Innovationsbezirke haben sich zu entscheidenden Triebkräften des Wirtschaftswachstums entwickelt, doch ihre Gestaltung übersieht oft die menschliche Erfahrung und die Zugänglichkeit von Annehmlichkeiten. Dieses Kapitel befasst sich mit dem Hangzhou Chengxi Sci-Tech Corridor, einem Bezirk, in dem Technologiegiganten wie Alibaba beheimatet sind, um die räumlichen Herausforderungen aufzudecken, die sein Potenzial behindern. Durch eine vergleichende Untersuchung dreier Bereiche innerhalb des Korridors und eines Innenstadtblocks zeigt das Kapitel krasse Unterschiede in der Vitalität und Begehbarkeit des öffentlichen Raums. Verhaltensdatenerfassung und innovative Visualisierungstechniken wie die Netzwerk-Reichweitenanalyse zeigen die Mängel des Korridors auf, darunter überdimensionale Blöcke und schlechte Konnektivität. Das Kapitel argumentiert, dass die Verbesserung der Durchlässigkeit des Straßennetzes und die Verbesserung der Zugänglichkeit der täglichen Dienstleistungen der Schlüssel zur Förderung einer dynamischen Innovationswirtschaft sind. Durch die Lösung dieser räumlichen Gestaltungsprobleme können Innovationsbezirke den Lebensstil "Arbeiten, Leben und Spielen" besser unterstützen und damit letztlich die wirtschaftliche Produktivität und die Lebensqualität ihrer Bewohner steigern.
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Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
The innovation economy is a crucial industry type currently valued highly by cities worldwide. In the suburb of Hangzhou in coastal area of China, driven by the leading enterprise Alibaba Corporation, a cluster of technology and innovation companies has gradually emerged. Since 2011, the local government has planned this area (covering an area of approximately 113 square kilometers) as an innovation district and provided tax and policy support. This article focuses on walkability, which refers to measuring how friendly an area is for walking. Although this concept is adopted by academics, it has not been easy to implement in the real world. For innovation districts, walkability is crucial for fostering public space vitality and catering to the lifestyle of young talents. However, according to a large-scale behavioural survey organized by the authors in spring 2023, the proportion of pedestrians is not high. Under the dual influence of “super blocks in the suburb and segregation by river network”, the current walkability of Hangzhou Chengxi Sci-tech Corridor is relatively lower than that of other famous innovation districts in the western world. This study also evaluates the walkability and density of daily service in this area with a new tool NRA (based on Space Syntax theory). A downtown innovation districts in the same city is chosen as a benchmark for understanding the quantitative results. The research shows the potential of this new method for evaluating walkability. The visualization of quantitative data can help the decision-makers and designers to identify weak spots, and compare different scenarios of regeneration plans.
1 Introduction
Urban innovation districts have become a core tool of economic development policy for economic developers, planners, and city officials. Brookings Institute civic boosters Bruck Katz and Julie Wagner define ‘innovation districts’ as “geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators” [1]. This definition explains the importance of innovation districts as a key source of economic productivity growth.
Productivity has experienced a significant slowdown globally in recent years, clusters and districts have the potential to combat this slowdown. Baily believes it can solve the problem of slow productivity growth by providing labs and talent to companies and fostering cooperation between them and universities [2].
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Second, innovation districts often emphasise density and connectivity. Kayanan believes this is necessary to maximize innovation and economic output by concentrating knowledge workers in a limited space and encouraging individuals to work throughout the day [3]. Ballard argues that promoting a combined ‘work, live and play’ function can help produce an integrated, ‘people-centred’ and compact city [4].
In addition, the development of innovation districts relies on weak ties. Weak ties exist between people or firms across industries. These are essential for transmitting new information, new contacts, and new business leads. Good physical space design can provide informal spaces for interaction to facilitate the composition of weak ties, thereby blurring the boundaries between work, life and play and allowing work to permeate all aspects of life effectively.
While it has been recognized that the density of economic activity brings productivity advantages to firms within clusters, there has been less research of this kind on the human experience, particularly on the ability of these innovators to access the amenities of life within innovation zones. Our study case, Hangzhou Chengxi Sci-tech Corridor, raises such questions. In terms of industry, it is home to head enterprises in the digital economy, such as Alibaba and Vivo, and has more than 2,500 national high-tech enterprises [5]. Regarding the composition of facilities, it has various cultural facilities such as a university and a conference centre. However, physical space accessibility still has many shortcomings.
