Visualizing space-time networks

I've said this on Twitter before but I should say it here as well. Craig Taylor and the Ito World team have some of the best data visualizations of geospatial data related to cities and transport networks.

Just a few days ago, Craig tweeted some of his latest work with neat visualizations of drive-time network for catchment area analysis. Here is a video comparing different cities in the UK and a brief explanation on how to read the dataviz.

"30 minute drive time analysis from major UK cities visualised as 3d coral geometry. 

The thickness of artery is proportional to the number of networks connected to it indicating busier routes. The falloff in height is linked to the proximity to the centre. 

Corals aren’t normalised in scale as the purpose of this is visualising the form and pattern the networks create. Animation is a boomerang motion scaling from 0 to 30 min and back again. Congestion/traffic not accounted for."

click at the bottom of the video to watch it in full screen and high definition

Yep, there are some obvious parallels here with Time Geography and in particular with the representation of space-time prisms. The static version of the space-time trees gives a sharper visualization of the data.

The space-time tree, or 3d coral geometry as Craig said.

and the inverted original dataviz, "the drive time web"

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

R Links

1. A new R package establishes an interface between R and QGIS - RQGIS , and a video tutorial showing the new ArcGIS interface for R
   
   
2. PISA 2015 – how to read/process/plot PISA data with R
   
   
3. bayesPop, the package for Bayesian population projections used by the UN, by Adrian Raftery via Bernardo L Queiroz. Here is the paper
   
   
4. Improving R animated GIFs with tweenr,by Leonard Kiefer
   
   
5. Simple and interactive R Graph Catalog
   
   
6. An interactive color chooser for #rstats
   
   
7. Creating tilted and stacked maps in R using ggplot2
   
   
8. Urška Demšar et al have written an script in R to visualize Stacked Space-Time Densities around trajectories. The related paper is here

credit: Urška Demšar

Detecting Spatial Clusters of Flow Data

Tao, R. and Thill, J.-C. (2016), Spatial Cluster Detection in Spatial Flow Data. Geographical Analysis. doi: 10.1111/gean.12100

Abstract:

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of spatial flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single-location spatial events or fail to preserve the integrity of spatial flow events. In this research a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K-function, while maintaining the integrity of flow data was introduced. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgraded the classical hot spot detection method to the stage of “hot flow” detection. Spatial proximity of flows was measured by a four-dimensional distance. Several specific aspects of the method were discussed to provide evidence of its robustness and expandability, such as the multiscale issue, relative importance control and adaptive scale detection, using a real dataset of vehicle theft and recovery location pairs in Charlotte, NC.

image credit: Tao, R., & Thill (2016)

Assorted Links

1. Night lights and regional income inequality in Africa
   
   
2. Will autonomous vehicles really be safer than conventional cars? Here is why answering this question is not so simple via Tyler Cowen
   
   
3. Real-time visualization of Ebola evolution and geographic spread
   
   
4. Higher Gender Wage Gap is associated with higher Domestic Violence rates
   
   
5. The Global Metro Monitor 2014
   
   
6. Some Beautiful Abandoned Places In Europe
   
   
7. How Americans Get to Work, neat interactive dataviz by Nathan Yau
   
   
8. Nice tool for comparing the size of countries, ht Pablo Astudillo
   
   
9. A terrific visual and interactive explanation of Gridlock vs. Bottlenecks, by Lewis Lehe