Correlation between Urban Commercial Nodes and the Development of Sci-Tech Enterprises in Hangzhou West High-Tech Corridor, China
Abstract
:1. Introduction
2. Review of Relevant Studies
3. Study Area, Data Source, and Data Preprocessing
3.1. Study Area
3.2. Data Source
3.2.1. Commercial Facilities Data
3.2.2. Development Data of STEs
3.2.3. Traffic Isochrones Data
3.3. Data Preprocessing: Filtering UCNs Based on Commercial Facilities’ Data
3.3.1. Search for Agglomeration Areas of Commercial Facilities by KDE and Hotspot Analysis
- Commercial facilities were spatially clustered, with higher density in the east than in the west. Commercial hotspots and high-value kernel density areas were basically overlapped. The boundaries of commercial hotspots were more clearly defined through hotspot analysis.
- The old urban area represented by Xihu, Lin’an, Yuhang, and Liuxia had a high density of commercial facilities, while the new urban areas represented by the Future Sci-Tech City, Qingshanhu Sci-Tech City, and Jiangcun had a relatively low density but had already formed a commercial cluster.
3.3.2. Eliminating Commercial Agglomerations that Are Not Relevant to This Study and Filtering UCN Research Samples
4. Methods
4.1. Spatial Correlation Test
4.2. Statistical Analysis
4.2.1. Research Samples
4.2.2. Independent Variables and Dependent Variables
4.2.3. Partial Least Squares Regression
5. Results
5.1. Spatial Correlations between UCNs and STEs
5.1.1. Spatial Correlation Test Using Global Bivariate Moran’s I and Bivariate Local Moran’s I Map
- The H–H (a high value of UCN facilities surrounded by a high value of STEs) areas were mainly found in the eastern part of the Corridor, dominated by Sandun, Zijingang Sci-Tech City, Jiangcun, the Future Sci-Tech City, the Liuxia-Xiaoheshan higher education zone, Shenhua, Wenxin, and Cuiyuan. A few were distributed in Lin’an, Yuhang, Qingshanhu Sci-Tech City, and Zhongtai Industrial Park.
- Almost all L–H (a low value of UCN facilities surrounded by a high value of STEs) areas appeared within 500 m of the H–H areas, indicating that commercial facilities were highly concentrated in the core of the enterprise clusters, which means that STEs and commercial facilities were already well mixed in the Corridor.
- The H–L (a high value of UCN facilities surrounded by a low value of STEs) areas were scattered near the residential areas, indicating that these commercial facilities mainly provided services to nearby residential areas.
5.1.2. Relevance of Location and Site Selection
- The UCN areas and their 500 m buffer zone cover 94.86% of STEs and 96.18% of the STE employees.
- Service sector STEs tend to be located closer to UCNs than manufacturing STEs.
- Smaller-scale STEs prefer to be located inside the UCNs, while larger-scale STEs prefer to be located within a 500 m buffer zone surrounding UCNs. This distance is not too far away from the UCN, and they maintain their own development space.
5.2. Correlations among Indicators of UCNs and the Development Indicators of STEs
- The scale of UCN had a positive correlation with the number of enterprises, the number of insured persons, the amount of registered capital, the number of software copyrights, and the comprehensive score of enterprises at the 0.01 significance level. There is no significant correlation between the number of commercial facilities and the other two dependent indicators.
- The number and the comprehensive score of catering restaurants have a significant positive correlation with the number of enterprises, the number of insured persons, the amount of registered capital, the number of software copyrights, the average salary, and the comprehensive score of enterprises. The consumption of catering per person only has a positive correlation with the number of insured persons of enterprises and has no significant correlation with other indicators of corporate development. That means, compared with the grade of restaurants, the number and the satisfaction of catering restaurants have a more significant relevance with corporate development.
