Assorted Links

1. Isoscope: work by Flavio Gortana (Twitter), showing how isochrones by car vary across space and time
   
2. Tutorial: Spatial Datasets and Urban Applications (code, slides + data)
   
   
3. Beautiful photographs of Forgotten Places, via Darran Anderson
   
   
4. Five tools that help you master map projections
   
   
5. The unfinished cities of Spain via Urban Living Lab
   
   
6. 15 years of urban growth in China, via Lazaro Gamio
   
   
7. Surnames and ancestry in Brazil, by Leo Monasterio
   
   
8. "Seeing Theory – A visual introduction to probability and statistics.”
   
   
9. paper: Combining satellite imagery and machine learning to predict poverty, by Neal Jean et al [code]

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

Brasilia, 32 years of urban expansion

Google Earth Engine has released new data for their project Timelapse, which combines over 5 million satellite images acquired over the past three decades and allows for a zoomable video of land transformations at a global scale. You can play around  procrastinate  on their website zooming in different areas. I find Dubai and Las Vegas particularly interesting. There is a good playlist on Youtube!

This is the timelapse of Brasilia (Federal District), my hometown. During this period between 1984 and 2016, the population of Brasilia went from approx. 1.2 million to 2.9 million. The video shows some very interesting transformations including the expansion of poor settlements largely undeserved by urban infra-structure in the south and southwest of the city, but also some illegal occupations of protected areas by middle- and high-income gated communities in the east and north-east parts of the city.

A long-term historical perspective of urban growth in Europe and the US

Marcelo Medeiros (who is secretly my guru) pointed me out to two papers on a long-term historical perspective of urban growth, by Rafael González-Val. I imagine some readers of the blog might find them  useful  interesting.

• The Evolution of US City Size Distribution from a Long Term Perspective (1900-2000)
• Historical Urban Growth in Europe (1300 - 1800)

ps. The AAG was great with some really good thought provoking sessions. I'm still in the Bay area for two more days, visiting the team at Berkeley Urban Planning to talk about transportation and accessibility modelling using Pandana .

R Links

1. Using R to model spatial patterns of urban growth in Africa
   
   
2. Great 'course' on network visualization with R, by Katherine Ognyanova
   
   
3. Building a Schelling segregation model in R, by Ben Davies
   
   
4. Basic mapping and attribute joins in R, by Robin Lovelace
   
   
5. A shiny app to explore the demographics of Texas counties, by Kyle Walker
   
   
6. Nathan Yau compares ggplot2 and R Base Graphics
   
   
7. A Compendium of Clean Graphs in R
   
   
8. I've gathered some code to show how to create Flow maps in R using ggplot2

Assorted Links

1. Interactive Map showing urban growth trends between 1950-2030, by Duncan Smith
   
   
2. Pictures of 11 Abandoned Monorails (and the perfect soundtrack for that)
   
   
3. The daily routines of a few creative minds
   
   
4. David Bowie Answers the Famous Proust Questionnaire
   
   
5. A book that is subway ticket in Brazil. Great idea. via Tyler Cowen
   
   
6. Great visualization of how Americans spend their days, based on the American Time Use Survey 2014 
   
   
7. Promising research project on Urban Governance, by the team at LSE Cities
   
   
8. A study on social habits and concerns about farting and pooping
   
   
9. “All roads lead to Rome”. Beautiful visualization of shortest path trees

credit: moovellab

Urbanization and Growth: A review of the past 500 years

Jedwab, R. and Vollrath, D. Urbanization without Growth in Historical Perspective. Working Papers from The George Washington University, Institute for International Economic Policy.

Abstract: 

The world is becoming more and more urbanized at every income level, and there has been a dramatic increase in the number of mega-cities in the developing world. This has led scholars to believe that development and urbanization are not always correlated, either across space or over time. In this paper, we use historical data at both the country level and city level over the five centuries between 1500-2010 to revisit the topic of “urbanization without growth” (Fay & Opal, 2000). In particular, we first establish that, although urbanization and income remain highly correlated within any given year, urbanization is 25-30 percentage points higher in 2010 than in 1500 at every level of income per capita. Second, while historically this shift in urbanization rates was more noticeable at the upper tail of the income distribution, i.e. for richer countries, it is now particularly visible at the lower tail, i.e. for poorer countries. Third, these patterns suggest that different factors may have explained the shift in different periods of time. We use the discussion of these factors as an opportunity to provide a survey of the literature and summarize our knowledge of what drives the urbanization process over time.

