Urban Picture

Rio de Janeiro, photo via CityDescriber

Building up or spreading out? Urban growth across 478 cities

New paper by the Seto Lab in collaboration with Anjali Mahendra (who is also onTwitter).

Mahtta, R., Mahendra, A., & Seto, K. C. (2019). Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+. Environmental Research Letters, 14(12), 124077.

Abstract

Urban form in both two- (2D) and three-dimensions (3D) has significant impacts on local and global environments. Here we developed the largest global dataset characterizing 2D and 3D urban growth for 478 cities with populations of one million or larger. Using remote sensing data from the SeaWinds scatterometer for 2001 and 2009, and the Global Human Settlement Layer for 2000 and 2014, we applied a cluster analysis and found five urban growth typologies: stabilized, outward, mature upward, budding outward, upward and outward. Budding outward is the dominant typology worldwide, per the largest total area. Cities characterized by upward and outward growth are few in number and concentrated primarily in China and South Korea, where there has been a large increase in high-rises during the study period. With the exception of East Asia, cities within a geographic region exhibit remarkably similar patterns of urban growth. Our results show that every city exhibits multiple urban growth typologies concurrently. Thus, while it is possible to describe a city by its dominant urban growth typology, a more accurate and comprehensive characterization would include some combination of the five typologies. The implications of the results for urban sustainability are multi-fold. First, the results suggest that there is considerable opportunity to shape future patterns of urbanization, given that most of the new urban growth is nascent and low magnitude outward expansion. Second, the clear geographic patterns and wide variations in the physical form of urban growth, within country and city, suggest that markets, national and subnational policies, including the absence of, can shape how cities grow. Third, the presence of different typologies within each city suggests the need for differentiated strategies for different parts of a single city. Finally, the new urban forms revealed in this analysis provide a first glimpse into the carbon lock-in of recently constructed energy-demanding infrastructure of urban settlements.

ps. The image below was taken from the WRI report that originated the paper.

How AI shaped online shopping in the 2010s

ht Guilherme Carrara Neto

The mobility patterns of historically notable individuals

A new study using Natural Language Processing techniques to retrieve historical information from Wikipedia and analyze the spatial mobility patterns of historically notable individuals. A nice and inventive method to study historical mobility patterns. Science can be incredible and fun. (HT Marco De Nadai)

Lucchini, L., Tonelli, S. & Lepri, B. Following the footsteps of giants: modeling the mobility of historically notable individuals using Wikipedia. EPJ Data Sci. 8, 36 (2019) doi:10.1140/epjds/s13688-019-0215-7

image credit: Lucchini et al 2019

How much time do we spend with other people as we grow old?

A couple of years ago, I posted this chart showing how much time we spend with other people as we grow old. The chart was created by Henrik Lindberg using data from the America Time Use Survey, and the code to recreate this chart in R is available here.

p.s It's my birthday today and birthdays are always a good moment to reflect about life :)

geobr v1.1 is on CRAN

Good news! The new version of geobr v1.1 has been published on CRAN.

The geobr package in R is probably the easiest and fastest way to download shapefiles and official spatial data sets of Brazil. The package includes a wide range of geospatial data available at various geographic scales and for various years with harmonized attributes, projection and topology.

You can find a simple tutorial on how to use the package here.

The new release of geobr v1.1 includes 19 data sets:

1. country
2. region
3. state
4. meso region
5. micro region
6. intermediate region
7. immediate region 
8. municipality
9. weighting area 
10. census tract 
11. statistical grid
12. urban areas
13. health facilities
14. indigenous land
15. conservation units 
16. biomes
17. legal Amazon 
18. semiarid
19.  disaster risk areas