement so to avoid what I call blind spots or no response.
As to allow the censors takers to enter in the Islams.
Why? Usually, these places are dominated by organized crimes, so only with the community we can get there.
And by doing this, we ensure that marginalized groups are represented in official statistics.
I one way we have done it was changing the name, we call Islams in Brazil.
In the old census, we call it substandard settlements and people didn't see their themselves represented.
So with the help of academia, we changed the name for favelas and urban communities.
Second, the always work for the integration of G special information with statistics.
In this point, we apply a survey questionnaire for the surroundings before the census.
We do that for a better integrated spatial statistical formation, but especially to see the conditions of the human area.
We ask for the existence.
We don't ask, we see it.
Of sidewalks, the existence of the condition, the type of the street floor, if it's dirt or asphalt, street wide, and the type of circulation, pedestrians, motorcycle, cars, trucks.
This is very important in the favelas.
Street lighting, manhole to see if there is sewage systems, wheelchair ramps, trees, bus stops, and bike lanes.
These especially allows policymakers to visualize inequality, especially in identifying regions with the poor infrastructure or with economical social risk.
And to is inclusive policy making.
Iger has worked on developing new environmental and social indicators, especially access to service.
We collect displacement data for education and work.
We produce multidimensional indicators to enable example, subsidized loan policies for buildings and remodeling based on low average income or likely.
We produce education and health statistics.
By comparing with existence of schools and hospitals to give a better access for them.
The last, make comparison with the census made before in 2010 to measure the impact of all the policies based in the difference of the data.
Thank you, Philip.
Now moving to Pete.
Open mapping and crowd source data have grown significantly and we see more SEGs being measured using data produced a true volunteer data.
Given the complexities in mapping form of settlements that often require very high resolution training data, how can crowd sourced approach be scaled to fast track better in form of settlements maps while maintaining quality and reliability.
Thank you.
I have 3 hours to answer this question.
Okay.
Okay.
I'd like to talk about, one thing to say about Open Geospatial data, there's many things to say, but it's reusable.
So once it's in the public domain, it's a digital public good.
Many people can use it and many people can build upon it.
So I'd like to talk a little bit about supply and demand and especially referring to quality and coverage.
So Sophie showcased a few different city authorities where there was a data demand, and that data now exists and it goes into the public domain, and that's great.
But it's not only cities that do this.
Grab, for example, in mapped Dhaka and Bangladesh because they wanted to do ride sharing, they wanted to do business.
TomTom invests heavily in open data.
The World Bank in Darsalam changed the data ecosystem in the city through open data.
And Freetown City Council who we work with have created an imagery dataset of very high quality that can be reused again and again.
And I think the demand for open data is really important for obviously the impact, but also for the quality and the recency, because the more demand there is, the more that quality and recency rises.
If open data is integrated into systems, into products, so not just thinking about it as a thing on its own, then the quality and the recency and the coverage will rise because people are using it and they need it.
On to supply, the GPSDD analysis said that for every dollar invested in data systems, there's a $32 return.
I think the reason that cities and these companies and NGOs invest in open data is because that return is potentially higher because of that reusability, but also because the generation of that data can be very cost effective.
So communities, your colleagues in Kampala, create open data to support their own objectives, and they do that for themselves and people might get paid, but it's cheaper than a commercial mapping system or a consultant.
And so There's the integration systems and products to build that demand side, but there's also the investment in local capability to produce really high quality and relevant data through mapping communities, through SDA federations, through universities that can really help to bring that quality to scale.
Thank you.
Thank you so much.
Now moving to SDI, Joan.
Community data can and has been transformative in many contexts.
SDI has been doing an amazing job in ensuring that vulnerable communities and slum dwellers get counted.
How can community generated data be better integrated into official systems? Thank you so much for the platform, and I'm glad slum Dealers is here today.
First of all, at times, why even our data is considered or is not used at times, government and municipalities feel that the community cannot collect their data.
But hey, why are you collecting the data? It is not ours without us.
That is what we say because we know the corners of the settlements where we come from.
We know the places where you don't know.
If you're planning for us, you have to be with us.
Also some of the things that they say, for example, the data standards, things concerning data have a lot of hard languages, the GSIs, the cadastro maps that tend to threaten the community members away.
But we feel, why doesn't the government come on board? Why don't we go to the field together? Why don't we move together door to door in the settlement profilings, the enumerations, we believe we can work together and have our data used.
