Statistics for Spatial Data.

The theory of affordances

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Urban Computing: Concepts, Methodologies, and Applications

Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks

A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

A survey on clustering algorithms for wireless sensor networks

The rapid development of on-line social networking sites has dramatically changed the way people live and communicate. One particularly interesting phenomena came along with this development is the prominent role of various on-line networking portals played in scheduling and organizing off-line group events and activities. In this paper, we focus on studying the face-to-face(f2f) group formed through, or facilitated by, on-line portals. We first show the distinct characteristics of such f2f groups by analyzing datasets collected from Whrrl and Meetup. Next, we propose a dynamic model for group gathering based on the process of friend invitation to interpret how a f2f group is formed on-line. The results of our model are confirmed by empirical observations. Finally, we demonstrate that using such group information can effectively improve the accuracies of social tie inference and friend recommendation.

From face-to-face gathering to social structure

In recent years, location-based social networks (LBSNs) have received high attention. While this new breed of social networks is nascent, there is no large-scale analysis conducted to investigate the associations among users in locales of the network. In this paper, we propose four locale based metrics, including Locale Clustering Coefficient, Inward Locale Transitivity, Locale Assortativity Coefficient, and Locale Assortability Coefficient to make association analysis on EveryTrail, a popular LBSN specialized on sharing trips. Based on the analysis result, we observe that people who share more trajectories will get more attention by other users, and people who are popular will connect to the people who are also popular.

/pdf/user-association-analysis-of-locales-on-location-based-2g72qw6ycg.pdf

User association analysis of locales on location based social networks

In this paper, we aim to develop a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users. In this service, theme locations in a travelogue are the core information to discover. Thus we aim to address the problem of theme location discovery to enable the above travelogue services. Due to the inherent ambiguity of location relevance, we perform location relevance mining (LRM) in two complementary angles, relevance classification and relevance ranking, to provide comprehensive understanding of locations. Furthermore, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to develop a co-training model for enhancement of classification performance. Built upon the mining result of LRM, we develop a series of techniques for provisioning of the aforementioned travelogue digests in our travelogue system. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance mining techniques and demonstrate the effectiveness of the travelogue service.

On theme location discovery for travelogue services

To bring data services through low-cost Internet access to remote rural areas, several projects proposed to use ferries, such as buses and cars,equipped with short-range WiFi to deliver messages between the Internet and remote villages. In such store-and-forward, ferry-based delay tolerant networks (DTNs), the messaging capacity on the ferries becomes a critical resource and thus needs to be used properly in order to reduce the overall transit delay. However, simply optimizing the overall transit delay may result in prolonged delays for certain individual villages. In this paper, we aim at ensuring a "fair" message ferrying service (in terms of transit delay) amongst the served villages while optimizing the overall performance of the whole system. To achieve our goal, we develop an application layer switch for message/ferry scheduling. Our scheduling is based on the notion of benefit that measures the advantages of ferry scheduling selections in a relative fashion. This approach allows us to properly model the delay minimization problem and fairness goal as mathematical constraints of a schedule optimization problem. Furthermore, we transform the two conflicting goals of this complex optimization problem into two interacting components, namely, performance optimization block and fairness assurance block, and treat them with bipartite matching and stochastic approximation  techniques, respectively. Through simulations, we validate our proposal and show that the overall system performance is optimized, together with the ensured fairness in terms of benefit and queuing time.

Fair Delay Tolerant Mobile Data Ferrying

Prior research on viral marketing mostly focuses on promoting one single product item. In this work, we explore the idea of bundling multiple items for viral marketing and formulate a new research problem, called Bundle Configuration for SpreAd Maximization (BCSAM). Efficiently obtaining an optimal product bundle under the setting of BCSAM is very challenging. Aiming to strike a balance between the quality of solution and the computational overhead, we systematically explore various heuristics to develop a suite of algorithms, including κ-Bundle Configuration and Aggregated Bundle Configuration. Moreover, we integrate all the proposed ideas into one efficient algorithm, called Aggregated Bundle Configuration (ABC). Finally, we conduct an extensive performance evaluation on our proposals. Experimental results show that ABC significantly outperforms its counterpart and two baseline approaches in terms of both computational overhead and bundle quality.

Mao Ye

Papers

From face-to-face gathering to social structure

User association analysis of locales on location based social networks

On theme location discovery for travelogue services

Fair Delay Tolerant Mobile Data Ferrying

On bundle configuration for viral marketing in social networks