Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this article is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely$k$-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.

With the advancement of the Internet of Things (IoT) and communication platform, large-scale sensor deployment can be easily implemented in an urban city to collect various information. To date, there are only a handful of research studies about understanding the usage of urban public spaces. Leveraging IoT, various sensors have been deployed in an urban residential area to monitor and study public space utilization patterns. In this article, we propose a data processing system to generate space-centric insights about the utilization of an urban residential region of multiple Points of Interests (PoIs) that consists of 190 000 m2real estate. We identify the activeness of each PoI based on the spectral clustering, and then study their corresponding static features, which are composed of transportation, commercial facilities, population density, along with other characteristics. Through the heuristic features inferring, the residential density and commercial facilities are the most significant factors affecting public place utilization.

Singapore University of Technology and Design (SUTD) in Singapore was set up in January 2010 to tackle global challenges of the 21stcentury and beyond. To truly address human-centric issues and problems, a design-focused curriculum using an outside-in approach was adopted in formulating its undergraduate degree programmes in engineering and architecture. This has led to the formation of degree programmes focusing on products, services, systems and built environment which the world needs. To break down the silo mentality and promote multidisciplinary education, SUTD adopts a very forward-looking approach in its academic structure in which majors are offered by four multidisciplinary pillars, supported by two clusters. The absence of conventional colleges, faculties, departments and even divisions encourages seamless and boundaryless collaboration in education and research across all pillars and clusters. With a strong focus on Big-Design (i.e., Big-D) throughout and across courses, semesters and domain areas, undergraduate students are well equipped with skills and attitudes through 20 to 35 team-based Big-D projects during their undergraduate education to provide learning experiences beyond book knowledge, preparing them well with various soft skills such as creativity, critical thinking, teamwork and others before embarking on their career in industry. Coupled with small group-cum-active or peer-to-peer learning in a dedicated cohort classroom environment, the SUTD undergraduate programme further encourages students by having them collaborate with each other across pillars to foster teamwork and a collaborative spirit in formulating and sharing solutions beyond the team. Besides being recognized by the "Global State-of-the-Art in Engineering Education" report commissioned by the MIT School of Engineering [1] as the top of the emerging leader in engineering education recently, the response from industry for the first 5 batches of undergraduates has been very positive.

Smart cities (SCs), as a novel solution built on top of large-scale Internet of Things (IoT) systems, experiences a rapid growth worldwide, in which, a synergetic orchestration among objects, data, and services is emphasized for innovative solutions to elevate the intelligence of cities based on the fusion of multisource and multimodal data. To enable such orchestration, this article proposes a smart service orchestration architecture (SSOA) to coordinate ubiquitous objects, create interlinked data, and implement versatile smart services. As a proof of concept of SSOA, an informed design platform (IDP) is presented to demonstrate how a smart service system can be designed and how a synergetic orchestration can be implemented to support an informed place design. Moreover, two dedicated mechanisms, namely, place utilization analysis mechanism (PUAM) and ensemble-based activity detection mechanism (EADM), are implemented in a multisource data processing flow to illustrate how massive geo-referenced data can be analyzed effectively and efficiently by machine learning algorithms to extract key information for a comprehensive data fusion required in using multimodal IoT systems. As evaluated, PUAM running in a distributed environment can dramatically improve the performance of geospatial clustering about 11 times from the baseline 53.6 to 4.7 s, and EADM with an ensemble activity classifier achieves the highest accuracy about 87.7% and also the highest f-score per activity category. Finally, various insights about the project testbed Jurong East, Singapore, are discussed to reveal its place design context.

The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability.

