Overview of techniques Part 1

1. Overview of techniques

1.1 Introduction

This year MUDP 2020 Values in Urban Design course, introduced variety of research techniques that were explored to help advance the knowledge of research techniques about urban design in the MUD Programme. These techniques are adaptable and flexible enough to be used for a wide range of research question about urban design. The techniques include well know analysis (environmental urban physics – solar, winds, thermal comfort, energy consumption) and emerging techniques such as 2D and 3D spatial design network analyst (updating space syntax to 3D), social media sentiment analysis, and image segmentation.

The aims are not to teach student coding but to empower the student with tools. As any tool, there is the risk that hammer only see the world as nail.

The intention of this compendium is to offer an overview of the technical capacity that was built through the course across the cohort. The compendium also provide detailed steps and processes of how to undertake different research with certain techniques. This compendium is for MUD teachers and supervisors to get an overview of the developed techniques, as well as enable any MUD student to use different techniques whilst undertaking their own research and/or dissertation. This compendium compiles together all the group work undertaken taken by students during MUDP2020 course. As part of the core group work, it was essential to develop a robust methodology for each of the techniques that was then explored individually The methodology are inclusive of software/platforms to use, data types and data manipulation, how to carry out certain analyses, visualisation, data quantification, limitation of the technique, alternative strategies etc. The intention of the group work was to create methodological know how across the class.

In principle it would enable any other student to understand a certain technique without more external research, tapping on the compendium and the know-how of the class.

MUDP2020 Values in Urban Design

Usefulness

For future MUD teachers/supervisors

• To gauge an understanding of the technical capacity that was built through the course.

• Demonstration of skills and knowledge gained by students

• Relevance of skills and techniques to urban research

For MUD students

• To achieve a step-by-step understanding of how to do different techniques.

• Developing a relational understanding between individual research and techniques.

• Expand knowledge through undertaking different techniques and develop research about urban design.

This section aims to highlight key findings and takeaways from each of the techniques.

Theme 1A: Macro-meso

Scale Network Science

Through the analysis of several existing and future MTR route cases and exiting road network, it can be found that the increase and adjustment of routes line will have a certain impact on regional accessibility, which has the certain impact on residents, jobs, workplaces, CBD, etc.

Metro network 2022 and 2031

• Existing metro network doesn’t have good potential flow in New Territories and Lantau.

• More loops can increase potential flow greatly.

• After connected with Shenzhen metro network, HK CBD has higher potential flow than Shenzhen CBD, because lines in the former one are denser and loops are more.

• As travel distance increases, New Territories will have more potential flow.

Road network

• The existing roads in Hong Kong have high potential traffic on the north side of Hong Kong Island and the south side of Kowloon District, while the roads with high value in Shenzhen are mainly located in Futian and Nanshan District on the south side.

• Increasing the radius can greatly increase the number of roads with high flow potential.

• When the roads in Hong Kong and Shenzhen are connected, it is clear that Hong Kong is more likely to form a high flow potential area because of the higher road density. And as the radius value increases, the flow potential of the road connecting Hong Kong and Shenzhen increases significantly.

• Within the Great Bay Area, there are high flow potential areas across cities, and as the radius increases, there is a tendency to form a highly connected super-city cluster along the Pearl River.

Theme 1B: Parametric urban design on topography

Difference from parametric design on flat ground

• Unlike designs on flat land, designers should consider the many influencing factors contained in the mountain into the design process, such as topography, landform, water distribution, climatic conditions, etc. This process will be more complex than parametric design on flat ground.

• The absolute elevation of the terrain affects the intensity of the city receiving solar radiation, and the relative slope and aspect of the terrain can change the shadow length of the building on it, the building located on the south slope, the shadow becomes shorter and the building on the north slope, the shadow becomes longer, the slope is larger, the more obvious the change.

Advantages and limitations of parametric tools

• Parametric tools are applied to the simulation and research of related urban design. But this does not mean that parametric tools can create a perfect system to predict people’s behavior in urban design. Intangible factors such as cultural background, social development and economic impact are rarely involved and considered in the parameterization process.

• Parameterization can liberate productivity. Parametric analysis is applicable to various types of terrain and sites. It can deepen the understanding and understanding of the site through early analysis and guide the later site design. The use of parameterization in UD project is more conducive to a basic understanding of this large project site, which are suitable for construction areas and which can be used as environmental protection areas, which can provide designers with early design entry points.

• As students majoring in design, we think rhino-grasshopper workflow is relatively more operable and does not need programming background knowledge like python. Grasshopper battery pack has written a lot of operation commands, which only needs to connect the battery according to logic, which is very convenient. At the same time, grasshopper can also use programming language to assist design, which is a more powerful platform.