If we can work on the weekends, if we can skip the drainage channels, if we can jump, why not you? So we say, without us, it is not ours.
And I believe if it is a joint process, if it is a joint program, our data can be considered, and yes, we have done it.
We have collected a lot of data as a community, and we are happy to see much of our data is being used by the municipalities, by the government, We normally do settlement profiles and at times we do periodical upgrade updating.
Many municipalities have been transformed to cities and we are like, yes, because we want things to be done our way, we shall do it our way.
We take the lead in profilings because we know ourselves, and even if it's not done, with the young people, I'm a young person, by the way, sorry to say, with the young people still have the energy to move in our settlements, our data aspect is totally community led.
It has two aspects, the New York City TV and the New York City 2.0, just as it is New York City.
What is the new settlement? What is the new city? Here comes the advocacy part of it.
We use the New York City TV.
New York City TV is a collective of young people that normally collects information in our settlements.
Hey, we have done a lot, please visit our pages, and we have collected a lot of evidence that has informed projects, that has informed different initiatives in our communities, be it climate related projects, be it youth economic empowerment projects, be it data projects.
I'm happy to say we are not yet there, but we are seeing light at the tunnel.
Much of it all has been said by our colleagues, the data we want.
Some of the municipalities, the government normally collect the data and they leave it there.
Why don't you come back to us and verify it together? Because we know our settlements very well.
You don't know them, we know them.
If you come and say that data says that this football pitch is here, and I say, Hey, look, it's not there, it's there.
Why are you saying it's not there yet? Me who lives in the settlement know it very well.
If we do it collectively right from data collection, verification, and also feedback, because in most cases when data is collected, the missing part is the feedback.
When you go back to the community, they don't even want to look at you.
They will tell you you collected data.
What happened? You didn't come back to tell us, you've collected the data, so what? If we are collecting data with our communities, please be mindful.
These are vulnerable communities, they are living in fear, and we believe that everyone deserves a dignified life.
Much as we are living in slums, there are spaces that we treasure.
They are our homes.
We have places that we call home.
These are social networking areas in our communities we share.
If I don't have salt, I can go to my neighbor to collect salt.
So these are socializing places.
They are our homes, much as we are not comfortable with the living conditions, but we believe with the data that has been collected, much has been said, we say unity is our strength.
Thank you.
Thank you.
Thank you so much.
Now moving to Francesca, satellite and Earth observation technologies are advancing rapidly, and ESA is soon launching a call for development of global solutions on global products on slums and informal settlements.
What do you envision as the main strengths and also the limitations for Earth observation in identifying and monitoring foral settlements at global scale? Thank you, Alina, for the question and for having me here and coming after my colleagues talk is really inspiring.
Thank you for that.
Earth observation has, of course, some big advantages, meaning that you can see many places in a consistent way, so you can scale your vision and compare areas, which is something that what I see is missing when we look at informal settlements monitoring because they're very city specific, if not country specific, or even neighborhood specific sometimes.
They have some very detailed specifications that are not easy to translate elsewhere.
Given these limitations, earth observation can maybe support in trying to find some commonalities, some aspects that help define informal settlements in a more harmonized way.
But without the ambition to know it all, this is for sure because at the end, local areas have some specificities, as I was saying, but as we heard, the community, of course, has the knowledge that satellites will never bring to the table.
So if I can summarize quickly what I believe, I do think that Earth observation and space agencies like the European Space Agency has a responsibility in pushing for our data to be used in a consistent and comparable way But without forgetting that we still need the very high re solution information, which from our openly available datasets are not coming, so we have to collaborate for sure with commercial satellite data, but more especially with the communities in the place.
I like the point also on verification.
Our methods go nowhere if we don't have ground truth data, meaning both for training our models, but also for validating them.
So Yes, the limitation is the Earth observation is not human, but it can become if we work together.
Thank you.
Thank you.
Thank you so much.
Now moving to Washington.
From a financing perspective, what do you consider as the main impediments to unlock investment in informal settlements transformation? Thank you.
Thank you, Ali.
That's a really good question.
I think first thing we should assume these multidimensional aspects of informal settlement.
Of course, we are very concerned about how those populations are highly exposed to climate risks, to spatial segregation, and also to social exclusion.
But we should also consider that this population, they also are eager to be more connected with their own society.
Bringing in some data from the Brazil from the data favela Institute.