With the explosion of social networks, people more often share their opinions on-line, which provides a great opportunity to detect the public sentiment of a place in an automatic and timely way comparing to the conventional approaches, e.g., surveys, workshops and interviews. Even through the application of social sentiment analysis is widely discussed in many domains, e.g., politics, e-commerce, economy, and health and environment, to the best of our knowledge, no research has ever studied the effects of public sentiments of social networks in the domain of place design. In order to fill this vacancy, a sentiment analysis service, called geo-sentiment analysis service, is required, whose cores are 1) a social sentiment analysis engine, and 2) an intuitive and interactive visualization service. Thus, this paper firstly proposes CGSA: a Crowd-calibrated Geo-Sentiment Analysis mechanism, which can 1) start the sentiment analysis process based on the design of CTS (Compound Training Samples), and SSF (Social Sentiment Features), 2) perform three analyses, namely sentiment, clustering and time series analysis on geotagged social network messages, and 3) collect crowd-labelled data based on a crowdsourced calibration service to gradually improve the classification accuracy. As proved by two detailed analyses, SSF has the best accuracy in training sentiment classifiers, and the performance of the calibrated classifier increases gradually and significantly from 74.71% to 80.05% in three calibration cycles. Moreover, as a part of a big project “Liveable Places”, “Sentiment in places” service with two visualization modes, namely 2D sentiment dashboard and 3D sentiment map, is implemented to support local authorities, urban designers and city planners better understand the effects of public sentiments regarding place (re)design in the testbed area: Jurong East, Singapore.

As a novel concept, "Informed Design" is being practiced in the Future Cities Laboratory at the Singapore-ETH Centre to innovate place design from empirical to evidential by harnessing geo-referenced "Big Data" for a responsive design. Initially, potentials of people sensing data derived from multi-sources, such as social networks, dedicated applications, sensors, etc., shall be explored to measure place utilization for a better understanding of design contexts and elicitation of design requirements. Therefore, an "Activities in Places" service is required to detect frequently used places, called hot places (HPs), and measure their utilizations in various dimensions. In order to fulfill emerging requirements and properly handle big and heterogeneous geo-located data in a near-real time manner for a responsive design, a unsupervised method, called Scalable "Activities in Places" Analysis Mechanism (SAPAM), is proposed with two main analysis mechanisms, namely 1) a scalable density-based spatial clustering of applications with noise (SDBSCAN), which dramatically improves the performance of DBSCAN through concurrent clusterings on data partitions, 2) a hot place detection procedure (HPDP) to extract HPs from clusters based on a continuous place usage pattern (CPUP), and analyze performed activities through a topic model trained by a corpus of daily documents of places. As proved by a comprehensive evaluation, 1) SDBSCAN, indeed, greatly improves the performance as shown by its best performance 4.71s, which is 11 times faster than DBSCAN, 2) HPDP can precisely detect HPs with a high recall of Singaporean regional centers, main transportation hubs and famous attractions, and 3) the utilization of HPs can be unveiled by three indicators, namely the number of visitors, the size of influence area, and the density of people, and also by performed activities in HPs. As a case study, three top 10 HP lists of three utilization indexes are created, and performed activities in a regional center Jurong East are analyzed.

People sensing data have been successfully utilized in various domains to support a more livable place with on-demand transport system, green environment, profitable economy and interactive governance, however, their potentials in supporting the design of places are not widely studied and explained. As an on-going multidisciplinary project in Singapore, “Livable Places” mins valuable insights from these data through a novel mechanism, called Scalable “Activities in Places” Analysis Mechanism (SAPAM), which conducts three kinds of analyses on their spatial, temporal and textual information respectively to reveal frequently used places, called Hot Places (HP), and to measure their utilization quantitively and qualitatively for a better understanding of design contexts. Accordingly, three analysis mechanisms are designed and implemented, namely 1) a Scalable sPace Clustering Mechanism (SPCM) based on spatial information to cluster geo-referenced data, 2) a Hot Place Detection Mechanism (HPDM) based on temporal information to detect frequently used places, and 3) a Discussing Topic Detection Mechanism (DTDM) based on textual information to explore people's activities in a place. As proved by a comprehensive evaluation, 1) SPCM, which implements a scalable version of DBSCAN, indeed, can dramatically improve the clustering performance from the baseline 53.57s to less than 5s; 2) HPDM can precisely detect HPs with a high recall of Singaporean regional centers, main transportation hubs and famous attractions; and 3) DTDM classifies discussing topics of a given HP with a high precision about 85%. As the project testbed, Jurong East is detailedly investigated, and comparing to other Singaporean regional centers, it is marked as a growing regional center with a prosperous and stable commercial ecosystem.