Monday, December 16, 2013

Human Mobility Patterns: Power Law or Exponential?

Brockmann, D., Hufnagel, L. & Geisel, T.
The scaling laws of human travel.
Nature 439,462–465 (2006).
Several studies in the literature have suggested that human mobility patterns follow a power law (scaling law):
Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439,462–465 (2006).
González, M.C., Hidalgo, C.A., Barabási, A.L., 2008. Understanding individual human mobility patterns. Nature 453, 779-782
Woolley-Meza, O., Thiemann, C., Grady, D., Lee, J.J., Seebens, H., Blasius, B., Brockmann, D., 2011. Complexity in human transportation networks: A comparative analysis of worldwide air transportation and global cargo ship movements. European Physical Journal B 84. 589-600.
Song, C., Koren, T., Wang, P. & Barabási, A.-L. Modelling the scaling properties of human mobility. Nat. Phys. 6, 818–823 (2010).
Jiang, B., Yin, J. & Zhao, S. Characterizing the human mobility pattern in a large street network. Phys. Rev. E 80, 1–11 (2009).
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C.
A tale of many cities: universal patterns in human urban mobility.
PLoS ONE 7, e37027 (2012).
Other studies proposed that an exponential function provides a better fit:
Liang, X., Zheng, X., Lv, W., Zhu, T. & Xu, K. The scaling of human mobility by taxis is exponential. Physica A 391, 2135–2144 (2012).
Peng, C., Jin, X., Wong, K.-C., Shi, M. & Liò, P. Collective Human Mobility Pattern from Taxi Trips in Urban Area. PLoS ONE 7, e34487 (2012).
Bazzani, A., Giorgini, B., Rambaldi, S., Gallotti, R. & Giovannini, L. Statistical laws in urban mobility from microscopic GPS data in the area of Florence. J. Stat. Mech. 2010, P05001(2010).
Roth, C., Kang, S. M., Batty, M. & Barthélemy, M. Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS ONE 6, e15923 (2011).
Kang, C., Ma, X., Tong, D. & Liu, Y. Intra-urban human mobility patterns: An urban morphology perspective. Physica A 391, 1702–1717 (2012).
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C. A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7, e37027 (2012).
The difference comes from the scale of the study and perhaps the data . The mobility patterns in the global scale (e.g. air transportation and cargo ship movements) or national scale (using bank notes and mobile data calls) tend to follow the power law. However, at the city scale, human mobility tends to follow an exponential law. Source of data could also affect the results. Use of mobile phone calls and taxi data do not completely represent individuals daily mobility patterns. Overall, the exponential law of human mobility in cities could be partly explained by the economies of agglomeration and thus, following a "natural decay". While at the global scale where the economies of agglomeration does not play a significant role, the power law seems to better describe the mobility patterns.

Success or Failure of Bike-share Systems

"But while Paris's bike-share scheme actually makes money for the city, London's 4,000 bikes cost local taxpayers an average of £1,400 per bike per year." - The Atlantic Cities
What are the driving factors that makes a bike-share system successful or financially self-sustained? I hope Chicago #Divvy does well in the coming years.
"Bank, whose logo covers thousands of 'Boris bikes' across London, will end association with flagship scheme in 2015." - The Guardian

Sunday, December 15, 2013

Household Car Ownership in London: Spatial Patterns

UK Data Explorer is an online visualization tool that uses UK Census 2011 data to visualize hundreds of different measures. Following is a series of maps showing the household car ownership in London including no car, 1 car, 3 cars, and 4 cars in the household. Dark blue roughly represents >2.5% of households and light blue to white represents <2.5% of households. Interestingly, as the number of cars in the households increases, the outer suburbs of London becomes darker and inner neighborhoods becomes pretty light or almost white. In other words, households with larger number of cars live in farther suburbs while households in the central areas have fewer cars. This is clearly linked to accessibility to destinations, cost of having a car, and perhaps number of persons in a household.

Saturday, December 14, 2013

Washington D.C.: The Changing City (Data Visualization)

Here are some nice visualizations of the demographic changes in Washington D.C over 10 years (2000-2010):

"Washington, DC, residents don't need census data to tell them what's obvious in their neighborhoods: the city is changing dramatically. But numbers can give us context. They can show us how shifts in population are reshaping the city and can help us prepare for changes to come.
In this series, we'll home in on changes from the past decade—2000 to 2010—when DC's population began growing again for the first time in 50 years. In this chapter, we look at demographic change, drilling down to wards and neighborhoods. Later, we'll explore changes in housing, crime, education, and more, using data from NeighborhoodInfo DCto tell the story of our changing city."

