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Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India

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Abstract

Unplanned and rapidly increasing urbanization has been observed worldwide, especially in developing countries which causes manifest many environmental issues. The Indian cities have experienced rapid urban growth due to increased population, migration activities, economic development, capitalization, and rising of services and amenities over the last few decades which would led to the expansion of the city structure toward peri-urban areas. Therefore, it causes environmental degradation like landscape fragmentation, adverse effects on urban ecological balance, urban climate change, etc. This paper aims to study the spatiotemporal urban dynamics of the Kolkata Metropolitan Area (KMA) in terms of urban development trends over the year 2031. The research study has focused on land use/land cover change and incorporated it with an open-source SLEUTH model to predict future urban scenarios. SLEUTH was applied two two parts of KMA such as North KMA and South KMA to compare the urban growth. The model initiation with calibration showed a high value of the spreading coefficient indicating the organic type of growth has happened. New urban centers have grown along with the road networks in the northern part of KMA. Spontaneous growth is noticed in the study area. Urban growth is  spreading toward the peripheral areas of Kolkata city over the periods. Therefore, the city and suburban areas have become compact. SLEUTH urban growth model can be considered as a planning-oriented model that would help planners to plan the Kolkata agglomeration city based on future scenarios. Sustainable policies can be introduced in those areas where the compact urbanization challenges are more.

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References

  • Aburas, M., Ho, Y., Ramli, M., & Ash’aari, Z. (2016). The Simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007

    Article  Google Scholar 

  • Al-shalabi, M., Billa, L., & Pradhan, B. (2013). Modeling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: The case of Sana’s metropolitan city. Yemen. Environmental Earth Science, 70(1), 425–437. https://doi.org/10.1007/s12665-012-2137-6

    Article  Google Scholar 

  • Alsharif, A. A., & Pradhan, B. (2014). urban sprawl analysis of tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of Indian Society of Remote Sensing, 42(1), 149–163. https://doi.org/10.1007/s12524-013-0299-7

    Article  Google Scholar 

  • Arsanjani, J. J., Kainz, W., & Mousivand, A. J. (2011). Tracking dynamic land-use change using spatially explicit Markov Chain based on Cellular automata: The case of Tehran. International Journal of Image and Data Fusion, 2(4), 329–345. https://doi.org/10.1080/19479832.2011.605397

    Article  Google Scholar 

  • Barredo, J. I., Demicheli, L., Lavalle, C., Kasanko, M., & McCormick, N. (2004). Modelling future urban scenarios in developing countries an application case study in Lagos, Nigeria. Environment and Planning B: Planning and Design, 32, 65–84.

    Article  Google Scholar 

  • Batty, M. (2008). The size, scale, and shape of cities. Science, 319(5864), 769–771. https://doi.org/10.1126/science.1151419

    Article  Google Scholar 

  • Batty, M., & Xie, Y. (1994). From cells to Cities. Environment and Planning B: Planning and Design, 21, 31–48.

    Article  Google Scholar 

  • Batty, M., Xie, Y. C., & Sun, Z. L. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233. https://doi.org/10.1016/S0198-9715(99)00015-0

    Article  Google Scholar 

  • Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata. India. International Journal of Remote Sensing, 30(18), 4733–4733. https://doi.org/10.1080/01431160802651967

    Article  Google Scholar 

  • Bihamta, N., Soffiannian, A., & Sima, F. (2014). Using the SLEUTH urban growth model to simulate FUTURE urban expansion of the isfahan metropolitan area, Iran. Journal of Indian Society of Remote Sensing, 43, 407–414. https://doi.org/10.1007/s12524-014-0402-8

    Article  Google Scholar 

  • Census of India (2011) District Census Handbooks of Kolkata district, North and South Parganas district, Hooghly district, Howrah district, and Nadia district, West Bengal. Census of India, New Delhi.

  • Chakravorty, S. (2000). From colonial city to globalising city? The far from- complete spatial transformatin of Calcutta. In P. Marcuse & R. Kempen (Eds.), Globalizing cities: A new spatial order? (pp. 56–77). Blackwell.