Theme 2: Value evaluation in existing areas

Looking for values in existing areas

• The assessment of existing values will allow us to gain a deeper understanding of the impact of urban design on urban life and its inhabitants, which will guide us to further complete our personal interest in specific aspects and explore the relationship between urban design and values.

• The assessment of existing sites allows the identification of existing urban problems and conflicts and the identification of directions for optimising the urban environment. By thinking about broader ‘urban design value’ in this way e as co-constructed between place and its stakeholders e we can also better understand the influence of the urban designer in the process of creating value in places. (Chiaradia et al., 2017)

• The use of existing site assessments can be of great help in many urban research topics. For example, urban form, street connectivity, area accessibility, block density, urban vitality, etc.

Conceptualising values

• Value as Net benefit or Value in Exchange : The potential of real estate to create investment returns generates exchange value, which is pursued by people who are interested in these returns. Homeowners, real estate brokers, landlords, developers, and financial institutions are all interested in the exchange value of land.

• Value as meaningful difference or Value in Use: Use value is the value of a property for a given purpose or to a certain user, indicating the property’s contribution to the enterprise’s utility or profitability.”

• Value as moral or social principles or Value in Common: Value in common generally refers to the common interests between partners. In urban design, it generally refers to the common value created by land developers and landowners and land users to serve everyone.

Theme 3: Social media data in urban research

Use of SVIs

• SVI has already been used to some extent in the field of urban street research. The use of computer technology can help people to process a huge number of street images. Green street coverage, the visual quality of street space, green visibility and even the impact of the natural environment on mental health all benefit from SVI.

• With the development of computer technology and the introduction of a variety of machine learning algorithms, it has become possible to use deep convolutional neural network architecture to line-of-sight accurate deep bottom processing of street view images and to effectively identify multiple elements such as sky, pavement, lanes, buildings and greenery.

Limitations and further studies

• The data source is limited by the fact that the streetscape data is captured on the driveway, while the public spaces that people use on a daily basis are mostly combined with pedestrian spaces, so there are deviations between the viewpoints and the actual viewpoints.

• In addition to the data about street characteristics obtained through SVI segmentation, we also need to obtain other spatial data about urban pollution, urban noise and other urban environmental burdens.

• By combining remote sensing and proximity sensing, it can help people to better understand the shape and quality of cities.

Limitations and further studies

• Different ways of using social media data explored in each individual process highlight the fact that this type of analysis needs to be conducted along with other research methods, such as network analysis, population character, building typology, and more.

• A major challenge of text based social media analysis is related to human versus machine (software) reading analysis. Complications occur throughout the whole process, from the very beginning. For instance, some reviews obtained from Google Maps, were directly translated by Google, mainly from Cantonese or Mandarin into English.

• More intelligent NLP (Natural Language Processing) software is needed for researchers to use. In manually filtering Google Maps comments, we will find many problems, and each problem needs a separate script to identify. This also happened on Weibo, a widely used social platform in China Because many people can make profits by sending irrelevant information, the automatic shielding system often fails to shield some hidden irrelevant information or shield relevant information. Malicious users are an important issue in social media.

• Before starting the research, pre-processing of text should be carefully considered as well, for instance with text normalization, getting rid of stop words, or repetitive words, etc. – the system might clean the text in a way that important words get omitted which would otherwise influence the analysis result (e.g., sentiment of aspect-qualifier categorization).

• Standards for social media data need to be established. This standard does not need to be particularly strict, because there is a large gap between users of different social media themes. However, if standards are not set, the research on a single social media cannot be applied to other social media. Cross social media research is needed to find the operation logic of different social media, so as to formulate a wide range of standards applicable to different social media.

• Image segmentation analyses do not yet offer a highly accurate identification of different elements, resulting in less accurate statistics of the final image data and correlational analysis.

