Human Spatial Behavior, Sensor Informatics, and Disaggregate Data

Petrenko, Anastasia; Bell, Scott; Stanley, Kevin G.; Qian, Winchel; Sizo, Anton; Knowles, Dylan

doi:10.1007/978-3-319-01790-7_13

Cited by 12 publications

(12 citation statements)

References 41 publications

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“…Overall results are replicated from Petrenko et al. () to provide overall context, and for the reader's convenience.…”

Section: Resultsmentioning

confidence: 62%

“…For a more in depth analysis of how recorded locations matched with participants' perceptions of their habits see Petrenko et al. ().…”

Section: Resultsmentioning

confidence: 99%

“…The WCN was created based on CentreLINE (CL) with the purpose of capturing and simplifying the geometry of building layout, improving positioning, and supporting indoor navigation (Jung and Bell ; Petrenko et al. ). The University of Saskatchewan is in a climatic zone characterized by harsh winters.…”

Section: Research Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

Petrenko

Sizo

Qian

et al. 2014

Transactions in GIS

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The popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system's inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT-resources. Our research outlines the potential for employing indoor wireless network positioning and sensor-based systems to improve the collection of tracking data indoors. By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method.

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“…Overall results are replicated from Petrenko et al. () to provide overall context, and for the reader's convenience.…”

Section: Resultsmentioning

confidence: 62%

“…For a more in depth analysis of how recorded locations matched with participants' perceptions of their habits see Petrenko et al. ().…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

Petrenko

Sizo

Qian

et al. 2014

Transactions in GIS

Self Cite

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“…In addition, in order to calibrate and validate a system with reasonable confidence margins, there is a need to have “enough” participants. While this is highly dependent on what aspects of food decision-making a model seeks to capture, datasets having only 37 participants [38] are not sufficient for modelling purposes. Several studies have been completed in the last years on the food environment using GPS, and they have both a sufficient time window and over a hundred individuals.…”

Section: Methodsmentioning

confidence: 99%

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Giabbanelli

Crutzen

2017

Computational and Mathematical Methods in Medicine

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Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps' process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.

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“…For example, on 5 March 2016, the 15 operation lines of the Beijing Subway handled a daily passenger volume of 9.6711 million [2]. Effectively managing the massive data of indoor cells and moving objects is particularly important for many applications, including object tracking [3], indoor navigation [4] and emergency evacuation [5].…”

Section: Introductionmentioning

confidence: 99%

Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries

Lin

Peng

Chen

et al. 2016

IJGI

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Abstract:With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that-compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)-the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries.

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Human Spatial Behavior, Sensor Informatics, and Disaggregate Data

Cited by 12 publications

References 41 publications

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries

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