15,550 research outputs found
StarBorn : Towards making in-situ land cover data generation fun with a location-based game
University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD
Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing
Gender differences is a phenomenon around the world actively researched by
social scientists. Traditionally, the data used to support such studies is
manually obtained, often through surveys with volunteers. However, due to their
inherent high costs because of manual steps, such traditional methods do not
quickly scale to large-size studies. We here investigate a particular aspect of
gender differences: preferences for venues. To that end we explore the use of
check-in data collected from Foursquare to estimate cultural gender preferences
for venues in the physical world. For that, we first demonstrate that by
analyzing the check-in data in various regions of the world we can find
significant differences in preferences for specific venues between gender
groups. Some of these significant differences reflect well-known cultural
patterns. Moreover, we also gathered evidence that our methodology offers
useful information about gender preference for venues in a given region in the
real world. This suggests that gender and venue preferences observed may not be
independent. Our results suggests that our proposed methodology could be a
promising tool to support studies on gender preferences for venues at different
spatial granularities around the world, being faster and cheaper than
traditional methods, besides quickly capturing changes in the real world
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
This paper is the first work to perform spatio-temporal mapping of human
activity using the visual content of geo-tagged videos. We utilize a recent
deep-learning based video analysis framework, termed hidden two-stream
networks, to recognize a range of activities in YouTube videos. This framework
is efficient and can run in real time or faster which is important for
recognizing events as they occur in streaming video or for reducing latency in
analyzing already captured video. This is, in turn, important for using video
in smart-city applications. We perform a series of experiments to show our
approach is able to accurately map activities both spatially and temporally. We
also demonstrate the advantages of using the visual content over the
tags/titles.Comment: Accepted at ACM SIGSPATIAL 201
Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO
User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the mapping community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future
Spatial Dynamic Modeling and Urban Land Use Transformation:
Assessing the economic impacts of urban land use transformation has become complex and acrimonious. Although community planners are beginning to comprehend the economic trade-offs inherent in transforming the urban fringe, they find it increasingly difficult to analyze and assess the trade-offs expediently and in ways that can influence local decisionmaking. New and sophisticated spatial modeling techniques are now being applied to urban systems that can quickly assess the probable spatial outcomes of given communal policies. Applying an economic impact assessment to the probable spatial patterns can provide to planners the tools needed to quickly assess scenarios for policy formation that will ultimately help inform decision makers. This paper focuses on the theoretical underpinnings and practical application of an economic impact analysis submodel developed within the Land use Evolution and Impact Assessment Modeling (LEAM) environment. The conceptual framework of LEAM is described, followed by an application of the model to the assessment of the cost of urban sprawl in Kane County, Illinois. The results show the effectiveness of spatially explicit modeling from a theoretical and a practical point of view. The agent-based approach of spatial dynamic modeling with a high spatial resolution allows for discerning the macro-level implications of micro-level behaviors. These phenomena are highlighted in the economic submodel in the discussion of the implications of land use change decisions on individual and communal costs; low-density development patterns favoring individual behaviors at the expense of the broader community.
Event Organization 101: Understanding Latent Factors of Event Popularity
The problem of understanding people's participation in real-world events has
been a subject of active research and can offer valuable insights for human
behavior analysis and event-related recommendation/advertisement. In this work,
we study the latent factors for determining event popularity using large-scale
datasets collected from the popular Meetup.com EBSN in three major cities
around the world. We have conducted modeling analysis of four contextual
factors (spatial, group, temporal, and semantic), and also developed a
group-based social influence propagation network to model group-specific
influences on events. By combining the Contextual features And Social Influence
NetwOrk, our integrated prediction framework CASINO can capture the diverse
influential factors of event participation and can be used by event organizers
to predict/improve the popularity of their events. Evaluations demonstrate that
our CASINO framework achieves high prediction accuracy with contributions from
all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017
https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557
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