    Chapter  Google Scholar 

  • Chandan, M., Nimish, G., & Bharath, H. (2019). Analyzing spatial patterns and trends of future urban expansion using SLEUTH. Spatial Information Research. https://doi.org/10.1007/s41324-019-00262-4

    Article  Google Scholar 

  • Chaudhuri, G. (2014). Temporal accuracy in urban growth forecasting: A study using the SLEUTH model. Transactions in GIS, 18(2), 302–320. https://doi.org/10.1111/tgis.12047

    Article  Google Scholar 

  • Chaudhuri, G., & Clarke, K. C. (2019). Modeling an Indian megalopolis- A case study on adapting the SLEUTH urban growth model. Computers, Environment, and Urban Systems. https://doi.org/10.1016/j.compenvurbsys.2019.101358

    Article  Google Scholar 

  • Clarke, K. (2008). Mapping and modeling land use change: an application of the SLEUTH model. In: C. Pettit, W. Cartwright, I. Bishop, K. Lowell, D. Pullar, D. Duncan (Eds.) Landscape analysis and visualisation: lecture notes in geoinformation and cartography. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-69168-6_17

    Chapter  Google Scholar 

  • Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and washing/Baltimore. International Journal of Geographical Information Science, 12(7), 699–714. https://doi.org/10.1080/136588198241617

    Article  Google Scholar 

  • Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–261.

    Article  Google Scholar 

  • Congalton, R., Green, K. (1999). Assessing the accuracy of Remote Sensed Data: Principles and Practices. Boca Raton.

  • Congalton, R. (1991). A review of assessing the accuracy of the classification of remotely sensed data. Remote Sensing Environment, 37, 35–46.

    Article  Google Scholar 

  • Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision. Remote Sensing Applications: Society and Environment, 19, 1–28. https://doi.org/10.1016/j.rsase.2020.100322

    Article  Google Scholar 

  • Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography. https://doi.org/10.1016/j.apgeog.2008.12.005

    Article  Google Scholar 

  • Dietzel, C., & Clarke, K. C. (2006). Decreasing Computational Time of Urban Cellular Automata Through Model Portability. GeoInformatica, 10, 197–211. https://doi.org/10.1007/s10707-006-7579-0

    Article  Google Scholar 

  • Dietzel, C., & Clarke, K. C. (2007). Toward optimal calibration of the SLEUTH land use change model. Transaction in GIS, 11(1), 29–45. https://doi.org/10.1111/j.1467-9671.2007.01031.x

    Article  Google Scholar 

  • Dietzel, C., Herold, M., Hemphill, J. J., & Clarke, K. C. (2005). Spatio-temporal dynamics in California’s Central Valley: Empirical links to Urban theory. International Journal of Geographical Information Science, 19(2), 175–195. https://doi.org/10.1080/13658810410001713407

    Article  Google Scholar 

  • Feng, H. H., Liu, H. P., & Lu, Y. (2012). Scenario prediction and analysis of urban growth using SLEUTH model. Pedosphere, 22(2), 206–216. https://doi.org/10.1016/S1002-0160(12)60007-1

    Article  Google Scholar 

  • Foody, G. (2002). Status of land cover classification accuracy assessment. Remote Sensing Environment, 80, 185–201.

    Article  Google Scholar 

  • Foody, G. (2004). Thematic map comparison: Evaluating the statistical significance of the difference in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70, 627–633.

    Article  Google Scholar 

  • Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land-use change by the integration of cellular automaton and Markov model. Ecological Modelling. https://doi.org/10.1016/j.ecolmodel.2011.09.009

    Article  Google Scholar 

  • He, C., Okada, N., Zhang, Q., Shi, P., & Zhang, J. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing. China. Applied Geography, 26(3–4), 323–345. https://doi.org/10.1016/j.apgeog.2006.09.006

    Article  Google Scholar 

  • Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis, and modeling. Remote Sensing of Environment, 86(3), 286–302. https://doi.org/10.1016/S0034-4257(03)00075-0

    Article  Google Scholar 

  • Hosseinali, F., Alesheikh, A., & Nourian, F. (2013). Agent-based modeling of urban land use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, 105–113. https://doi.org/10.1016/j.cities.2012.09.002

    Article  Google Scholar 

  • Hu, Z., & Lo, C. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31, 667–688.

