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...sits made by the user i from other locations into the origin location O. The third dimension is related to the temporal information. We break down the temporal measurement intervals into days as our baseline temporal cuboid. Evidently, by interacting the temporal dimension with spatial-user dimensions, we can quantify 10 human mobility flow patterns between cubiod pairs over time and space. 2.3 Aggregation function Incorporating individuals‟ space-time trajectories into the dimensional mobility geometrics measures requires appropriate aggregation functions for efficient data query operations (Gray et al., 1997). Assume that U is an aggregated spatiotemporal hierarchy corresponding to a set of human mobility patterns of social media users ui between a pair-wise cities: p1 and p2. Specifically, p1 and p2 represent the higher levels of the hierarchy cubiods (e.g. month&city-based cubiod), aggregated by a series of basic cubiod measures (e.g. day&grid unit-based cubiod): k i i pp 1 ,11 ,where kip i ,...2,1, ,1 ; and k j j pp 1 ,22 , where kjp j ,...2,1, ,2 . Thus we can write the aggregation function for measuring mobility flows between p1 and p2 as follows: kji ji ji ccFccF , 1, ,2,121 ,, . In addition...

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...d successful runs were implemented by using the super-machines installed in the Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Science. 3 Empirical implementation We focus on the most commonly used location-based social media data available in China---Weibo. Weibo (literally means „microblog‟ in English), which is often seen as the Chinese version of Twitter, is essentially a web-based social media platform. Similar like Twitter, Weibo users can post a short text message (with a 140-character limit) for showing subjective impressions and daily activities (Java et al. 2007). With more than 100 million daily active users and more than 1 billion monthly active users, Weibo (www.weibo.com) has provided us a new way to explore real-time migrant flows between city pairs in China. We improve on the applications of Weibo data in two ways: First, we design a sophisticated big data mining programme to search, gather and extract billions of Weibo records from the Weibo‟s public application programming interface (API) system. Second, Weibo provides a location-tracked application tool (commonly known as a „geo-tagged‟ service) for identifying users‟ 14 geographical location...

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... light of precision issues, we restrict our focus on social media users‟ inter-city mobility activities, rather than intra-city mobility behaviors. Building on the sociology literature, people are likely to share their activities if they are visiting a city that is different from their origins or current residences. But one fundamental threat to identification is how to identify where is a social media user‟s origin city. Existing studies have often used to the most frequently visited city as the origin city of the social media user, and used a spatial radius to track individuals‟ footprints (Gonzalez et al., 2008; Cao et al., 2015). Cities that have higher visiting frequency rates are potentially very different from those that do not. These differences may arise through disparities in individuals‟ initial motivations. For example, it is likely that the most frequent visited city for a businessman is his project‟s location, rather than his home location. For a more rigorous assessment, we apply the text mining methods (Rao et al., 2010; Burger et al., 2011; Wang et al., 2013) to derive social media users‟ current residence information. To be specific, we define a social media user‟s current residence a...

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...lic policy. We are–for the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillover...

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...ties and balancing regional disparities. However, despite intense policy and public enthusiasms, there is virtually no direct evidence on exploring the configuration of urban network patterns by using social media users‟ 3 mobility flows within a large developing country context. The scarcity of empirical evidence is not surprising, given that mining location-based social media data faces serious identification challenges. First, location-based social media data, as a type of big data resource, are often featured by the dynamic, massive information generated by billions of users across space (Manyika et al., 2011). In truth, despite of the recent development of intensive-computational geographic information system (GIS) modeling programs, social media data with precise individual-level location information is still extremely large to proceed by using the GIS techniques at multiple geographical scales (Wang, 2010; Wright &Wang, 2011). Furthermore, conventional GIS-based computational methods cannot directly read the unstructured social media datasets (e.g. words, pictures, videos). Additional big data mining methods are often needed to transform social media data information from unstructured data forma...

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...plicit aim of exploiting urban network patterns. This research contributes to several strands of literature. First, it adds to the work on applications of social media data resources. Despite the booming trend of social media users in developing countries, the empirical literature has mostly focused on the U.S. and European countries. These studies have allowed for the simulation and modeling of distribution and dynamics of traffic flows (Steiger et al., 2014), mobile users (Malleson and Andresen, 2014), urban population (Aubrecht et al., 2011), LBSM users‟ social network (Ahern et al., 2007; Backstrom et al., 2010; Sun et al., 2013) and food health (Widener and Li, 2014), as well as spatio-temporal predictions of natural disaster progresses (e.g. earthquake, forest fire). These studies are of interest in their own right and are important for the development of optimal public policy. We are–for the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors...

