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Urban Informatics for Smart City: A Case Study of Singapore

Table of Contents

1.0      Introduction. 3

2.0      Specific Technologies used in Singapore’ Urban Computing and Urban Sensing. 4

2.1      Specific Technologies for Urban Sensing in Singapore. 5

2.2      Specific Technologies or Models for Urban Computing in Singapore. 6

3.0      Using Existing Technologies and Models to Promote Development of Singapore’s Smart Cities  7

4.0      Suggestions of Technologies and Models for Singapore’s Urban Computing and Urban Sensing  10

5.0      Using Suggested Technologies and Models to Promote Development of Smart Cities. 12

6.0      Conclusion. 13

7.0      References. 15

Urban Informatics for Smart City: A Case Study of Singapore

Introduction

            The global population is rapidly surging. With more people relocating to the urban areas, it is inevitable that the ballooning demographic dynamics will strain the current infrastructure and challenge services delivery. According to Kosowatz (2020), more than half of the world’s population resides in cities. The United Nations (UN) estimates that by mid-century, the number of city dwellers would have increased to 68%. The question, therefore, is how the local governments will accommodate these residents and facilitate their effortless living within the cities. Will the upsurge of people affect movement, provision of services such as sanitation, water, energy, and other basic needs? These questions have prompted increased research into the place of technology in the creation of futuristic cities and countries. Today, more than 102 cities globally are finding new ways of integrating technologies into their infrastructure and programs to ease service delivery and optimize the living standards of the residents. The whole initiative is labelled as smart cities. According to Thales Group (2021), smart cities denote frameworks predominantly dependent on Information and Communication Technologies (ICT) to promote, deploy, and enable development practices that are sustainable and capable of addressing the bourgeoning urbanization challenges.  Kosowatz (2020) shares in these sentiments noting that smart cities are urban areas that predominantly use varieties of embedded electronic sensors and methods to collect, analyze, and disseminate data essential in fostering efficient and effective use and management of resources, services, and assets.

The dependence on technological systems, especially the Internet of Things (IoT), wireless technologies, and cloud computing among other ICT systems has greatly contributed towards the growth of the smart city concept. The latest ranking of smart cities and governments globally identifies cities and countries such as Singapore, London, Dubai, New York, Seoul, Oslo, Copenhagen, Boston, Amsterdam, Hong Kong, and Barcelona (Kosowatz, 2020). Out of these cities, Singapore has been the most lauded for its progressive investment in projects that have significantly transformed the way the government and citizens interact. To acknowledge these efforts, this urban informatics report for smart cities focuses on Singapore. It identifies the specific technologies, models, or methods used in the Singapore for urban computing and urban sensing. The other sections of the report address the question on how existing technologies, models, or methods can be used to promote the development of the selected smart city. Besides, it presents suggestions for Singapore’s urban sensing and urban computing technologies. The last section evaluates how the suggested technologies, models, and methods can be used to promote the development of Singapore’s smart city.

Specific Technologies used in Singapore’ Urban Computing and Urban Sensing

The Republic of Singapore is an island city state located in the maritime Southeast Asia. According to the 2019 census report by the World Bank, the country had a population of 5.705 million people. Singapore is renowned as a wealthy city state and a thriving financial hub. It is often described as one of the economic tigers in Asia. The country thrives on security and stability. Reports by Kosowatz (2020) indicate that urban centers in Singapore have a dense population of 8,000 people in each square kilometer. The country’s demographics are described as aging. As a result, their productivity is waning and for this reason, and the need for sustained security and stability, the government has focused on embracing digital advancements that have elevated Singapore’s position as one of the smartest countries and cities in the globe. In fact, it ranks second with a score of 32.3 compared to London’s 33.5 (Smart City Governments, 2021). Singapore has a Smart Nation Vision which guides its selection of models, methods, and technologies for urban computing and urban sensing.

