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.

Using deep learning and satellite imagery to improve land use classification in cities

Marta Gonzalez and colleagues have a recent paper using deep learning and satellite image data to improve land use classification. The authors have made documented code and Jupyter notebooks available here. I'm self recommitting the paper and code to my future self. HT Marco De Nadai.

Albert, A., Kaur, J., & Gonzalez, M. C. (2017, August). Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1357-1366). ACM.

Abstact:

Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.

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

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]

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