It's a research institute focused on informal settlements in Brazil.
They have shown that if we put together all the favelas of Brazil, that would be the fifth state of the country.
All favelas of Brazil, they have together the equivalent buying power of countries like Paraguay or Bolivia We should also look to informal settlements as gateway to opportunities to have access to labor market and also having access to better opportunities.
Dealing with this such complexity of deprivation, but also a collection of opportunities, it's our understanding how providing data and how putting those communities on the map, it's really important because from one side, we need much better policy making process and allowing local and national governments to have a better decision and really being able to look to critical situations like natural disaster.
So talk about Latin America, some Caribbean countries, they are sadly exposed to hurricanes and that means a potential reduce 1-2% of GDP in just a matter of one natural disaster event.
So that's the relevance of that in that sense or the policy making aspect.
Really being able to quantify and especially to provide solutions for those specific situation.
But the other way, we should also consider, and that's so important to engage communities in that process, also to allow them to have a formal address, also, as I have been saying, also to allow them to order a pizza and get the pizza delivery at their home.
We are talking about people living in their homes, and we are talking about families, we are talking about householods.
They are desiring the same kind of access and opportunity that we consider for the formal city, I would say.
So, As a development bank, as a multilateral bank, especially we understand, Ali, that that's something that Tony and I, we have been trying to do.
It's pushing that agenda and especially considering the role of the finance system and on funding those projects.
I think it's really important to have this global conversation, but we also understand that we need more implementation projects and also using loan operations also to to try to push that idea with national and local governments in a way that they should consider GIS components anytime they ask a development bank for a loan, for a credit operation, considering these possibilities and the value of geoscience and especially the value of data, not only as a neutral evidence only for for policy making, but also considering that data as opportunity for development.
We really understand that it's a really transformative role for the multilateral banks and we want to really assume that commitment with this global coalition.
Oh, thank you.
Thank you so much.
Thank you all of you.
I actually have a proposition.
So initially we would have a second round of questions, but we are over time, but thankfully they allowed us to stay an additional 10 minutes.
But what I would like to suggest is that maybe we could open for questions from participants that they can also benefit from your experiences and at the end, each of you have 1 minute to share a final thought and also any ideas that you may want to.
Is this fine with you? Can we do that? Yes.
Sorry for being disruptive, but I also think they could benefit from you.
Yes.
Okay.
So colleagues, do you have any questions, any comments to our panelists or to our presenters, Al, Robert, Sophie, and Monica, who are still here with us.
Any comments that you want to raise or question that you may have? I also ask you to be brief, but just looking around.
Yes.
One question, please.
Please introduce yourself.
Abdul Ibrahim Abdul Ibraim with Director of Urban Development in Dhakaar.
My question deals with the European agency.
Now, we listen perfectly to the gentleman, but he's speaking French.
How can we benefit from the studies of the European Special Agency? My other question is for Francesca.
Is it possible that the municipalities that are near the principle of subsidiarity could participate? What is their role in the struggle against informal settlements? Well, you need interpretation, but lady, there is a translation service.
Just checking the question.
Yeah, I know.
There is a translation service, misses Alene.
So the first question is, as local authority, how can we get the images from the European Spatial Agency? How can we obtain these images? And the other question is related to the participation of municipalities with the principle of subsidiarity in the struggle against informal settlements.
Question how to observation, our data? I think so.
If this is the question, our data is openly available and there are different data portals where you can find our data coming from the satellites, such as the Copernicus ecosystem.
It's freely available in terms of satellite data.
Also, the products that we develop from our datasets are openly available, such as the project done by Monica.
So it's openly available freely available, both the raw data from satellite and the project data.
I missed the second question.
If it was for me.
It was.
It was for me.
Actually, it wasn't the participatory process, so I think also Joan could compliment you.
He's just clarifying.
Any other questions? I will go back to you, but let's just move here, and then we have one there.
Actually, it's just a follow up question because it's really interesting for me.
Apart from the open data that you have on your website, let's say, can we collaborate to have more detailed data, let's say, for a specific project? For instance, we saw an example, I forgot the name of the city.
It was iti.
Yes, they did something in 27 hours.
I don't know if it was also satellite data or not, but if you want to do something like this, can we collaborate with this organization and get more detailed data? Okay.
I think that this is.
For specific projects, we do projects with users.
At all our projects, also the project that Monica was mentioning earlier is done really with the users from the very beginning, from the code design of the solution and so on.