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...measured with the explicit aim of exploiting urban network patterns. This research contributes to several strands of literature. First, it adds to the work on applications of social media data resources. Despite the booming trend of social media users in developing countries, the empirical literature has mostly focused on the U.S. and European countries. These studies have allowed for the simulation and modeling of distribution and dynamics of traffic flows (Steiger et al., 2014), mobile users (Malleson and Andresen, 2014), urban population (Aubrecht et al., 2011), LBSM users‟ social network (Ahern et al., 2007; Backstrom et al., 2010; Sun et al., 2013) and food health (Widener and Li, 2014), as well as spatio-temporal predictions of natural disaster progresses (e.g. earthquake, forest fire). These studies are of interest in their own right and are important for the development of optimal public policy. We are–for the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of ...

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...studies have often used to the most frequently visited city as the origin city of the social media user, and used a spatial radius to track individuals‟ footprints (Gonzalez et al., 2008; Cao et al., 2015). Cities that have higher visiting frequency rates are potentially very different from those that do not. These differences may arise through disparities in individuals‟ initial motivations. For example, it is likely that the most frequent visited city for a businessman is his project‟s location, rather than his home location. For a more rigorous assessment, we apply the text mining methods (Rao et al., 2010; Burger et al., 2011; Wang et al., 2013) to derive social media users‟ current residence information. To be specific, we define a social media user‟s current residence as his or her origin city, while other cities in space-time trajectories as 9 visited destination cities. 2.2 Dimensional mobility algorithm Adopted from Leonardi et al. (2014), a graphical framework for data warehousing and data cuboid, is employed in this study to represent the dimensional mobility algorithm of social media users across cities. In the social media data cuboid, we stratify three dimensions: First, user dimensi...

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...the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effe...

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... 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from periphery cities tend to cluster into large metropolitan cities, recent economic studies suggest that external economies of agglomeration are substantial, but sharply attenuated by geographical distance across cities. But migration flow information from conventional census data cannot capture the real-time dynamics of human mobility flows between city pairs. In our analytical framework, we use space–time trajectories to track the spatial and temporal dimensions of social media users‟ activities. Our examination of changes in ...

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... used to the most frequently visited city as the origin city of the social media user, and used a spatial radius to track individuals‟ footprints (Gonzalez et al., 2008; Cao et al., 2015). Cities that have higher visiting frequency rates are potentially very different from those that do not. These differences may arise through disparities in individuals‟ initial motivations. For example, it is likely that the most frequent visited city for a businessman is his project‟s location, rather than his home location. For a more rigorous assessment, we apply the text mining methods (Rao et al., 2010; Burger et al., 2011; Wang et al., 2013) to derive social media users‟ current residence information. To be specific, we define a social media user‟s current residence as his or her origin city, while other cities in space-time trajectories as 9 visited destination cities. 2.2 Dimensional mobility algorithm Adopted from Leonardi et al. (2014), a graphical framework for data warehousing and data cuboid, is employed in this study to represent the dimensional mobility algorithm of social media users across cities. In the social media data cuboid, we stratify three dimensions: First, user dimension. We apply the text...

Citation Context

...use the space-time trajectories of social media users for identifying individuals‟ footprints in a geographic space. Assume that there is a country space which contains M cities available for individuals‟ mobility. A set of N individuals would post their daily social activities (e.g., traveling) through a location-based social media platform1. We seek to use a credible measure of “space– time trajectory "for capturing human mobility pattern. Our guiding principle in defining the space–time trajectory has been to follow Hägerstraand (1970)‟s implicit function used in the geographical analysis (Zheng & Zhou, 2011; Cao et al., 2015). We define that a social media user, Niu i ,1 , has a true space–time trajectory Wi within a country. This real-life trajectory Wi is approximately identified by WTi; Where WTi represents a set of geographically-tagged footprints of location (li), timestamp (ti) and message content (ci) posted in social media. So, for each user ui, WTi={( j i j i j i ctl ,, ),( 111 ,, j i j i j i ctl ),( 222 ,, j i j i j i ctl ),,,( kj i kj i kj i ctl ,, ),,,}.where j i kj i kj i tttkj ...;0;0 1 . One thing to note is that, unlike traditional spatial trajectories of geographic objects, soci...

