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A road by any other name: street naming and property addressing system in Accra, Ghana

Linus Pott's picture
Street names in Accra, Ghana
Street names in Accra, Ghana. Photo credit: Ben Welle/ Flickr CC
When I used to work in Rwanda, I lived on a small street in Kigali. Every time I invited friends over, I would tell them to “walk past the Embassy, look out for the Church, and then continue to the house with the black gate.” The day a street sign was erected on my street was a game changer.
 
So how do more than two million citizens of Accra navigate the busy city without the help of street names? While some street names are commonly known, most streets do not have any official name, street sign or house number. Instead, people usually refer to palm trees, speed bumps, street vendors, etc.

But, what happens when the palm tree is cut or when the street vendor changes the location?

The absence of street names poses not only challenges for orientation, but also for property tax collection, postal services, emergency services, and the private sector. Especially, new economy companies, such as Amazon or Uber, depend on street addressing systems and are eager to cater to market demands of a growing middle class.

To address these challenges, the Accra Metropolitan Assembly (AMA), financed by the World Bank’s second Land Administration Project , is implementing a street addressing and property numbering system in Accra. Other Metropolitan areas received funding from other World Bank-funded projects for similar purposes.
 

What can satellite imagery tell us about secondary cities? (Part 2/2)

Sarah Elizabeth Antos's picture
In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.

Can we rely only on satellite? How accurate are these results?

It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.

So, it’s important to learn how well the resulting maps reflect the reality on the ground.

Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.

Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.
 
Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).

What can satellite imagery tell us about secondary cities? (Part 1/2)

Sarah Elizabeth Antos's picture

The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.

But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.

The question remains: How can we efficiently transform big data into valuable products that help urban planners?

In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.

A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.

Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.

What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.

Want to explore our maps? Download our data here. Click here for an interactive land cover map of La Ceiba.

Using ICTs to Map the Future of Humanitarian Aid (part 2)

Dana Rawls's picture
Satellite image and analysis of damage caused by Tropical Cyclone Evan in Samoa. Credit: UNITAR-UNOSAT

With crisis mapping’s increasing profile, other organizations have joined the fray. Just this month, Facebook announced that it was partnering with UNICEF, the World Food Programme, and other partners to “share real-time data to help respond after natural disasters,” and the United Nations has also contributed to the field with its Office for the Coordination of Humanitarian Affairs (OCHA) founding MicroMappers along with Meier, as well as creating UNOSAT, the UN Operational Satellite Applications Programme of the United Nations Institute for Training and Research.

In a 2013 interview, UNOSAT Manager Dr. Einar Bjorgo described the work of his office.

“When a disaster strikes, the humanitarian community typically calls on UNOSAT to provide analysis of satellite imagery over the affected area… to have an updated global view of the situation on the ground. How many buildings have been destroyed after an earthquake and what access roads are available for providing emergency relief to the affected population? We get these answers by requiring the satellites to take new pictures and comparing them to pre-disaster imagery held in the archives to assess the situation objectively and efficiently.”

Four years later, UNOSAT’s work seems to have become even more important and has evolved from the early days when the group used mostly freely available imagery and only did maps.

Using ICTs to Map the Future of Humanitarian Aid (part 1)

Dana Rawls's picture
Haiti map after the 2010 earthquake. Over 450 OpenStreetMap volunteers from an estimated 29 countries digitized roads, landmarks and buildings to assist with disaster response and reconstruction. OpenStreetMap/ITO World

The word “disruption” is frequently used to describe technology’s impact on every facet of human existence, including how people travel, learn, and even speak.

Now a growing cadre of digital humanitarians and technology enthusiasts are applying this disruption to the way humanitarian aid and disaster response are administered and monitored.

Humanitarian, or crisis, mapping refers to the real-time gathering and analysis of data during a crisis. Mapping projects allows people directly affected by humanitarian crises or physically located on the other side of the world to contribute information utilizing ICTs as diverse as mobile and web-based applications, aggregated data from social media, aerial and satellite imagery, and geospatial platforms such as geographic information systems (GIS).

