Disentangling the impacts of collective mobility of residents and non-residents on burglary levels

Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data

The study on individual mobility patterns supports our better understanding of spatiotemporal characteristics of people’s travel behavior and social activities. The mobile phone GPS data are advantageous due to the large size of their data coverage. This paper aims to identify individual activity anchor places and to analyze related patterns based on the GPS data collected from thousands of mobile phone users over four months in Greater Paris, France. We propose a method to refine the identification of home and secondary activities. Based on this, the mobility spatial characteristics are aggregated by applying a three-stage clustering method. As a consequence, the clusters of activity types, the daily mobility patterns (day types), and the user groups with similar daily mobility patterns are obtained stage by stage. This allows us to analyze the obtained clusters in a cascading maneuver by three different levels: activity level, day level, and individual level. Inversely, the mobility characteristics per user group are interpreted with respect to the interpretation of day types and then activity types. From the interpretable clusters, it is facilitated for us to find the daily mobility differences by user groups across weekdays and weekends, transport modes, as well as the mobility variability over the study period.

To evaluate the association between social distancing quantified by mobile phone data and the current prevalence of COVID-19 infections in the U.S. per capita. Data were accessed on April 4, 2020, from Centers for Disease Control and Prevention, Google COVID-19 Community Mobility Report, and the United States Census Bureau to report prevalence of COVID-19 infections, mobility data, and population per state, respectively. Mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from February 7 - March 29, 2020. Multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing. Mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. Multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (R= 0.604, P= 0.002). Time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% CI, -0.003 to 0.036; P= 0.09). Based on mobility reports from mobile phone GPS data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of COVID-19 infections in the U.S. per capita. Mobile phone data utilizing 'location history' may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of COVID-19 in the U.S.

This chapter describes a framework for using a mobile phone GPS data to investigate the effects of weather on people’s daily activity routines. Temperature, rainfall, and wind speed are among the weather variables considered in our case study discussed in this chapter. We describe a method for inferring people’s daily activity patterns, including the places they visit and when they do so, as well as the duration of the visit, based on GPS position traces of their mobile phones and Yellow Pages information. An analysis of 31,855 mobile phone users reveals that people are more likely to stay at restaurants or food outlets for longer periods of time, and to a lesser extent at retail or shopping sites, when the weather is extremely cold or the ambiance is calm (non-windy). People’s activity habits are affected by certain weather conditions when compared to their usual patterns. People’s motions and activities are evident at different times of the day. The weather has a wide range of effects on different geographical areas of a large city. When urban infrastructure data is employed to characterize areas, significant connections between weather conditions and people’s accessibility to public rail network are observed. This chapter gives a new perspective of how mobile phone GPS data can be utilized in the context of weather’s influence on human behavior, specifically choices of daily activities, as well as the impact of environmental factors on urban life dynamics. The conceptual framework and analysis discussed in this chapter are based on the original research by Phithakkitnukoon et al. (PLoS One. 8:12, 2013; PLoS One. 7:10, 2012; Activity-aware map: Identifying human daily activity pattern using mobile phone data. LNCS, 2010).KeywordsGPS dataWeather effectAtmospheric conditionDaily activity patternsWeather variablesHuman behavior

The traffic speed information of an urban road network is generally estimated using the widely available taxi GPS data. However, taxi usages are preponderantly restricted to areas with high population density, which results in limited spatial coverage of collected taxi GPS data. Moreover, the traffic speeds of taxies are not guaranteed to well represent the traffic speeds of other types of vehicles. In this study, we address these issues by introducing an infinite Gaussian mixture model to estimate traffic speed distribution. The variational inference method is employed to deal with the complicated parameter estimation problem. The proposed mixture model simultaneously combines taxi GPS data, bus GPS data, and mobile phone GPS data, which not only generates the mixed traffic-speed distribution of different types of vehicles but also improves the spatial coverage and the quality of traffic speed estimation. Surprisingly, we find that the incorporation of mobile phone GPS data can considerably improve the model’s ability to sense anomalous traffic conditions. Finally, the mixed traffic-speed distribution is validated using the license plate recognition data.

