Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data Sources and Pre-Processing
2.2.1. Mobile Phone Signaling Data
2.2.2. POI Data
3. Methodology
3.1. Research Framework
3.2. Calculation of Attractiveness Indicators
- Space Density
- 2.
- Space Distance
- 3.
- Space Diversity
- 4.
- Dwell Time
- 5.
- Dwell Frequency
- 6.
- Dwell Diversity
3.3. TOPSIS
3.4. OLS Regression Model
4. Results
4.1. Spatiotemporal Distribution Characteristics of Attractiveness
4.1.1. Space Density
4.1.2. Space Distance
4.1.3. Space Diversity
4.1.4. Dwell Time
4.1.5. Dwell Frequency
4.1.6. Time Diversity
- The difference between the spatial dimensions on the weekends and weekdays was larger than that between the time dimensions.
- The distribution of high values of various indicators was highly varied, presenting four main types: centripetal aggregation, marginal distribution, regional aggregation, and scattered distribution. Among the indicators, space density showed a centripetal distribution, dwell time a marginal distribution, space distance, space diversity, and time diversity a regional aggregation, and dwell frequency a scattered distribution.
- A significant correlation existed between the type of research unit and the high value of the indicator. The research units with a high space density were mostly cultural spots and landscape parks; the research units with a high space distance were mostly transportation hubs, cultural spots, and landscape parks; the research units with high space diversity and dwell frequency were landscape parks and residential areas; and the research units with high dwell time and time diversity were landscape parks, public buildings, and residential area.
4.2. TOPSIS Evaluation Result
4.2.1. Weight Calculation Result
4.2.2. Overall Attractiveness Evaluation Result
4.3. OLS Regression Result
4.4. Type Analysis
- The “HHHH” category was mainly distributed in the central area of the city such as the unit where the Zhiyinhao Dock is located, which is a famous urban cultural tourism area in Wuhan. This category of area often had strong centrality and attractiveness and belonged to the relatively economically developed region, where the POI variety and quantity were also very high.
- The “LLLL” category was mainly distributed in the urban fringe area and closer to the suburbs than the “LLLH” category. This category belonged to a relatively underdeveloped area with low POI density, POI mix, and attractiveness.
- The “HLLL” category was scattered and mainly located in well-constructed landscape parks in the riverside area. This category’s high attraction was mainly its well-designed and high-quality open spaces, which provide opportunities for the surrounding crowd to commune with nature. People were attracted by the scenery and environment here rather than the variety of POIs.
- In the “LHHH” category, research units were mainly distributed in the central area of the city. This type of area was low in attractiveness, but the density and mix of the POIs were high. The surrounding areas were mostly residential areas with complete infrastructure, and the part along the river is dominated by cargo terminals and linear walks. Thus, the area is unfavorable for people to stay, and the landscape and activities of the place are relatively monotonous, resulting in low attractiveness to the crowd.
5. Discussion
- The high-value distribution of attractiveness of the river waterfronts in Wuhan presented regional aggregation characteristics, and the attractiveness of the economically developed areas was high.
- CPOIs and outdoor RPOIs had a positive effect on the attractiveness of the riverside in Wuhan, while HPOIs, OPOIs, and the high degree of POI mixing had a negative impact on the attractiveness of urban riverside.
- The high–high agglomeration spaces were mainly concentrated in the economically developed areas of the city center and were mainly open spaces where urban cultural activities are held, while the low–low agglomeration spaces were mostly gathered in the suburban areas. The spatial distribution of the high–low agglomeration spaces, which are mainly green open spaces, was relatively fragmented, while the low–high clusters, which are mainly freight terminals, linear walks, and residential areas, were near the city center.
