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Shum LC, Faieghi R, Borsook T, Faruk T, Kassam S, Nabavi H, Spasojevic S, Tung J, Khan SS, Iaboni A. Indoor Location Data for Tracking Human Behaviours: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1220. [PMID: 35161964 PMCID: PMC8839091 DOI: 10.3390/s22031220] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/04/2022]
Abstract
Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.
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Affiliation(s)
- Leia C. Shum
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
| | - Reza Faieghi
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
- Department of Aerospace Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Terry Borsook
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
| | - Tamim Faruk
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
| | - Souraiya Kassam
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
| | - Hoda Nabavi
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
| | - Sofija Spasojevic
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | - James Tung
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Shehroz S. Khan
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | - Andrea Iaboni
- KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada; (L.C.S.); (R.F.); (T.B.); (T.F.); (S.K.); (H.N.); (S.S.); (S.S.K.)
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
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Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities. LAND 2021. [DOI: 10.3390/land10111209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of urban big data analytics based on AI-related tools to support the design and planning of cities. To this end, the author introduces a conceptual framework to assess the influence of the emergence of these tools on the design and planning of the cities in the context of urban change. In this paper, the implications of the application of artificial-intelligence-based tools and geo-localised big data, both in solving specific research problems in the field of urban planning and design as well as on planning practice, are discussed. The paper is concluded with both cognitive conclusions and recommendations for planning practice. It is directed towards urban planners interested in the emerging urban big data analytics based on AI-related tools and towards urban theorists working on new methods of describing urban change.
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Heat Maps: Perfect Maps for Quick Reading? Comparing Usability of Heat Maps with Different Levels of Generalization. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10080562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Recently, due to Web 2.0 and neocartography, heat maps have become a popular map type for quick reading. Heat maps are graphical representations of geographic data density in the form of raster maps, elaborated by applying kernel density estimation with a given radius on point- or linear-input data. The aim of this study was to compare the usability of heat maps with different levels of generalization (defined by radii of 10, 20, 30, and 40 pixels) for basic map user tasks. A user study with 412 participants (16–20 years old, high school students) was carried out in order to compare heat maps that showed the same input data. The study was conducted in schools during geography or IT lessons. Objective (the correctness of the answer, response times) and subjective (response time self-assessment, task difficulty, preferences) metrics were measured. The results show that the smaller radius resulted in the higher correctness of the answers. A larger radius did not result in faster response times. The participants perceived the more generalized maps as easier to use, although this result did not match the performance metrics. Overall, we believe that heat maps, in given circumstances and appropriate design settings, can be considered an efficient method for spatial data presentation.
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