Inferring the heterogeneous effect of urban land use on building height with causal machine learning

References

• Athey, S. 2015. “Machine Learning And Causal Inference For Policy Evaluation.” In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney NSW Australia, 5–16.
   Google Scholar
• Athey, S., and G. Imbens. 2016. “Recursive Partitioning For Heterogeneous Causal Effects.” Proceedings of the National Academy of Sciences 113 (27): 7353–7360. https://doi.org/10.1073/pnas.1510489113.
   PubMed Web of Science ®Google Scholar
• Athey, S., J. TIbshirani, and S. Wager. 2019. “Generalized Random Forests.” The Annals of Statistics 47 (2): 1148–1178. https://doi.org/10.1214/18-AOS1709.
   Web of Science ®Google Scholar
• Brenning, A. 2022. “Spatial Machine-learning Model Diagnostics: A Model-agnostic Distance-based Approach.” International Journal of Geographical Information Science 37 (3): 1–23. https://doi.org/10.1080/13658816.2022.2131789.
   Google Scholar
• Cai, J., and Y. Chen. 2022. “A Novel Unsupervised Deep Learning Method for the Generalization of Urban Form.” Geo-Spatial Information Science 25 (4): 568–587. https://doi.org/10.1080/10095020.2022.2068384.
   Web of Science ®Google Scholar
• Cao, K., M. Batty, B. Huang, Y. Liu, L. Yu, and J. Chen. 2011. “Spatial Multi-Objective Land Use Optimization: Extensions to the Non-Dominated Sorting Genetic Algorithm-II.” International Journal of Geographical Information Science 25 (12): 1949–1969. https://doi.org/10.1080/13658816.2011.570269.
   Web of Science ®Google Scholar
• Chen, Y. 2022. “An Extended Patch-Based Cellular Automaton to Simulate Horizontal and Vertical Urban Growth Under the Shared Socioeconomic Pathways.” Computers, Environment and Urban Systems 91:101727. https://doi.org/10.1016/j.compenvurbsys.2021.101727.
   Web of Science ®Google Scholar
• Chen, Y., and M. Feng. 2022. “Urban Form Simulation in 3D Based on Cellular Automata and Building Objects Generation.” Building and Environment 226:109727. https://doi.org/10.1016/j.buildenv.2022.109727.
   Web of Science ®Google Scholar
• Chen, G., X. Li, X. Liu, Y. Chen, X. Liang, J. Leng, X. Xu, et al. 2020. “Global Projections of Future Urban Land Expansion Under Shared Socioeconomic Pathways.” Nature Communications 11 (1): 537. https://doi.org/10.1038/s41467-020-14386-x.
   PubMed Web of Science ®Google Scholar
• Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, J. Robins, et al. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1): C1–C68. https://doi.org/10.1111/ectj.12097.
   Web of Science ®Google Scholar
• Clarke, K., and L. Gaydos. 1998. “Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore.” International Journal of Geographical Information Science 12 (7): 699–714. https://doi.org/10.1080/136588198241617.
   PubMed Web of Science ®Google Scholar
• Cui, P. 2020. “Causal Inference Meets Machine Learning.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 3527–3528.
   Google Scholar
• Cui, P., and S. Athey. 2022. “Stable Learning Establishes Some Common Ground Between Causal Inference and Machine Learning.” Nature Machine Intelligence 4 (2): 110–115. https://doi.org/10.1038/s42256-022-00445-z.
   Google Scholar
• Eikelboom, T., R. Janssen, and T. Stewart. 2015. “A Spatial Optimization Algorithm for Geodesign.” Landscape and Urban Planning 144:10–21. https://doi.org/10.1016/j.landurbplan.2015.08.011.
   Web of Science ®Google Scholar
• Feng, Y., and X. Tong. 2020. “A New Cellular Automata Framework of Urban Growth Modeling by Incorporating Statistical and Heuristic Methods.” International Journal of Geographical Information Science 34 (1): 74–97. https://doi.org/10.1080/13658816.2019.1648813.
   Web of Science ®Google Scholar
• Foster, D. J., and V. Syrgkanis. 2023. “Orthogonal Statistical Learning.” Annals of Statistics 51 (3): 879–908. https://doi.org/10.1214/23-AOS2258.
   Google Scholar
• Funderburg, R. G., H. Nixon, M. G. Boarnet, and G. Ferguson. 2010. “New Highways and Land Use Change: Results from a Quasi-Experimental Research Design.” Transportation Research Part A: Policy and Practice 44 (2): 76–98. https://doi.org/10.1016/j.tra.2009.11.003.