Maybe we can have a chat of what the needs are because we don't do this for Everyone and for all the cases, it's not our mandate, but if there's a specific need that is shared among a group of users, then we can start to see if something is doable.
Yeah.
Also from our side, from UNTx side, we are happy to connect with you and to see how we can support.
It's not freely data that we're using.
We're using very high resolutional aerial or satellite imagery, but we can see what is available in the country in the city you are interested and how we can support, so happy to follow up with you.
Hello.
Thank you for the presentations.
I will have a question for John.
I think very interesting to have the collaborated and co produced data initiatives that you referred to.
I'm curious about, is there any examples that you created data or you collaborated with the government or the other organizations which data used to inform planning processes for the governmental procedures and the formal planning systems in your settlements, just as a case study or benchmark that you can refer to.
Okay, thank you so much.
For the case of Uganda, I'll give a case study of the Kampala Ginger Expressway.
We are given the task of community mobilization, preparing the ground for the different stakeholders for the Uganda National Road authority to come and engage their community members for compensation.
But because it is a very big infrastructure, it was going to displace a lot of people.
And for the data and the community voices that we collected, a lot was not yet done in regards to compensation, in regards to relocation, in regards to having the right of way cleared for the final works.
And when we shared this data, I must say the road is not yet constructed.
I'm not happy because the infrastructure has not yet been set up, but at least I'm happy they considered the community voices.
They have to compensate them.
They have to give them a relocation plan because they try to look for possible alternative resettlement areas for them, not far away from the settlements because they have been working within the settlements.
And time has passed still and the value of land keeps on increasing day by day.
So those are some of the examples I can relate to.
And in addition to that, we've organized communities into saving groups because if you don't save, we are low income and as well we will get the money for all the basic services.
So they have not yet built up the roads, and we are trying to organize the communities such that government programs do not leave them more soft than before.
Thank you.
Okay.
Colleagues, any of you wants to complement the questions that we have? I'm just looking around.
We have time for more, one question and then I will hand yes.
One more question, final question.
We have one here, one here, and then we close.
Sorry.
We can take two, but you can follow up with them.
They will continue here.
So one there and one here, and then we close.
Just to show follow up on data availability, data is free to use the Risken Daspace ecosystem and third party providers that actually are obliged to provide free access, which we do.
So you have free processing power, so you can use it.
Also, there is open data science from European Commission and they have Epoch system that can also run algorithms models there.
It is very free and you can use it, you can try it actually operationally, so you can use it on constant basis.
It's very free.
Thank you so much.
Thank you for complimenting.
Please.
Final question.
It's a question, but also a comment.
My name is Irene from Mozambique Mapou.
One question, it's for Mr.
Washington to know about this American I understand it's an inter American development bank.
If do you have an experience of, let us say, having a corporation working with some cities in Africa.
That's one aspect.
A comment, it's related to Uganda, but probably for other municipalities because in fact, data, I think it has more to give to cities, let us say, only for projects.
I understand that was one example.
I'll give an example of my city.
In my city we are struggling with what we call tax collection.
And it is a problem of my city, but also, I know that many cities in my country they are also facing the same challenge because we know and we understand that at the time we have data, it will enable us to easily go and collect what municipality supposed to collect.
It means a contribution of our citizens.
The funds that will revert building the infrastructure of urban.
The challenge now is that, for example, for informal resettlement is we don't know, in fact, she said very well, we don't know how many people are living there.
We don't know how many houses we have the and we don't have detailed information that can allow us even to help those communities to upgrade them.
Yesterday, I saw a very good experience, and I think we have been learning a lot of this forum.
It's really, really useful because we have many lessons taking home.
The last aspect regarding the data of European space, I ask you to conclude, please.
Yes, I'm concluding was just to learn from them how you can cooperate if you have an experience of an African city and how we can both probably starting off a pilot project and see how this data can help us to target in the informal resettlement and how we can easily get there to go in detail.
Thank you very much.
Thank you appreciate.
Thank you.
Thank you for your question.
Yeah.
At the Inter American Development Bank, we only have mandate to work with the Latin American countries and the Caribbean countries.
But we do have been talking also a lot with other multilateral banks, the African Development Bank, the Asian Development Bank, the Islamic Development Bank, and of course, the World Bank.
I and that's why your question is really and thank you for that to exactly address the relevance of a community of practice in global scale.