6. Conclusions
6.1. Research Innovation
6.2. Future Construction Suggestions
6.3. Deficiencies and Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sairinen, R.; Kumpulainen, S. Assessing social impacts in urban waterfront regeneration. Environ. Impact Assess. Rev. 2006, 26, 120–135. [Google Scholar] [CrossRef]
- Shah, S.; Roy, A.K. Social Sustainability of Urban Waterfront—The Case of Carter Road Waterfront in Mumbai, India. Proc. Environ. Sci. 2017, 37, 195–204. [Google Scholar] [CrossRef]
- Hagerman, C. Shaping neighborhoods and nature: Urban political ecologies of urban waterfront transformations in Portland, Oregon. Cities 2007, 24, 285–297. [Google Scholar] [CrossRef]
- Sepe, M. Urban history and cultural resources in urban regeneration: A case of creative waterfront renewal. Plan. Perspect. 2013, 28, 595–613. [Google Scholar] [CrossRef]
- Follmann, A. Urban mega-projects for a ‘world-class’ riverfront—The interplay of informality, flexibility and exceptionality along the Yamuna in Delhi, India. Habitat Int. 2015, 45, 213–222. [Google Scholar] [CrossRef]
- Desfor, G.; Jørgensen, J. Flexible urban governance. The case of Copenhagen’s recent waterfront development. Eur. Plan. Stud. 2004, 12, 479–496. [Google Scholar] [CrossRef]
- Shafaghat, A.; Keyvanfar, A.; Manteghi, G.; Bin Lamit, H. Environmental-conscious factors affecting street microclimate and individuals’ respiratory health in tropical coastal cities. Sustain. Cities Soc. Nat. Resour. 2016, 21, 35–50. [Google Scholar] [CrossRef]
- Romero, V.P.; Maffei, L.; Brambilla, G.; Ciaburro, G. Modelling the soundscape quality of urban waterfronts by artificial neural networks. Appl. Acoust. 2016, 111, 121–128. [Google Scholar] [CrossRef]
- Wong, T.-C. Revitalising Singapore’s central city through gentrification: The role of waterfront housing. Urban Policy Res. Transp. Bus. Manag. 2006, 24, 181–199. [Google Scholar] [CrossRef]
- Ali, M.S.; Nawawi, A.H. The Social Impact of Urban Waterfront Landscapes: Malaysian Perspectives. 2009. Available online: https://programm.corp.at/cdrom2009/papers2009/CORP2009_15.pdf (accessed on 24 August 2022).
- Wrenn, D.M. Waterfronts: Cities Reclaim Their Edge. Landsc. J. 1995, 14, 95–96. [Google Scholar] [CrossRef]
- Hoyle, B. Urban waterfront revitalization in developing countries: The example of Zanzibar’s Stone Town. Geogr. J. 2002, 168, 141–162. [Google Scholar] [CrossRef]
- Jones, A.L. Regenerating urban waterfronts—Creating better futures—From commercial and leisure market places to cultural quarters and innovation districts. Plan. Pract. Res. 2017, 32, 333–344. [Google Scholar] [CrossRef]
- Gospodini, A. Urban waterfront redevelopment in Greek cities—A framework for redesigning space. Cities 2001, 18, 285–295. [Google Scholar] [CrossRef]
- Feldman, M. Urban Waterfront Regeneration and Local Governance in Tallinn. Eur. Stud. 2000, 52, 829–850. [Google Scholar] [CrossRef]
- Chang, C.T.; Huang, S. Reclaiming the City: Waterfront Development in Singapore. Urban Stud. 2011, 48, 2085–2100. [Google Scholar] [CrossRef]
- Vollmer, D. Urban waterfront rehabilitation: Can it contribute to environmental improvements in the developing world? Environ. Res. Lett. 2009, 4, 024003. [Google Scholar] [CrossRef]
- Yang, C.; Shao, B. Influence of Waterfront Public Space Elements on Lingering Vitality and Strategies: Taking Two Typical Waterfronts Along Huangpu River, Shanghai as Examples. Urban. Archit. 2018, 4, 40–47. [Google Scholar]
- Jacobs, J. The Death and Life of Great American Cities; Penguin: Harmondsworth, UK, 1961. [Google Scholar]
- Boivin, M.; Tanguay, G.A. Analysis of the determinants of urban tourism attractiveness: The case of Québec City and Bordeaux. J. Destin. Mark. Manag. 2019, 11, 67–79. [Google Scholar] [CrossRef]
