The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games. In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.
Our work is further related to a stream of research on that aims to extract insights from location and mobility data in order to aid urban planning and the development of smart cities. @cite_21 investigate the interplay between citizen mobility and the well-being of London's census areas through a public transport fare dataset. and @cite_2 @cite_22 , on the other hand, focus on mobility pattern modeling to characterize the structure of cities with respect to neighborhood dynamics and functional regions. Our approach to extracting mobility features in a neighborhood is related to these works in the sense that we qualitatively assess micro-areas in the city to uncover the underlying factors contributing to venue popularity during the Olympics.
@cite_21 @cite_22 @cite_2
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A key facet of urban design, planning, and monitoring is measuring communities' well-being. Historically, researchers have established a link between well-being and visibility of city neighbourhoods and have measured visibility via quantitative studies with willing participants, a process that is invariably manual and cumbersome. However, the influx of the world's population into urban centres now calls for methods that can easily be implemented, scaled, and analysed. We propose that one such method is offered by pervasive technology: we test whether urban mobility--as measured by public transport fare collection sensors--is a viable proxy for the visibility of a city's communities. We validate this hypothesis by examining the correlation between London urban flow of public transport and census-based indices of the well-being of London's census areas. We find that not only are the two correlated, but a number of insights into the flow between areas of varying social standing can be uncovered with readily available transport data. For example, we find that deprived areas tend to preferentially attract people living in other deprived areas, suggesting a segregation effect. In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development. The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones. The rapid developments of ubiquitous mobile computing provide planners and researchers with new opportunities to understand and build smart cities by mining the massive spatial-temporal mobility data. However, given the increasing complexity and volume of the emerging mobility datasets, it also becomes challenging to build novel analytical framework that is capable of understanding the structural properties and critical features. In this paper, we introduce an analytical framework to deal with high-dimensional human mobility data. To this end, we formulate mobility data in a probabilistic setting and consider each record a multivariate observation sampled from an underlying distribution. In order to characterize this distribution, we use a multi-way probabilistic factorization model based on the concept of tensor decomposition and probabilistic latent semantic analysis (PLSA). The model provides us with a flexible approach to understand multi-way mobility involving higher-order interactions—which are difficult to characterize with conventional approaches—using simple latent structures. The model can be efficiently estimated using the expectation maximization (EM) algorithm. As a numerical example, this model is applied on a four-way dataset recording 14 million public transport journeys extracted from smart card transactions in Singapore. This framework can shed light on the modeling of urban structure by understanding mobility flows in both spatial and temporal dimensions.