   Web of Science ®Google Scholar
• Hagenauer, J., H. Omrani, and M. Helbich. 2019. “Assessing the Performance of 38 Machine Learning Models: The Case of Land Consumption Rates in Bavaria, Germany.” International Journal of Geographical Information Science 33 (7): 1399–1419. https://doi.org/10.1080/13658816.2019.1579333.
   Web of Science ®Google Scholar
• Hewitt, R. J., M. Shadmanroodposhti, and B. A. Bryan. 2022. “There’s No Best Model! Addressing Limitations of Land-Use Scenario Modelling Through Multi-Model Ensembles.” International Journal of Geographical Information Science 36 (12): 2352–2385. https://doi.org/10.1080/13658816.2022.2098299.
   Web of Science ®Google Scholar
• Hidalgo, C. A., E. Castañer, and A. Sevtsuk. 2020. “The Amenity Mix of Urban Neighborhoods.” Habitat International 106:102205. https://doi.org/10.1016/j.habitatint.2020.102205.
   Web of Science ®Google Scholar
• Imbens, G. W., and D. B. Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. New York, USA: Cambridge University Press.
   Google Scholar
• Jacob, D. 2021. “CATE Meets ML: Conditional Average Treatment Effect and Machine Learning.” Digital Finance 3 (2): 99–148. https://doi.org/10.1007/s42521-021-00033-7.
   Google Scholar
• Jedwab, R., P. Loungani, and A. Yezer. 2021. “Comparing Cities in Developed and Developing Countries: Population, Land Area, Building Height and Crowding.” Regional Science and Urban Economics 86:103609. https://doi.org/10.1016/j.regsciurbeco.2020.103609.
   Web of Science ®Google Scholar
• Jones, K. W., D. J. Lewis, and M. S. Crowther. 2015. “Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.” Public Library of Science ONE 10 (10): e0141380. https://doi.org/10.1371/journal.pone.0141380.
   Google Scholar
• Kang, M., Y. Liu, M. Wang, L. Li, and M. Weng. 2022. “A Random Forest Classifier with Cost-Sensitive Learning to Extract Urban Landmarks from an Imbalanced Dataset.” International Journal of Geographical Information Science 36 (3): 496–513. https://doi.org/10.1080/13658816.2021.1977814.
   Web of Science ®Google Scholar
• Künzel, S. R., J. S. Sekhon, P. J. Bickel, and B. Yu. 2019. “Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning.” Proceedings of the National Academy of Sciences 116 (10): 4156–4165. https://doi.org/10.1073/pnas.1804597116.
   PubMed Web of Science ®Google Scholar
• Lecca, P. 2021. “Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.” Frontiers in Bioinformatics 1:746712. https://doi.org/10.3389/fbinf.2021.746712.
   PubMedGoogle Scholar
• Lechner, T. 2018. Domain Adaptation Under Causal Assumptions. Tübingen, Germany: Eberhard Karls Universität Tübingen Tübingen.
   Google Scholar
• Leist, A. K., M. Klee, J. H. Kim, D. H. Rehkopf, S. P. A. Bordas, G. Muniz-Terrera, and S. Wade. 2022. “Mapping of Machine Learning Approaches for Description, Prediction, and Causal Inference in the Social and Health Sciences.” Science Advances 8 (42): eabk1942. https://doi.org/10.1126/sciadv.abk1942.
   PubMed Web of Science ®Google Scholar
• Li, B. 2022. “Prospects on Causal Inferences in GIS.” In New Thinking in GIScience, edited by B. Li, X. Shi, A-X. Zhu, C. Wang, and H. Lin, 109–118. Singapore, Singapore: Springer.
   Google Scholar
• Liu, Y., M. Batty, S. Wang, and J. Corcoran. 2021. “Modelling Urban Change with Cellular Automata: Contemporary Issues and Future Research Directions.” Progress in Human Geography 45 (1): 3–24. https://doi.org/10.1177/0309132519895305.
   Web of Science ®Google Scholar
• Liu, X., Y. Huang, X. Xu, X. Li, X. Li, P. Ciais, P. Lin, et al. 2020. “High-Spatiotemporal-Resolution Mapping of Global Urban Change from 1985 to 2015.” Nature Sustainability 3 (7): 564–570. https://doi.org/10.1038/s41893-020-0521-x.
   Web of Science ®Google Scholar
• Liu, X., X. Liang, X. Li, X. Xu, J. Ou, Y. Chen, S. Li, 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. https://doi.org/10.1016/j.landurbplan.2017.09.019.
