The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

/pdf/understanding-individual-human-mobility-patterns-3k1tyob274.pdf

Understanding individual human mobility patterns

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

http://www.ced.berkeley.edu/downloads/research/LUP.parks.pdf

Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’

Transactions of the Institute of British Geographers

OVERVIEW The GRA Marketing Internship Program is offered to students who are interested in gaining valuable work experience through efforts in marketing, membership, sales, and events. Interns work directly to assist the Director of Marketing and Communications on various tasks relating to upcoming GRA events. During this internship, students will work a minimum of 10 hours a week and a maximum of 20 hours a week. Students are encouraged to earn credit for their internship, however this is a paid internship. Students interested in obtaining credit for their internship must consult their academic advisor or the intern coordinator at their academic unit.

Role of WHO.

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10251725

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

• A detailed understanding of the travel dynamics of SunRail riders is presented. • The instantaneous-balance Bayesian model identified OD flows in the transit system. • Linking the working population with business zones is a good target for the expansion of transit systems. • No straightforward relationship between population numbers and ridership was identified. • There is no prominent factor increasing the ridership numbers at transit stations. Accessibility, livability, and public health in urban areas can be improved by promoting sustainable and eco-friendly modes of transport such as rail transit. The success and feasibility of a rail transit system, however, rely on maintaining sufficient ridership depending on several factors. This study focuses on two of these factors in examining the SunRail transit system: 1) the ridership dynamics in terms of passenger flows between transit stations, and 2) the socio-demographic characteristics of the population living around and travelling between these stations. The SunRail system is the flagship commuter rail line of Greater Orlando, Florida, encouraging denser and more walkable development and promoting a less car-dependent multimodal transportation system. For this purpose, we utilized an instantaneous-balance Bayesian model estimating the origin–destination passenger flows and conducted a comprehensive assessment of the characteristics of residents living within each SunRail station area. The analyses help provide a more detailed understanding of the travel dynamics of SunRail riders as well as who lives and works in all 12 SunRail station areas. Consequently, the findings and insights obtained from the analyses aim to serve urban and transport planners in devising strategies to influence ridership based on the passenger flows and unique characteristics of the station areas. 

Ridership dynamics and characteristics of potential riders of a transit system: The SunRail of Central Florida

Dispersed urban growth following from rising affluence and mobility presents serious challenges to the future operation of urban transport systems. Indeed, recent demographic and social trends in the United States and other industrialized countries seem counter to the sustainability of urban transport. First, already large metropolitan populations grew substantially during past decades, particularly in the United States, where they expanded by 13.9 percent from 1990 to 2000. As of 2000, metropolitan persons accounted for roughly 80 percent of the total U.S. population (Perry and Mackun 2001). Second, quarterly U.S. gross domestic product (GDP) grew at robust rates (2 to 8 percent annualized) during the boom period of the last decade (1996 to 2000) (Bureau of Economic Analysis 2001). This prosperity has facilitated an ability to obtain material goods, particularly automobiles, thereby adding to levels of personal mobility. Today, increased numbers of automobiles are appearing on highways; evidence from the 2000 U.S. census shows that automobile ownership is approaching almost 2 cars (1.7) per household (Pisarski 2002). Third, homeownership has reached unprecedented levels, with about two-thirds of households owning their own home (Pisarski 2002; U.S. Census 2002).

Commuting, Congestion, and Urban Transport Sustainability

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have ident...

Mark W. Horner

Papers

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

Ridership dynamics and characteristics of potential riders of a transit system: The SunRail of Central Florida

Commuting, Congestion, and Urban Transport Sustainability

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data