An Agent-Based Simulation of the Urban Growth in Latin American cities

Barros, Joana (2012). Exploring urban dynamics in Latin American cities using an agent-based simulation approach. In Agent-based models of geographical systems (pp. 571-589). Springer Netherlands. [Ungated version here, and PhD Thesis here]

Abstract:

This paper focuses on a specific kind of urban growth that happens in Latin American cities, called 'Peripherisation'. This is characterised by the formation of low-income residential areas in the peripheral ring of the city and a perpetuation of a dynamic core-periphery spatial pattern. The dynamics of growth and change in Latin American cities are explored using agent-based simulation. The objective is to increase the understanding of urban spatial phenomena in Latin American cities, which is essential to providing a basis for future planning actions and policies. Simulation exercises were used to revisit assumptions about urbanisation issues in Latin American cities and investigate important aspects of growth and change in these cities. These exercises allowed the problem of urban growth in Latin American cities to be unfolded through their dynamics, relating these dynamics to urban morphology, and thus presenting a new and important perspective on the phenomenon.

Top 10 most populous cities in 2014 and 2030

[image credit: Thompson Reuters, via Pedro Damiao]

The Timing of Urbanization: a global analysis

Motamed, M.J. et al. (2014) Agriculture, transportation and the timing of urbanization: Global analysis at the grid cell level. Journal of Economic Growth, v.19,issue 3. 

(thanks Leo Monasterio for the tip) 

Abstract:

This paper addresses the timing of a location’s historical transition from rural to urban activity. We test whether urbanization occurs sooner in places with higher agricultural potential and comparatively lower transport costs, using worldwide data that divide the earth’s surface at half-degree intervals into 62,290 cells. From an independent estimate of each cell’s rural and urban population history over the last 2,000 years, we identify the date at which each cell achieves various thresholds of urbanization. Controlling for unobserved heterogeneity across countries through fixed effects and using a variety of spatial econometric techniques, we find a robust association between earlier urbanization and agro-climatic suitability for cultivation, having seasonal frosts, better access to the ocean or navigable rivers, and lower elevation. These geographic correlations become smaller in magnitude as urbanization proceeds, and there is some variation in the effects across continents. Aggregating cells into countries, we show that an earlier urbanization date is associated with higher per capita income today.

Related Links:

• Global forecasts of urban expansion
• 200 years of Urban Expansion
• Urban Growth Trajectories

200 years of Urban Expansion

We have mentioned the Atlas of Urban Expansion here at Urban Demographics a while ago. It is an amazing project supported by the Lincoln Institute of Land Policy that gathers loads of data for a number of cities across the globe.

Using part of these data, Shlomo Angel and the team at NYU Stern Urbanization Project have recently started working on a new series of great animations showing the expansion of 30 global cities over the last 200 years. Here are the first five cases: São Paulo, Chicago, Paris, London and Los Angeles:

obs. It's a great work and I'm looking forward for the next videos. The only reservation I would make is that these animations can be a bit deceiving as they give the impression of concentric urban expansion. 

Related Links:

• Las Vegas Urbanization (1984-2009)
• Global forecasts of urban expansion
• Buenos Aires Urban Expansion
• Visualizing surface changes over time in every corner on Earth
• The Dynamics of Global Urban Expansion

The Haussmann Effect

A recent study by Marc Barthelemy and colleagues analyzing more than 200 years (1789–2010) of the evolution of the street network of Paris. This paper has just been added to my reading list. (via Emily Badger). By the way, Micahel Batty and his team at UCL have done a similar study analyzing the evolution of London’s street-network over a 224 year-period (1786-2010).

Barthelemy's Abstract:

Interventions of central, top-down planning are serious limitations to the possibility of modelling the dynamics of cities. An example is the city of Paris (France), which during the 19th century experienced large modifications supervised by a central authority, the ‘Haussmann period’. In this article, we report an empirical analysis of more than 200 years (1789–2010) of the evolution of the street network of Paris. We show that the usual network measures display a smooth behavior and that the most important quantitative signatures of central planning is the spatial reorganization of centrality and the modification of the block shape distribution. Such effects can only be obtained by structural modifications at a large-scale level, with the creation of new roads not constrained by the existing geometry. The evolution of a city thus seems to result from the superimposition of continuous, local growth processes and punctual changes operating at large spatial scales.

Figure 2: (a) Map of Paris in 1789 superimposed on the map of current 2010 Paris.In the whole study, we focus on the Haussmann modifications and limited ourselves to the 1789 portion of the street network. (b) Map of Haussmann modifications.