Specific Technologies for Urban Sensing in Singapore

Singapore’s smart cities relies on urban sensing and urban computing technologies, methods, and models. Evidently, the country ticks all the dimensions of smart cities which are smart mobility, smart people, smart environment, smart living, smart governance, and smart economy (Mondal, Rao & Madria, 2018). Singapore has invested intensively in urban sensing. One of the technologies used in Singapore is optical urban remote sensing. These technologies are managed by agencies such as Singapore Center for Remote Imaging, Sensing, and Processing which works in affiliation with the Urban Redevelopment Authority (URA), National University of Singapore (NUS), and the National Parks (NP) to capture and assess ground truth imagery used in smart urban planning (Sidhu, Pebesma & Wang, 2017). The remote sensing technology is used to capture satellite imagery of earthly phenomena and collect global scale data for big data repositories such as Amazon Cloud and Google Earth engine. The information guides governments land usability studies and also facilitates land cover monitoring. The second technology is photogrammetry for 3D urban mapping. It has been helpful in the creation of the dynamic 3D city models since it automates the generation of 3D models of cities. The application of this technology in Singapore is backed by the LiDAR 3D laser scanning technology which is useful in providing airborne extraction of images of buildings (Sidhu et al. 2017). This system is being embraced to monitor heat use and heat emissions in cities. Apart from these technologies, Singapore is using ground based LiDAR for mobile mapping of spaces.

Specific Technologies or Models for Urban Computing in Singapore

Singapore has embraced powerful computational methods aided by edge and cloud computing technologies. The operability of these technologies are backed by urban models, especially the population-level human mobility model. They collectively help in knowledge discovery and spatial data mining. According to the US National Institute of Standards and Technology (NIST), cloud computing refers to the on-demand availability of system resources such as computing power and data storage without the need for direct ownership or active management of the systems by the end users (Sidhu et al., 2017). The term generally denotes the availability of data centers for multiple users over internet protocols. The cloud computing model enables convenient, ubiquitous, on-demand access to pooled configurable computing resources that can be accessed from any location at any time through an internet network (Mell & Grance, 2011).  The service models used in Singapore include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS models include operating systems in smartphones and other digital devices. The PaaS models include Google app engine used to deploy acquired applications and consumer-created applications to cloud infrastructure. IaaS models are used in Singapore to support high-performance computations.

The other key technologies for urban computing used to enable smart operations in Singapore include web technologies namely the web servers and web client, proxies, routers, gateways, and cache services (Singh & Chatterjee, 2017). These technologies enabled internet technologies and broadband network as well as data center technologies and multi-tenant technology. These technologies are interoperable with deployment models namely private cloud, community cloud, public cloud, and hybrid cloud where the private clouds are used by companies especially in the financial services sectors, public clouds are used for managing public infrastructure such as universities and corporations while community cloud are useful to the government departments. Hybrid clouds mostly handle sensitive and general data shared across the different entities (Sunyaev, 2020). The use of these technologies and models has proven cost effective, high quality and supportive of many core capabilities. On the contrary, some of the issues with cloud computing technologies include possibility of data loss and data breach, data scavenging, and insecure interfaces as well as shared technology vulnerabilities.

Singapore uses edge computing. Shi, Cao and Zhang (2016) refer to edge computing as technologies that allow computing to be performed at the edges of networks, especially on downstream and upstream data on behalf of cloud services and IoT services respectively. They include gateways installed in smart homes to facilitate computing offload, data caching and storage, data processing, request distribution and service delivery. Edge computing supports most of the dimensions of smart cities such as smart medicine where phones and wearable devices are used to provide medical alerts in real time (Singleton, Spielman & Floch, 2018). The technology also supports smart policing dimension as it supports real time video analytics on suspected criminal activities in the Singapore cities. These technologies work collaboratively with the population-level human mobility models to estimate human population and traffic flow across different places (Zheng et al., 2019). The model works by constructing origin-destination (OD) matrix which helps in mapping people and their movement.

Using Existing Technologies and Models to Promote Development of Singapore’s Smart Cities

Singapore uses urban computing and urban sensing technologies and models to manage all of its dimensions of smart cities. For instance, it uses smart sensors interconnected to aggregation boxes for digital collection of information and data. The country collects most of the data on pedestrian activities and traffic volume using population-level human mobility modelling aided by the construction of OD matrices to locate people and predict their movement around cities (Arasaratnam, 2011). The operations of these technologies are aided by cloud and edge computing. The integration of private cloud, public cloud, community cloud and hybrid clouds facilitates storage and dissemination of data. The information is then disseminated to various agencies for analysis. The resultant findings are used for decision making on how to optimize service delivery. The whole process is facilitated by the availability of broadband access to 95% of the households in the cities and the country as a whole (Kosowatz, 2020). Broadband access interconnects with open sourcing to share the information with the citizens as well as the private sectors thus leveraging data for business and personal use.