Many previous studies showed that house rent is affected by residential property characteristics, house surrounding environment, facilities, and so on. However, there are few researches on finding the relationship between house rent and people's activities. Thus, we used hourly location-based big data collected by mobile phone GPS data to monitor people's activities all over the city. Multiple residential property characteristics and environments helped to verify if there is a relationship between house rent and people congestion in Tokyo. We find that people congestion has relationship with house rent and make more accurate prediction. We also employed linear and regularization regression and artificial neural network as algorithm and find artificial neural network might be the best calculation method.

Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis

Exploring the associations between neighborhood greenness and level of physical activity of older adults in shanghai

Mobile phone GPS data in urban customized bus: Dynamic line design and emission reduction potentials analysis

Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential

Elevated HIV prevalence has been observed among urban U.S. individuals who use drugs and who lack stable housing. This article synthesizes extant research on this population and situates it in a multilevel, ecologically based model of HIV risk. Based on a multidisciplinary review of the literature, the model applies social-ecological theory on human development to identify factors shaping the HIV risk context for individuals who use drugs and who are unstably housed at global, societal, neighborhood, household, and individual levels of influence. At the global level, the model includes neoliberal ideologies contributing to the social inequalities that frame the HIV epidemic. U.S. housing and drug policy, including urban renewal, HOPE VI, and the War on Drugs, is the focus of the societal level. At the neighborhood level, mechanisms of the built environment and psychosocial mechanisms are explored for their salience to HIV risk. Research on the association between housing instability and HIV risk is reviewed at the household level. At the last level, relevant individual differences in biology, psychology, and cognition are discussed. Modeling risk at multiple levels of the environment underscores the need to expand the focus of research, treatment, and prevention interventions for HIV/AIDS and addictions beyond individuals and their risk behaviors to address facets of structural violence and incorporate the broader social, political, and economic contexts of risk and health.

Abstract. Nowadays, for the estimation of traffic demand or people flow, modelling route choice activity in road networks is an important task and many algorithms have been developed to generate route choice sets. However, developing an algorithm based on a small amount of data that can be applied generally within a metropolitan area is difficult. This is because the characteristics of road networks vary widely. On the other hand, recently, the collection of people movement data has lately become much easier, especially through mobile phones. Lately, most mobile phones include GPS functionality. Given this background, we propose a data-oriented algorithm to generate route choice sets using mobile phone GPS data. GPS data contain a number of measurement errors; hence, they must be adjusted to account for these errors before use in advanced people movement analysis. However, this is time-consuming and expensive, because an enormous amount of daily data can be obtained. Hence, the objective of this study is to develop an algorithm that can easily manage GPS data. Specifically, at first movement data from all GPS data are selected by calculating the speed. Next, the nearest roads in the road network are selected from the GPS location and count such data for each road. Then An algorithm based on the GSP (Gateway Shortest Path) algorithm is proposed, which searches the shortest path through a given gateway. In the proposed algorithm, the road for which the utilization volume calculated by GPS data is large is selected as the gateway. Thus, route choice sets that are based on trends in real GPS data are generated. To evaluate the proposed method, GPS data from 0.7 million people a year in Japan and DRM (Digital Road Map) as the road network are used. DRM is one of the most detailed road networks in Japan. Route choice sets using the proposed algorithm are generated and the cover rate of the utilization volume of each road under evaluation is calculated. As a result, the proposed route generation algorithm and GPS data cleaning process work well and a huge variety of routes that have high potential to be used in the real world can be generated.

When considering evacuation center plans for earthquake disasters, it is necessary to know how many people will evacuate in each stage after the disaster over the long term. In this paper, by using mobile phone GPS data and tsunami survey data for the 2011 Tohoku Earthquake Tsunami disaster, we developed a home-return model. The model can estimate the rate of people who will have returned home any number of days after an earthquake disaster. As a result, we obtained high the root-mean-square error (RMSE) accuracy of the model. The study leads to a new understanding of the quantitative relationship between people returning home after evacuation and local vulnerability and tsunami hazards.

Predicting travel time reliability using mobile phone GPS data

Economic analysis based on the mobile phone GPS data and monitoring consumer behavior during the COVID-19 pandemic