    Article  Google Scholar 

  • Hua, L., Tang, L., Cui, S., & Yin, K. (2014). Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability, 6, 3899–3914. https://doi.org/10.3390/su6063899

    Article  Google Scholar 

  • Jantz, C. A., Goetz, S. J., Donato, D., & Claggett, P. (2010). Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems, 34, 1–16. https://doi.org/10.1016/j.compenvurbsys.2009.08.003

    Article  Google Scholar 

  • Jat, M. K., Choudhary, M., & Saxena, A. (2017). Application of geo-spatial techniques and cellular automata for modelling growth of a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Sciences, 20(2), 223–241. https://doi.org/10.1016/j.ejrs.2017.02.002

    Article  Google Scholar 

  • Jat, M. K., Garg, P., & Khare, D. (2008). Monitoring and modeling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43. https://doi.org/10.1016/j.jag.2007.04.002

    Article  Google Scholar 

  • Jensen, J. R. (2007). Remote sensing of the environment: An earth resource perspective. Pearson.

    Google Scholar 

  • Kantakumar, L. N., Kumar, S., & Schneider, K. (2016). Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat International, 51, 11–22. https://doi.org/10.1016/j.habitatint.2015.10.007

    Article  Google Scholar 

  • KantaKumar, L., Sawant, N. G., & Kumar, S. (2011). Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area. International Journal of Geomatics and Geosciences, 2(2), 568–579.

    Google Scholar 

  • Karolien, V., Anton, V. R., Maarten, L., Eria, S., & Paul, M. (2012). Urban growth of Kampala, Uganda: Pattern analysis and scenario development. Landscape and Urban Planning, 106, 199–206.

    Article  Google Scholar 

  • Knox, P. L. (1994). The restless urban landscape. Englewood Cliffs. Prentice Hall.

    Google Scholar 

  • Kuang, W. (2012). Spatio-temporal patterns of intra-urban land-use change in Beijing, China between 1984 and 2008. Chinese Geographical Science, 22(2), 210–220.

    Article  Google Scholar 

  • Kundu, A. (2011). Trends and processes of urbanisation in India. Urbanization and Emerging Population Issues 6. London: Human Settlements Group International, Institute for Environment and Development (IIED) and United Nations Population Fund (UNFPA). https://pubs.iied.org/pdfs/10597IIED.pdf

  • Kundu, K., Halder, P., & Mandal, J. (2020). Urban change detection analysis during 1978–2017 at Kolkata, India, using multi-temporal satellite data. Journal of the Indian Society of Remote Sensing, 48(11), 1535–1554.

    Article  Google Scholar 

  • Lea, C., & Curtis, A. (2010). Thematic accuracy assessment procedures: National Park Service Vegetation Inventory, version 2.0. Fort Collins, Colorado: Natural resource report NPS/2010/NRP-2010/204, National Park Service.

  • Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), 389–411. https://doi.org/10.5721/EuJRS20144723

    Article  Google Scholar 

  • Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land-use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343.

    Article  Google Scholar 

  • Li, X., & Yeh, A. (2004). Analyzing spatial restructuring of land use patterns in a fast-growing region using remote sensing and GIS. Landscape and Urban Planning, 69(4), 335–354. https://doi.org/10.1016/j.landurbplan.2003.10.033

    Article  Google Scholar 

  • Lillesand, T., & Keifer, R. (1987). Remote sensing and image interpretation. John Willey and Sons.