   Web of Science ®Google Scholar
• Liu, Y., A.-X. Zhu, J. Wang, W. Li, G. Hu, and Y. Hu. 2019. “Land-Use Decision Support in Brownfield Redevelopment for Urban Renewal Based on Crowdsourced Data and a Presence-And-Background Learning (PBL) Method.” Land Use Policy 88:104188. https://doi.org/10.1016/j.landusepol.2019.104188.
   Web of Science ®Google Scholar
• Li, X., and A. G. O. Yeh. 2001. “Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems.” Environment and Planning A 33 (8): 1445–1462. https://doi.org/10.1068/a33210.
   Web of Science ®Google Scholar
• Li, X., and A. G. O. Yeh. 2004. “Data Mining Of Cellular Automata’s Transition Rules.” International Journal of Geographical Information Science 18 (8): 723–744. https://doi.org/10.1080/13658810410001705325.
   Web of Science ®Google Scholar
• Lu, Y., S. Laffan, C. Pettit, and M. Cao. 2020. “Land Use Change Simulation and Analysis Using a Vector Cellular Automata (CA) Model: A Case Study of Ipswich City, Queensland, Australia.” Environment & Planning B: Urban Analytics & City Science 47 (9): 1605–1621. https://doi.org/10.1177/2399808319830971.
   Google Scholar
• Magliocca, N. R., P. Dhungana, and C. D. SInk. 2023. “Review of Counterfactual Land Change Modeling for Causal Inference in Land System Science.” Journal of Land Use Science 18 (1): 1–24. https://doi.org/10.1080/1747423X.2023.2173325.
   Web of Science ®Google Scholar
• Meng, L., Y. Sun, and S. Zhao. 2020. “Comparing the Spatial and Temporal Dynamics of Urban Expansion in Guangzhou and Shenzhen from 1975 to 2015: A Case Study of Pioneer Cities in China’s Rapid Urbanization.” Land Use Policy 97:104753. https://doi.org/10.1016/j.landusepol.2020.104753.
   Web of Science ®Google Scholar
• Meyfroidt, P. 2016. “Approaches and Terminology for Causal Analysis in Land Systems Science.” Journal of Land Use Science 11 (5): 501–522. https://doi.org/10.1080/1747423X.2015.1117530.
   Web of Science ®Google Scholar
• Meyfroidt, P., A. de Bremond, C. M. Ryan, E. Archer, R. Aspinall, A. Chhabra, G. Camara, et al. 2022. “Ten Facts About Land Systems for Sustainability.” Proceedings of the National Academy of Sciences 119 (7): e2109217118. https://doi.org/10.1073/pnas.2109217118.
   PubMed Web of Science ®Google Scholar
• Millington, J. D., D. O’sullivan, and G. L. Perry. 2012. “Model Histories: Narrative Explanation In Generative Simulation Modelling.” Geoforum; Journal of Physical, Human, and Regional Geosciences 43 (6): 1025–1034. https://doi.org/10.1016/j.geoforum.2012.06.017.
   Web of Science ®Google Scholar
• Miyauchi, T., T. Setoguchi, and T. Ito. 2021. “Quantitative Estimation Method for Urban Areas to Develop Compact Cities in View of Unprecedented Population Decline.” Cities 114:103151. https://doi.org/10.1016/j.cities.2021.103151.
   Web of Science ®Google Scholar
• Nie, X., and S. Wager. 2021. “Quasi-Oracle Estimation of Heterogeneous Treatment Effects.” Biometrika 108 (2): 299–319. https://doi.org/10.1093/biomet/asaa076.
   Web of Science ®Google Scholar
• Nugroho, N. Y., S. Triyadi, and S. Wonorahardjo. 2022. “Effect of High-Rise Buildings on the Surrounding Thermal Environment.” Building and Environment 207:108393. https://doi.org/10.1016/j.buildenv.2021.108393.
   Web of Science ®Google Scholar
• Patel, P., S. Karmakar, S. Ghosh, and D. Niyogi. 2020. “Improved Simulation of Very Heavy Rainfall Events by Incorporating WUDAPT Urban Land Use/Land Cover in WRF.” Urban Climate 32:100616. https://doi.org/10.1016/j.uclim.2020.100616.
   Web of Science ®Google Scholar
• Pettit, C., S. Biermann, C. Pelizaro, and A. Bakelmun. 2020. “A Data-Driven Approach to Exploring Future Land Use and Transport Scenarios: The Online What If? Tool.” Journal of Urban Technology 27 (2): 21–44. https://doi.org/10.1080/10630732.2020.1739503.