- Biernacka, M.; Kronenberg, J. Classification of institutional barriers affecting the availability, accessibility and attractiveness of urban green spaces. Urban For. Urban Green. 2018, 36, 22–33. [Google Scholar] [CrossRef]
- Kronenberg, J.; Haase, A.; Łaszkiewicz, E.; Antal, A.; Baravikova, A.; Biernacka, M.; Dushkova, D.; Filčak, R.; Haase, D.; Ignatieva, M.; et al. Environmental justice in the context of urban green space availability, accessibility, and attractiveness in postsocialist cities. Cities 2020, 106, 102862. [Google Scholar] [CrossRef]
- Dziecielski, M.; Kourtit, K.; Nijkamp, P.; Ratajczak, W. Basins of attraction around large cities—A study of urban interaction spaces in Europe. Cities 2021, 119, 103366. [Google Scholar] [CrossRef]
- Crevoisier, O.; Rime, D. Anchoring Urban Development: Globalisation, Attractiveness and Complexity. Urban Stud. 2021, 58, 36–52. [Google Scholar] [CrossRef]
- De Souza, A.L.B.; Maraschin, C. Spatial configuration in urban agglomerations: Effects of retailing attractiveness in Serra Gaucha Region, Brazil. Urbe-Rev. Bras. Gest. Urbana 2019, 11. [Google Scholar] [CrossRef]
- Jayasinghe, A.; Sano, K.; Rattanaporn, K. Application for developing countries: Estimating trip attraction in urban zones based on centrality. J. Traffic Transp. Eng. 2017, 4, 464–476. [Google Scholar] [CrossRef]
- Beiró, M.G.; Bravo, L.; Caro, D.; Cattuto, C.; Ferres, L.; Graells-Garrido, E. Shopping mall attraction and social mixing at a city scale. EPJ Data Sci. 2018, 7, 28. [Google Scholar] [CrossRef]
- Lessa, D.A.; Lobo, C. Mobility and Urban Centralities: An Analysis Based on the Motorized Flows Attraction in Belo Horizonte/State of Minas Gerais/Brazil. Sustainability 2021, 13, 10128. [Google Scholar] [CrossRef]
- Fofanova, K.V.; Sychev, A.A. Factors in Migration Attractiveness of a Provincial City: The Case Study of the City of Saransk. Reg. Russ. J. Reg. Stud. 2019, 27, 756–778. [Google Scholar] [CrossRef]
- Mazza, A.; Punzo, A. Spatial attraction in migrants’ settlement patterns in the city of Catania. Demogr. Res. 2016, 35, 117–138. [Google Scholar] [CrossRef]
- Koprowska, K.; Kronenberg, J.; Kuźma, I.B.; Łaszkiewicz, E. Condemned to green? Accessibility and attractiveness of urban green spaces to people experiencing homelessness. Geoforum 2020, 113, 1–13. [Google Scholar] [CrossRef]
- Panti, A.M.; Rojas, M.A.B.; Camara, B.L.C. Perception of public space: Loss of attractiveness in the Urban Commercial Corridor Avenida de los Heroes de Chetumal, Quintana Roo, Mexico. Rev. Urban. 2020, 43, 79–95. [Google Scholar] [CrossRef]
- Guedoudj, W.; Ghenouchi, A.; Toussaint, J.-Y. Urban attractiveness in public squares: The mutual influence of the urban environment and the social activities in Batna. Urbe-Rev. Bras. Gest. Urbana 2020, 12. [Google Scholar] [CrossRef]
- Ćwik, A.; Kasprzyk, I.; Wójcik, T.; Borycka, K.; Cariñanos, P. Attractiveness of urban parks for visitors versus their potential allergenic hazard: A case study in Rzeszów, Poland. Urban For. Urban Green. 2018, 35, 221–229. [Google Scholar] [CrossRef]
- Paletto, A.; Guerrini, S.; De Meo, I. Exploring visitors’ perceptions of silvicultural treatments to increase the destination attractiveness of peri-urban forests: A case study in Tuscany Region (Italy). Urban For. Urban Green. 2017, 27, 314–323. [Google Scholar] [CrossRef]
- Chen, M.-S.; Ko, Y.-T.; Lee, L.-H. The Relation Between Urban Riverbank Reconstruction and Tourism Attractiveness Shaping- A Case Study of Love River in Kaohsiung, Taiwan. J. Asian Arch. Build. Eng. 2018, 17, 353–360. [Google Scholar] [CrossRef]