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...lly-integrated data cuboid model allows to transform the massive, dynamic and unstructured location-based social media data into a structured framework for exploring spatiotemporal patterns of urban networks in a large developing country context. This is novel. 4 Exploration of social interaction patterns in China Seeing city and regional disparities in a large developing country through social 16 media user migration flows is new to the existing literature. The visualized flow mapping methods have been recently applied in the literature to represent dynamics of goods and people across space (Guo et al., 2006; Wang, 2010; Verbeek et al, 2011). By using real-time space-time trajectories of location-based social media data, we are able to investigate geographical implications of migration flows of social media users across spatiotemporal scales. The model results are presented below in two ways. First, we monitor the transition trajectories of social media users‟ migration flows and characterize the aggregated spatiotemporal outcomes of city and regional disparities. The second way of presentation assesses spatiotemporal patterns of pair-wise social connection networks by different geographical scal...

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...3; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from periphery cities tend to cluster into large metropolitan cities, recent economic studies suggest that external economies of agglomeration are substantial, but sharply attenuated by geographical distance across cities. But migration flow information from conventional census data cannot capture the real-time dynamics of human mobility flows between city pairs. In our analytical framework, we use space–time trajectories to track the spatial and temporal dimensions of social media users‟ activities. Our examination of changes in human mobility patterns by mon...

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...ation-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from periphery cities tend to cluste...

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...iterature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et ...

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... et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from periphery cities tend to cluster into large metropolitan cities, recent economic studies suggest that external economies of agglomeration are substantial, but sharply attenuated by geographical distance across cities. But migration flow information from conventional census data cannot capture the real-time dynamics of human mobility flows between city pairs. In our analytical framework, we use space–time trajectories to track the spatial and temporal dimensions of social media users‟ activities. O...

Citation Context

... is not surprising, given that mining location-based social media data faces serious identification challenges. First, location-based social media data, as a type of big data resource, are often featured by the dynamic, massive information generated by billions of users across space (Manyika et al., 2011). In truth, despite of the recent development of intensive-computational geographic information system (GIS) modeling programs, social media data with precise individual-level location information is still extremely large to proceed by using the GIS techniques at multiple geographical scales (Wang, 2010; Wright &Wang, 2011). Furthermore, conventional GIS-based computational methods cannot directly read the unstructured social media datasets (e.g. words, pictures, videos). Additional big data mining methods are often needed to transform social media data information from unstructured data formats to structured, and ready-to-use spatial datasets. In this paper, we tackle these problems by analysing the configuration of intercity connection patterns in China to provide new evidence to the applications of location-based social media data in urban and regional studies. China provides an ideal lab...

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...el allows to transform the massive, dynamic and unstructured location-based social media data into a structured framework for exploring spatiotemporal patterns of urban networks in a large developing country context. This is novel. 4 Exploration of social interaction patterns in China Seeing city and regional disparities in a large developing country through social 16 media user migration flows is new to the existing literature. The visualized flow mapping methods have been recently applied in the literature to represent dynamics of goods and people across space (Guo et al., 2006; Wang, 2010; Verbeek et al, 2011). By using real-time space-time trajectories of location-based social media data, we are able to investigate geographical implications of migration flows of social media users across spatiotemporal scales. The model results are presented below in two ways. First, we monitor the transition trajectories of social media users‟ migration flows and characterize the aggregated spatiotemporal outcomes of city and regional disparities. The second way of presentation assesses spatiotemporal patterns of pair-wise social connection networks by different geographical scales (city pairs, province pairs and...

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...proximately identified by WTi; Where WTi represents a set of geographically-tagged footprints of location (li), timestamp (ti) and message content (ci) posted in social media. So, for each user ui, WTi={( j i j i j i ctl ,, ),( 111 ,, j i j i j i ctl ),( 222 ,, j i j i j i ctl ),,,( kj i kj i kj i ctl ,, ),,,}.where j i kj i kj i tttkj ...;0;0 1 . One thing to note is that, unlike traditional spatial trajectories of geographic objects, social media users‟ space-time trajectories (WTi) are not likely to be sampled at regular time intervals (Zheng & Zhou, 2011; Gao & Liu, 2013). As suggested by Andrienko et al. (2012), space-time trajectories of social media users have the „episodic” nature. Indeed, it is possible that users may not frequently share their mobility behaviors, and users can even choose to disable 1 To avoid personal confidential data concerns, we assume that the location-based social media data company can protect the information about individual socioeconomic characteristics. 8 location positions when posting their social activities. Conventional methods (e.g., interpolation and map-matching) in used spatial trajectory analysis would at best capture the estimated footprint patterns of socia...