   Web of Science ®Google Scholar
• Pontius, R. G., JR, J. D. Cornell, and C. A. Hall. 2001. “Modeling the Spatial Pattern of Land-Use Change with GEOMOD2: Application and Validation for Costa Rica.” Agriculture, Ecosystems and Environment 85 (1–3): 191–203. https://doi.org/10.1016/S0167-8809(01)00183-9.
   Web of Science ®Google Scholar
• Rudin, C. 2019. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence 1 (5): 206–215. https://doi.org/10.1038/s42256-019-0048-x.
   PubMedGoogle Scholar
• Said, M., C. Hyandye, H. C. Komakech, I. C. Mjemah, and L. K. Munishi. 2021. “Predicting Land Use/Cover Changes and Its Association to Agricultural Production on the Slopes of Mount Kilimanjaro, Tanzania.” Annals of GIS 27 (2): 189–209. https://doi.org/10.1080/19475683.2020.1871406.
   Web of Science ®Google Scholar
• Sanchez, P., J. P. Voisey, T. Xia, H. I. Watson, A. Q. O’Neil, and S. A. Tsaftaris. 2022. “Causal Machine Learning for Healthcare and Precision Medicine.” Royal Society Open Science 9 (8): 220638. https://doi.org/10.1098/rsos.220638.
   PubMed Web of Science ®Google Scholar
• Schuler, A.,Baiocchi, M., Tibshirani, R. and Shah, N. 2018. “A Comparison of Methods for Model Selection When Estimating Individual Treatment Effects.” ArXiv Preprint 1804:05146. https://doi.org/10.48550/arXiv.1804.05146.
   Google Scholar
• Sharma, A., and E. KIciman. 2020. “Dowhy: An End-to-end Library For Causal Inference.” ArXiv Preprint. https://doi.org/10.48550/arXiv.2011.04216.
   PubMedGoogle Scholar
• Syrgkanis, V., Lewis, G., Oprescu, M., Hei, M., Battocchi, K., Dillon, E., Pan, J., Wu, Y., Lo, P., Chen, H. and Harinen, T. 2021. “Causal Inference and Machine Learning in Practice with Econml and Causalml: Industrial Use Cases at Microsoft, Tripadvisor, Uber.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, USA, 4072–4073.
   Google Scholar
• Tu, W., J. Cao, Y. Yue, S.-L. Shaw, M. Zhou, Z. Wang, X. Chang, et al. 2017. “Coupling Mobile Phone and Social Media Data: A New Approach to Understanding Urban Functions and Diurnal Patterns.” International Journal of Geographical Information Science 31 (12): 2331–2358. https://doi.org/10.1080/13658816.2017.1356464.
   Web of Science ®Google Scholar
• Verburg, P. H., and K. P. Overmars. 2009. “Combining Top-Down and Bottom-Up Dynamics in Land Use Modeling: Exploring the Future of Abandoned Farmlands in Europe with the Dyna-CLUE Model.” Landscape Ecology 24 (9): 1167–1181. https://doi.org/10.1007/s10980-009-9355-7.
   Web of Science ®Google Scholar
• Wager, S., and S. Athey. 2018. “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests.” Journal of the American Statistical Association 113 (523): 1228–1242. https://doi.org/10.1080/01621459.2017.1319839.
   Web of Science ®Google Scholar
• Wang, W., D. Wang, H. Chen, B. Wang, and X. Chen. 2022. “Identifying Urban Ventilation Corridors Through Quantitative Analysis of Ventilation Potential and Wind Characteristics.” Building and Environment 214:108943. https://doi.org/10.1016/j.buildenv.2022.108943.
   Web of Science ®Google Scholar
• Wolff, C., T. Nikoletopoulos, J. Hinkel, and A. T. Vafeidis. 2020. “Future Urban Development Exacerbates Coastal Exposure In The Mediterranean.” Scientific Reports 10 (1): 1–11. https://doi.org/10.1038/s41598-020-70928-9.
   PubMed Web of Science ®Google Scholar
• Zhai, Y., Y. Yao, Q. Guan, X. Liang, X. Li, Y. Pan, H. Yue, 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. https://doi.org/10.1080/13658816.2020.1711915.
   Web of Science ®Google Scholar
• Zhang, H., X. Li, X. Liu, Y. Chen, J. Ou, N. Niu, Y. Jin, et al. 2019. “Will the Development of a High-Speed Railway Have Impacts on Land Use Patterns in China?” Annals of the American Association of Geographers 109 (3): 979–1005. https://doi.org/10.1080/24694452.2018.1500438.
   Web of Science ®Google Scholar