Thursday, December 12, 2013

Gender Disparities in Science

Recently published in Science: Bibliometrics: Global gender disparities in science
"Despite many good intentions and initiatives, gender inequality is still rife in science. Although there are more female than male undergraduate and graduate students in many countries, there are relatively few female full professors, and gender inequalities in hiring, earnings, funding, satisfaction and patenting persist."
Since I am a member of the Traffic Flow Theory and Characteristics Committee (AHB45) of the Transportation Research Board of the National Academies, I was thinking that maybe I could start looking at our own committee. As of December 2013, the TFTC committee has 36 members (including 4 young and 2 emeritus). Only 6 out of 36 members are female resulting in a female/male ratio of 0.167

Also, the Network Modeling Committee (ADB30) has 42 members (including 4 young and 4 emeritus). Only 8 out 42 members are female resulting in a female/male ratio of 0.190

Predictive vs. Prescriptive Analytics

Here is an interview with Jack Levis from UPS discussing predictive vs. prescriptive analytics. He actually discusses more about implementation of ORION in figuring out the best route to deliver. Despite the low scholarly value of the article, it's nice to read what UPS does to improve their system.
"Our digital journey started with an early adoption of data and analytics tools for improving our operations. As our operations became more complex and distributed in nature, the focus has been to improve business processes, increase efficiency and cut costs. We had been following a descriptive and predictive analytics-based system for a long time but what has recently changed is our shift to prescriptive analytics. I can safely say that UPS is one of the few companies to effectively use prescriptive analytics to gain insight for successful optimization."
See the entire January issue of "Digital Transformation Review" here:

Tuesday, December 10, 2013

Featured Article: The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
Read the full article here: Zaltz Austwick M, O’Brien O, Strano E, Viana M (2013) The Structure of Spatial Networks and Communities in Bicycle Sharing Systems. PLoS ONE 8(9): e74685. doi:10.1371/journal.pone.0074685

Monday, December 9, 2013

Verification of the Zipf's Law for Cities in Iran

Zipf's law establishes a simple relationship between the size/population of N samples and their frequency ranking. The original study by Zipf in 1935* proposed that the frequency of any word in a natural language is inversely proportional to its rank in the frequency table. Before Zipf, others including Auerbach (1913)** proposed a similar law that the size distribution of cities in a country can be approximated by a Pareto distribution meaning that the size of cities is inversely related to their ranking. In other words, if you list cities of a country and rank them by their population, the population of each city would be inversely related to its ranking. If you're interested to learn more, see the following articles:
Jiang B. and Jia T (2011). Zipf’s Law for All the Natural Cities in the United States: A Geospatial Perspective. International Journal of Geographical Information Science, Volume 25, Issue 8, pp. 1269-1281.
Cristelli M, Batty M, Pietronero L (2012). There is more than a power law in Zipf. Nature, Scientific reports 2, pp. 1-7.
I decided to verify whether the Zipf's law holds for cities in Iran (my birth country). Following is a bar chart showing the first 20 cities in Iran sorted from the largest to the smallest, based on population data from 2006. Obviously, Tehran has the largest population with near 8 million followed by Mashhad (my hometown), Isfahan, Tabriz, Karaj, and Shiraz.

Now let's plot the log (population) against the log (ranking). In fact, results imply that the Zipf's law holds (approximately) for these cities (R-squared = 0.9752). Therefore one could predict the population of a city based on its ranking in a country or vice versa. Note that doing a simple regression here to get the coefficients of the Zipf's law is not exactly correct. More correct methods exist in the literature for estimating the Zipf's coefficients which I do not discuss in this post. The performed regression gives a reasonable approximation in my opinion. The underlying mechanism of the Zipf's law is not yet fully understood specially in the context of cities. It would be interesting to see how the following graph has evolved over time when cities shift in ranking and with increase/decline of population. Does the Zipf's law holds true for other self-formed communities (e.g. at the neighborhood level)? And most importantly, why do we see what we see here? Honestly, I am a little skeptical about the Zipf's law and its application in predicting cities population. I think there is something there that we're missing. The recent paper published in Nature by Cristelli et al. (listed above) sheds some light into it.
* George K. Zipf (1935) The Psychobiology of Language. Houghton-Mifflin.
** Auerbach F. (1913) Das gesetz der bevolkerungskoncentration (The Law of Population Concentration). Petermanns Geographische Mitteilungen, 59, pp. 74–76.

