The delineation and growth of metropolitan areas in the world between 2000 and 2015

A talented team at the OECD and the European Union have developed a consistent method to delineate metropolitan areas – or functional urban areas (FUAs) – in the entire world. They recently published an open access paper where the explain the method and use it to analyze the population growth of metropolitan areas in the world between 2000 and 2015. You can find more info about the paper below and  a geopackage data set of  FUAs can downloaded from here. 

Moreno-Monroy, A. I., Schiavina, M., & Veneri, P. (2020). Metropolitan areas in the world. Delineation and population trends. Journal of Urban Economics, 103242. 

Abstract

This paper presents a novel method to delineate metropolitan areas – or functional urban areas (FUAs) – in the entire world and assesses their population trends. According to the definition developed by the OECD and the European Union, FUAs are composed of high-density urban centres with at least 50 thousand people plus their surrounding commuting zones. The latter represent the urban centres’ areas of influence in terms of labour market flows. The proposed method combines a functional and a morphological approach to overcome the dependency on travel-to-work data to define commuting zones and allow a global delineation. It relies on a probabilistic approach and the use of population and travel impedance gridded data across the globe. Results show that around 3.9 billion people, making up 53% of the world population, live in 8,790 FUAs, out of which 17% live in their commuting zones. Between 2000 and 2015, population growth was higher in larger FUAs, highlighting a general trend toward higher concentration of the metropolitan population. Commuting zones grew faster than urban centres, though with heterogeneous patterns across world regions, income levels and metropolitan size.

Related posts:

• Plotting the metropolitan areas of Brazil in R
• Delineating Geographical Regions with telephone call networks
• The urban footprint of the largest metropolitan areas of Europe

Assorted Links

1. Evolution of European Motorways 1920-2020
   
   
2. Dataviz of spatial and temporal spread in networks
   
   
3. paper: Open office architecture seems to decrease the volume of face-to-face employees' interaction and increase electronic interaction.
   
   
4. Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery
   
   
5. Microsoft releases 18M building footprints in Uganda and Tanzania to enable AI Assisted Mapping
   
   
6. OpenRailwayMap, the open online map of the worldwide railroad infrastructure
   
   
7. In 2019, Helsinki had 0 pedestrian or cyclist fatalities for the first time since 1960. How Lowering speeds and traffic calming.

Plotting the metropolitan areas of Brazil in R

Here is a simple R script to download the shape files of Brazilian metropolitan areas by year and plot them in R.

obs. It's important to note that, since the 1988, metro areas are created by state governments. As a rule, this is done with absolutely no transparency nor any technical criteria. It is this legal issue, rather than the urbanization process in the country itself, that led to such an inflated increase in the number of metro areas in the country over the past two decades.

Number of metro areas in Brazil:

1970: 9

2001: 24 

2018: 77

geobr: data updates

Some of you are already familiar with geobr, an R package that we developed in Ipea to facilitate downloading official spatial data sets of Brazil (a quick intro to geobr here). The stable version 1.0 was published on CRAN a couple of months ago. Since then, we have added some new data sets.

geobr now brings official spatial data of natural biomes, indigenous lands of all ethnicities in Brazil according to stage of demarcation, and risk areas prone to landslides and floods in Brazil. These data sets are currently only available in the development version of the package, wich can be installed with devtools::install_github("ipeaGIT/geobr") .

geobr: easy access to official spatial data sets of Brazil

I'm glad to annouce that geobr is now officially available on CRAN. Here and here are quick intros on how to use geobr to get easy and quick access to shape files and other official spatial data sets of Brazil. The package currently includes several data sets for various years such as states, regions, municipalities, census tracts, statistical grid and others. The github repository is this, in case you want to keep track of the latest developments.

The package was developed by my team and I at the Institute for Applied Economic Research (Ipea). We are constantly working to expand and improve the package, so if you have any suggestions/contributions, please feel free to open an issue on Github.

A spatial database of health facilities in sub Saharan Africa

Interesting new paper analyzing accessibility to emergency hospital care in sub-Saharan Africa in 2015. The authors have done a laborious work to map public health facilities and the data is openly available here. HT Moritz Kraemer.

Ouma, P. O., et al. (2018). Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis. The Lancet Global Health, 6(3), e342-e350.

Abstract:

Background

Timely access to emergency care can substantially reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, no Pan-African database of where hospitals are located exists; therefore, we aimed to complete a geocoded inventory of hospital services in Africa in relation to how populations might access these services in 2015, with focus on women of child bearing age.

Methods

We assembled a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan Africa, including Zanzibar, using data from various sources. We only included public hospitals with emergency services that were managed by governments at national or local levels and faith-based or non-governmental organisations. For hospital listings without geographical coordinates, we geocoded each facility using Microsoft Encarta (version 2009), Google Earth (version 7.3), Geonames, Fallingrain, OpenStreetMap, and other national digital gazetteers. We obtained estimates for total population and women of child bearing age (15–49 years) at a 1 km2 spatial resolution from the WorldPop database for 2015. Additionally, we assembled road network data from Google Map Maker Project and OpenStreetMap using ArcMap (version 10.5). We then combined the road network and the population locations to form a travel impedance surface. Subsequently, we formulated a cost distance algorithm based on the location of public hospitals and the travel impedance surface in AccessMod (version 5) to compute the proportion of populations living within a combined walking and motorised travel time of 2 h to emergency hospital services.

Findings

We consulted 100 databases from 48 sub-Saharan countries and islands, including Zanzibar, and identified 4908 public hospitals. 2701 hospitals had either full or partial information about their geographical coordinates. We estimated that 287 282 013 (29·0%) people and 64 495 526 (28·2%) women of child bearing age are located more than 2-h travel time from the nearest hospital. Marked differences were observed within and between countries, ranging from less than 25% of the population within 2-h travel time of a public hospital in South Sudan to more than 90% in Nigeria, Kenya, Cape Verde, Swaziland, South Africa, Burundi, Comoros, São Tomé and Príncipe, and Zanzibar. Only 16 countries reached the international benchmark of more than 80% of their populations living within a 2-h travel time of the nearest hospital.

Interpretation

Physical access to emergency hospital care provided by the public sector in Africa remains poor and varies substantially within and between countries. Innovative targeting of emergency care services is necessary to reduce these inequities. This study provides the first spatial census of public hospital services in Africa.

geobr: shapefiles and official spatial data sets of Brazil in R

In 2012, I published here a blog post about where to find shapefiles of Brazil. Since then, this has become one of the most popular posts in 9 years of the blog. However, the links to the original data sets change every now and then, and it gets a bit tricky to find the most up to date data. My team and I at the Institute for Applied Economic Research (Ipea) have created geobr, an R package that allows users to easily access shapefiles of the Brazilian Institute of Geography and Statistics (IBGE) and other official spatial data sets of Brazil.

The geobr package currently includes a variety of data sets, such as the shapefiles of municipalities and states (from 1872 to 2018), census weighting areas, a spatial grid with population count at a resolution of 200 x 200 meters, a geolocated database of health facilities in the country etc. All the data sets are read into R as sf data. We will gradually add other databases to the package, but feel free to make specific requests and suggestions by opening new issues on the GitHub page of geobr or tweeting the hashtag #geobr.

The advantage of geobr: Intuitive syntax that provides easy and quick access to a wide variety shapefiles and official spatial data sets with updated geometries for various years using harmonazied attributes and geographic projections across geographies and years.

Here is a quick intro to geobr:

Assorted links

1. 25% of students think they are in the top 1% of social skills. 94% of professors think their work is better than their peers. The Social Psychology of Biased Self-Assessment (ht Leo Monasterio)
   
   
2. What happened when New York City (randomly) increased street lighting? Crime fell by 36% as a direct result. HT John B. Holbein ‏, who is great at finding this kind of gems by the way.
   
   
3. Sacred Spaces: a series on modernist churches' by Thibaud Poirier, HT Darran Anderson
   
   
4. Interesting report comparing housing in London , New York City, Paris and Tokyo, by Jim Gleeson. Some key results summarized in this short thread.
   
