10 minute read
CONTENTS
Residencial Input
Social System
Advertisement
Public Health
Transportation
Green Infrastructure
STATEMENT:
Cities are dynamic and multifaceted entities that offer endless opportunities for exploration and imagining of future possibilities. Traditionally, understanding cities was limited to personal experiences and observations, but in the digital age, data analytics provides a new lens to explore and analyze cities. By leveraging data, we can not only gain new insights into urban environments but also create new possibilities for their future development.
01 Public Health Strategies Of Pandemic
● COVID-19 Dashboard & Trend Analysis of Taipei City
Category: Professional Project, Team Work, 2020-2022
Competent Authority: Department of Health, Taipei City Government
Collaborator: Iima Yu (Data engineer)
Adviser: Roy Lin
Role: Project Lead, Data Analyst
Work: Project management, Dashboard develop, Data Analysis
02 A Comprehensive Analysis Of Urban Mobility
● Decoding the Commute Pattern of Neihu Tech Park
Category: Professional Project, Team Work , 2021
Competent Authority: Department of Transportation, Taipei City Government
Adviser: Roy Lin
Collaborator:Ingrid Kao (Front-End Engineer)
Role: Project Lead, Data Analyst
Work: Policy Research, Data Analysis , Report, web design
03 A Future Of Urban Living
● Home For Tomorrow, New Co-Living Movement
Category: Professional Project, Team Work, 2020
Client: 9floor ,Co-Living Brand
Porject lead: Hao-Che Hung,
Collaborator: Xin-Yi Xie, Yu-Ming Wu, Hao-Lun Hung
Role: designer
Work: Field Work, User Research, Concept Design
04 NATURE-BASED SOLUTIONS
● Transition river, The new urban water management system
Category: Academic Graduation Project, Team Work, 2017
Collaborator: Ting-Chun Wang
Advisers: Chung-Shsun Wu, Pao-Chun Chen
Work: Site analysis, Planning & Spatial Design
05
Other Works
● 5-1 Taipei City Dashboard | Professional Project
● 5-2 Co-creating a future with civic data | Professional Project
● 5-3 Making Taxi Amenities Safer and More Effective | Professional Project
● 5-4 Environmental Embeddedness and Social Characteristics | Academic Research
● 5-5 Editorial design | Side Project
● 5-6 10th CYLA, Gradution Exhibition | Spatia1 Design
01 Public Health Strategies Of Pandemic
COVID-19 Dashboard & Trend Analysis of Taipei City
Category: Professional Project, Team Work
Competent Authority: Department of Health, Taipei City Government
Collaborator: Iima Yu (Data engineer)
Role: Project Lead, Data Analyst
Work: Project management, Dashboard develop, Data Analysis
Introduction
As COVID-19 rapidly spread across the globe in February 2020, monitoring pandemic-related information became a top priority for major cities worldwide. In response to this urgent need, the Taipei Urban Intelligence Center initiated the Taipei City Government COVID-19 Dashboard Project, which implemented an automated system to converge and integrate pandemic-related data. These data are utilized by the Taipei City Pandemic Command Center as a crucial reference during their assessment and decision-making processes.
What Data is Essential for a Pandemic Command Center to Monitor
In terms of overall content, the dashboard is the product of discussions and revisions made during meetings involving experts from the Pandemic Prevention Project Office and the Department of Health, as well as other relevant agencies. It is divided into three main sections: 1. Pandemic spread and vaccine coverage rates globally, 2. Tracking of COVID-19 spread in Taiwan, and 3. Analysis of Taipei's pandemic-related resources and spread. This information allows users to get a comprehensive understanding of the COVID-19 situation from various angles.
Confirmed Cases & Death Rate
Global Vaccination Rate
Effective reproductive number
Degree of Lockdown Severity
Airline Recovery
Confirmed Cases & Death Rate
Taiwan Vaccination Rate
Confirmed Cases Age & Sex Ratio
COVID-19 Community Mobility
Taiwan CDC Crucial Policy
Confirmed Cases Location Alalysis
Confirmed Cases Network Analysis
Hospital Covid-19 Unit Caapacity
Quarantine Hotel Capacity
Rapid Test Capacity
Local government primary Covid-19 healthcare facilities
We utilize Geospatial analysis to set up rapid test stations in hotspot areas.
We have to predict pandemic trends to prepare medical capacity.
How to Effectively Integrate Pandemic Data from Collection to Application
The process of creating a COVID-19 dashboard involves four steps: collecting data from various sources, processing and organizing the data, analyzing it to uncover insights, and using those insights to inform decision-making and build the dashboard. This process allows for effective monitoring and management of the pandemic.