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...ajectories of social media users The starting point for our analysis is to use the space-time trajectories of social media users for identifying individuals‟ footprints in a geographic space. Assume that there is a country space which contains M cities available for individuals‟ mobility. A set of N individuals would post their daily social activities (e.g., traveling) through a location-based social media platform1. We seek to use a credible measure of “space– time trajectory "for capturing human mobility pattern. Our guiding principle in defining the space–time trajectory has been to follow Hägerstraand (1970)‟s implicit function used in the geographical analysis (Zheng & Zhou, 2011; Cao et al., 2015). We define that a social media user, Niu i ,1 , has a true space–time trajectory Wi within a country. This real-life trajectory Wi is approximately identified by WTi; Where WTi represents a set of geographically-tagged footprints of location (li), timestamp (ti) and message content (ci) posted in social media. So, for each user ui, WTi={( j i j i j i ctl ,, ),( 111 ,, j i j i j i ctl ),( 222 ,, j i j i j i ctl ),,,( kj i kj i kj i ctl ,, ),,,}.where j i kj i kj i tttkj ...;0;0 1 . One thing to note is...

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...gree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing tha...

Citation Context

...This real-life trajectory Wi is approximately identified by WTi; Where WTi represents a set of geographically-tagged footprints of location (li), timestamp (ti) and message content (ci) posted in social media. So, for each user ui, WTi={( j i j i j i ctl ,, ),( 111 ,, j i j i j i ctl ),( 222 ,, j i j i j i ctl ),,,( kj i kj i kj i ctl ,, ),,,}.where j i kj i kj i tttkj ...;0;0 1 . One thing to note is that, unlike traditional spatial trajectories of geographic objects, social media users‟ space-time trajectories (WTi) are not likely to be sampled at regular time intervals (Zheng & Zhou, 2011; Gao & Liu, 2013). As suggested by Andrienko et al. (2012), space-time trajectories of social media users have the „episodic” nature. Indeed, it is possible that users may not frequently share their mobility behaviors, and users can even choose to disable 1 To avoid personal confidential data concerns, we assume that the location-based social media data company can protect the information about individual socioeconomic characteristics. 8 location positions when posting their social activities. Conventional methods (e.g., interpolation and map-matching) in used spatial trajectory analysis would at best capture ...

Citation Context

...through disparities in individuals‟ initial motivations. For example, it is likely that the most frequent visited city for a businessman is his project‟s location, rather than his home location. For a more rigorous assessment, we apply the text mining methods (Rao et al., 2010; Burger et al., 2011; Wang et al., 2013) to derive social media users‟ current residence information. To be specific, we define a social media user‟s current residence as his or her origin city, while other cities in space-time trajectories as 9 visited destination cities. 2.2 Dimensional mobility algorithm Adopted from Leonardi et al. (2014), a graphical framework for data warehousing and data cuboid, is employed in this study to represent the dimensional mobility algorithm of social media users across cities. In the social media data cuboid, we stratify three dimensions: First, user dimension. We apply the text mining methods to read the individuals‟ socioeconomic information (such as gender, number of friends). To avoid data privacy concerns, we restrict our focus onto extracting users‟ origin city information and geo-tagged footprints information from text-based contents. Second, spatial dimension. It creates 1km2 grid cells, ...

Citation Context

... at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 200...

Citation Context

...re urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; A...

Citation Context

... urban network patterns. This research contributes to several strands of literature. First, it adds to the work on applications of social media data resources. Despite the booming trend of social media users in developing countries, the empirical literature has mostly focused on the U.S. and European countries. These studies have allowed for the simulation and modeling of distribution and dynamics of traffic flows (Steiger et al., 2014), mobile users (Malleson and Andresen, 2014), urban population (Aubrecht et al., 2011), LBSM users‟ social network (Ahern et al., 2007; Backstrom et al., 2010; Sun et al., 2013) and food health (Widener and Li, 2014), as well as spatio-temporal predictions of natural disaster progresses (e.g. earthquake, forest fire). These studies are of interest in their own right and are important for the development of optimal public policy. We are–for the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is con...

