Macro-Meso MTR & Road Network Science Analytics 1A

Shi Yongli, Xi Ni, Fan Zitian, Yu Chenfei

For further details please get in touch with:

• Shi Yongli (u3589419@connect.hku.hk)

• Xi Ni (u3589461@connect.hku.hk)

• Fan Zitian (zitian@connect.hku.hk)

• Yu Chenfei (ycf1006@connect.hku.hk)

• Alain Chiaradia (alainjfc@hku.hk)

Related handouts:

Network Analysis using Rhino-GH-DST step-by-step guide https://1drv.ms/u/s!AvexlkjTjldzj5t5lrFZmSqUzesx9Q?e=HUE5iN

Network Analysis using sDNA-ArcGIS Pro step-by-step guide https://1drv.ms/u/s!AvexlkjTjldzj5t8c3ehByuYWyS1tg?e=iAGEY7 https://sdna.cardiff.ac.uk/sdna/wp-content/downloads/documentation/manual/sDNA_manual_v3_4_5/

Network Analysis using sDNA-QGIS step-by-step guide https://1drv.ms/b/s!AvexlkjTjldzj5t9BUanqFh6EfCe2w?e=aCoFDZ

DeCodingSpaces (DST) Toolbox https://toolbox.decodingspaces.net/

2.1 Introduction and Background

The Group Work (GW) report spirit is to produce a practical that anyone could use and follow to undertake network sciences data collection and generation techniques to answer research questions that are of the types outlined in the research background section. The practical need to be tested as follow: if I follow it step by step, can I achieve the same results. Are all necessary information included to do replicate the practical?

The second important learning objective is to demonstrate an understanding and use of the techniques. The GW is focusing on exploring baseline and policy options spatial model creation, processing, and display with descriptions of results.

Within a background of existing research, research question and results, the GW aims to accomplish the two goals outlined above. The GW report is to demonstrate and report on step-by-step use of research techniques,

• data collection (policy and map, road network...)

• spatial model creation (mapping MTR...)

• data preparation

• analysis procedures

• comparable display, description of the results.

• awareness of limitations of the study

The Individual Work (IW) report aim to explore and answer specific question and includes baseline and individual option explorations, as necessary, descriptions, and interpretation of results.

As an alternative, IW could also undertake network design explorations of idealized city networks MTR and/or Road – i.e., without particular site yet at a sensible spatial scale (e.g., distance between station, etc.)

2.2 Summary of extant theory related to macro-meso network analysis

This pilot study suggest that network configurational analysis can lead to a better understanding of the influence on the level of all day movement of the configuration of a public transport network.

Chiaradia, A., Moreau, E. and Raford, N., 2005, June. Configurational exploration of public transport movement networks: a case study, the London Underground. In 5th International Space Syntax Symposium (pp. 541-552).

Network resilience is becoming more and more important with increased needs for transport and future mobility on one side and climate changes on the other.

Bešinović, N., 2020. Resilience in railway transport systems: a literature review and research agenda. Transport Reviews, 40(4), pp.457-478.

The results indicated that the network accessibility of rail lines had a statistically significant capitalisation effect on property prices that varied across different submarkets.

He, S.Y., 2020. Regional impact of rail network accessibility on residential property price: Modelling spatial heterogeneous capitalisation effects in Hong Kong. Transportation Research Part A: Policy and Practice, 135, pp.244-263.

The study of the temporal evolution of the structure of the world’s largest subway networks shows that, remarkably, all these networks converge to a shape which shares similar generic features despite their geographic and economic differences – if transport planning use similar modeling techniques this similarity should be expected.

Roth, C., Kang, S.M., Batty, M. and Barthelemy, M., 2012. A long-time limit for world subway networks. Journal of The Royal Society Interface, 9(75), pp.2540-2550.

Partial improvements of the trans-European road network at the mid-term scenario will have limited effect on the overall accessibility, while the long-term completion of the trans-European network combined with the accession of candidate and potential candidate member states will enhance significantly the inter-regional accessibility within South East Europe and thus the prospect of regional development and convergence with the European Union.

Gavanas, N. and Pavlidou, N., 2011. The impact of the trans-European road network and the process of enlargement on regional accessibility in South East Europe. In Proc., 51st European Regional Science Association Conference, Barcelona, Spain (p. 20).

Key findings from existing and future MTR and Road Networks

Networks are pervasive. They characterise the movements of people, goods and information. These transportation networks are codified, designed, implemented, maintained, and modified continuously. In recent decades, scholars in diverse domains, including mathematics, physics, computer science, sociology, urban planning and design, biology, etc. have extensively investigated all kind of networks. This has resulted in a new research field of network theory or a “science of network” (Newman, et al., 2006) also called complexity sciences. Among these domains a shared key research question is: “how the presence and [morphology] of networks affect the way that events play out.” (Newman, et al., 2006). Much research shows that network morphology is associated with flow along network and many other aspects of urban planning and design such as land use type distribution. Network analysis is a powerful proxy of flow in the city based on multi-scale accessibility analysis.

The analysis of several existing and future MTR route system cases and exiting road network are investigated. The results show increase and adjustment of routes line will have impacts on regional accessibility, which has impacts on residents, jobs, workplaces, CBD extension and dynamic, sub-centre spatial distribution and ranking. A key metric is flow potential (betweenness centrality based on human spatial cognition).

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.

2.4 Operationalizing Macro-meso network design analysis MTR

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.

Tips for successful operation of sdna: Pirated software shall not be used. The data source and saved path shall be as short as possible (e.g., D: // folder name). The folder shall be named in English without spaces.

Using Rhino and Grasshopper

For the use of rhinoceros, please refer to the handout of MUDP1020 Session 5 Metro Network Mapping and Analysis (Appendix-2). And for parameter settings, please refer to MUDP1020_GH Script and Theory Handout (Appendix-3).

Drawing the Network

Pay attention to check that all line segments need to be connected, especially some transfer places.

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.

MTR with Euclidean Radius

The radius is set to 12,000m (12,000m means about 10 stations, and 10 stations are the average subway travel distance in Hong Kong (ARUP 2014))

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

Prepare, processing, displaying

When using Euclidean analysis

• Before running, use the ‘explode’ command to break all polylines and select all to run. The main metric is Euclidean distance using average travel time/distance in HK (Arup 2014)

Explain

Red represents the route with the highest flow potential, blue represents the route with the lowest flow potential. The legend has been normalised from 0 to 1 (dividing all value by the maximum value) to allow comparison across different options.