Human diffusion and city influence

Lenormand, Maxime; Gonçalves, Bruno; Tugores, Antònia; Ramasco, José J.

doi:10.1098/rsif.2015.0473

Cited by 50 publications

(55 citation statements)

References 59 publications

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“…It is important to note that these most visited touristic sites are not necessarily the ones with the higher attractiveness presented in the previous section. That is the advantage of the method proposed in [38], which allows us to measure the influence and the power of attraction of regions of the world with different number of local and non-local visitors.…”

Section: Touristic Site's Visiting Figures By Country Of Residencementioning

confidence: 99%

“…Many different rankings of most visited touristic sites exist but they are often based on the number of visitors, which does not really tell us much about their attractiveness at a global scale. Here we apply an alternative method proposed in [38] to measure the influence of cities. The purpose of this method is to analyze the influence and the attractiveness of a site based on the average radius traveled and the area covered by individuals visiting this site.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this method is to analyze the influence and the attractiveness of a site based on the average radius traveled and the area covered by individuals visiting this site. More specifically, we select 20 out of the most popular touristic sites of the world and analyze their attractiveness using a dataset containing about 10 million geolocated tweets, which have already demonstrated their efficiency as useful source of data to study mobility at a world scale [18,38]. In particular, we propose three rankings of the touristic sites' attractiveness based on the spatial distribution of the visitors' place of residence, we show that the Taj Mahal, the Pisa Tower and the Eiffel Tower appear always in the top 5.…”

Section: Introductionmentioning

confidence: 99%

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Touristic site attractiveness seen through Twitter

et al. 2016

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Tourism is becoming a significant contributor to medium and long range travels in an increasingly globalized world. Leisure traveling has an important impact on the local and global economy as well as on the environment. The study of touristic trips is thus raising a considerable interest. In this work, we apply a method to assess the attractiveness of 20 of the most popular touristic sites worldwide using geolocated tweets as a proxy for human mobility. We first rank the touristic sites based on the spatial distribution of the visitors' place of residence. The Taj Mahal, the Pisa Tower and the Eiffel Tower appear consistently in the top 5 in these rankings. We then pass to a coarser scale and classify the travelers by country of residence. Touristic site's visiting figures are then studied by country of residence showing that the Eiffel Tower, Times Square and the London Tower welcome the majority of the visitors of each country. Finally, we build a network linking sites whenever a user has been detected in more than one site. This allow us to unveil relations between touristic sites and find which ones are more tightly interconnected.

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Section: Touristic Site's Visiting Figures By Country Of Residencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Touristic site attractiveness seen through Twitter

et al. 2016

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“…Taxi drivers can also benefit from studying their driving behavior while passive (no passengers) [6] and by predicting the number of passengers they could potentially get in a certain hotspot in the next time interval [22]. Cities can benefit as well by correlating mobility patterns and energy consumption to provide better sustainable urban forms [20], and by measuring their influence in the world on attracting visitors [21], and by quantifying their resilience to extreme events [7]. In [4,9], the authors implemented visualization tools to understand urban flows across time and space from taxi rides and social media.…”

Section: Related Workmentioning

confidence: 99%

Discovering and Characterizing Mobility Patterns in Urban Spaces

Noboa

Lemmerich

Singer

et al. 2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for understanding these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses we utilize data that is publicly available on the Web, namely Foursquare data and census data provided by an open data platform. By applying this combination of approaches to taxi data in Manhattan, we can discover and characterize different patterns in human mobility that cannot be identified in a collective analysis. As one example, we can find a group of taxi rides that end at locations with a high number of party venues (according to Foursquare) on weekend nights. Overall, our work demonstrates that human mobility is not one-dimensional but rather contains different facets both in time and space which we explain by utilizing online data. The findings of this paper argue for a more fine-grained analysis of human mobility in order to make more informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.

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“…Geo-tagged positional information within tweets can be relayed as point with exact geographic coordinates or as bounding box when the location is expressed as a place, which is less precise. Despite the small percentage of tweets that carry positional information (either as exact coordinates or as bounding box), tweets have been used for the analysis of travel patterns at the local [13], national [14,15] and global [16,17] scale, and for the detection and tracking of spatial events [18]. Being restrained by the scarcity of geo-tagged tweets in low-density population areas and in countries with low Twitter penetration rates, most studies analyze travel behavior in metropolitan areas, base their movement analysis on aggregated user flows between large areal units (e.g., counties, states, or countries), or provide statistical summaries about movements (e.g., peak times and distances) rather than visualizing individual movement trajectories or trajectory clusters in maps.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Hübl

Cvetojevic

Hochmair

et al. 2017

IJGI

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Abstract:Over the past few years, analysts have begun to materialize the "Citizen as Sensors" principle by analyzing human movements, trends and opinions, as well as the occurrence of events from tweets. This study aims to use geo-tagged tweets to identify and visualize refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015, which was caused by war and political and economic instability in those regions. The focus of this study is on exploratory data analysis, which includes refugee trajectory extraction and aggregation as well as the detection of topical clusters along migration routes using the V-Analytics toolkit. Results suggest that only few refugees use Twitter, limiting the number of extracted travel trajectories to Europe. Iterative exploration of filter parameters, dynamic result mapping, and content analysis were essential for the refinement of trajectory extraction and cluster detection. Whereas trajectory extraction suffers from data scarcity, hashtag-based topical clustering draws a clearer picture about general refugee routes and is able to find geographic areas of high tweet activities on refugee related topics. Identified spatio-temporal clusters can complement migration flow data published by international authorities, which typically come at the aggregated (e.g., national) level. The paper concludes with suggestions to address the scarcity of geo-tagged tweets in order to obtain more detailed results on refugee migration patterns.

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Human diffusion and city influence

Cited by 50 publications

References 59 publications

Touristic site attractiveness seen through Twitter

Touristic site attractiveness seen through Twitter

Discovering and Characterizing Mobility Patterns in Urban Spaces

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

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