   
5. The power of a single book. Beautiful metaphor for how ideas can have real impact
   
   
6. The data that was missing in your research: an incredibly detailed 3-D maps of the lunar surface
   
   
7. Microsoft Researchers trained a neural network to analyze satellite imagery and generate the footprints of 125,192,184 building in all 50 US states. The data are available on GitHub. Great coverage in the NYT, by Tim Wallace et al.

image credit: NYT

The suburbs of Mesa, Arizona

image credit: NYT

Creating a simple world map of cities in R

Mike (from cool but useless) asked on twitter if there is any package in R with with lats/longs of lots of cities. In fact there is. Here is a simple code to create a world map of cities with population larger than 40K in R using maps::world.cities and ggplot2.

ps. These population data in the maps package refers to 2016 estimates. If you want city population data from previous years, you might remember that we have previously posted about this open dataset with 6,000 years of global urbanization.

Assorted Links

1. Paper: "exposure to socioeconomic inequality in an everyday setting negatively affects willingness to publicly support a redistributive economic policy" by Melissa Sands
   
   
2. Peak Artificial Intelligence
   
   
3. Interactive historic map of every UK railway line that's ever existed, via Tim Dunn
   
   
4. Oxford DEMIG databases with unprecedented coverage of migration & migration policy data now available for free download, via Hein de Haas
   
   
5. Using machine learning in R to predict Automobile Collision and Bike Share Demand and subsidy via Ken Steif
   
   
6. Awful AI: a curated list to track current scary usages of AI - hoping to raise awareness, HT Will Geary
   
   
7. The software used to create the black hole in the movie 'Interstellar' is a full implementation of Einstein's equations in 40,000 lines of C++, and rendered thousands of 23-megapixel IMAX frames on a 32,000-core render farm at about 20 core-hours per frame via John Arundel
   
   
8. What to consider when choosing colors for data visualization, by Lisa Rost

credit: Lisa Rost

How complete is OpenStreetMap data coverage?

Mikel Maron (MapBox) has addressed this question in 2015 using the CIA World Factbook as a reference. The coverage varies from country to country, as expected, but it's pretty good overall and it only gets better with time. You can check the results for your country here.

More recently, Barrington-Leigh and Millard-Ball addressed the same questions with different methods (ht Ralph Straumann). The authors arrived at a similar conclusion but they also draw some other interesting findings (see the abstract below).  Obs. I'm curious to know why Bolivia stands out from Latin America.

Barrington-Leigh, C., & Millard-Ball, A. (2017). The world’s user-generated road map is more than 80% complete. PloS one, 12(8), e0180698.
Abstract:

OpenStreetMap, a crowdsourced geographic database, provides the only global-level, openly licensed source of geospatial road data, and the only national-level source in many countries. However, researchers, policy makers, and citizens who want to make use of OpenStreetMap (OSM) have little information about whether it can be relied upon in a particular geographic setting. In this paper, we use two complementary, independent methods to assess the completeness of OSM road data in each country in the world. First, we undertake a visual assessment of OSM data against satellite imagery, which provides the input for estimates based on a multilevel regression and poststratification model. Second, we fit sigmoid curves to the cumulative length of contributions, and use them to estimate the saturation level for each country. Both techniques may have more general use for assessing the development and saturation of crowd-sourced data. Our results show that in many places, researchers and policymakers can rely on the completeness of OSM, or will soon be able to do so. We find (i) that globally, OSM is ∼83% complete, and more than 40% of countries—including several in the developing world—have a fully mapped street network; (ii) that well-governed countries with good Internet access tend to be more complete, and that completeness has a U-shaped relationship with population density—both sparsely populated areas and dense cities are the best mapped; and (iii) that existing global datasets used by the World Bank undercount roads by more than 30%.

