Exploring space syntax on entrepreneurial opportunities with Wi-Fi analytics

Mashhadi, Afra; Acer, Utku Günay; Boran, Aidan; Scholl, Philipp M.; Forlivesi, Claudio; Vanderhulst, Geert; Kawsar, Fahim

doi:10.1145/2971648.2971745

Cited by 9 publications

(11 citation statements)

References 39 publications

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“…Finally, it is worth mentioning the domain of social sensing (e.g., Eagle and Pentland 2006;Atzmueller et al 2014;Liu et al 2015;Mashhadi et al 2016). Social sensing refers to the collection of social data at a large scale (Atzmueller et al 2014).…”

Section: Dyadic Interactionsmentioning

confidence: 99%

The promise of social signal processing for research on decision-making in entrepreneurial contexts

et al. 2019

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In this conceptual paper, we demonstrate how modern data science techniques can advance our understanding of important decisions in the context of entrepreneurship that involve social interactions. We know that individuals' decision-making is strongly affected by nonverbal behavior. The emerging domain of social signal processing aims at accurate computerized analysis of such behavior. Behavioral cues stemming from, for example, gestures, posture, facial expressions, and vocal expressions can now be detected and analyzed by state-of-the-art technologies utilizing artificial intelligence. This paper discusses and illustrates their potential value for future research on decision-making by entrepreneurs as well as by others yet directly affecting them (e.g., investors). In brief, social signal processing is more accurate and more efficient than conventional research methods and may reveal important characteristics that so far have been omitted in explaining decisions that are vital for firm survival and growth. We derive a total of five propositions from our newly developed conceptual framework, which we hope will be subject to extensive empirical scrutiny in future research.

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Section: Dyadic Interactionsmentioning

confidence: 99%

The promise of social signal processing for research on decision-making in entrepreneurial contexts

et al. 2019

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“…coffee breaks and meetings) starting from social contacts and location information [20]. Also other technologies have been proposed, for instance, RFID [11], WiFi [37] and hybrid approaches with radio and ultrasound sensors [24].…”

Section: Mobile Sensing For Office Analyticsmentioning

confidence: 99%

Detecting Emerging Activity-Based Working Traits through Wearable Technology

Montanari

Mascolo

Sailer

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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A recent trend in corporate real-estate is Activity-Based Working (ABW). The ABW concept removes designated desks but offers different work settings designed to support typical work activities. In this context there is still a need for objective data to understand the implications of these design decisions. We aim to contribute by using automated data collection to study how ABW's principles impact office usage and dynamics. To this aim we analyse team dynamics and employees' tie strength in relation to space usage and organisational hierarchy using data collected with wearable devices in a company adopting ABW principles. Our findings show that the office fosters interactions across team boundaries and among the lower levels of the hierarchy suggesting a strong lateral communication. Employees also tend to have low space exploration on a daily basis which is instead more prevalent during an average week and strong social clusters seem to be resisting the ABW principles of space dynamics. With the availability of two additional data sets about social encounters in traditional offices we highlight traits emerging from the application of ABW's principles. In particular, we observe how the absence of designated desks might be responsible for more rapid dynamics inside the office. In more general terms, this work opens the door to new and scalable technology-based methodologies to study dynamic office usage and social interactions.

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“…Wi-Fi-based approaches [7][2] [19][16] [28] by analyzing wireless packets provide more flexible and low-cost options to perform crowd detection and human mobility monitoring in large-scale environments. The work in [7] proposes some filtering algorithms to handle uncertain and noisy data from Wi-Fi sniffers due to MAC address randomization, overlapping coverage between Wi-Fi sniffers, and high variance in Wi-Fi sensing ranges.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], Wi-Fi sniffers are deployed in an industrial exhibition to capture the Wi-Fi probes from mobile devices of attendees, and mobility patterns in each monitored zone are analyzed such as the number of unique MAC addresses, the number of Wi-Fi probes, and the brand statistics of mobile devices in each zone. The work in [19] extends [2] to analyze not only crowd dynamics but also correlations between the spatial configuration and entrepreneurial opportunities in these zones based on attendees' mobility. In [16], the people flows are analyzed using Wi-Fi probes based on a sequence of frequently visited sensing zones.…”

Section: Related Workmentioning

confidence: 99%

CrowdEstimator

Solmaz²

2018

Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services

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Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fionly crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems;©ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM MobiSys'18, http://doi.

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Exploring space syntax on entrepreneurial opportunities with Wi-Fi analytics

Cited by 9 publications

References 39 publications

The promise of social signal processing for research on decision-making in entrepreneurial contexts

The promise of social signal processing for research on decision-making in entrepreneurial contexts

Detecting Emerging Activity-Based Working Traits through Wearable Technology

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