Mapping and measuring urban places: Are we there yet? (Part 2/2)

David Mason's picture
Photo by Anton Balazh via Shutterstock

My previous blog post surveyed some of the recent trends in developing global measures of urbanization. In this post, I want to turn to a brief discussion for scholars and practitioners on some possible applications and areas of focus for ongoing work:
 
[Download draft paper "Bright Lights, Big Cities: a Review of Research and Findings on Global Urban Expansion"]
 
While there are a number of different maps for documenting urban expansion, each has different strengths and weaknesses in application. Coarser resolution maps such as MODIS can be used for mapping the basic contours of artificial built-up areas in regional and comparative scales. On the other hand, high-resolution maps are best suited for individual cities, as algorithms can be used to identify and classify observed colors, textures, shading, and patterns into different types of land uses. These levels of detail are difficult to use for reliable comparisons between cities as the types of building materials, structure shapes, light reflectivity, and other factors can vary widely between countries and regions.
 
Nonetheless, there are a number of applications for policymakers in this regard, from identifying and mapping green spaces and natural hazard risks to identifying and tracking areas of new growth, such as informal settlements. However, such approaches to land use detection require careful calibration of these automated methods, such as cross referencing with other available maps, or by “ground truthing” with a sample of  street-level photos of various types of buildings and land cover as reference inputs for automation. One solution to this is the use of social media and geo-coded data to confirm and monitor changes in urban environments alongside the use of high-resolution satellite imagery.
 
Nighttime light maps also have gained traction as measures of urban extent and as ways to gauge changes in economic activity in large urban centers. They are probably less useful for documenting smaller settlements, which may be dimmer or have little significant variation in brightness. It is important to correct these types of maps for “overglow” measurement effects—where certain light may “bleed” or obscure the shapes and forms of very large, bright urban areas in relation to adjacent smaller and dimmer settlements (newer VIIRs maps have made some important advances in correcting this).

Mapping and measuring urban places: Are we there yet? (Part 1/2)

David Mason's picture
Source: Deuskar, C., and Stewart B.. 2016. “Measuring global urbanization using a standard definition of urban areas: Analysis of preliminary results” World Bank
This satellite image shows Sao Paolo's estimated “urban areas” based on a WorldPop gridded population layer. Areas in yellow are areas with at least 300 people per km2 and a known settlement size of 5,000 people. Red areas represent a population density threshold of at least 1,500 people per km2 and a known settlement size of 50,000 people.
There remains a surprising amount of disagreement over precisely what “urban” means despite the ubiquity of the term in our work. Are urban areas defined by a certain amount of artificial land cover such as permanent buildings and roads? Or are they more accurately described as spatially concentrated populations? The answer often depends on what country you are in, as their administrative definitions of urban areas can vary widely across and between these two dimensions.
 
Without a globally consistent measure of urban areas, it can be difficult to track changes in built-up areas (land surface coverage comprised of buildings and roads) and population growth across time and space. This impacts how policymakers may understand and prioritize the challenges cities face and what investments or reforms may be needed. In a new paper, “Bright Lights, Big Cities: a Review of Research and Findings on Global Urban Expansion,” I provide a brief introduction to some of the current approaches for measuring urban expansion and review the comparative findings of some recent studies.
The UN’s World Urbanization Prospects (WUP), perhaps the most comprehensive and widely cited measure of urbanization across the world, draws from a compilation of country-level population totals based on administrative definitions. A key weakness with this set is that since each country defines “urban” differently, it is difficult to accurately compare one country’s urbanization to another, as well as to estimate the urban population of a group of countries or the world itself. Recent work has provided more sophisticated ways to measure urban growth and expansion using both satellite map data and careful application of population data.