- Gao, T.; Zhu, L.; Zhang, T.; Song, R.; Zhang, Y.; Qiu, L. Is an Environment with High Biodiversity the Most Attractive for Human Recreation? A Case Study in Baoji, China. Sustainability 2019, 11, 4086. [Google Scholar] [CrossRef]
- Qiu, L.; Lindberg, S.; Nielsen, A.B. Is biodiversity attractive?—On-site perception of recreational and biodiversity values in urban green space. Landsc. Urban Plan. 2013, 119, 136–146. [Google Scholar] [CrossRef]
- Ramlee, M.; Omar, D.; Yunus, R.M.; Samadi, Z. Successful Attractions of Public Space through Users Perception. In Proceedings of the Annual Serial Landmark International Conferences on Quality of Life (ASLI QoL2015)/2nd ABRA International Conference on Quality of Life (AQoL2015), Izmir, Turkey, 3–6 November 2015. [Google Scholar]
- Banet, K.; Naumov, V.; Kucharski, R. Using city-bike stopovers to reveal spatial patterns of urban attractiveness. Curr. Issues Tour. 2022, 25, 2887–2904. [Google Scholar] [CrossRef]
- Cai, L.; Jiang, F.; Zhou, W.; Li, K. Design and Application of an Attractiveness Indicator for Urban Hotspots Based on GPS Trajectory Data. IEEE Access 2018, 6, 55976–55985. [Google Scholar] [CrossRef]
- Guo, X.M.; Weiqiang, C.; Tiantian, L.; Shumeng, H. Research on dynamic visual attraction evaluation method of commercial street based on eye movement perception. J. Asian Archit. Build. Eng. 2021, 21, 1779–1791. [Google Scholar]
- Mor, M.; Fisher-Gewirtzman, D.; Yosifof, R.; Dalyot, S. 3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos. ISPRS Int. J. Geoinf. 2021, 10, 275. [Google Scholar] [CrossRef]
- Deelen, I.; Janssen, M.; Vos, S.; Kamphuis, C.; Ettema, D. Attractive running environments for all? A cross-sectional study on physical environmental characteristics and runners’ motives and attitudes, in relation to the experience of the running environment. BMC Public Health 2019, 19, 366. [Google Scholar] [CrossRef] [PubMed]
- Zawadzka, A. Architectural and Urban Attractiveness of Small Towns: A Case Study of Polish Coastal Cittaslow Towns on the Pomeranian Way of St. James. Land 2021, 10, 724. [Google Scholar] [CrossRef]
- Moskalonek, Z.M.; Połom, M.; Puzdrakiewicz, K. Changes in the Function of Allotment Gardens in an Attractive Location Based on the Example of Tri-City in Poland. Land 2020, 9, 464. [Google Scholar] [CrossRef]
- Giles-Corti, B.; Broomhall, M.H.; Knuiman, M.; Collins, C.; Douglas, K.; Ng, K.; Lange, A.; Donovan, R.J. Increasing walking—How important is distance to, attractiveness, and size of public open space? Am. J. Prev. Med. 2005, 28, 169–176. [Google Scholar] [CrossRef]
- Subramanian, D.; Jana, A. Assessing urban recreational open spaces for the elderly: A case of three Indian cities. Urban For. Urban Green. 2018, 35, 115–128. [Google Scholar] [CrossRef]
- Niu, H.F.; Silva, E.A. Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods. J. Urban Plan. Dev. 2020, 146, 04020007. [Google Scholar] [CrossRef]
- Huang, H.; Wu, J.; Liu, F.; Wang, Y. Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data. Sustainability 2021, 13, 112. [Google Scholar] [CrossRef]
- Manfredini, F.; Pucci, P.; Tagliolato, P. Toward a Systemic Use of Manifold Cell Phone Network Data for Urban Analysis and Planning. J. Urban Technol. 2014, 21, 39–59. [Google Scholar] [CrossRef]
- Steenbruggen, J.E.; Tranos, E.; Nijkamp, P. Data from mobile phone operators: A tool for smarter cities? Telecommun. Policy 2015, 39, 335–346. [Google Scholar] [CrossRef]
- Zhang, S.; Yang, Y.; Zhen, F.; Lobsang, T. Exploring Temporal Activity Patterns of Urban Areas Using Aggregated Network-driven Mobile Phone Data: A Case Study of Wuhu, China. Chin. Geogr. Sci. 2020, 30, 695–709. [Google Scholar] [CrossRef]