Currently, Singapore is working with the National Research Foundation to develop a Virtual Singapore which is a dynamic model of a 3D city with platforms for data collaboration. The initiative has invited private and public firms to develop tools required in testing the concepts. Yang (2011) speculates that the government will prefer its 3D urban mapping to be done using the LiDAR 3D laser scanning technology to provide airborne images of buildings. Currently, the system and technologies are used in monitoring heat use and heat emissions in cities. Apart from these technologies, Singapore is using ground based LiDAR for mobile mapping of spaces. The process involves simulating crowd dispersions from sporting venues. Additionally, since 80% of the Singaporeans reside in public housing, the public agencies are collaborating with private entities to embrace smart home technologies essential in efficient management of home energy, water management, and monitoring of the elderly.

Some of the progress made in the housing sector across the cities include embedding frameworks for smart mobility, healthcare, security, water, energy, smart community and engagement, smart housing and economic development, and smart waste management. Singapore has already laid the first layer for smart cities which entails the installation of critical mass sensors integrated into high-speed communication networks. Another important part of this layer is the availability of smartphones. Kosowatz (2020) notes that the second layer of smart cities in Singapore involves introducing specific applications that ensure constant streaming of raw information and data. The real time sharing of this information contributes towards synchronal alerts, actions, and insights.  The third layer of converting the country and its cities into smart cities is buying-in the participation of the public. Singapore has moved beyond this stage as most of its citizens support Smart Nation Vision. For instance, the pedestrians and drivers allow applications to guide their motoring and mobility by following alerts on better travel routes at a moment’s notice (Kosowatz, 2020). Their willingness to accept smart mobility systems has speed up traffic for everyone in the cities thus preventing congestion.

Other applications that have proven useful in the smarting of Singapore include crime mapping applications which help in policing, telemedicine applications enable instantaneous access to health practitioners, digital waste tracking receptacles communicate with the waste management agencies when the waste cans are full. Open databases ease information sharing and access while mobile connection platforms provide on the go battery charging and internet access for personal devices. One major element of smart city initiatives in Singapore is that these technologies are being adopted into the city-states building projects and signature infrastructure. They are embracing the Internet of Things (IoT) to collect data and analyze solar penetration and wind flow which guides the design of new buildings and shared spaces in the cities and around the country. By 2022, the Singaporean government intends to implement energy efficient lighting and intelligent systems on all the public roads. Furthermore, there are ongoing projects to install solar panels on the rooftops of more than 6,000 buildings.

Suggestions of Technologies and Models for Singapore’s Urban Computing and Urban Sensing

Looking into the future, it is important that Singapore embraces urban informatics systems that are scalable and adaptable. The process demands the use of new approaches to managing urban computing and urban sensing. In regard to urban sensing, the latest developments encase the use of cellphones in tracking users and their activities thus making predictions on how to direct their mobility. Yang (2011) refers to this concept as social sensing, which focuses on more accurate collection of information to ease human mobility and enhance human interactions. Examples of technologies that will be capitalized in improving this suggested process is the use of Global Position Systems (GPS) applications on accelerometers, location sensors, and mobile devices to track vehicular and human traffic (Aggarwal & Abdelzaher, 2013). The data collected can be used together with trajectory mining techniques to guide social patterns. As much as this suggestion is viable in improving human mobility and interactions, its main disadvantage is that it creates privacy concerns and could further expose the users to cyber security threats.

The second suggestion that could improve urban sensing is by increasing the urban big data infrastructure through 3D city modelling. Even though Singapore is working on 3D modelling of its cities and towns, it is important that the developers create the models in five levels to capture more details (Kolbe & Donaubauer, 2021). It is important that Singapore moves away from the use of building information modelling and instead use the semantic 3D city modelling which is more robust and reliable in supporting urban sensing. The third suggestion that could be adopted by Singapore is the use of Artificial Intelligence (AI) and deep learning for aspects of landslide mapping and congestion prediction (Shi et al. (2020). Wang et al. (2016) add to this suggestion noting that Singapore could achieve higher levels of pollution management by using advanced urban sensing that identifies the normal routes used by most traffic and pedestrians and instead, propose healthy routes that generate lower environmental pollution in the cities (Zheng et al., 2019). Likewise, the systems could be used to improve the use of urban energy. In fact, integrating AI and deep learning to the sustainable energy systems being introduced in Singapore could significantly reduce urban energy consumption.

Apart from these techniques and models, Liu et al. (2019) add that Singapore could benefit from ambient sensing through the use of strategically located and dedicated cameras for street views. The cameras can be mounted on cars, embedded into smart phones and smart lampposts. Ambient sensing measures the ratio of urban blue and green spaces. The results are important in mitigating mental illnesses as weighted against the self-rated Geriatric Depression Scale (Helbich et al. 2019). Also, social sensing could be useful in the realization of active social sensing. The process requires collaborative mapping of devices. Social sensing could further be enhanced by use of typical passive social sensing through smartphone mapping and tracking of navigation systems and other location based services including social media check-ins.

Regarding urban computing, there is a need for Singapore to capitalize and explore the advantages presented by spatial knowledge discovery and data mining. The transition means that the country adopts the spatial association rule mining (SARM) to measure housing pricing and how best to regulate the housing sector. SARM considers two rules when measuring the house prices, namely the rule of proximity to water and the age of the building. It further measures the interestingness of the neighborhood to ascertain that the housing prices are determined accurately. Liu et al. (2019) lament that as much as SARMS will help solve the challenge with pricing public and private houses, the model has issues such as inability to handle uncertainty. It is also vulnerable to vague spatial concepts, uncertain data discretization, and data noises which could affect the accuracy of pricing houses by the smart systems. Liu et al. (2019) add that SPARM can also be useful in pricing hotel rooms for instance, it could use algorithms such as nearness to certain features could affect the price of a hotel room. Surprisingly, Zheng et al. (2019) note that sequential pattern mining (SPM) could be used for predicting traffic trajectories. The data computed can be used for various applications.

Using Suggested Technologies and Models to Promote Development of Smart Cities

The usefulness of urban informatics is determined by the effectiveness with which the urban informatics are used. The main approaches influencing urban informatics include urban sciences, urban applications and systems, urban sensing, urban big data infrastructure, and urban computing. All these approaches are required to work together in promoting the wholesome development of smart cities. Focusing on one approach at the expense of the others only limits the usefulness of smart city informatics. In the case of Singapore, using all these identified approaches could contribute towards higher returns on investments from smart city initiatives. However, this report only focuses on urban sensing and urban computing. Liu et al. (2019) explain that the suggestions made on urban sensing and urban computing will enrich Singapore’s smart cities in various ways.

First, the application of Kontokosta (2018) to new methods and existing methods of urban sensing will ease urban planning and align smart city development to the goals and objectives of the government and its citizens. As highlighted in the previous section suggesting urban sensing technologies for Singapore, it is notable that social sensing will be support the development of Singapore’s smart cities in a number of ways. Social sensing using volunteered geographical information (VGI) (Longley et al., 2015). VGI ensures that data is collected form a large number of citizens who voluntarily shared on the available knowledge sharing systems. The process ensures that most people have accurate geographic information that enable them to better plan their day and activities. The features of VGI that enable it to share geographic information cheaply unlike the professional maps is that it allows for voluntary sharing of private information with the public on the state of the climate and other geographic systems (Crooks et al. 2019).  The resultant data promotes development of smart cities such as encompasses elements such as positional accuracy, geometric accuracy, and topological consistency required to guide urban mobility.

The use of urban computing, especially SARM, is helpful in the development of smart cities in Singapore since it enhances land use and directs the government and individuals on how to make socioeconomic changes (Liu et al., 2019). The systems are used in setting land use rules. For instance, it could help identify polluted areas and uncertain regions which can then be reclaimed or improved to enhance the quality of life of the urban dwellers. SARM can be used together with other technologies such as GPS trajectories and check-in trajectories to extract data and information. This particular activity analyzes trip flow of individuals between shops and restaurants among other places (Liu et al., 2019). The information is important in the development of smart cities since it guides investment decisions.

Conclusion

This report answers three main questions regarding urban informatics, particularly urban sensing and urban computing. The report has discussed the technologies and models used in urban computing and sensing in Singapore. The country uses optical urban remote sensing which collects data used to improve smart mobility, smart people, smart environment, smart living, smart governance, and smart economy. On the other hand, the country’s urban computation uses powerful computational methods aided by edge and cloud computing technologies together with population-level human mobility model to empower knowledge discovery and data mining. Together with the cloud system, Singapore uses IaaS, SaaS, and PaaS). Other technologies for computing used in Singapore include; web technologies namely web client, servers, proxies, routers, gateways, and cache services.

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