Because in the IDB, in our institution, we do provide open source solutions, and I do recommend everyone here to check online.
It's called it open source Urban toolbox.
And those are open source applications that we provide then to anyone, it doesn't matter where, can be also in different countries.
And those solutions, although they are open source, they can also can be customized to a specific geographic conditions.
But that's only one example of this, the relevance of a community of practicing, allowing us to check what different parties have been doing in that sense.
Because we'll be always doing with a really wide and diverse aspects of informality definition and also techniques to map those informations.
But we have a common ground in that sense.
It's always relevant, it's always mandatory to have community engaged of the process of confirming data or especially having them also as active stakeholders in the process of data collection.
We can start that from different sites, from different perspectives, but at the end, we should always consider the relevance and how it's mandatory to have community confirming that data is especially working as data warden for the adoption and utilization of that data.
Consider that that data should always be managed for a public good and not for a different applications.
We understand that open source environment is really irrelevant.
It doesn't matter your geography.
Because at the end, we are talking about technical solutions.
But if we could share there in more global scale, we understand that we could achieve a better solution that could be deployed in different situations.
Thank you.
Thank you, Washington for that.
Colleagues, 1 minute for each of you to share your final thoughts, your key message that you want to share with participants here, please.
Thank you.
Thank you so much.
As we are thinking about informal settlement upgrading, I think one of the issues that we need to consider is special planning so that Once the settlement has been upgraded, it can be integrated into the urban framework, and then the cans the cancs can benefit.
Then to do this better because you cannot use the normal standard, I think it is better that you have a slum upgrading strategy for upgrading as well as preventing the growth of slum.
In my country, this is what we have started doing to ensure that If an institution comes to my country, they say they want to upgrade an informal settlement, they should be able to do the way we feel is best for our country.
Thank you.
Like my friend, I would like to move from policy making to upgrade in formal sentiment.
I would like to comment about the example of the Hejinium.
We have 33-years-old program called favela B.
We would translate like slums to neighborhood.
Its objective is to treat favelas as neighborhoods, bring pavement, street lights, garbage collection, sewer systems, and other cultural equipment.
I have the honor the pleasure to say that the Dead of this program is here, Sj Mags.
He changed the life of many people.
It's an honor.
Beyond the programs, I would like to say that the access to employment and income is fundamental for the poorest population to break the cycle of sausage exclusion and achieve dignified living conditions.
I'd like to build a little bit on Washington's point about community of practice.
I think good communities of practice create cumulative value and they build together and share.
I'd say in my experience in data and data for impact, it's surprising where your collaborators end up coming from and they come from all sorts of places.
I guess I reach out to people who you think are doing interesting things, people who you think you can trust, people who may have tried something before, and I think let's get busy doing things and working out how to replicate them.
Thank you so much.
My last word to the house is that let us complete all the data processes and let us put the data to use.
Let us produce projects for these people living in the informal settlements because they deserve a dignified or a good life as well, and let us not use the data as a threat to forced evictions.
Thank you.
So I would say very quickly that when we produce new products, we want them, first of all, to be useful and not only looking good.
And I get back also to your questions.
If you're interested to stay in the loop of the new developments that we will be doing in the next month on informal settlements, come to me and we'll make sure that your feedback is in the loop.
Thank you.
I just want to say thank you to you, Ali, to Hubert and Dennis, and also to Monica and Sophie for creating space for this conversation.
We have seen the increase of the conversation and also the increase of partnership.
The last word room Buforo in Cairo was somehow not exactly the beginning, but was the trigger of that and hopefully in the next room Bufo we'll be having a much bigger dialog and a much bigger contribution to the topic.
I know that looking to us, we are mapping nerds.
People do not do mapping all day on their lives, but at the end, what we are talking about especially seeing people and making voices to be listened and also understanding that we should work for this idea of a more inclusionary process and it's really beautiful having this opportunity to have this conversation, Li.
Thank you so much.
Thank you.
Thank you all for joining us today for sharing your experience, your knowledge with all of us.
Thank you all also for staying with us and participating and most of all, thank you, Robert.
Thank you, Dennis, for organizing this amazing panel.
A round of applause to all of you.
Our thank you also to the interpreters, the invisible work that we don't see and also to all the team here supporting us today.
Thank you so much.
I would just ask all the speakers to line up here so that we can take a photo.
Thank you, colleagues.
I hope you enjoyed the rest of your evening.
Thank you.