Abstract
In recent years, many cities in the world have been hit by the high temperature heat wave and suffered heavy losses. And many coastal cities have been affected to a certain extent. Through reasonable urban form and architectural design to cope with the heat wave, improving the city’s ability to cope with high temperature has become an important planning and mitigation strategy to adapt to urban high temperature. In this paper, big data is applied to the study of coastal weather characteristics. From the perspective of urban planning and architecture, the interaction mechanism of coastal weather characteristics is discussed by using quantitative analysis methods such as correlation analysis and spatial regression model, which provides an important basis for the planning and urban design of high temperature heat wave. Then, taking urban morphology parameters, land use parameters and LST as variables, Pearson correlation coefficient was calculated by SPSS and GeoDa tools, and a spatial regression model was established to explore the quantitative relationship between coastal weather characteristics and land surface temperature. In the Pearson correlation coefficient, the correlation between vegetation coverage and LST is the largest, showing a negative correlation, with the coefficient of −0.595; 595. In addition to coastal climate types, the correlation between building density and LST is the largest, the coefficient is 0.360, positive correlation, the correlation is the smallest. With the application of big data technology, the tax collection and management mode will develop in the direction of intelligence, efficiency, fairness, and accuracy. Big data technology will provide a new direction for the research of coastal meteorological characteristics and the optimization of urban enterprise finance and taxation. In this paper, through the study of coastal meteorological characteristics of big data, it is applied in the city enterprise financial tax, to promote the enterprise financial tax more standardized.
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Change history
17 November 2021
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-021-08988-y
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08471-8
References
Aminu M, Matori AN, Yusof KW, Malakahmad A, Zainol RB (2017) Analytic network process (ANP)—based spatial decision support system (SDSS) for sustainable tourism planning in Cameron Highlands, Malaysia. Arab J Geosci 10(13). https://doi.org/10.1007/s12517-017-3067-0
Banerjee P, Ghose MK, Pradhan R (2018) Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya. Arab J Geosci 11(7):1–8. https://doi.org/10.1007/s12517-018-3488-4
Bibri SE (2018) A foundational framework for smart sustainable city development: theoretical, disciplinary, and discursive dimensions and their synergies. Sustain Cities Soc 38:758–794. https://doi.org/10.1016/j.scs.2017.12.032
Canepa B (2007) Bursting the bubble: determining the transit-oriented development’s walkable limits. Transp Res Rec 1992(1):28–34. https://doi.org/10.3141/1992-04
Celik E, Aydin N, Gumus AT (2014) A multiattribute customer satisfaction evaluation approach for rail transit network: a real case study for Istanbul, Turkey. Transp Policy 36:283–293. https://doi.org/10.1016/j.tranpol.2014.09.005
Ewing R, Cervero R (2010) Travel and the built environment. J Am Plan Assoc 76(3):265–294. https://doi.org/10.1080/01944361003766766
Habibian M, Hosseinzadeh A (2018) Walkability index across trip purposes. Sustain Cities Soc 42:216–225. https://doi.org/10.1016/j.scs.2018.07.005
Hollevoet J, De Witte A, Macharis C (2011) Improving insight in modal choice determinants: an approach towards more sustainable transport. WIT Transactions on the Built Environment 116:129–141. https://doi.org/10.2495/UT110121
Jones E (2003) Walkable towns: the liveable neighbourhoods strategy. Sustainable Transport:314–325. https://doi.org/10.1016/B978-1-85573-614-6.50029-X
Kamruzzaman M, Baker D, Washington S, Turrell G (2014) Advance transit oriented development typology: case study in brisbane. Australia J Transp Geogr 34:54–70. https://doi.org/10.1016/j.jtrangeo.2013.11.002
Loo BPY, Du Verle F (2016) Transit-oriented development in future cities: towards a two-level sustainable mobility strategy. Int J Urban Sci 21(sup1):54–67. https://doi.org/10.1080/12265934.2016.1235488
Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20(7):703–726. https://doi.org/10.1080/13658810600661508
Motlagh MG, Amraei B, Halimi M (2020) Evaluating the hazardous potential of the dieback of the Zagros Oak forests using the multi-criteria decision-making methods. Arab J Geosci 13:995. https://doi.org/10.1007/s12517-020-05992-6
Schlossberg M, Brown N (2004) Comparing transit-oriented development sites by walkability performance measures. Transp Res Rec 34–42:34–42. https://doi.org/10.3141/1887-05
Singh MP, Singh P (2017) Multi-criteria GIS modeling for optimum route alignment planning in outer region of Allahabad City, India. Arab J Geosci 10:294. https://doi.org/10.1007/s12517-017-3076-z
Singh YJ, Fard P, Zuidgeest M, Brussel M, van Maarseveen M (2014) Measuring transit oriented development: a spatial multi criteria assessment approach for the City Region Arnhem and Nijmegen J. Transp Geogr 35:130–143. https://doi.org/10.1016/j.jtrangeo.2014.01.014
Singh YJ, Lukman A, Flacke J, Zuidgeest M, Van Maarseveen MF (2017) Measuring TOD around transit nodes-towards TOD policy. Transp Policy 56:5696–5111. https://doi.org/10.1016/j.tranpol.2017.03.013
Zhao C, Nielsen TA, Olafsson AS, Carstensen TA, Meng X (2018) Urban form, demographic and socio-economic correlates of walking, cycling, and e-biking: evidence from eight neighborhoods in Beijing. Transp Policy 64:102–112. https://doi.org/10.1016/j.tranpol.2018.01.018
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Resposible Editor: Sheldon Williamson
This article is part of the Topical Collection on Environment and Low Carbon Transportation
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-08988-y
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Li, Z., Ping, C. RETRACTED ARTICLE: Coastal meteorological characteristics based on big data and financial tax optimization of urban enterprises. Arab J Geosci 14, 1499 (2021). https://doi.org/10.1007/s12517-021-07892-9
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DOI: https://doi.org/10.1007/s12517-021-07892-9