Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy

Sainio, Jani; Westerholm, Jan; Oksanen, Juha

doi:10.3390/ijgi4041813

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…Perhaps the most common concern relates to privacy issues associated with data gathered from personal mobile phones (Meijles et al, 2013;Taczanowska et al, 2008). For example, disclosing start and end points of a GPS track or displaying routine route of an individual can reveal a home or work location (Sainio et al, 2015). This study and others (e.g.…”

Section: Discussionmentioning

confidence: 82%

Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement

Korpilo

Virtanen

Lehvävirta

2017

Landscape and Urban Planning

111

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Section: Discussionmentioning

confidence: 82%

Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement

Korpilo

Virtanen

Lehvävirta

2017

Landscape and Urban Planning

111

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“…Shelton, Poorthuis, and Zook (2015) also provided a conceptual and methodological framework for the analysis of geo-tagged tweets to explore the spatial imaginaries and processes of segregation and mobility. Sainio, Westerholm, and Oksanen (2015) introduced a fast map server to generate and visualize heat maps of popular routes online based on client preferences from massive track data collected by the Sports Tracker mobile application. Toole et al (2015) presented a flexible, modular and computationally efficient software system to transform and integrate raw, massive data of call detail records (CDRs) from mobile phones into estimates of travel demand.…”

Section: Mobile Data Collection Computing and Near Field Communicationmentioning

confidence: 99%

Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

625

289

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Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers-Big Data and cloud computingand reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

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“…Considering the displaying problem, we design a pyramid tile structure (MacLean & Tsotsos, ; Masó, Pomakis, & Julia, ; Sainio, Westerholm & Oksanen, ) to manage and store panoramic images. We place the original images in the first layer, and the data of the n layer are resampled from the n‐1 layer according to a given scale factor, such as 4:1, which means every four adjacent pixels of the n‐1 layer are resampled to one pixel to generate a new image of the n layer.…”

Section: Spherical Lod Model Of 360° Panoramic Viewmentioning

confidence: 99%

An effective spherical panoramic LoD model for a mobile street view service

Xianxiong

et al. 2016

Transactions in GIS

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Street view (panoramic view) services have begun to shift from PC platforms to mobile smart devices. Considering the capabilities of smart terminal devices, a 3608 spherical level of detail (LoD) model for mobile street view services that renders panoramic images on the inner surface of a sphere via LoD is proposed in this article. Panoramic images are segmented into tiles and organized in a pyramid tile structure for LoD rendering to improve the rendering efficiency of the proposed model. A projection model between panoramic images and the spherical surface is presented to map the panoramic tiles on the spherical graticule. A street view-rendering algorithm of panoramic images is proposed with the rendering function of OpenGL for Embedded Systems (OpenGL ES).A street view service app running on Android, based on the proposed approach, is implemented to assess two aspects of the panoramic view model, namely visualization effect and efficiency. Experiment results reveal that the 3608 spherical LoD model of the panoramic image can display 3D street view scenes better than the cubic panoramic view model. The proposed model also has an excellent 3D visualization effect and high-efficiency rendering ability for mobile street view services.Therefore, it is applicable in large-scale mobile street view services both online and offline and in augmented reality navigation. K E Y W O R D S level of detail, mobile device, panoramic image, rendering, spherical, street view 1 | I NTR OD U CTI ON In the past, people expressed the real world abstractly through all types of maps, such as general, thematic, image, and 3D maps. In May 2007, in a collaboration with Stanford University called CityBlock, Google launched Google Street View (GSV) with 3608 panoramic images for the first time (Anguelov et al., 2010). The idea was to provide a street view of driving along every street in the world by taking pictures of all buildings and roadsides. At present, worldwide Transactions in GIS. 2017;21:897-915.wileyonlinelibrary.com/journal/tgis street view environments can be routinely captured with land-based mobile mapping systems (Li, Hu, & Chen, 2009;Anguelov et al., 2010). The extensive availability of street-level image data has proven the popularity of the street view environment among users; this environment delivers useful information that was not previously available. Street view (panoramic view) with a 3608 panoramic image has been and continues to be an exciting adventure in globalscale and street-level photo collection and service. As an online virtual imaging application, GSV allows a user to view and navigate through 3608 panoramic images and observe streets and neighborhoods virtually (Li et al., 2009;Fang, Li, & Zhang, 2011;Lewis, Fotheringham, & Winstanley, 2011). One can navigate forward and backward and then up and down as well as zoom in and out. Chinese Internet service providers, such as Tencent and Baidu, also recently launched panoramic view services in succession; these services can provide real 3D scenes for the exp...

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Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy

Cited by 20 publications

References 17 publications

Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement

Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement

Big Data and cloud computing: innovation opportunities and challenges

An effective spherical panoramic LoD model for a mobile street view service

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