Completeness of the OSM dataset, by grid cell, January 2016.

credit: Barrington-Leigh & Millard-Ball (2017)

Globally consistent estimate of carbon footprints of 189 countries and 13,000 cities

Daniel D Moran et colleagues developed the Global Gridded Model of Carbon Footprints (GGMCF). This model provides a globally consistent and spatially resolved (250m) estimate of carbon footprints in per capita and absolute terms across 189 countries. Their paper got recently accepted for publication (see below) and their data is freely available. Kudos to the team!

Moran, D., Kanemoto, K., Jiborn, M., Wood, R., Többen, J., & Seto, K. (2018). Carbon footprints of 13,000 cities. Environmental Research Letters.

Abstract:

While it is understood that cities generate the majority of carbon emissions, for most cities, towns, and rural areas around the world no carbon footprint (CF) has been estimated. The Gridded Global Model of City Footprints (GGMCF) presented here downscales national CFs into a 250m gridded model using data on population, purchasing power, and existing subnational CF studies from the US, China, EU, and Japan. Studies have shown that CFs are highly concentrated by income, with the top decile of earners driving 30-45% of emissions. Even allowing for significant modeling uncertainties, we find that emissions are similarly concentrated in a small number of cities. The highest emitting 100 urban areas (defined as contiguous population clusters) account for 18% of the global carbon footprint. While many of the cities with the highest footprints are in countries with high carbon footprints, nearly one quarter of the top cities (41 of the top 200) are in countries with relatively low emissions. In these cities population and affluence combine to drive footprints at a scale similar to those of cities in high-income countries. We conclude that concerted action by a limited number of local governments can have a disproportionate impact on global emissions.

credit: Moran et al

New tool to get population estimates for any user-defined area

Last year, I wrote a post indicating where you can find open source data on the spatial distribution of population at reasonably high spatial resolutions. One of those sources is Sedac / Nasa hosted by CIESIN at Columbia University. Since my post (almost a year ago), these guys have done some important improvements to their methods and data, which now bring for the first time a global data set on population estimates by age and sex available at 30-arcsecond resolution.

They have also developed a new interactive tool that lets you estimate the total population within a polygon you draw on the map. I saw this tool on Twitter thanks to David McClendon.

Happy procrastinating, everyone!

Related posts:

• Global Grid of Probabilities of Urban Expansion to 2030
• An open dataset with 6,000 years of global urbanization 
• High-resolution data sets on global man-made impervious surfaces and urban extents

Assorted Links

1. Post-doctoral position at Lund University to work on sustainable urban mobility and accessibility with a focus on economics and transportation equity, HT Jean Ryan
   
   
2. Rainbows make horrible colour maps... let's stop using them
   
   
3. paper : Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes
   
   
4. The Internet of Things in unexpected things
   
   
5. An Open Letter from 1,470 Economists on Immigration
   
   
6. There is an app to crowdsource data on shootings in Rio. Great initiative by Cecilia Olliveira
   
   
7. A Catalog of Civic Data Use Cases, via Igor Calzada
   
   
8. An interactive global overview of the magnitude, disparities and trend of infant mortality in the world between 1950 and 2011, by Ramon Martinez

Assorted Links

1. paper: carpooling options from companies like Uber and Lyft could reduce the number of vehicles on the road by a factor of three without significantly impacting travel time
   
   
2. Istanbul Urban Database: digitizing the historic urban archives of Istanbul , via @geog.an.mod
   
   
3. "Bussed Out: How America moves its homeless"
   
   
4. The effect of population age structure on innovation. In short, younger societies are more innovative. Via Tyler Cowen
   
   
5. Loud orgies of Mexican fish could deafen dolphins, say scientists.
   
   
6. Place matters: A longitudinal study estimating the influence of neighbourhood walkability on walking behaviour in across different life-stages via Ahmed el Geneidy
   
   
7. The science myths that will not die. The `population bomb` myth is included in the list
   
   
8. Carl Schmertmann‏ pointed out on Twitter to a new open access book "Visualizing Mortality Dynamics in the Lexis Diagram". You might also like this really great interactive interface to explore how mortality patterns change over time and across countries , developed by Jonas Schöley

High-resolution data sets on global man-made impervious surfaces and urban extents

In 2012, we posted about a big research project on global forecasts of urban expansion and its environmental impacts, by Karen C. Seto and her lab at Yale. On a related topic, SEDAC (a NASA data center hosted at CIESIN in Columbia University) has released two new high-resolution data sets that some of you might find useful for your own research:

1. Global Human Built-up And Settlement Extent for the target year 2010, derived from global 30m Landsat satellite data
   
   
2. Global Man-made Impervious Surface for the target year 2010, derived from global 30m Landsat satellite data

Happy data crunching!

