Considering Regional Connectivity and Policy Factors in the Simulation of Land Use Change in New Areas: A Case Study of Nansha New District, China
Source: By:Zehuan Zheng, Shi Xian
DOI: https://doi.org/10.30564/jgr.v6i3.5814
Abstract:Numerous emerging development areas worldwide are receiving attention; however, current research on land use change simulation primarily concentrates on cities, urban clusters, or larger scales. Moreover, there is a limited focus on understanding the impact of regional connectivity with surrounding cities and policy factors on land use change in these new areas. In this context, the present study utilizes a cellular automata (CA) model to investigate land use changes in the case of Nansha New District in Guangzhou, China. Three scenarios are examined, emphasizing conventional locational factors, policy considerations, and the influence of regional connectivity with surrounding cities. The results reveal several key findings: (1) Between 2015 and 2021, Nansha New District experienced significant land use changes, with the most notable shifts observed in cultivated land, water area, and construction land. (2) The comprehensive scenario exhibited the highest simulation accuracy, indicating that Nansha New District, as an emerging area, is notably influenced by policy factors and regional connectivity with surrounding cities. (3) Predictions for land use changes in Nansha by 2030, based on the scenario with the highest level of simulation accuracy, suggest an increase in the proportion of cultivated and forest land areas, alongside a decrease in the proportion of construction land and water area. This study contributes valuable insights to relevant studies and policymakers alike.
References:[1] Liu, X., Hu, G., Ai, B., et al., 2018. Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics. International Journal of Geographical Information Science. 32(1), 73-101. DOI: https://doi.org/10.1080/13658816.2017.1376065 [2] Liu, X., Li, X., Shi, X., et al., 2010. Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science. 24(5), 783-802. DOI: https://doi.org/10.1080/13658810903270551 [3] Zhou, R., Lin, M., Gong, J., et al., 2019. Spatiotemporal heterogeneity and influencing mechanism of ecosystem services in the Pearl River Delta from the perspective of LUCC. Journal of Geographical Sciences. 29(5), 831-845. DOI: https://doi.org/10.1007/s11442-019-1631-0 [4] Beroho, M., Briak, H., Cherif, E.K., et al., 2023. Future scenarios of land use/land cover (LULC) based on a CA-markov simulation model: Case of a Mediterranean watershed in Morocco. Remote Sensing. 15(4), 1162. DOI: https://doi.org/10.3390/rs15041162 [5] Li, X., Yang, Q., Liu, X., 2007. Genetic algorithms for determining the parameters of cellular automata in urban simulation. Science in China Series D: Earth Sciences. 50(12), 1857-1866. DOI: https://doi.org/10.1007/s11430-007-0127-4 [6] Lin, W., Sun, Y., Nijhuis, S., et al., 2020. Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study. Science of the Total Environment. 739, 139899. DOI: https://doi.org/10.1016/j.scitotenv.2020.139899 [7] Hasan, S., Shi, W., Zhu, X., et al., 2020. Future simulation of land use changes in rapidly urbanizing South China based on land change modeler and remote sensing data. Sustainability. 12(11), 4350. DOI: https://doi.org/10.3390/su12114350 [8] Li, D., Li, X., Liu, X., et al., 2012. GPU-CA model for large-scale land-use change simulation. Chinese Science Bulletin. 57(19), 2442-2452. DOI: https://doi.org/10.1007/s11434-012-5085-3 [9] Liu, X., Liang, X., Li, X., et al., 2017. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning. 168, 94-116. DOI: https://doi.org/10.1016/j.landurbplan.2017.09.019 [10]Liu, X., Li, X., Shi, X., et al., 2012. A multitype ant colony optimization (MACO) method for optimal land use allocation in large areas. International Journal of Geographical Information Science. 26(7), 1325-1343. DOI: https://doi.org/10.1080/13658816.2011.635594 [11] van Vliet, J., Hurkens, J., White, R., et al., 2012. An activity-based cellular automaton model to simulate land-use dynamics. Environment and Planning B: Planning and Design. 39(2), 198-212. DOI: https://doi.org/10.1068/b36015 [12]Xiong, N., Yu, R., Yan, F., et al., 2022. Land use and land cover changes and prediction based on multi-scenario simulation: A case study of Qishan County, China. Remote Sensing. 14(16), 4041. DOI: https://doi.org/10.3390/rs14164041 [13]Xu, X., Zhang, D., Liu, X., et al., 2022. Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: A case study on city of Toronto. Geo-spatial Information Science. 25(3), 439-456. DOI: https://doi.org/10.1080/10095020.2022.2043730 [14]Liu, X., Li, X., Shi, X., et al., 2010. Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science. 24(5), 783-802. DOI: https://doi.org/10.1080/13658810903270551 [15]Wu, F., 2002. Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science. 16(8), 795-818. DOI: https://doi.org/10.1080/13658810210157769 [16]Zhang, J., Hou, Y., Dong, Y., et al., 2022. Land use change simulation in rapid urbanizing regions: a case study of Wuhan urban areas. International Journal of Environmental Research and Public Health. 