The Evolution of a Prototype into a Comprehensive Dashboard
At the outset, we lacked access to data from relevant units, so we began by designing a UI prototype and demonstrating the dashboard's capabilities using simulated data. Through data integration, we aimed to achieve optimal results. Upon receiving approval from the mayor, we collaborated with public health and medical experts to enhance data completeness and automation.
Comparing with Nations Around the World
We utilizes open data from around the world, including confirmed cases, deaths, fatality rate, and vaccine coverage, to help decisionmakers understand global pandemic trends. There have been four stages of explosive growth globally and in Taiwan, including the onset of the Omicron variant. While Taiwan has avoided major outbreaks, it's crucial to remain vigilant. Vaccination rates are also crucial for pandemic prevention policies, helping determine the degree of regulation loosening for public activities.
Mobile Populations Are Key In Prevention
The domestic outbreak of COVID-19 in Taiwan began in Wanhua District in May 2021 and spread to New Taipei City due to frequent commuting and overlapping activity circles. Stopping the spread relied on a combined overall pandemic prevention strategy, contact-tracing operations, and allocation of medical resources. Successful coordination among these aspects limited the impact of the May 2021 wave. we also uses Google COVID-19 Community Mobility Reports to understand crowdflow changes before and after policy implementation.
Medical Capacity & Chain Of Infection
The dashboard provides real-time data on pandemic prevention indicators in Taipei, including positive cases, home isolation, and resource capacity. This also assists in contact-tracing operations to identify possible super-spreaders and prevent further transmission. By monitoring critical resources like hospital bed capacity, that helps authorities respond promptly to potential medical resource overload. Overall, the dashboard plays a crucial role in mitigating the spread of COVID-19 in Taipei.
Enhancing Pandemic Response With Digital Tools
Integrating data from multiple departments to create a comprehensive dashboard for pandemic prevention is a crucial step in enhancing the efficiency of information synthesis. By making the dashboard accessible through both tablets and large screens, the relevant data can be presented effectively in various meeting venues. Such measures enable better management of pandemic prevention efforts and lead to optimal outcomes.
02 Comprehensive Analysis Of Urban Mobility
Decoding the Commute Pattern of Neihu Tech Park
Category: Professional Project, Team Work
Competent Authority: Department of Transportation, Taipei City Government
Adviser: Roy Lin
Role: Project Lead, Data Analyst
Work: Policy Research, Data Analysis , Report
Introduction
The persistent problem of traffic congestion in Neihu has posed a significant challenge for the Taipei City Government. Despite implementing various solutions, the issue persists due to previous urban development plans being underestimated. In 2021, the Taipei Urban Intelligence Center and Department of Transportation collaborated to conduct a comprehensive study, taking into account surrounding districts and adopting a more holistic approach to planning in an effort to tackle the issue effectively.
Learning More about the Greater Neihu Park
According to traffic flow statistics from 2019, the hotspots of traffic congestions in Neihu are mostly located within the “Greater Neihu Technology Park” (hereafter referred to as “GNTP”) which comprises the Neihu Technology Park and the Dawan South Section Industrial Zone. The GNTP takes up roughly 2.8 km², where the majority of properties are designated for industrial-use designation and some for residential-use.
About Industries
There are over 6,000 businesses established in GNTP, among which are headquarters of large corporations. The park plays a key role in the “Taiwan Tech Industry Axial Belt.” The majority of companies in the park belong to supply chains related to R&D, marketing, service, and knowledge industry chains, as well as businesses in high added-value sectors such as ICT, digital content, and biotech.
About Traffic
Major thoroughfares in the surroundings of GNTP serve the needs of those working there, but they also carry the burden of accommodating traffic traversing the neighborhood. Based on the analysis commissioned by the Department of Transportation, 42.5% of the vehicle flow from neighboring areas into GNTP during commuting hours are heading to destinations inside the park, while 57.5% are ones passing through the park to reach other destinations.
About Population
This study utilizes Telecom data estimate the population characteristics of GNTP. There are a total of 133,500 working at the GNTP. From the graph to the right, we can tell that the majority of the GNTP consists of workplaces and offices, with relatively few properties that are residential mixuses. The majority of workers have to travel to their work at the tech park, requiring relatively longdistance commutes.
In addition, when compared to the built area Taipei City, the working population density of GNTP is approximately 53,400 people/km² – which is roughly 2.5 times the working population of the former.
Where Do the Workers Come from
The working population of GTNP, in addition to those who live in Neihu, come to work from nearby locations such as Shilin, Nangang, and Xizhi districts – these account for roughly 15%. In addition, another 16% of the workers are commuters from downtown districts of Taipei City: Songshan, Daan, Xinyi, and Zhongshan.