    Google Scholar 

  • Liu, Y., Li, L., Chen, L., Cheng, L., Zhou, X., & Cui, Y. (2019). Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei. East China PloS ONE. https://doi.org/10.1371/journal.pone.0224998

    Article  Google Scholar 

  • Lopez, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe A case in Morelia city, Mexico. Landscape and Urban Planning, 55, 271–285.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2005). Urban classification using full spectral information of landsat ETM imagery in marion country Indiana. Photogrammetric Engineering & Remote Sensing. https://doi.org/10.14358/PERS.71.11.1275

    Article  Google Scholar 

  • Mandal, J., Ghosh, N., & Mukhopadhyay, A. (2019). Urban growth dynamics and changing land-use land-cover of megacity Kolkata and its environs. Journal of the Indian Society of Remote Sensing, 47(10), 1707–1725. https://doi.org/10.1007/s12524-019-01020-7

    Article  Google Scholar 

  • Mithun, S., Chattopadhyay, S., & Bhatta, B. (2016). Analyzing urban dynamics of Metropolitan Kolkata, India by using landscape metrics. Papers in Applied Geography, 2(3), 1–14. https://doi.org/10.1080/23754931.2016.1148069

    Article  Google Scholar 

  • Mondal, B., Das, D. N., & Bhatta, B. (2016). Integrating cellular automata and Markov techniques to generate urban development potential surface: A study on Kolkata agglomeration. Geocarto International, 32(4), 1–20. https://doi.org/10.1080/10106049.2016.1155656

    Article  Google Scholar 

  • Mondal, B., Das, D. N., & Dolui, G. (2015). Modeling spatial variation of explanatory factors of urban expansion of Kolkata: A geographically weighted regression approach. Modeling Earth Systems and Environment, 1(29), 1–13. https://doi.org/10.1007/s40808-015-0026-1

    Article  Google Scholar 

  • Mondal, B., Dolui, G., Pramanik, M., Maity, S., Biswas, S. S., & Pal, R. (2017). Urban expansion and wetland shrinkage estimation using a GIS-based model in the East Kolkata Wetland, India. Ecological Indicators, 83, 62–73. https://doi.org/10.1016/j.ecolind.2017.07.037

    Article  Google Scholar 

  • Musa, S., Hashim, M., & Roba, M. (2017). A review of geospatial-based urban growth models and modeling initiatives. Geocarto International, 32(8), 813–833.

    Article  Google Scholar 

  • Pal, A. (2006). Scope for bottom-up planning in Kolkata: rhetoric vs reality. Environment and Urbanization, 18(2), 501–521.

    Article  Google Scholar 

  • Parveen, M. T., & Ilahi, R. A. (2022). Assessment of land-use change and its impact on the environment using GIS techniques: a case of Kolkata Municipal Corporation, West Bengal, India. Geojournal. https://doi.org/10.1007/s10708-022-10581-z

    Article  Google Scholar 

  • Pramanik, M. M., & Stathakis, D. (2015). Forecasting urban sprawl in Dhaka city of Bangladesh. Environment and Planning B: Planning and Design. https://doi.org/10.1177/0265813515595406

    Article  Google Scholar 

  • Rafiee, R., Mahiny, A. S., Khorasani, N., Darvishsefat, A. A., & Danekar, A. (2009). Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities, 26, 19–26.

    Article  Google Scholar 

  • Rahaman, M., Dutta, S., & Sahana, M. (2018). Analyzing urban sprawl and spatial expansion of Kolkata urban agglomeration using geospatial approach. In P. Kumar (Ed.), Applications and challenges of geospatial technology. Springer.

    Google Scholar 

  • Ramachandra, T., Aithal, B. H., & Sowmyashree, M. (2014). Urban structure in Kolkata: Metrics and modelling through geo-informatics. Applied Geomatics, 6, 229–244. https://doi.org/10.1007/s12518-014-0135-y

    Article  Google Scholar 

  • Rukhsana, & Hasnine, M. (2021). Population pressure and urban sprawl in kolkata metropolitan area. In Rukhsana (Ed.), Habitat, Ecology and Ekistics, Advances in Asian Human-Envionmental Research. Springer Nature.

    Chapter  Google Scholar 

  • Sahana, M., Hong, H., & Sajjad, H. (2018). Analyzing urban spatial pattern and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Science of the Total Environment, 628(629), 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170

    Article  Google Scholar 

  • Sante, I., Garcia, A., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes. A review and analysis. Land use and Urban planning, 96(2), 108–122. https://doi.org/10.1016/j.landurbplan.2010.03.001

    Article  Google Scholar 

  • Saxena, A., & Jat, M. K. (2019). Capturing heterogeneous urban growth using the SLEUTH model. Remote Sensing Applications: Society and Environment, 13, 426–434. https://doi.org/10.1016/j.rsase.2018.12.012

    Article  Google Scholar 

  • Sengupta, U. (2007). Housing reform in Kolkata: Change and Challenges. Housing Studies, 22(6), 965–979.