- The number of hotels and the average number of guest rooms have a significant positive correlation with the number of enterprises, the number of insured persons, the amount of registered capital, the number of software copyrights, the average salary, and the comprehensive score of enterprises. The comprehensive score of hotels has a positive correlation with the number, the number of insured persons, the amount of registered capital, the average salary, and the comprehensive score of enterprises. The average star-rating of hotels only has a significant positive correlation with the number of insured persons and the number of software copyrights. That means, compared with the grade of hotels, the number, scale, and satisfaction have a more significant relevance with corporate development.
- The number of retail stores only has a significant positive correlation with the comprehensive score of enterprises and has no significant correlation with other indicators of corporate development. The floor space of commercial complexes has a significant positive correlation with the number of enterprises, the amount of registered capital, and the comprehensive score of enterprises.
- The number of resident service stores has a significant positive correlation with the number of enterprises, the number of insured persons, the number of software copyrights, and the comprehensive score of enterprises. The number of culture, sports, and entertainment stores has a significant positive correlation with the number of enterprises, the number of insured persons, the amount of registered capital, the number of software copyrights, the average salary, and the comprehensive score of enterprises. The number of finance and insurance business halls has a significant positive correlation with the number of enterprises, the number of insured persons, the amount of registered capital, and the comprehensive score of enterprises.
- The four most correlated independent variables with the number of enterprises (Y1) are the number of catering restaurants (X2), the comprehensive score of catering restaurants (X4), the number of hotels (X5), and the number of culture, sports, and entertainment stores (X12).
- The four most correlated independent variables with the number of insured persons (Y2) are the comprehensive score of catering restaurants (X4); the average number of guest rooms (X7); the number of culture, sports, and entertainment stores (X12); and the number of finance and insurance business hall (X13).
- The four most correlated independent variables with the registered capital of enterprises (Y3) are the comprehensive score of catering restaurants (X4), the number of hotels (X5), the average number of guest rooms (X7), and the comprehensive score of hotels (X8).
- The four most correlated independent variables with the number of software copyrights (Y5) are the comprehensive score of catering restaurants (X4), the number of hotels (X5), the average star-rating of hotels (X6), and the average number of guest rooms (X7).
- The four most correlated independent variables with the average salary of employees (Y6) are the comprehensive score of catering restaurants (X4), the number of hotels (X5), the comprehensive score of hotels (X8), and the number of culture, sports, and entertainment stores (X12).
- The four most correlated independent variables with the comprehensive score of STEs (Y7) are the number of catering restaurants (X2), the comprehensive score of catering restaurants (X4), the number of hotels (X5), and the average number of guest rooms (X7).
6. Key Findings
- There was a significant correlation related to the spatial distribution between UCNs and STEs. Almost all L–H areas were within 500 m of the H–H areas. The UCN areas, and the 500 m buffer zone surrounding them, covered 94.86% of STEs and 96.18% of the STE employees. The service sector STEs tended to be located closer to UCNs than the manufacturing STEs. The distance from small-scale STEs to UCNs was shorter than that of large-scale STEs.
- UCNs may promote the development of surrounding STEs. The scale of UCN had a significant positive correlation on the agglomeration, scale, and comprehensive development level of STEs. Improving the number and upgrading the comprehensive score of restaurants and hotels may promote the agglomeration, development scale, salary, and comprehensive development level of STEs. The scales of the resident service industry; culture, sports, and entertainment industry; and finance and insurance industry all contributed to a certain extent to different aspects of corporate development.
- There were differences in the degree of mutual influences between the six commercial service industries and the development of surrounding STEs. Catering and hotel industries were the two most correlated industries for corporate development. The scale and satisfaction of catering and hotel industries might be conducive to the development of STEs. The culture, sports, and entertainment industry also made a relatively high contribution to STE development, while the number of retail stores had almost no significant correlation with the development of STEs.
