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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.

Call for Feedback: How-To Note on Community Mapping for Better Services

Samhir Vasdev's picture

The review process for this How-to Note has ended. The paper has been downloaded 36 times and we received 5 comments.

We are grateful to the many reviewers for their valuable comments. The author will carefully review and consider all comments when finalizing the note. The final version of the How-To Note will be published on the Open Development Technology Alliance website and announced in the World Bank blog forum

Using GIS to Manage the Urban Ecosystem

Henry Jewell's picture

Growing up on a farm meant I spent very little time in cities. I felt more at home when surrounded by green than grey. As a kid, I saw cities as noisy, bright, busy and quite frankly, confusing. I always thought to myself why would anyone want to live in them? However, when I grew up, I moved to a city to take advantage of the opportunities it provided. I am not alone. More than 50 percent of the world’s population lives in cities and this number will rise. Cities are hubs of productivity, innovation and vast human capital; but once you live in them you begin to see that they are like any other ecosystem: complex and fragile, whose balance can be easily disturbed. With many cities rapidly growing and evolving, how do you manage this increasing complexity without destroying the ecosystem?

GIS Image.  Source - University of Texas at DallasGeographical Information System (GIS) techniques have proven successful in mapping, analyzing and managing natural ecosystems. It is now time to make use of the same technology to manage, model and design our expanding global system of cities. GIS consists of a proven set of tools that can provide information to leaders at the local and national level to facilitate evidence-driven decision making. It allows us to move beyond 2D paper maps and incorporate everything that lies below, above and around a city to create a 3D digital representation of the city’s ecosystem. By integrating this information into the planning process, it will hopefully lead to harmonized planning across sectors. For example, integrated transport and land use planning and development will allow for economic, social and environmental benefits. More sectors can then be incorporated, with this integration not only happening within the city limits but including the urban periphery, where a lot of urban expansion is currently occurring. This holistic view will allow planners to make cities more livable.

The Mouse that Severed the Red Tape from Guruvayoor Municipality in Kerala, India

Kalesh Kumar's picture

When 150 marriages are solemnized in a day within 60 minutes in the same venue, the challenges are not just with the brides and grooms to stick to their own soul mates, but also to the municipal authorities to keep track and issue marriage certificates in a reasonable time frame. As many Keralites located all over the world chooses Guruvayoor Temple for their marriage, delivering their marriage certificates adds to the troubles of a small municipality with less than 10 staff in the section.

On a recent visit to Kerala as part of the World Bank supported Kerala Local Government Service Delivery Project (KLGSDP), I found that in 2010 September, Guruvayoor Municipality solved the problems with marriage certificates, and opened a window of transparency and efficiency in its service delivery to the general public, through an e-governance platform. Meeting us in his current office in the Attingal Municipality, N Vijayakumar, former Municipal Secretary of Guruvayoor, took us through the journey he and a highly committed team made for bringing an e-revolution in the Municipality.

“They are sitting on a gold mine and don’t even know it….”

Holly Krambeck's picture

The other day, my colleague Roger Gorham, a transport economist working in Africa, shared with me an interesting story. He was in Lagos, meeting with stakeholders about setting up public-private partnerships for transport initiatives. One meeting revealed that, in an effort to improve service, a private entity had invested in new taxis for Lagos and in each had installed a GPS unit. This little revelation may not seem interesting, but it was very exciting to Roger, who also learned that the company has amassed more than 3 years of GPS tracking data for these taxis (which, incidentally, troll the city like perfect probes, nearly 24 hours a day, 7 days a week) and that this data could be made available to him, if he thought he might make some use of it.

Now, if you are reading this blog, chances are that you realize that with this kind of data and a little analysis, we can quickly and easily reveal powerful insights about a city’s transport network – when and where congestion occurs, average traffic volumes, key traffic generators (from taxi pick-up point data), occurrence of accidents and traffic blockages in real time, and even the estimated effects of congestion and drive cycle on fuel efficiency.

As Roger said, “They are sitting on a gold mine and don’t even know it….”