1.3 Techniques explored by each student in MUDP 2020

MUDP1003 Supervisor and Students

Casey Wong

Bian Ming

Cheng Zixuan

Gao Jinyuan

Li Yifan

Zhu Jiaxuan

MUDP2020 Technique epxlored

Theme 3 Social Media in Urban research

Theme 1B Parametric UD on topography

Theme 3 Social Media in Urban research

Theme 3 Social Media in Urban research

Theme 3 Social Media in Urban research

MUDP1003 Supervisor and Students

Jason Hilgefort

Wei Yumeng

Xiao Di

Zhang Mengdi

Zhao Jixuan

Zhou Ning

MUDP1003 Supervisor and Students

Laurence Liauw

Chen Mengdi

Kong Fanding

Lu Yilin

MUDP2020 Technique epxlored

Theme 3 Social Media in Urban research

Theme 4 Streetview Image in Urban research

Theme 3 Social Media in Urban research

Theme 2 Value evaluation in existing areas

Theme 2 Value evaluation in existing areas

MUDP2020 Technique epxlored

Theme 4 Streetview Image in Urban research

Theme 4 Streetview Image in Urban research

Theme 3 Social Media in Urban research

MUDP1003 Supervisor and Students

Massimiliano Dappero

Wang Yangxi Xi Ni

Xiao Yiyun

MUDP1003 Supervisor and Students

Sid Khakhar

Ahmed Sazdik

Li Shiyu

MUDP1003 Supervisor and Students

Sunnie Lau

Cui Yuechen

Lin Zhuowei

Shi Yongli

Wen Jing

Yu Chenfei

MUDP1003 Supervisor and Students

Sunny Choi

Wang Xinran

Wu Yingxian

MUDP2020 Technique epxlored

Theme 2 Value evaluation in existing areas

Theme 1A Macro-Meso Network Science

Theme 2 Value evaluation in existing areas

MUDP2020 Technique epxlored

Theme 2 Value evaluation in existing areas

Theme 3 Social Media in Urban research

MUDP2020 Technique epxlored

Theme 1B Parametric UD on topography

Theme 4 Streetview Image in Urban research

Theme 1A Macro-Meso Network Science

Theme 4 Streetview Image in Urban research

Theme 1A Macro-Meso Network Science

MUDP2020 Technique epxlored

Theme 1B Parametric UD on topography

Theme 4 Streetview Image in Urban research

MUDP1003 Supervisor and Students

Vera Kleesattel

Liu Weiran

Tundokova Reka

Yin Yue

MUDP1003 Supervisor and Students

Alain Chiaradia

Fan Zitian

Yang Yuan

Yu Xuechun

Zhang Jin

MUDP2020 Technique epxlored

Theme 4 Streetview Image in Urban research

Theme 3 Social Media in Urban research

Theme 4 Streetview Image in Urban research

MUDP2020 Technique epxlored

Theme 1A Macro-Meso Network Science

Theme 2 Value evaluation in existing areas

Theme 2 Value evaluation in existing areas

Theme 1B Parametric UD on topography

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Theme 1A: Macro-meso scale network science

Theme 1B: Parametric urban design on topography

• Zhang, Y & Liu,C., 2021. Parametric Urbanism and Environment Optimization Toward a Quality Environmental Urban Morphology. International journal of Environmental research and Public health.

• Chen, Y et al., 2021. From Separation to Incorporation. A full circle application of computational approaches to performance-based architectural design. Proceedings of the 2021 Digital Futures.

• Osintseva, L et al., 2020. Automated Parametric Building Volume Generation: A Case Study of Urban Blocks. SimAUD 2020.

• Beriao, J, Arrobas P, Duarte, J., Parametric Urban Design: Joining morphology and urban indicators in a single interactive model. City Modelling - Volume 1 eCAADe30

• Whitaker, C, Pniewski, M, Harding, J., Parametric Massing for Effective City Design. Ramboll 3D Reid.

• Wilson, L., 2019. How to generate a thousand master plans: A framework for computational urban design. SimAUD2019

Theme 2: Value Evaluation in Existing Areas

• Chiaradia, A. Waters, F. 2020. The Creative Transformation of Island East and Development of Taikoo Place. Places Imact Report, Swire Properties

• Sieh, L, Chiaradia, A, Jones, S. 2021. Urban Design Research in Practice: The Value Gradient Map. 2021. Urban Design Group Journal.

• Chiaradia, A, Jones, S, Sieh, L. 2021: TOD, Towards a value assessment tool: A multi-scale value gradient map.

Theme 3: Social media Data in Urban Research

• Mayr, P & Weller, K.. Think before you collect. Setting up a data collection approach for social media studies.

• Ilieva, R, McPhearson, T. Oct 2018. Social-media data for urban sustainability. Nature Sustainability Volume 1.

• Marti P et al., 2019. Social Media Data: Challenges, Opportunities, and Limitations in Urban Studies. Computers, Environment and Urban Systems 74 (2019) 161-174

• Chen T et al., 2019. Identifying Urban Spatial Structure and Urban Vibrancy in Highly Dense Cities using Geo-referenced Social Media Data. Habitat International 89 (2019) 102005

• Sim, J, Miller, P, Swarup, S., Oct 2020. Tweeting the High Line Life: A Social Media Lens on Urban Green Spaces. Sustainability 2020 12, 8895;

Theme 4: Street-view Imagery (SVIs) in Urban Research

• Cinnamon, J.; Jahiu, L. Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations. ISPRS Int. J. Geo-Inf. 2021, 10, 471. https:// doi.org/10.3390/ijgi10070471

• Shen Q et al., 2017. StreetVizor: Visual Exploration of Human-scale urban forms based on street views. IEEE Transactions on visualisation and computer graphics.