- The commercial complex may be one of the physical forms that were conducive to the development of surrounding STEs. The floor space of the commercial complex had a significant positive correlation on the agglomeration, scale, and comprehensive development level of STEs. It can be inferred that the highly developed Internet economy has already digested much of consumer demand. Only when massive retail stores gather into commercial complexes will they promote the development of surrounding STEs.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Commercial Service Industries | Classification | Data Dimension | Data Size | Data Source |
---|---|---|---|---|---|
Inside the Corridor/Collection Area | |||||
Internet Assessment Data | Catering Industry | Chinese food, Western food, Korean food, Japanese food, fast food, bar, café, bread and dessert, drinks shop, teahouse, hotpot, barbecue, crayfish, seafood, noodle, morning tea, buffet, fresh food, health care, etc. | Location; consumption per person; comprehensive score | 11,430/64,688 | Dianping. com |
Hotel Industry | Hotel, B&B, serviced apartment, youth hostel, villa, inn, agritainment, etc. | Location; star rating; number of guest rooms; comprehensive score | 587/4519 | Ctrip. com | |
Internet Map POI Data | Retail Industry | Shopping mall, department store, supermarket, convenience store, building material market, household appliances market, retail stores, etc. | Location; floor space of commercial complex | 4411/35,670 | Baidu Map |
Resident Service Industry | Communication service hall, post office, courier service, ticket office, laundry, print shop, photo studio, real estate agency, maintenance station, household service, pet service, newsstand, beauty salon, barber shop, nail salon, etc. | Location | 2817/20,947 | ||
Culture, Sports, and Entertainment Industry | Art gallery, exhibition hall, cultural center, stadium, fitness center, resort, cinema, KTV, theater, opera house, ballroom, Internet bar, game center, bath center, massage, recreation club, etc. | Location | 1029/7408 | ||
Finance and Insurance Industry | Bank, ATM, credit cooperative, investment service, pawnshop, etc. | Location | 314/3284 |
Type | Sector Classification | Digital Economy Industries | Data Dimension | Data Size | Data Source |
---|---|---|---|---|---|
Inside the Corridor | |||||
Enterprise Credit Information Data | Manufacturing Sector | Computer, communications, and other electronic equipment manufacturing | Location; registered capital; number of insured persons; average salary; number of intellectual properties; number of software copyrights; comprehensive score | 307 | Qichacha; Tianyancha; Qixinbao; |
Electronic information and electromechanical manufacturing | |||||
Special electronic equipment manufacturing | |||||
Service Sector | The telecom, radio and television, and satellite transmission service industry | 5377 | |||
Internet and related service industry | |||||
The software and information technology service industry | |||||
The cultural digital content service industry |
Category | Name of UCNs | Explanation |
---|---|---|
Built more than 20 years ago | Cuiyuan | Cuiyuan subdistrict. Close to the central business district of Hangzhou and the Yuquan Campus of Zhejiang University. The proportion of old residential areas is relatively high. |