Citation Context

...quently visited city as the origin city of the social media user, and used a spatial radius to track individuals‟ footprints (Gonzalez et al., 2008; Cao et al., 2015). Cities that have higher visiting frequency rates are potentially very different from those that do not. These differences may arise through disparities in individuals‟ initial motivations. For example, it is likely that the most frequent visited city for a businessman is his project‟s location, rather than his home location. For a more rigorous assessment, we apply the text mining methods (Rao et al., 2010; Burger et al., 2011; Wang et al., 2013) to derive social media users‟ current residence information. To be specific, we define a social media user‟s current residence as his or her origin city, while other cities in space-time trajectories as 9 visited destination cities. 2.2 Dimensional mobility algorithm Adopted from Leonardi et al. (2014), a graphical framework for data warehousing and data cuboid, is employed in this study to represent the dimensional mobility algorithm of social media users across cities. In the social media data cuboid, we stratify three dimensions: First, user dimension. We apply the text mining methods to r...

Citation Context

...rajectories of social media users for identifying individuals‟ footprints in a geographic space. Assume that there is a country space which contains M cities available for individuals‟ mobility. A set of N individuals would post their daily social activities (e.g., traveling) through a location-based social media platform1. We seek to use a credible measure of “space– time trajectory "for capturing human mobility pattern. Our guiding principle in defining the space–time trajectory has been to follow Hägerstraand (1970)‟s implicit function used in the geographical analysis (Zheng & Zhou, 2011; Cao et al., 2015). We define that a social media user, Niu i ,1 , has a true space–time trajectory Wi within a country. This real-life trajectory Wi is approximately identified by WTi; Where WTi represents a set of geographically-tagged footprints of location (li), timestamp (ti) and message content (ci) posted in social media. So, for each user ui, WTi={( j i j i j i ctl ,, ),( 111 ,, j i j i j i ctl ),( 222 ,, j i j i j i ctl ),,,( kj i kj i kj i ctl ,, ),,,}.where j i kj i kj i tttkj ...;0;0 1 . One thing to note is that, unlike traditional spatial trajectories of geographic objects, social media users‟ spa...

Citation Context

...literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from periphery cities tend to cluster into large metropolitan cities, recent economic studies suggest that external econom...

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...r level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosenthal and Strange, 2008; Arzaghi and Henderson, 2008). By showing that migration flows from ...

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...d social interactions. By applying big data mining techniques, „geo-tagged‟ social media users‟ mobility flows between city-pairs can be measured with the explicit aim of exploiting urban network patterns. This research contributes to several strands of literature. First, it adds to the work on applications of social media data resources. Despite the booming trend of social media users in developing countries, the empirical literature has mostly focused on the U.S. and European countries. These studies have allowed for the simulation and modeling of distribution and dynamics of traffic flows (Steiger et al., 2014), mobile users (Malleson and Andresen, 2014), urban population (Aubrecht et al., 2011), LBSM users‟ social network (Ahern et al., 2007; Backstrom et al., 2010; Sun et al., 2013) and food health (Widener and Li, 2014), as well as spatio-temporal predictions of natural disaster progresses (e.g. earthquake, forest fire). These studies are of interest in their own right and are important for the development of optimal public policy. We are–for the first time in the literature–to comprehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-ba...

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...ehensively measure urban network patterns at a detailed spatial degree (the city-pair level) based on 5 location-based social media data from a large developing country context. There is a substantial literature that investigates various aspects of human mobility behaviors. Much of it is concerned with variation in local amenities (e.g. crimes) and population distribution within cities, an issue not directly related to our work. Only a small a number of papers look at the social and spatial interactions of individuals and cities (Crandall et al., 2009; Cranshaw et al., 2012; Gao et al., 2012; Stephens, 2013; Rosler and Liebig, 2013; Stefanidis et al., 2013; Liu et al., 2014; Lovelace et al., 2014; Hollenstein and Purves, 2014). In what is probably the most closely related paper to our own, Jiang and Miao (2014) uses location-based social media data to examine the evolution of the rank-size distribution of cities in the mainland US. Put differently, we look at the dynamics of inter-city connection patterns in China, an important complementary inquiry. Finally, our work is related to the spatial economic literature dealing with the spillovers of agglomeration effects (Andersson et al., 2004; Rosen...