During our visit, we found two significant problems with the Corridor. First, jobs and housing are separated, with a ‘Pendulum flow’ of nearly 600,000 people per day [6]. Second, it has a weak sense of urbanity. Residents familiar with it agree that this new area has no street life and that the hierarchical pattern of roads is rigid. As Montgomery says, these areas lack urbanity and good urban places [7].
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The plan for the Corridor was developed by China’s top design institute (China Academy of Urban Planning and Design) in 2020 [5]. It drew on many famous examples of innovative districts and underwent several program modifications. However, the implementation status is not satisfactory. Therefore, this paper explores effective visualization methods to reveal the mistakes made in permeability in this district.
This study was conducted in two parts. Firstly, behavioral data on public space was collected to assess the vitality of public space in the Corridor. Behavioral data was collected for the Corridor and the Downtown block over the same period to compare the data. In the second part, spatial form walkability was measured for the area. A new tool, Network Reach Analysis (NRA), released by Professor Haofeng Wang from Shenzhen University, was used [8]. The algorithm is based on John Peponis’s work [9].
2 Research Question and Methodology
The purpose of this article is twofold. Firstly, does the Corridor indeed have low vitality in public spaces? Secondly, is there a more accurate method for measuring walkability? We use behavioral data to answer question one, as detailed in Sect. 2.2. For question two, we will proceed in steps through data comparison and emphasize data visualization. Please refer to Sects. 2.3 and 2.4 for details.
2.1 Introduction to Three Case Regions
We chose Jiangcun and the Huanglong area downtown for our comparative study. Jiangcun and Hangzhou Future Sci-tech City (FSTC) belong to the Corridor, and the former is within the bypass highway, which is closer to the downtown area. As shown in Fig. 1., FSTC is Object 1, the Jiangcun area is Object 2, and the Huanglong area is Object 3. After this, we refer to these three objects as Areas A, B and C.
The behavioral data collection was conducted in May 2023. The actual counting work was performed at each gate for 5 min in 6 different periods (8:00–9:30, 10:00–11:30, 12:00–13:30, 13:30–15:00, 15:30–17:00, 17:30–19:00). The counting time includes weekdays and weekends.
This study aims to assess the compactness of urban regions. Therefore, we do not consider motor vehicles and only record non-motor vehicles and pedestrians, which are smaller-scale travel modes. Non-motor vehicles include bicycles and e-bikes. For e-bikes, a distinction is made as to whether they are take-out e-bikes or not. The former is mainly used for transport, while the latter is used for daily life.
2.3 Comparison of the Block Size
The definition of walkability can be divided into two types of attributes. First, the street network’s permeability is high [10]; second, the function points are close and accessible [11]. In his 1998 paper, Montgomery argues that Human Scale, City Blocks, and Permeability are essential in creating urban vitality. He measured street permeability by measuring the number of intersections within a 10-min walk. The higher the number of intersections, the more prosperous urban places are [7].
We use the block size map to visualize the number of intersections. Based on the research results of several scholars, we believe that the upper limit of the suitable walking distance for humans is 300 m, and most people are more inclined to accept a walking distance of less than 150 m. This area is walkable if the block size map is mainly yellow and red. If light blue and dark blue appear, the side length of a block is greater than 400 m, which is not conducive to walking activities.
2.4 POI Assignment and Calculation of Urbanity Index
As A is a new urban area, its spatial structure is still being developed. We use the Network Reach Analysis algorithm and UR tool to assess the density of daily service patterns in the current street. We name it the “Urbanity Index”.
The steps are as follows: First, the POI data, which function most closely related to daily life around the block, were selected. Based on the shape and length of the road network, assign values to the total number of POIs obtained from screening.
In the first analysis, the direct use of POI data assignments produced results that were not common sense. A review of the raw data revealed that this was due to underground shopping districts or multi-storey commercial complexes in the area. Therefore, we designed an improved assignment method.
According to the five levels surveyed by Gehl, the number of effective POIs within 10 m is 6. Use the formula per10 = 10 * poi total/shape length. The total number of POIs per 10 m is greater than or equal to 6, assigned a value of 5. Between 3 and 6, assigned a value of 4. Between 1.5 and 3, assigned a value of 3. Between 1 and 1.5, assigned a value of 2. Between 0.2 and 1, assigned a value of 1. Below 0.2, assigned a value of 0. Finally, the Combined Reach function in the UR was used to calculate the sum of POI levels that could be reached within two turns greater than 20 degrees at a given radius.
3 Comparison of the Use of Public Space
The results of the behavioral data collection are shown in Table 1. Overall, the data does not differ much between the three areas, with similar proportions of pedestrians and e-bikes.
Table 1.