- Tang, L.; Lin, Y.; Li, S.; Li, S.; Li, J.; Ren, F.; Wu, C. Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data. Sustainability 2018, 10, 4565. [Google Scholar] [CrossRef]
- Jiang, S.J.; Ferreira, J.; Gonzalez, M.C. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Trans. Big Data 2017, 3, 208–219. [Google Scholar] [CrossRef] [Green Version]
- Diao, M.; Zhu, Y.; Ferreira, J.J.; Ratti, C. Inferring individual daily activities from mobile phone traces: A Boston example. Environ. Plan. B Plan. Des. 2016, 43, 920–940. [Google Scholar] [CrossRef]
- Du, N.R.; Ottens, H.; Sliuzas, R. Spatial impact of urban expansion on surface water bodies—A case study of Wuhan, China. Landsc. Urban Plan. 2010, 94, 175–185. [Google Scholar] [CrossRef]
- Hu, Q.; Wu, W.; Xia, T.; Yu, Q.; Yang, P.; Li, Z.; Song, Q. Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping. Remote Sens. 2013, 5, 6026–6042. [Google Scholar] [CrossRef]
- Xu, G.; Jiao, L.; Zhao, S.; Yuan, M.; Li, X.; Han, Y.; Zhang, B.; Dong, T. Examining the Impacts of Land Use on Air Quality from a Spatio-Temporal Perspective in Wuhan, China. Atmosphere 2016, 7, 62. [Google Scholar] [CrossRef]
- Liu, S.; Lai, S.-Q.; Liu, C.; Jiang, L. What influenced the vitality of the waterfront open space? A case study of Huangpu River in Shanghai, China. Cities 2021, 114, 103197. [Google Scholar] [CrossRef]
- Ertuğrul, I.; Karakaşoğlu, N. Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst. Appl. 2009, 36, 702–715. [Google Scholar] [CrossRef]
- Akram, M.W.A.; Dudek, W.A.; Ilyas, F. Group decision-making based on pythagorean fuzzy TOPSIS method. Int. J. Intell. Syst. 2019, 34, 1455–1475. [Google Scholar] [CrossRef]
ID | Name | Category | District | ID | Name | Category | District |
---|---|---|---|---|---|---|---|
1 | River Landscape Park | Landscape park | Hankou | 24 | Hongshan Beach Park | Landscape park | Wuchang |
2 | Hankou River Beach Park Phase IV | Landscape park | Hankou | 25 | Yangsigang Yangtze River Bridge Park | Landscape park | Wuchang |
3 | Hankou River Beach Park Phase III | Landscape park | Hankou | 26 | BAPU Street embankment Beach | Landscape park | Wuchang |
4 | Hankou River Beach Park Phase III | Landscape park | Hankou | 27 | Changjiang Zidu residential community | Residential community | Wuchang |
5 | Fengfan Square, Hankou River Beach Park Phase II | Cultural spot, landscape park | Hankou | 28 | Under building | / | Wuchang |
6 | Zhiyin Dock, Hankou River Beach Park Phase II | Cultural spot, landscape park | Hankou | 29 | Meihuayuan residential community | Residential community | Wuchang |
7 | Hankou River Beach Park Phase I | Landscape park | Hankou | 30 | Jiefanglu residential community | Residential community | Wuchang |
8 | Culture Plaza of Hankou River Beach Park, Hankou River Beach Park Phase I | Landscape park | Hankou | 31 | Zhonghualu Dock | Transportation hub | Wuchang |
9 | Wuhan Harbor, Wuhan Science and Technology Museum | Transportation hub, public building | Hankou | 32 | Dadikou Square, Wuchang Beach Park | Landscape park | Wuchang |
10 | Wuhanguan Dock | Cultural spot | Hankou | 33 | Wuchang Beach Park | Landscape park | Wuchang |
11 | Temple of the Dragon King | Cultural spot, landscape park | Hankou | 34 | Wuchang Beach Park | Landscape park | Wuchang |
12 | Nananzui Park | Landscape park | Hanyang | 35 | Moon Bay Square of Wuchang River Beach Park | Landscape park | Wuchang |
13 | Qingchuan Pavilion, Dayu Square Park | Cultural spot, landscape park | Hanyang | 36 | Moon Bay Dock | Transportation hub | Wuchang |
14 | Chaozong Park | Landscape park | Hanyang | 37 | Wuchang Beach Park | Landscape park | Wuchang |
15 | Yingwuzhou Culture Plaza | Cultural spot | Hanyang | 38 | Wuchang Beach Park | Landscape park | Wuchang |