Sunday, December 8, 2013

20-minute neighborhood and bikeability

Following the previous post on the idea of "twenty-minute city", here are presentation slides by Nathan MacNeil from Portland State University on "Exploring How Infrastructure and Destinations Influence Bicycle Accessibility":

"This paper explores a methodology for assessing a neighborhood’s bikeability based on its mix of infrastructure and destinations –essentially the 20-minute neighborhood for bicycles.
Background: Dense, well-connected neighborhoods where residents can access services, shopping, transit, restaurants and employment centers without the use of a car are often lauded as an important next step in urban and suburban development. These goals have come up in the aftermath of decades of federally-subsidized automobile and highway-centric planning that encouraged development of cheap land on the periphery of metropolitan areas, tore up existing urban streetcar systems, and disconnected urban neighborhoods with highway projects. Given that much of the current urban landscape was created for the automobile, it is no surprise that most people view the car as a necessity.
However, many places are now embracing the idea that auto-dependent cities are not sustainable from an environmental, economic and national-security standpoint. Efforts to recreate neighborhoods where residents can manage (and want to manage) without cars usually focus on providing transportation options, attracting a diversity of uses (including all essential uses) and attaining a certain threshold of population density within a limited space.
The area of outer east Portland provides an interesting case study of a community largely shaped by the automobile, but struggling to become increasingly urban and decreasingly auto-dependent. Among the goals expressed in the 2009 plan are to improve the area's land use mix by encourage mixed-use development and multi-use commercial areas, to increase the safety and accessibility of bicycling, and to improve connectivity.
This paper explores a methodology for assessing a neighborhood's bikeability based on its mix of infrastructure and destinations – essentially the 20-minute neighborhood for bicycles. The area of outer east Portland, an area east of 82nd Avenue with substantially lower bicycling rates than other Portland neighborhoods, is used as a case study and compared to an assessment of neighborhoods that are considered to be bike-friendly (downtown, inner-east and north Portland). The paper examines prior approaches to assessing bikeability, details a new method to measure bikeability, presents the findings, and explores what impact expected or potential transportation and land use changes might have on bikeability."
The full paper can be downloaded here:

Saturday, December 7, 2013

20-minute City

The idea of a "20-minute city" is appealing but could it really be achieved when a huge employment hub called CBD and almost-purely residential suburbs around it exist? The complex relationship of mobility and accessibility need to be well understood first.
"One of the better objects of Plan Melbourne is to create '20-minute neighbourhoods' in which jobs, schools, shops and community services within a 20-minute walk, bike or public transport ride from home. Matching employment with residential location is difficult at the best of times. Proximity can be encouraged via a good distribution of affordable housing and job types, with multiple transport links so people have choices about where they live and work."

Thursday, December 5, 2013

Integrated Corridor Management Analysis, Modeling, and Simulation Guide

See the report from the RITA, U.S. DOT on "Integrated Corridor Management 
Analysis, Modeling, and Simulation Guide" as part of the "Traffic Analysis Toolbox":
"As part of the Federal Highway Administration (FHWA) Traffic Analysis Toolbox (Volume XIII), this guide was designed to help corridor stakeholders implement the ICM AMS methodology successfully and effectively. It provides a step-by-step approach to implementation of the ICM AMS methodology and reflects lessons learned in its application to the three ICM Pioneer Sites and a test corridor. It is specifically targeted at technical and/or program managers in transportation agencies at the State or local level who may oversee implementation of ICM and/or an ICM AMS initiative. This Guide will also be a helpful reference to all stakeholders involved in AMS, including technical modelers, by providing a framework for developing an effective analysis plan to support selection and application of available tools and models specifically conducive to ICM." - Traffic Analysis Toolbox

Monday, December 2, 2013

UrtheCast (pronounced as Earth Cast)

Urthecast (pronounced as Earth+Cast) will provide near-real time satellite images. You can view time-lapses of your selected places and see how they evolve over time: