Data for Amenities Planning

09 March 2020

Amenities are essential in our everyday life. Data collected from various sources are important secondary information to assess and analyse the needs and user patterns of residents. These data quantifies and supports decision in planning the location and size of the amenities to be provided.

However, big data could be problematic because there might be too much data, missed opportunities and concerns over privacy. Datasets might contain data that are irrelevant, incomplete or inaccurate. Urban planners and data analysts needs to filter noise to extract important information from the datasets to make logical urban analysis. As urban planners are only able to analyse problems they know, urban planners needs to be trained to identify problems and opportunities beyond their current knowledge. This could be overcome through experience and discussion with experts of other domains. Furthermore, data might create a ‘blind spot’ that urban planners would overlooked. An amenity could be available but might not be convenient for residents in the vicinity. For example, a residential block might be within 1km from a market; however, residents within the buffer might not be able to walk there due to lack of crossing or terrain constraint. Residents in Tampines could travel to Woodlands by MRT, but most residents would prefer to take bus 168 as it is a direct route with shorter travel time.

I agreed that there is a lack of data on first and last mile connectivity in Singapore, as such data could not be captured through Ez-link transactions or DataSpark. These data would be available if the bike-sharing model took off.

Singapore is not the only city/country that uses data collected from public transport and telco for planning of infrastructures, amenities and services. Seoul uses big data from taxi ride (meter, booking call and apps etc.) and telco to design the route of Night Owl Bus services to address demand and reduce operating cost.[1] The data is also used to analyse commuting and behaviour patterns, which is used to predict demand for other municipal services (e.g. daytime bus route, senior day care opportunities etc.).[2] Seoul government takes measures to encrypt the data and protect the privacy of citizens. Seoul is creating “Seoul Smart Data” (S-data), a storage of public data from over 500 sources that can be utilized to identify and solve urban issues.[3]

Over in Japan, the Kashiwa-no-ha Smart Center is an energy management and monitoring room that monitors the electricity usage of buildings within the entire Kashiwa-no-ha Smart City. The Area Energy Management System (AEMS) and Home Energy Management System (HEMS) are smart power grid that uses artificial intelligence to visualize the consumption of energy for each building and unit respectively. The visualization enables the Smart Center to efficiently optimize the distribution of energy from various local renewable energy producers (e.g. solar panels, storage battery, etc.) and power companies to buildings within the district. The Smart Center will share information on electricity usage with residents to educate them on energy consumption and conservation. The AEMS and HEMS successfully reduces peak consumption by 26%, effectively reduces carbon emissions. The Smart Center also manage electricity sharing during disasters.[4]

In the age of digitalization, data are important source of secondary information that helps urban planners in analysing, planning and solving urban issues. However, it is still important for urban planners to conduct site observations and community engagements to collect primary data. Offline efforts are still essential to ensure that amenities planned are what residents need and want.

References
1. Night Bus (called Owl Bus_: Route Design Using Big Data. (2014, October 14). Retrieved March 9, 2020, from https://seoulsolution.kr/en/content/night-bus-called-owl-bus-route-design-using-big-data.
2. 2019 Transportation Data of Seoul Analyzed through Big Data. (2020, February 18). Retrieved March 9, 2020, from http://english.seoul.go.kr/2019-transportation-data-of-seoul-analyzed-through-big-data.
3. Seoul to set up “S-data”, an integrated storage for all public data. (2019, July 11). Retrieved March 9, 2020, from http://english.seoul.go.kr/seoul-to-set-up-s-data-an-integrated-storage-for-all-public-data.
4. See, B. P. (2019). More than a Smart City: Kashiwa-no-ha is Healthy, Efficient and Innovative. Singapore: Centre for Liveable Cities. Retrieved March 9, 2020, from https://www.clc.gov.sg/docs/default-source/reports/bc-2019-01-more-than-a-smart-city.pdf.

[This is an essay submitted for "DEP5111 Planning Technologies" module on 9 March 2020 for AY2019/2020 Semester 2. "DEP5111 Planning Technologies" is offered by National University of Singapore (NUS) Department of Architecture (DoA) Master of Urban Planning (MUP).]

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