16 | Hanyang beach Park | Landscape park | Hanyang | 39 | Wuchang Beach Park | Landscape park | Wuchang |
17 | Under building | / | Hanyang | 40 | Qingshan River Beach Park Phase I | Landscape park | Wuchang |
18 | Under building | / | Hanyang | 41 | Qingshan River Beach Park Phase I | Landscape park | Wuchang |
19 | Under building | / | Hanyang | 42 | Qingshan River Beach Park Phase I | Landscape park | Wuchang |
20 | Wuhan International EXPO Center | Public building | Hanyang | 43 | Qingshan River Beach Park Phase I | Landscape park | Wuchang |
21 | Yangsigang bridge Beach Park | Landscape park | Hanyang | 44 | Qingshan River Beach Park Phase II | Landscape park | Wuchang |
22 | Hongshan Beach Park | Landscape park | Wuchang | 45 | Jieteng jianjiu Music and Sports Park | Cultural spot, landscape park | Wuchang |
23 | Hongshan Beach Park | Landscape park | Wuchang | 46 | Jieteng jianjiu Music and Sports Park | Cultural spot, landscape park | Wuchang |
Length | Num | Length | Num | Length | Num |
---|---|---|---|---|---|
250 m | 73,469 | 2000 m | 4299 | 16,000 m | 135 |
500 m | 64,206 | 4000 m | 2154 | 32,000 m | 37 |
1000 m | 17,177 | 8000 m | 539 | 96,000 m | 4 |
Total number: 162,020 |
Dimension | Indicator | Calculation Formula | Description |
---|---|---|---|
Spatial | Space Density | is the number of users of each research unit, is the area of each research unit. | |
Space Distance | is the sum of user travel distance of each research unit, is the number of users of each research unit. | ||
Space Diversity | is the number of origins of users in research unit i. | ||
Temporal | Dwell Time | is the sum of the user Dwell Time per study unit, is the number of users of each research unit. | |
Dwell Frequency | is the sum of the user Dwell Time per study unit, is the number of users of each research unit. | ||
Time Diversity | refers to the proportion of the number of users arriving in research unit i to the total number of users arriving in 24 h a day during period j. |
Dimension | Indicator | Work Day Average | Work Day Standard Deviation | Work Day Weight | Rest Day Average | Rest Day Standard Deviation | Rest Day Weight |
---|---|---|---|---|---|---|---|
Spatial | Space Density Space Diversity | 0.069 0.201 | 0.148 0.156 | 40.31% 10.21% | 0.07 0.159 | 0.149 0.145 | 37.23% 10.28% |
Space Distance | 0.619 | 0.298 | 7.02% | 0.5 | 0.315 | 10.67% | |
Temporal | Dwell Time Dwell Frequency | 0.551 0.14 | 0.203 0.217 | 3.70% 35.61% | 0.518 0.145 | 0.22 0.219 | 4.26% 32.43% |
Time Diversity | 0.756 | 0.237 | 3.15% | 0.7 | 0.294 | 5.13% |
Variables | Beta (Weekday) | t (Weekday) | Beta (Weekend) | t (Weekend) | VIF |
---|---|---|---|---|---|
CPOI | 1.361 | 7.466 *** | 1.424 | 7.183 *** | 4.811 |
RPOI | 0.291 | 2.716 ** | 0.344 | 2.951 ** | 1.666 |
HPOI | −0.758 | −5.099 *** | −0.721 | −4.457 *** | 3.198 |
OPOI | −0.379 | −2.231 ** | −0.598 | −3.237 ** | 4.173 |
Simpson | −0.212 | −2.268 ** | −0.181 | −1.788 * | 1.259 |
Adjusted R2: 0.689 | Adjusted R2: 0.632 |
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Chen, Y.; Jia, B.; Wu, J.; Liu, X.; Luo, T. Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling. Land 2022, 11, 1434. https://doi.org/10.3390/land11091434
Chen Y, Jia B, Wu J, Liu X, Luo T. Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling. Land. 2022; 11(9):1434. https://doi.org/10.3390/land11091434
Chicago/Turabian StyleChen, Yuting, Bingyao Jia, Jing Wu, Xuejun Liu, and Tianyue Luo. 2022. "Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling" Land 11, no. 9: 1434. https://doi.org/10.3390/land11091434
APA StyleChen, Y., Jia, B., Wu, J., Liu, X., & Luo, T. (2022). Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling. Land, 11(9), 1434. https://doi.org/10.3390/land11091434