Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data

Abstract

Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data.

Big (Geo)Data en Ciencias Sociales: Retos y Oportunidades

“…Si se procesan los datos según identificador de usuario, se puede tener una aproximación de los lugares que visita cada usuario en los distintos momentos del día y días de la semana, es decir, su perfil espacio-temporal. Así es posible utilizar esta fuente como una proxy para analizar las densidades de población cambiantes a lo largo del día en la ciudad (Ciuccarelli et al, 2014) y las pautas de movilidad de la población (Wu et al, 2014). La fiabilidad de este tipo de datos ha sido validada en el trabajo de Lenormand et al (2014), quienes tras contrastar datos de Twitter con información de telefónos móviles y datos oficiales (censos) concluyeron que las tres fuentes ofrecen resultados comparables en términos de distribución espacial de la población, evolución temporal de las densidades de población y pautas de movilidad de los individuos.…”

Investigating “Locality” of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

“…On the one hand, investigating spatial interaction patterns may be beneficial to businesses, e.g., by helping to identify a good location based on personalized user preferences, or selecting a good site for a new shop. The former has been explored by studies through the use of social media data [4,6], while the latter is a new field within mobility studies using social media data. Investigating spatial interaction distances may help when selecting the site of a new shop, restaurant, or other service facility.…”

“…Foursquare has over 50 million users worldwide, and over 6 billion check-ins had been made using the website by May 2014, with millions of new check-ins taking place every day [2]. Although check-in data has some limitations in terms of how it represents human mobility-for example, it shows age group and place category bias-such data has the ability to identify human mobility patterns in accordance with certain mechanisms [3,4]. When compared to some other data sources (e.g., survey data and mobile phone data), LBSN check-in data has some advantages as an indicator of human activity categories such as dining, working, and shopping, as it provides a fine-grained resolution, and is readily available.…”

Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users

“…Specifically, as a popular type of volunteered geographic information (VGI), georeferenced check-in data offered by location-based social networks (LBSNs) (Foursquare, Google Latitude, Facebook Places, etc.) create potential for analyzing human mobility [5,6]. Despite some limitations on representing human mobility, e.g., the bias of age group and place category, check-in data has the ability to uncover human mobility according to certain mechanisms [5,6].…”

“…create potential for analyzing human mobility [5,6]. Despite some limitations on representing human mobility, e.g., the bias of age group and place category, check-in data has the ability to uncover human mobility according to certain mechanisms [5,6]. Compared to the aforementioned travel data types, LBSN data are highly available and low cost.…”