A first look at Facebook’s high-resolution population maps

Talip Kilic's picture

Facebook recently announced the public release of unprecedentedly high-resolution population maps for Ghana, Haiti, Malawi, South Africa, and Sri Lanka. These maps have been produced jointly by the Facebook Connectivity Lab and the Center for International Earth Science Information Network (CIESIN), and provide data on the distribution of human populations at 30-meter spatial resolution. Facebook conducted this research to inform the development of wireless communication technologies and platforms to bring Internet to the globally unconnected as part of the internet.org initiative.

Figure 1 conveys the spatial resolution of the Facebook dataset, unmatched in its ability to identify settlements. We are looking at approximately a 1 km2 area covering a rural village in Malawi. Previous efforts to map population would have represented this area with only a single grid cell (LandScan), or 100 cells (WorldPop), but Facebook has achieved the highest level of spatial refinement yet, with 900 cells. The blue areas identify the populated pixels in Facebook’s impressive map of the Warm Heart of Africa.
 

Figure 1: Digital Globe Imagery from Rural Malawi Overlaid with Facebook Populated Cells

Facebook’s computer vision approach is a very fast method to produce spatially-explicit country-wide population estimates. Using their method, Facebook successfully generated at-scale, high-resolution insights on the distribution of buildings, unmatched by any other remote sensing effort to date.  These maps demonstrate the value of artificial intelligence for filling data gaps and creating new datasets, and they could provide a promising complement to household surveys and censuses. 

Beginning in March 2016, we started collaborating with Facebook to assess the precision of the maps and explore their potential uses in development efforts. Here, we describe the analyses undertaken to date by the Living Standards Measurement Study (LSMS) team at the World Bank to compare the high-resolution population projections against the ground truth data. Among the countries that were part of the initial release, Malawi was of particular interest for the validation exercise given the range of data at our disposal.

How geospatial technology can help cities plan for a sustainable future

Xueman Wang's picture
In this video, representatives from the World Bank, GEF, and City of Johannesburg discuss the impact of geospatial tools on urban planning.

Many urban residents these days will find it hard to imagine a life without mobile apps that help us locate a restaurant, hail a cab, or find a subway station—usually in a matter of seconds. If geospatial technology and data already make our everyday lives this easier, imagine what they can do for our cities: for example, geospatial data on land-use change and built-up land expansion can provide for more responsive urban planning, while information on traffic conditions, road networks, and solid waste sites can help optimize management and enhance the quality of urban living.

The “urban geo-data gap”
 
However, information and data that provide the latest big picture on urban land and services often fail to keep up with rapid population growth and land expansion. This is especially the case for cities in developing countries—home to the fastest growing urban and vulnerable populations.

​Using open tools to create the digital map of Cairo’s public transit

Tatiana Peralta Quiros's picture
Follow the authors, Tatiana (@tatipq) and Diego (@canaless) on Twitter 

The first step in any transit planning process involves understanding the current supply and demand of transit services. In most of the countries where we work, understanding the supply of services is a messy, costly and lengthy process, since most cities have little knowledge of bus routes, services and operational schemes.
 
Having a digital map (GIS) and General Transit Feed Specifications (GTFS) details of a network allows a transit agency to do better service planning and monitoring, as well as provide information to its users. A traditional GIS software approach often requires a team of consultants and months of work.  Last month, however, we were presented with the challenge to use innovative tools do the same work in less than two weeks.
 
This was our first visit to Cairo, Egypt, and there we were tasked with the goal of mapping the city’s entire bus network (approximately 450 formal bus routes) in order to conduct an accessibility analysis with our new Accessibility Tool. At first hand this task seemed daunting, and a few days after arriving we were not certain that we could accomplish it in two weeks.
 
Before our trip, we had agreed on a somewhat flexible work plan, laying out an array of potential open-access, free tools that we could use depending on the scenarios we would encounter, mostly dependent on the availability of data.

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