Wenxin | Wenxin subdistrict, with the Xixi wetland on the west side. Mixed spatial forms of industry (including sci-tech parks, office buildings, etc.), residential functions (including newly built residences, old communities, urban villages, etc.), and commercial functions (including commercial complexes, commercial streets, etc.). | |
Sandun | Sandun town. Rapid development in recent years due to the construction of Zijingang Campus, Zhejiang University, and the planning of Zijingang Sci-Tech City. | |
Liuxia | Liuxia subdistrict. A major transportation route connecting the east and west parts of Hangzhou, with residential areas as the main function. Enterprises are mostly concentrated in the Liuxia industrial district. Commercial facilities are mainly inside a giant commercial complex, namely, Xixi InCity. | |
Xianlin | Xianlin subdistrict. The southwest part is a mix of traditional industrial areas and sci-tech parks, and the north and east parts are mainly residential areas. | |
Yuhang | Yuhang old town, located at the westernmost end of the Future Sci-Tech City and the east of South Lake, with residential areas as the main function. Commercial facilities are mainly in the form of commercial streets. | |
Lin’an | Center of Lin’an old town. Commercial facilities are mainly in the form of commercial streets. | |
Built in the past 20 years | Shenhua | Shenhua subdistrict. Located in the east of Zijingang Campus, Zhejiang University. Rapidly developed because of the construction of Zhejiang University. Almost all industrial, residential, and commercial facilities inside the node were newly built in the last 20 years. |
Zijingang | Core area of Zijingang Sci-Tech City. Close to Zijingang Campus, Zhejiang University, and Westlake University. Core industries: digital economy and intelligent manufacturing. | |
Wuchang | Wuchang subdistrict. Traditional industrial parks, sci-tech parks, and residential areas are highly mixed. | |
Xiaoheshan | Xiaoheshan higher education area. Many campuses of universities are located inside the node. Commercial facilities mainly serve these universities and surrounding sci-tech parks. | |
Zhongtai | The industrial and residential areas formed with the construction of Zhongtai Industrial Park. | |
Qingshanhu | Core area of Qingshanhu Sci-Tech City. Core industries: advanced manufacturing, represented by core components of high-end equipment and new energy materials. The number of commercial facilities is significantly lower than in the eastern part of the Corridor. | |
A&F University | Donghu Campus, Zhejiang A&F University. College student entrepreneurship parks, newly built high-rise residential areas, and urban villages are highly mixed. Commercial facilities are mainly in the form of commercial streets. | |
Built in the past 10 years | Jiangcun | Jiangcun commercial and residential district. Rapid development with the construction of the Zijingang Campus and Subway Line 5. A series of large commercial complexes such as Xixi Intime City and Xixi Paradise Walk were built in the past five years. |
Alibaba | Around Alibaba global headquarters. The Alibaba headquarters is a group of giant sci-tech parks for a single corporation. It is divided into three parks from south to north. Commercial facilities are distributed around these three parks. | |