    Article  Google Scholar 

  • Serasinghe Pathiranage, I. S., Kantakumar, L. N., & Sundaramoorthy, S. (2018). Remote sensing data, and SLEUTH urban growth model: as decision support tools for urban planning. Chin. Geogr. Sci. 28, 274–286. https://doi.org/10.1007/s11769-018-0946-6

    Article  Google Scholar 

  • Sevik, O. (2006). Application of SLEUTH model in Antalya. The graduate school of natural and applied sciences of Middle East Technical University. https://etd.lib.metu.edu.tr/upload/12607190/index.pdf

  • Silva, E., & Clarke, K. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525–552. https://doi.org/10.1016/S0198-9715(01)00014-X

    Article  Google Scholar 

  • Sivaramakrishnan, K. C., Kundu, A., & Singh, B. (2005). Handbook of urbanization in India: An analysis of trends and process. Oxford University Press.

    Google Scholar 

  • Smits, P. C., Dellepiane, S. G., & Schowengerdt, R. A. (1999). Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461–1486.

    Article  Google Scholar 

  • Story, M., & Congalton, R. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing, 52, 397–399.

    Google Scholar 

  • Strahler, A. H. (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10, 135–163.

    Article  Google Scholar 

  • Sudhira, H., Ramachandran, T., & Kaup, J. (2003). Urban Sprawl pattern recognition and modeling using GIS. Proc Map India, New Delhi.

  • Turner, B., II. (2009). Land change science. In R. Kitchen & N. Thrift (Eds.), International encyclopedia of human geography (pp. 107–111). Elsevier.

    Chapter  Google Scholar 

  • Verburg, P., Kok, K., Pontius, R., & Veldkamp, A. (2006). Land-use and land- cover change. Global change-The IGBP series. In: Modeling land-use and land-cover change. (E. Lambin, & H. Geist, Trans.). Springer.

  • Wegener, M. (1994). Operational urban model’s state of the art. Journal of the American Planning Association, 60(1), 17–29.

    Article  Google Scholar 

  • Wu, Q., Li, H. Q., Wang, R. S., Paulussen, J., He, Y., Wang, M., et al. (2006). Monitoring and predicting land-use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78, 322–333.

    Article  Google Scholar 

  • Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., & Liang, Y. (2006). Evaluating urban expansion and land use change in Shijiazhuang China, by using GIS and remote sensing. Landscape and Urban Planning, 75, 69–80.

    Article  Google Scholar 

  • Xin, Y., Xin, Q. Z., & Li-Na, L. (2012). A spatiotemporal model of land-use change based on ant colony optimization, Markov chain, and cellular automata. Ecological Modelling, 233, 11–19.

    Article  Google Scholar 

  • Yadav, V., & Bhagat, R. (2015). Spatial dynamics of population in Kolkata urban agglomeration. In R. Singh (Ed.), Urban development challenges, risks and resilience in asian mega cities. Springer.

    Google Scholar 

  • Yang, X., & Lo, C. (2003). Modelling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17(5), 463–488.

    Article  Google Scholar 

  • Youssef, A. M., Pradhan, B., & Tarabees, E. (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: Contribution from the analytic hierarchy process. Arabian Journal of Geoscience, 4, 463–473.

    Article  Google Scholar 

  • Zhou, Y., Varquez, A. C., & Kanda, M. (2019). High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Scientific Data, 6, 34.

    Article  Google Scholar 

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  1. Department of Geography, Mangalore University, Mangalagangotri, Mangalore, Karnataka, 574199, India

    Sandipta Das

  2. CGDD/VRG/EPS, Space Application Centre, ISRO, Ahmadabad, India

    Gaurav V. Jain

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Das, S., Jain, G.V. Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India. J Indian Soc Remote Sens 50, 2277–2302 (2022). https://doi.org/10.1007/s12524-022-01602-y

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