Data collection
Location
Points
Take-out electric bike
Ordinary electric bike
Pedestrian-Weekdays
Pedestrian-Weekends
A
114
6891
14424
7930
7172
B
46
4634
9385
5984
6767
C
19
1731
3626
2046
1326
Next, we compare the activity levels in the three regions using medians (see Fig. 2). For all four activity types, the median is lower in areas A and B than in C. These phenomena indicate that travelling in A and B is much worse than in C.
Fig. 2.
The collected data from three area (Created by the authors)
Visualization helps people understand the liveliness intuitively. Figure 3 and Fig. 4 represent the results for pedestrians and non-motor vehicles. Both area A and B show poorer movement conditions than C. There are a lot of blue gates within A and B. This is consistent with the pre-study feeling that A and B have employment and industry no less than the downtown, but the vitality of the public space is relatively weak. There is a 300 * 300 m grids to give the scale of the study areas. Area C has denser street network.
Fig. 3.
Hourly pedestrian flow distribution, left: A, middle: B, right: C (Created by the authors)
Fig. 4.
Hourly non-motor vehicle flow distribution: left: A, middle: B, right: C (Created by the authors)
4 Comparison of Walkability of Three Blocks
4.1 Visualization for Block Sizes
By comparing the block sizes, the difference in the size of the street scales in the three areas can be obtained. Figure 5 shows the box plot of block sizes in the three areas. The median and mean block sizes are the largest in A, second in B, and last in C.
Fig. 5.
Block area distribution of three regions (Created by the authors)
The hierarchical visualization of the Block can give us more information. (see Fig. 6). Only a few blocks within C area are grey blue and dark blue (grey blue is the campus, and dark blue is the scenic area). However, both are not conducive to the block’s walkability and still functionally contribute to the area’s vibrancy.
While some core blocks in A and B are compact, they are surrounded by many large-scale blocks that greatly affect their walkability. B is bounded by scenic views to the south, campus to the east, and highways to the north and west border. A is bordered by a scenic area to the south and a university campus to the north. Alibaba, a large block, also cuts off its core, and the urban fabric is even less dense.
Fig. 6.
Block hierarchical visualization, left: A, middle: B, right: C (Drawn by the authors)
4.2 Reach Length Analysis
Block analysis will give us an overall understanding of the differences in block scales in the three areas. Next, we use the function of combined reach in UR. It reveals the potential for the public space street network to be used.
Due to space limitations, only some of the calculations are given. As seen from Fig. 7, the proportion of longer accessible street segments is much lower in areas A and B than in C. This result is consistent with the pedestrian flow data. Our research found many observations with few walkers in areas A and B. The reason for this is most likely because these areas are less accessible.
Fig. 7.
The calculation results of reach length (Created by the authors)
4.3 Urbanity Index Analysis
Reachable streets total length analysis does not contain a function. In the function of combined reach, we can choose the number of POIs, as weighting, to calculate the total number of points of a specific function that are reachable by a resident from any street segment at a particular radius, with 2 turns of 20° or more. We call it the “Urbanity Index” in the article because it expresses the density and accessibility of daily life-related functional points.
It presents a great divergence from the analyzed structure of Reachable streets’ total length in A. This is because, in A, many street segments exist, consisting of fences, with few shops along the street. Due to space limitations, only the visualization results for 300 m are shown (see Fig. 8). Area C presents the best accessibility, B the second, and A the last.
Fig. 8.
The calculation result of Combined Reach 300 m, left: A, middle: B, right: C (Created by the authors)
5 Conclusions and Discussions
This paper takes the famous Hangzhou Innovation District as an example to argue for the success of innovation districts. This study collects actual pedestrian and non-motor traffic data to compare the differences between the three areas. Compared to the urban case, the new area has fewer walkers, and the vitality of the public space is weak. This is a mismatch with the Corridor’s strong industrial capacity and functional composition advantages. Therefore, we conjecture that this case has severe spatial design problems concerning the goals of fostering an innovation economy.
Firstly, the block-scale visualization shows many oversized blocks in the Corridor, making it unsuitable for pedestrian activity. The site study found that this could be due to the river network and the blockage of large flats. Therefore, if the public space vitality of these blocks is to be enhanced, improving the permeability of the road network should have a significant effect.
Secondly, with the help of the Urconnect tool, we calculated the Reachable streets total length, which indicates the area’s walkability potential, and the Urbanity Index, which expresses the accessibility and vibrancy of commercial services experienced by residents. Compared with traditional spatial syntax measures, these two measures have a unit, and their values can be understood directly. They show extreme explanatory power in this study. It is worthwhile to test them further in future studies.
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