[click on the image to enlarge it]

[Image Credit: Barthelemy et al (2013)]

Related links/posts:

• The evolution of road networks
• Assorted links on Space Syntax
• Assorted Links on S. Syntax and Accessibility
• Extracting Feature Geometries from OpenStreetMap
• The amazing David Rumsey Database of historical maps

Visualizing surface changes over time in every corner on Earth

A while ago we had a few posts with satellites images showing the urban expansion of Phoenix (1989 - 2009), São Paulo (1957-2007) and Las Vegas ( 1984-2009 or 1976-2010)*. Now you may check similiar timelapse satellite videos for many (many!) other places on the planet from 1984 through 2012 (yearly pictures amounting a total of 909 terabytes of data).

Search anywhere here and say thanks to NASA, Google, Time and the U.S. Geological Survey (USGS).

* Reletad Link:

• Atlas of Urban Expansion - comparing the urban expansion of London, Cariro, Chicago and São Paulo from 1800's through 2000.

Demographic evolution of cities, 1950-2025

A nice chart showing the U.N. estimate for the demographic evolution of cities between 1950 and 2025. (via RC21 Network). 

Here are some other charts based on a similiar idea:

• Urban Hierarchy of 590 cities (1950-2050)
• Relative Rank of American States for Nine Decades (1790-1870)
• Urban hierarchy of the United States over time (1790-2000)
• The Richest Cities in the US in 1978 and Now

A cellular automata intraurban model with prices and income-differentiated actors

Bernardo A. Furtado (a friend of mine from Ipea) have recently published this paper that I'd like to share with you:


A cellular automata intraurban model with prices and income-differentiated actors 
Environment and Planning B: Planning and Design 39(5) 897 – 924

Abstract:

This paper presents an intraurban cellular automata model that is an extension to White and Engelen’s pioneering model. The paper’s main contribution is to distinguish between agglomerative effects, determined by the attraction of the neighbourhood, and disagglomerative effects, driven by land prices, or land affordability. In order to do that, social heterogeneity is introduced in the model at the intraurban level. As a result, we can simulate both the evolution of land use and land prices. An application of the model and a sensitivity analysis indicate that neighborhood influence is the main driving force of cities’ spatial configurations. Prices, however, exert an important countereffect. Actually, the higher the influence of land prices, the faster land succession is observed. Finally, an important conclusion of the model is that intraurban models should not fail to differentiate actors by income level.

Evolution of actors in the Metropolitan Region of Belo Horizonte (Brazil), 1900-2000

[Image Credit: Furtado et al (2012) ]

Label:

Related Links:

• Agent-based modeling of Slums formation
• Cities in Motion: Game or Urban Transport Model?
• A five-minute talk by Michael Batty on cellular automata modelling
• Modelling Cities from 1977
• Simulations from the Early Days

CAMUSS Symposium

For those interested in cellular automata models, you may watch a live broadcast of the CAMUSS* Symposium (November 8–10) here.

*Camuss - International Symposium on Cellular Automata Modeling for Urban and Spatial Systems

Global forecasts of urban expansion

A cutting edge study by Karen C. Seto (Yale) and others on urban growth projections around the world and some of its environmental impacts (via  Urbanization Project). Read a briefing of the study here or here.

Abstract:

"[...] despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km2, nearly tripling the global urban land area circa 2000."

[Image Credit: Yale]

Related Links:

• Las Vegas Urbanization (1976-2010) - more here
• Seto Urbanization and Global Change Group
• Atlas of Urban Expansion

Quote of the day

"You tell me the size of any city in the United States and I can tell you with 80 to 90 percent accuracy almost everything about it. The scaling laws tell you that despite all of the efforts of planners, geographers, economists, architects, and politicians, and all of the local history, geography, and culture, somehow cities end up having to obey these scaling laws. We need to be aware of those forces when we design and redesign cities." (Geoffrey West)

Soundtrack for the weekend: Jazz for Cows

One Hundred Years of Land Values

Gabriel Ahlfeldt (LSE) presents at the Lincoln Institute his project on digitizing data from the Olcott's land values blue book of Chicago. The Olcott's Blue Books (under public domain) bring a unique dataset of historical land values, land uses, building heights, and other information in Chicago and its suburbs, published annually between 1900 and 1990 (!)

You can  get green with envy and  watch the presentation here.

Buenos Aires Urban Expansion

A gif depicting Buenos Aires urban expansion in three different time periods: until 1947; from 1947 to 1972 and 1972 onwards (thanks Leonardo Garateguy for the tip).

It's important to stress the avant-garde urbanization of Buenos Aires in contrast to other Latin American cities. Much could be said about this, but it would be out of our purpose with this blog. Besides, Guillhermo Tella probably has much more interesting things to say about it.

[image credit: Guillhermo Tella. A summary of his book here]