EFC | Euro-America Financial City is a landmark building in the core area of the Future Sci-Tech City. The main space form is a super high-rise office building and ground floor commercial center. | |
The Dream Town | The Dream Town is a model for the construction of a Chinese special town, with the main function of incubating small- and medium-sized Internet enterprises. Commercial facilities are mainly in the form of commercial streets. |
Category | Independent Variables | Indicator | Explanation |
---|---|---|---|
Scale of UCN | X1 | Total Number of Commercial Facilities | The total number of all commercial facilities in each sample. |
Catering Industry | X2 | Number of Catering Restaurants | The number of catering restaurants in each sample. |
X3 | Consumption of Catering per Person | The average consumption per person of all restaurants in each sample. | |
X4 | Comprehensive Score of Catering Restaurants | The average comprehensive score of all restaurants in each sample. | |
Hotel Industry | X5 | Number of Hotels | The number of hotels in each sample. |
X6 | Average Star-Rating of Hotels | The average star-rating of all hotels in each sample. | |
X7 | Average Number of Guest Rooms | The average number of guest rooms of all hotels in each sample. | |
X8 | Comprehensive Score of Hotels | The average comprehensive score of all hotels in each sample. | |
Retail Industry | X9 | Number of Retail Stores | The number of all retail stores in each sample. |
X10 | Floor Space of Commercial Complex | The total floor space of all commercial complexes in each sample. | |
Resident Service Industry | X11 | Number of Resident Service Stores | The number of all resident service stores in each sample. |
Culture, Sports, Entertainment Industry | X12 | Number of Culture, Sports, and Entertainment Stores | The number of all culture, sports, and entertainment stores in each sample. |
Finance and Insurance Industry | X13 | Number of Finance and Insurance Business Halls | The number of all finance and insurance business halls in each sample. |
Category | Dependent Variables | Indicator | Explanation |
---|---|---|---|
Agglomeration Degree | Y1 | Number of Enterprises | The total number of all commercial facilities in each sample. |
Development Scale | Y2 | Number of Insured Persons | The average number of insured persons of all enterprises in each sample. |
Y3 | Registered Capital of Enterprises | The average amount of registered capital of all enterprises in each sample. | |
Innovation Ability | Y4 | Number of Intellectual Properties | The average number of intellectual properties of enterprises in each sample. |
Y5 | Number of Software Copyrights | The average number of software copyrights of enterprises in each sample. | |
Salary Package | Y6 | Average Salary of Employees | The average salary of employees in each sample. |
Comprehensive Development Level | Y7 | Comprehensive Score | The average comprehensive score of all enterprises in each sample. Published by the Tianyancha platform. This score involves more than 300 dimensions of the enterprises’ public information. |
h | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Y7 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SS | Qh2 | SS | Qh2 | SS | Qh2 | SS | Qh2 | SS | Qh2 | SS | Qh2 | SS | Qh2 | |
