Geo-located Twitter as the proxy for global mobility patterns

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.Comment: 17 pages, 13 figure

Analyzing the Data-Rich-But-Information-Poor Syndrome in Dutch Water Management in Historical Perspective

Water quality monitoring has developed over the past century from an unplanned, isolated activity into an important discipline in water management. This development also brought about a discontent between information users and information producers about the usefulness and usability of information, in literature often referred to as the data-rich-but-information-poor syndrome. This article aims to gain a better understanding of this issue by studying the developments over some five decades of Dutch national water quality monitoring, by analyzing four studies in which the role and use of information are discussed from different perspectives, and by relating this to what is considered in literature as useful information. The article concludes that a “water information gap” exists which is rooted in different mutual perceptions and expectations between the two groups on what useful information is, that can be overcome by improving the communication. Such communication should be based on willingness to understand and deal with different mindframes and should be based on a methodology that guides and structures the interactions

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches

From Motion Activity to Geo-Embeddings : Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people.(VLID)355285

Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services

Environmental rehabilitation: efficiency and effectiveness in soil remediation

Soil cleaning-up operations have become a priority in most western countries. In the Netherlands, in particular, a systematic effort to restore the environmental quality of polluted sites has started in the early eighties. The cornerstone of the Dutch legislation is that of restoring soil multifunctionality, which allows the cleaned site to be used for any purpose, without functional constraints. In more than ten years of application, this approach has shown some weak points. First, the costs of cleaning-up may be extremely high. Many companies tend to delay as much as possible the operations, either to delay expenditures or to wait for the development of more effective cleaning-up technologies. Second, many cleaning-up techniques achieve very good results in terms of soil quality, but result into a transfer of pollution to other environmental media (for instance, air and water) and require an intensive use of scarce resources (for instance, energy, groundwater and space). Third, in many instances the site has a unique destination, an industrial site for instance, and cleaning-up beyond the level strictly necessary is very cost-inefficient. These considerations have lead to the development of a new approaches for soil cleaning-up and to the development of methodologies and instruments for addressing effectiveness and efficiency in soil remediation. The paper shows a Decision Support System which assists the planning of cleaning-up operations on the basis of: (1) their effectiveness in reducing the risks for the specific needs of the site; (2) their capacity of minimising the negative influences on the environment and on the depletion of scarce resources; (3) the possibility of minimising the costs of operation and of timing the cleaning-up investments. The paper focuses on the environmental quality part, showing how the negative influences of cleaning-up operations can be taken into account in the evaluation of cleaning-up alternatives. Application examples are also provided.