Abstract
Urban expansion models are widely used to understand, analyze and predict any peculiar scenario based on input probabilities. Modelling and uncertainty are concomitant, and can occur due to reasons ranging from–discrepancies in input variables, unpredictable model parameters, spatio-temporal variability between observations, or malfunction in linking model variables under two different spatio-temporal scenarios. However, uncertainties often occur because of the interplay of model elements, structures, and the quality of data sources employed; as input parameters influence the behavior of cellular automaton (CA) models. Our study aims to address these uncertainties. While most studies consider neighborhood effects, timestep and spatial resolution, our study uniquely focuses on the susceptibility of multi density classes and varying cell size on uncertainty. Hence this chapter offers a theoretical elucidation of the concepts, sources, and strategies for managing uncertainty under various criteria as well as an algorithm for enumerating the model’s accuracy for Wallonia, Belgium.
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References
Almeida CM, Monteiro AMV, Câmara G, Soares-Filho BS, Cerqueira GC, Araújo WL, Pantuzzo AE (2003) Modeling the urban evolution of land use transitions using cellular automata and logistic regression. Int Geosci Remote Sens Symp (IGARSS) 3:1564–1566. https://doi.org/10.1109/IGARSS.2003.1294176
Angel S, Lamson-Hall P, Blei A, Shingade S, Kumar S (2021) Densify and expand: a global analysis of recent urban growth. Sustainability 13(7):3835. https://doi.org/10.3390/SU13073835
Batisani N, Yarnal B (2009) Uncertainty awareness in urban sprawl simulations: lessons from a small US metropolitan region. Land Use Policy 26(2):178–185. https://doi.org/10.1016/J.LANDUSEPOL.2008.01.013
Batty M (2016) Urban Evolution on the desktop: simulation with the use of extended cellular automata. Environ Plan A Econ Space 30(11):1943–1967. https://doi.org/10.1068/A301943
Cao M, Tang G, Shen Q, Wang Y (2015) A new discovery of transition rules for cellular automata by using cuckoo search algorithm. Int J Geogr Inf Sci 29(5):806–824. https://doi.org/10.1080/13658816.2014.999245
Chakraborty A, Omrani H, Teller J (2022) Modelling the drivers of urban densification to evaluate built-up areas extension: A data-modelling solution towards zero net land take. In: Lecture notes in computer science (Including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 13376 LNCS. pp 260–270. https://doi.org/10.1007/978-3-031-10450-3_21/COVER
Chaudhuri G, Clarke KC (2014) Temporal accuracy in urban growth forecasting: a study using the SLEUTH model. Trans GIS 18(2):302–320. https://doi.org/10.1111/TGIS.12047
Chen W, Henebry GM (2009) Change of spatial information under rescaling: a case study using multi-resolution image series. ISPRS J Photogramm Remote Sens 64(6):592–597. https://doi.org/10.1016/J.ISPRSJPRS.2009.05.002
Clarke KC (2008) A decade of cellular urban modeling with SLEUTH: unresolved issues and problems, Ch. 3. In: Planning support systems for cities and regions, pp 47–60. http://bbs.geog.ucsb.edu
Crosetto M, Tarantola S (2010) Uncertainty and sensitivity analysis: tools for GIS-based model implementation. Int J Geogr Inf Sci 15(5):415–437. https://doi.org/10.1080/13658810110053125
Dahal KR, Chow TE (2015) Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth. Int J Geogr Inf Sci 29(3):475–497. https://doi.org/10.1080/13658816.2014.987779
Feng Y, Wang J, Tong X, Liu Y, Lei Z, Gao C, Chen S (2018) The effect of observation scale on urban growth simulation using particle swarm optimization-based CA models. Sustainability 10:4002. https://doi.org/10.3390/SU10114002
Feng Y, Liu Y (2013) A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. Int J Geogr Inf Sci 27(3):449–466. https://doi.org/10.1080/13658816.2012.695377
Feng Y, Tong X (2018) Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. Gisci Remote Sens 55(5):678–698. https://doi.org/10.1080/15481603.2018.1426262
Gao C, Feng Y, Tong X, Lei Z, Chen S, Zhai S (2020) Modeling urban growth using spatially heterogeneous cellular automata models: comparison of spatial lag, spatial error and GWR. Comput Environ Urban Syst 81:101459. https://doi.org/10.1016/J.COMPENVURBSYS.2020.101459