PRESS | PRESS | PRESS | PRESS | PRESS | PRESS | PRESS | ||||||||
1 | 1.96 × 106 2.81 × 106 | 1 | 9.31 × 106 1.19 × 107 | 1 | 2.59 × 106 3.41 × 106 | 1 | 4.47 × 104 6.67 × 104 | 1 | 6.91 × 106 9.17 × 106 | 1 | 3.71 × 108 5.49 × 108 | 1 | 7.83 × 103 1.02 × 104 | 1 |
2 | 7.30 × 105 2.28 × 106 | −0.163 | 5.57 × 106 1.05 × 107 | −0.127 | 1.06 × 106 1.79 × 106 | 0.311 | 1.67 × 104 7.13 × 104 | −0.593 | 4.34 × 106 8.73 × 106 | −0.263 | 2.36 × 108 5.39 × 108 | −0.453 | 5.50 × 103 1.22 × 104 | −0.562 |
3 | 2.96 × 105 1.15 × 106 | −0.581 | 2.70 × 106 1.44 × 107 | −1.594 | 6.14 × 105 1.45 × 106 | −0.377 | 1.12 × 104 6.03 × 104 | −2.608 | 2.97 × 106 8.94 × 106 | −1.058 | 1.82 × 108 5.02 × 108 | −1.132 | 2.65 × 103 1.61 × 104 | −1.925 |
4 | 2.36 × 105 9.65 × 105 | −2.261 | 1.55 × 106 2.21 × 107 | −7.217 | 4.62 × 105 1.85 × 106 | −2.007 | 1.02 × 104 5.08 × 104 | −3.538 | 2.44 × 106 8.28 × 106 | −1.789 | 1.70 × 108 5.55 × 108 | −2.045 | 1.89 × 103 1.23 × 104 | −3.629 |
5 | 1.85 × 105 1.18 × 106 | −4.002 | 9.34 × 105 2.25 × 107 | −13.56 | 3.73 × 105 2.86 × 106 | −5.196 | 9.70 × 103 6.07 × 104 | −4.964 | 2.26 × 106 8.83 × 106 | −2.615 | 1.48 × 108 7.36 × 108 | −3.337 | 1.68 × 103 9.80 × 103 | −4.192 |
6 | 1.45 × 105 1.31 × 106 | −6.086 | 6.55 × 105 2.12 × 107 | −21.71 | 2.68 × 105 3.19 × 106 | −7.553 | 9.22 × 103 7.92 × 104 | −7.173 | 2.14 × 106 9.36 × 106 | −3.145 | 1.41 × 108 7.71 × 108 | −4.219 | 1.55 × 103 1.12 × 104 | −5.641 |
7 | 1.22 × 105 1.52 × 106 | −9.487 | 5.92 × 105 2.05 × 107 | −30.26 | 2.52 × 105 3.10 × 106 | −10.55 | 7.90 × 103 1.25 × 105 | −12.55 | 2.10 × 106 9.76 × 106 | −3.556 | 1.37 × 108 8.41 × 108 | −4.968 | 1.52 × 103 1.22 × 104 | −6.872 |
8 | 1.15 × 105 1.83 × 106 | −14.04 | 4.68 × 105 2.37 × 107 | −38.98 | 2.25 × 105 3.35 × 106 | −12.29 | 7.35 × 103 1.55 × 105 | −18.6 | 2.04 × 106 1.57 × 107 | −6.471 | 1.31 × 108 9.94 × 108 | −6.26 | 1.52 × 103 2.28 × 104 | −13.99 |
9 | 9.98 × 104 1.81 × 106 | −14.81 | 4.04 × 105 2.93 × 107 | −61.7 | 2.12 × 105 3.72 × 106 | −15.54 | 7.05 × 103 1.69 × 105 | −21.96 | 1.97 × 106 1.65 × 107 | −7.062 | 1.27 × 108 1.49 × 109 | −10.36 | 1.49 × 103 2.83 × 104 | −17.68 |
10 | 9.58 × 104 3.61 × 106 | −35.19 | 3.22 × 105 3.52 × 107 | −86.04 | 1.91 × 105 8.97 × 106 | −41.38 | 7.00 × 103 1.76 × 105 | −23.92 | 1.96 × 106 1.85 × 107 | −8.409 | 1.17 × 108 2.09 × 109 | −15.43 | 1.49 × 103 6.11 × 104 | −40.06 |
11 | 8.65 × 104 1.49 × 107 | −154.7 | 2.69 × 105 3.67 × 107 | −112.9 | 1.83 × 105 1.02 × 107 | −52.67 | 6.87 × 103 2.36 × 105 | −32.75 | 1.86 × 106 4.86 × 107 | −23.75 | 9.98 × 107 3.73 × 109 | −30.95 | 1.48 × 103 8.39 × 104 | −55.5 |
12 | 7.78 × 104 1.54 × 107 | −177.3 | 1.58 × 105 2.44 × 107 | −89.63 | 1.79 × 105 1.32 × 107 | −71.26 | 6.77 × 103 3.24 × 105 | −46.1 | 1.83 × 106 5.16 × 107 | −26.83 | 7.11 × 107 5.64 × 109 | −55.48 | 1.48 × 103 1.04 × 105 | −68.82 |
13 | 8.40 × 103 7.91 × 106 | −100.8 | 1.32 × 105 2.75 × 107 | −173.6 | 7.98 × 104 2.19 × 107 | −121.3 | 6.40 × 103 3.96 × 105 | −57.47 | 1.60 × 106 1.69 × 108 | −91.27 | 7.11 × 107 1.16 × 1010 | −162.4 | 1.38 × 103 1.26 × 105 | −84.09 |
X | Principal Component U1 | ||||||
---|---|---|---|---|---|---|---|
Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Y7 | |
X1 | 0.912 | 0.827 | 0.615 | 0.711 | 0.782 | 0.840 | 0.905 |
X2 | 0.947 | 0.880 | 0.735 | 0.762 | 0.860 | 0.910 | 0.940 |