• Yuhao Kang, Fan Zhang, Song Gao, Hui Lin & Yu Liu (2020) A review of urban physical environment sensing using street view imagery in public health studies, Annals of GIS, 26:3, 261-275, DOI: 10.1080/19475683.2020.1791954

1.5 Snapshot of methodology and process

Theme 1A: Macro-meso scale network science

In general, QGIS, ArcGIS pro, ArcGIS and rhinoceros can complete network analysis, but after our test, only rhinoceros can complete the analysis.

Issues with QGIS

You can refer to the QGIS handout: SPATIAL DESIGN NETWORK ANALYSIS sDNA in QGIS, but in the preparation stage, there will be problems. These may be caused by the installation path which is not the original path, or the path appears in Chinese, or the Chinese software installation package.

Issues with ArcGIS

Pirated software may have problems loading the toolbox (Fig 2), and the official software will prompt errors when running. These may be caused by the installation path which is not the original path, or the path appears in Chinese, or the Chinese software installation package.

Some solutions can refer to the following Web links: https://mp.weixin.qq.com/s/GuI4hjF1-kudboNrMiCLug https://mp.weixin.qq.com/s/RJvS4JvWA22jvflppvKC3g

Network at Interchange Stations

Make sure there is a short line connection at the transfer station, as shown in the figure below,there is only one station, but the transfer should be reached by walking, so each crossing line needs stub for connection.

The software used in this design study is Rhino and Grasshopper. Rhino is a 3D modeling software that is widely used in industrial design, architectural design and urban design. Grasshopper is a parametric built-in plugin for the Rhino platform. Its advantages are fast interaction efficiency with rhino modeling platform and user-friendly programming, its limitation is that it requires a certain grasshopper programming foundation.

ArcGIS can also be used to perform this series of analyses and calculations. ArcGIS has powerful geographic data management and analysis capabilities, but the disadvantage is that ArcGIS has a lower degree of freedom. For designers, Grasshopper allows designers to modify analysis algorithms and drawing methods, and Grasshopper is more efficient and intuitive for 3D analysis.

Used software: ArcGIS pro-Population density, Floor area ratio, Open space ratio, Ground space index, Block Size city scale, Building height, Road space, POI. sDNA-analyze the accessibility of 3D pedestrian network. Grasshopper- Accessible density, Area density, Vehicle network analysis, Pedestrian network analysis. Google map- Movement and place analysis There also have other software applications that can be used for urban design analysis. For example, in term of networks analysis, we use the ArcGIS to analysis Network density and sDNA to analysis the accessibility and permeability of network.

There are also other techniques could be used in analysis spatial network, for instance previous technique Space Syntax developed by UCL (1970s), and GIS also could analysis the network. Urban Network Analysis (UNA) developed by MIT is also useful, unlike previous software tools that operate with two network elements (nodes and edges), the UNA tools include a third network element - buildings - which are used as the spatial units of analysis for all measures. Hence, when considering the impact of typology of building on the network accessibility in a super block context TSW, there are some new findings we could explore.

In terms of social media data, manual extraction of less data can be achieved. But when researchers want to get enough data, machines are often more suitable. There are three ways to extract data: manual, API Wrappers like Stevesie, data extraction software like octopus.

Manually extracting data can achieve the extraction of less data, but it encounters difficulties when faced with a large amount of data. Python/coding, API Wrappers and data extraction software can all extract large amounts of data. But python/coding requires a certain programming foundation and is not suitable for everyone. So we explored the other two directions. Both API Wrappers and data extraction software can obtain more comprehensive data, and API Wrappers are easier to use, so we finally chose Stevesie as the first choice, and the free data extraction software Octopus as another option.

The most open and easily accessible street view resources should be Google Street View. In the Google Street View interface people are able to change their position and rotate their visual field freely to simulate the real feeling of street view. However, Google Street View is web-based and unable to be downloaded and processed directly.

The group chooses to use Street View Download 360 Pro, an app for downloading 360° panoramas (example see Figure 4 2) from Google Street View, to extract the necessary images. With the area download tool it is easy to get all panoramas within an area during a certain period at one time. Moreover, the exif metadata are added to the image such as geolocation, elevation, date taken, camera rotation, etc. This may facilitate some future analysis.

The basic concept of semantic segmentation is to label each point of the target category on the image according to the "semantics", so that different kinds of things can be distinguished on the image, which can be understood as the classification task at the pixel level (Xiaobaixueshijue, 2021).

2. Macro-Meso MTR & Road Network Science Analytics