Related links:

• Where to find high spatial resolution data on population distribution
• 200 years of Urban Expansion
• Visualizing surface changes over time in every corner on Earth
• The Dynamics of Global Urban Expansion

Heads up for some useful R packages

As you can see from this post, the community of R users and developers is alive and kicking on Twitter. If you would like to recommend other packages, send me an email or leave a comment on this post.

1. data.table: high-performance data manipulation, by Matt Dowle and Arun Srinivasan. This is certainly among my favourite packages. I've been working with datasets of a few hundreds of millions observations and it makes things much faster. I stopped using dplyr long time ago
   
   
   
   
2. tidycensus: a new library to get the US Census Bureau spatial and demographic data in R ready for use with sf and the tidyverse. This package was created by Kyle Walker, who is a must-follow if you're into R and spatial analysis
   
   
   
   
3. mapview: interactive viewing of spatial objects in R, by Tim Salabim
   
   
   
   
4. mapedit: interactive editing of spatial data in R, by Kent Russel
   
   
   
   
5. europop: Historical Populations of European Cities, 1500-1800, by Matthew Lincoln
   
   
   
   
6. corrr: a good package for exploring correlations in R, by Simon Jackson
   
   
   
   
7. ggsci, a collection color palettes inspired by colors used in scientific journals to be used in ggplot2, by Nan Xiao
   
   
   
   
8. ourworldindata: a package by Simon Jackson to access the datasets from OurWorldInData.org, which is a great project by Max Roser
   
   
   
   
9. magick: advanced image-processing in R, which can be really useful for including gifs in your plots and impress reviewer #2 . ht Danielp Hadley

Assorted links

1. Combining satellite imagery and machine learning to predict poverty
   
   
2. Bill Rankin's new work on Mapping Slavery Against US Incarceration
   
   
3. A Review of Temporal Visualizations based on Generalized Space-Time Cube Operation
   
   
4. Charles Darwin was born on this day 208 years ago. All of Darwin's published works are available here.
   
   
5. An extensive list of good blogs and websites on urban- and transport-related topics. ht Kyle Zheng
   
   
6. A 15-min video giving an interesting summary of the largest US health insurance experiment in history - ht Carl Schmertmann
   
   
7. There is an app to crowdsource data on shootings in Rio. Great project by Cecilia Olliveira in face of harsh reality
   
   
8. Can you name a city just by looking at its streets?
   
   
9. Inequality Is Killing The American Dream

An open dataset with 6,000 years of global urbanization

Patrick Gerland points to a new paper/open dataset that provides the first spatially explicit archive of the location and size of urban population settlements over the last 6,000 years. We have previously posted in this blog about the fantastic work of Karen Seto and her group at Yale at the Seto lab on Urbanization and Global Change (here and here). This one adds up to a great and continuous effort on improving spatially explicit models of population growth and urbanization, with important contributions to historical and prospective research.

Reba, M., Reitsma, F., & Seto, K. C. (2016). Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000. Scientific data, 3.

Abstract:

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.

credit: Reba, Reitsma and Seto

Comparing house price trends worldwide

Leonard Kiefer has a new post where he presents some interesting analysis of global house price trends. Lonards has used data from the international house price database, organized by the Dallas Federal Reserve Bank.

The analysis is written in R and the code is available at the bottom of his post. Thanks Leonard!

ps. don't forget to check the related links at the bottom of this post.

credit: Leonard Kiefer

Related Links:

• Aggregate homeownership rates for different countries
• Commuting times and housing costs in eight major cities
• 3D dataviz of house prices in England and Wales
• The importance of titling and property rights