Separate Tech park into three Zones
If we divide GTNP into three zones A, B and C notebased on the distance to the MRT stations, we can acquire more detailed readings on where the working population living at different places need to head for their workplace in either northern Neihu or southern Neihu. As zone A is located right next to the Wenhu Line, the working population hail from area spanning both Taipei and New Taipei – even as far as Keelung. For zones B and C, the majority of the working populations here are either Neihu locals or from neighboring districts.
How to Analyze Commute Patterns in Public Transportation
MRT and buses are the most important public transit systems in Taipei. The reasearch used Transit card transaction data and geo-spatial analytics understand that there are about 42.5 thousand passengers taking MRT, buses, or YouBikes to work during morning peak hours. Compared to the total workers of 133.5 thousand, the public tranist utilization rate is merely 30% whereas 70% of the total workers took private vehicles.
Digging Insights into Public Transportation
To increase public transportation usage, we conducted a spatial analysis and identified hotspots for MRT, buses, and YouBike. People traveling to Neihu tend to take the MRT to Xihu, Gangqian, or Songshan stations and then use buses or YouBike to reach their final destination. However, multiple transfers are often required, which discourages some commuters. For example, those traveling from Shilin station may need to transfer at least three times.
About MRT
The majority of MRT commuters can be traced to the surrounding area along MRT Wenhu Line.
The largest number of transferring commuters are coming from Bannan Line, followed by those coming from Songshan-Xindian, and ZhongheLuzhou Lines.
The worker population from Shilin and Beitou districts account for 9.2% of the total worker population, but only a small number of these people take the MRT to work.
About Bus
In addition to taking the bus directly to Neihu Technology Park, a large number of office workers take the MRT and transfer to the bus to get to their destinations. Statistics indicate that MRT Songshan Station is the most popular hub for transfers.
Due to the lack of MRT coverage, nearby areas such as Shilin, Neihu, Songshan, Nangang, Zhongshan, Xinzhuang, and Xizhi districts are hotspots for those commuting by bus.
About YouBike
The hotspots for YouBike utilization are mostly located in the vicinity of MRT stations located near Neihu Technology Park. The majority of commuters ride the YouBike as last mile connection to MRT stations.
MRT Gangqian Station is the most popular station for those transferring via YouBike. Other stations that follow closely behind include Wende, Jiannan Road, Neihu, and Songshan.
Which area's residents have a preference for using Vehicles
To better understand where GNTP workers live and how it relates to their utilization rate of public transportation, we isolated the trips arriving at the MRT stations, bus stations, and YouBike stations within GNTP and categorize them based on passengers' trip origins. Throughout the stats below, we can examine the total number and percentage of commuters using public transit versus private vehicles. Those areas that have high private vehicle usage, shall be the area we'd need to address the most.
Furthermore, the study also interactively demonstrated the utilization rate of public transportation and private vehicles through a spatial grid, making it easier for the public to comprehend.
Zhongshan area, which is adjacent to GNTP, has convenient access to both the metro and bus routes. Most workers who reside in this area choose to use public transportation to commute to work.
Neihu area is the most common residential location for GNTP employees. Despite its close proximity, most people choose to use private transportation such as cars or scooters to commute to work.
Although Xizhi area is adjacent to GNTP, there is only bus transportation available in the area and no metro. As a result, most people choose to drive to work at GNTP. This area can be considered in need of improved public transportation.
What are Some Opportunities to the Ease the Traffic Problems of GNTP
The traffic problem of Neihu Science Park is at large degree constrained by existing urban planning and road network infrastructure, making a solution that fixes everything once-and-for-all would be rather unlikely. However, through the studies of commute patterns, we think there are some opportunities worth further exploration.
Making Public Transportation More Convenient
1. Providing shuttle transfer service for demand hotspots.
2. Increase the convenience for transferring between buses and MRT, such as improving the walkability of paths to stations, optimizing the bus routes and service intervals.
3. Work with private sector partners to offer diversified transportation alternatives such as shared vehicles and ride-sharing.
Providing Better Options for Last Mile Connection
1. Expand the number of YouBike stations at demand hotspots or increase the frequency of bike deployment to enhance usability.
2. Taking advantage of the ongoing construction of MRT Circular Line to establish a more comprehensive slow-mode traffic system including walkways, bike lanes, etc.
Regulating or Easing the Total Volume of Traffic：
1. The new global trend of “hybrid work mode” saw growing popularity in the post-pandemic era. This concept could help reduce the total traffic volume accessing Neihu Technology Park.
2. Some nations had rolled out designated low-carbon-emission urban zones to reduce traffic by imposing a higher cost of private driving, which could also be a possible measure.