X3 | 0.000 | 0.016 | 0.095 | 0.009 | 0.026 | 0.004 | 0.000 |
X4 | 0.726 | 0.712 | 0.689 | 0.729 | 0.738 | 0.763 | 0.725 |
X5 | 0.921 | 0.883 | 0.811 | 0.862 | 0.883 | 0.929 | 0.913 |
X6 | 0.205 | 0.270 | 0.282 | 0.234 | 0.313 | 0.223 | 0.207 |
X7 | 0.459 | 0.571 | 0.698 | 0.498 | 0.572 | 0.517 | 0.481 |
X8 | 0.200 | 0.278 | 0.476 | 0.376 | 0.302 | 0.283 | 0.207 |
X9 | 0.492 | 0.407 | 0.201 | 0.315 | 0.346 | 0.393 | 0.486 |
X10 | 0.694 | 0.675 | 0.640 | 0.447 | 0.657 | 0.672 | 0.694 |
X11 | 0.815 | 0.728 | 0.493 | 0.609 | 0.671 | 0.725 | 0.810 |
X12 | 0.844 | 0.802 | 0.665 | 0.732 | 0.786 | 0.814 | 0.834 |
X13 | 0.687 | 0.644 | 0.475 | 0.756 | 0.605 | 0.661 | 0.687 |
Comprehensive result | 0.608 | 0.592 | 0.529 | 0.542 | 0.580 | 0.595 | 0.607 |
Y1 Number of Enterprises | Y2 Number of Insured Persons | Y3 Registered Capital of Enterprises | Y4 Number of Intellectual Properties | Y5 Number of Software Copyrights | Y6 Average Salary of Employees | Y7 Comprehensive Score | ||
---|---|---|---|---|---|---|---|---|
Con. | −1392.765 | −755.667 | −358.997 | 52.975 | 5.085 | −1505.27 | 1.811 | |
X1 | Total Number of Commercial Facilities | 0.04 ** | 0.029 ** | 0.01 ** | 0.001 | 0.017 ** | 0.152 | 0.001 ** |
X2 | Number of Catering Restaurants | 0.081 ** | 0.056 ** | 0.03 * | 0.001 | 0.039 * | 0.338 * | 0.002 ** |
X3 | Consumption of Catering per Person | 1.039 | 7.317 * | 5.72 | 0.1 | 3.897 | 11.913 | 0.091 |
X4 | Comprehensive Score of Catering Restaurants | 148.746 ** | 126.755 ** | 74.326 * | 5.153 | 102.349 ** | 873.306 ** | 4.854 ** |
X5 | Number of Hotels | 1.661 ** | 1.236 ** | 0.837 * | 0.05 | 0.927 * | 7.871 * | 0.046 ** |
X6 | Average Star-Rating of Hotel | 22.276 | 33.204 * | 20.728 | 1.171 | 34.261 * | 127.577 | 0.557 |
X7 | Average Number of Guest Rooms | 8.579 ** | 12.057 ** | 7.955 * | 0.293 | 7.043 ** | 45.835 * | 0.331 ** |
X8 | Comprehensive Score of Hotels | 53.799 ** | 61.865 ** | 69.596 * | 5.394 | 44.529 | 426.133 ** | 1.387 * |
X9 | Number of Retail Stores | 0.092 | 0.063 | −0.024 | 0.001 | 0.008 | 0.15 | 0.003 * |
X10 | Floor Space of Commercial Complex | 4.615 ** | 2.635 | 1.754 * | −0.162 | 1.616 | 15.583 | 0.126 ** |
X11 | Number of Resident Service Stores | 0.214 ** | 0.167 ** | 0.031 | 0.004 | 0.078 * | 0.745 | 0.007 ** |
X12 | Number of Culture, Sports, and Entertainment Stores | 0.628 ** | 0.557 ** | 0.267 * | 0.02 | 0.352 ** | 2.853 * | 0.017 ** |
X13 | Number of Finance and Insurance Business Halls | 1.112 ** | 1.102 ** | 0.324 ** | 0.101 | 0.548 | 5.373 | 0.037 ** |
R-Squared | 0.882 | 0.532 | 0.534 | 0.216 | 0.389 | 0.445 | 0.627 |
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Wu, Y.; Li, X.; Yang, Y.; Xu, W. Correlation between Urban Commercial Nodes and the Development of Sci-Tech Enterprises in Hangzhou West High-Tech Corridor, China. Land 2022, 11, 2086. https://doi.org/10.3390/land11112086
Wu Y, Li X, Yang Y, Xu W. Correlation between Urban Commercial Nodes and the Development of Sci-Tech Enterprises in Hangzhou West High-Tech Corridor, China. Land. 2022; 11(11):2086. https://doi.org/10.3390/land11112086
Chicago/Turabian StyleWu, Yue, Xiangyi Li, Yue Yang, and Weishun Xu. 2022. "Correlation between Urban Commercial Nodes and the Development of Sci-Tech Enterprises in Hangzhou West High-Tech Corridor, China" Land 11, no. 11: 2086. https://doi.org/10.3390/land11112086