19(14), 8785. DOI: https://doi.org/10.3390/ijerph19148785 [17]Chen, C., Liu, Y., 2021. Spatiotemporal changes of ecosystem services value by incorporating planning policies: A case of the Pearl River Delta, China. Ecological Modelling. 461, 109777. DOI: https://doi.org/10.1016/j.ecolmodel.2021.109777 [18]Lai, Z., Chen, C., Chen, J., et al., 2022. Multi-scenario simulation of land-use change and delineation of urban growth boundaries in county area: A case study of Xinxing County, Guangdong Province. Land. 11(9), 1598. DOI: https://doi.org/10.3390/land11091598 [19]Liao, W., Liu, X., Xu, X., et al., 2020. Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China. Science Bulletin. 65(22), 1935-1947. DOI: https://doi.org/10.1016/j.scib.2020.07.014 [20]Li, L., Huang, X., Yang, H., 2023. Scenario-based urban growth simulation by incorporating ecological-agricultural-urban suitability into a future land use simulation model. Cities. 137,104334. DOI: https://doi.org/10.1016/j.cities.2023.104334 [21]Zhai, Y., Yao, Y., Guan, Q., et al., 2020. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. International Journal of Geographical Information Science. 34(7), 1475-1499. DOI: https://doi.org/10.1080/13658816.2020.1711915 [22]Yao, Y., Liu, X., Li, X., et al., 2017. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science.31(12), 2452-2479. DOI: https://doi.org/10.1080/13658816.2017.1360494 [23]Liu, L., Yu, S., Zhang, H., et al., 2023. Analysis of land use change drivers and simulation of different future scenarios: Taking Shanxi Province of China as an example. International Journal of Environmental Research and Public Health. 20(2), 1626. DOI: https://doi.org/10.3390/ijerph20021626 [24]Zhang, D., Liu, X., Wu, X., et al., 2019. Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China. GIScience & Remote Sensing. 56(2), 282-308. DOI: https://doi.org/10.1080/15481603.2018.1507074 [25]Arficho, M., Thiel, A., 2020. Does land-use policy moderate impacts of climate anomalies on lulc change in dry-lands? An empirical enquiry into drivers and moderators of LULC change in Southern Ethiopia. Sustainability. 12(15), 6261. DOI: https://doi.org/10.3390/su12156261 [26]Kolb, M., Galicia, L., 2018. Scenarios and story lines: Drivers of land use change in southern Mexico. Environment, Development and Sustainability. 20(2), 681-702. DOI: https://doi.org/10.1007/s10668-016-9905-5 [27]Fan, T., 2006. Improvements in intra-European inter-city flight connectivity: 1996-2004. Journal of Transport Geography. 14(4), 273-286. DOI: https://doi.org/10.1016/j.jtrangeo.2005.08.006 [28]Allard, R.F., Moura, F., 2016. The incorporation of passenger connectivity and intermodal considerations in intercity transport planning.Transport Reviews. 36(2), 251-277. DOI: https://doi.org/10.1080/01441647.2015.1059379 [29]Yang, Y., Li, D., Li, X., 2019. Public transport connectivity and intercity tourist flows. Journal of Travel Research. 58(1), 25-41. DOI: https://doi.org/10.1177/0047287517741997 [30]Lin, J., Wu, Z., Li, X., 2019. Measuring inter-city connectivity in an urban agglomeration based on multi-source data. International Journal of Geographical Information Science. 33(5), 1062-1081. DOI: https://doi.org/10.1080/13658816.2018.1563302 [31]GB/T 21010-2017 (GBT21010-2017). Current land use classification [Internet]. China National Standardization Administration. Available from:https://www.chinesestandard.net/PDF/English.aspx/GBT21010-2017 [32]Yan, J., Zhang, Y., Liu, L., et al., 2002. Land use and landscape pattern change: A linkage to the construction of the Qinghai-Xizang Highway.Journal of Geographical Sciences. 12(3), 253-265. DOI: https://doi.org/10.1007/bf02837543 [33]Liu, X., Li, X., Anthony, G.O.Y., 2006. Multiagent systems for simulating spatial decision behaviors and land-use dynamics. Science in China Series D: Earth Sciences. 49(11), 1184-1194.DOI: https://doi.org/10.1007/s11430-006-1184-9 [34] Barrat, A., Barthelemy, M., Pastor-Satorras, R., et al., 2004. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America. 101(11), 3747-3752. DOI: https://doi.org/10.1073/pnas.0400087101 [35]Batty, M., 2018. The new science of cities. The MIT Press: Cambridge. [36]Newman, M.E., 2004. Analysis of weighted networks. Physical Review E. 70(5), 056131. DOI: https://doi.org/10.1103/PhysRevE.70.056131 [37]Onnela, J.P., Saramäki, J., Hyvönen, J., et al., 2007. Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics. 9(6), 179. DOI: https://doi.org/10.1088/1367-2630/9/6/179 [38]Opsahl, T., Agneessens, F., Skvoretz, J., 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks. 32(3), 245-251. DOI: https://doi.org/10.1016/j.socnet.2010.03.006 [39]Zhou, Z.H., Wu, J., Tang, W., 2002. Ensembling neural networks: Many could be better than all. Artificial Intelligence. 137(1-2), 239-263.DOI: https://doi.org/10.1016/S0004-3702(02)00190-X [40]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. DOI: https://doi.org/10.1080/13658810210137004 [41]Pontius Jr, R.G., Walker, R., Yao-Kumah, R., et al., 2007. Accuracy assessment for a simulation model of Amazonian deforestation. Annals of the Association of American Geographers. 97(4), 677-695. DOI: https://doi.org/10.1111/j.1467-8306.2007.00577.x [42]Gerard, B.M.H., 1998. Error propagation in environmental modelling with GIS. CRC Press:Boca Raton.