García AM, Santé I, Crecente R, Miranda D (2011) An analysis of the effect of the stochastic component of urban cellular automata models. Comput Environ Urban Syst 35(4):289–296. https://doi.org/10.1016/J.COMPENVURBSYS.2010.11.001
García AM, Santé I, Boullón M, Crecente R (2013) Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain. Int J Geograph Inform Sci 27(8):1593–1611. https://doi.org/10.1080/13658816.2012.762454
García-Álvarez D, Camacho Olmedo MT, Paegelow M (2019a) Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Comput Environ Urban Syst 78:101389. https://doi.org/10.1016/J.COMPENVURBSYS.2019.101389
García-Álvarez D, van Delden H, Teresa Camacho Olmedo M, Paegelow M, García-Álvarez D, Camacho Olmedo MT, van Delden H, Paegelow Geode M, Paegelow M (2019b). Uncertainty challenge in geospatial analysis: an approximation from the land use cover change modelling perspective. 289–314. https://doi.org/10.1007/978-3-030-04750-4_15
Gar-On Yeh A, Li X (n.d.) Error propagation and model uncertainties of cellular automata in urban simulation with GIS
Jantz CA, Goetz SJ (2007) Analysis of scale dependencies in an urban land-use-change model. Int J Geograph Inform Sci 19(2):217–241. https://doi.org/10.1080/13658810410001713425
Jenerette GD, Wu J (2001) Analysis and simulation of land-use change in the central Arizona—Phoenix region, USA. Landscape Ecol 16(7):611–626. https://doi.org/10.1023/A:1013170528551
Jiang H, Guo H, Sun Z, Xing Q, Zhang H, Ma Y, Li S (2022) Projections of urban built-up area expansion and urbanization sustainability in China’s cities through 2030. J Clean Prod 367:133086. https://doi.org/10.1016/J.JCLEPRO.2022.133086
Kamusoko C, Aniya M, Adi B, Manjoro M (2009) Rural sustainability under threat in Zimbabwe—simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Appl Geogr 29(3):435–447. https://doi.org/10.1016/J.APGEOG.2008.10.002
Kocabas V, Dragicevic S (2006) Assessing cellular automata model behaviour using a sensitivity analysis approach. Comput Environ Urban Syst 30(6):921–953. https://doi.org/10.1016/J.COMPENVURBSYS.2006.01.001
Kok K, Veldkamp A (2001) Evaluating impact of spatial scales on land use pattern analysis in Central America. Agr Ecosyst Environ 85(1–3):205–221. https://doi.org/10.1016/S0167-8809(01)00185-2
Li X, Gong P (2016) Urban growth models: progress and perspective. Sci Bull 61(21):1637–1650. https://doi.org/10.1007/S11434-016-1111-1
Li X, Yeh AGO (2010a) Data mining of cellular automata’s transition rules. Int J Geogr Inf Sci 18(8):723–744. https://doi.org/10.1080/13658810410001705325
Li X, Yeh AGO (2010b) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343. https://doi.org/10.1080/13658810210137004
Li S, Colson V, Lejeune P, Speybroeck N, Vanwambeke SO (2015) Agent-based modelling of the spatial pattern of leisure visitation in forests: a case study in Wallonia, south Belgium. Environ Model Softw 71:111–125. https://doi.org/10.1016/J.ENVSOFT.2015.06.001
Li X, Yu L, Sohl T, Clinton N, Li W, Zhu Z, Liu X, Gong P (2016) A cellular automata downscaling based 1 km global land use datasets (2010–2100). Sci Bull 61(21):1651–1661. https://doi.org/10.1007/S11434-016-1148-1
Li X, Liu X, Yu L (2014) A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. 28(7):1317–1335. https://doi.org/10.1080/13658816.2014.883079
Liu X, Li X, Shi X, Wu S, Liu T (2008) Simulating complex urban development using kernel-based non-linear cellular automata. Ecol Model 211(1–2):169–181. https://doi.org/10.1016/J.ECOLMODEL.2007.08.024
Liu X, Li X, Liu L, He J, Ai B (2010) A bottom-up approach to discover transition rules of cellular automata using ant intelligence. Int J Geogr Inf Sci 22(11–12):1247–1269. https://doi.org/10.1080/13658810701757510
Liu Y (2008) Modelling urban development with geographical information systems and cellular automata. CRC PRESS. https://www.routledge.com/Modelling-Urban-Development-with-Geographical-Information-Systems-and-Cellular/Liu/p/book/9780367577438
Loucks DP, van Beek E, Stedinger JR, Dijkman JPM, Villars MT (2005) Water resources systems planning and management an introduction to methods, models and applications
Lu Y, Laffan S, Pettit C, Cao M (2019) Land use change simulation and analysis using a vector cellular automata (CA) model: a case study of Ipswich City, Queensland, Australia. Environ Plan B Urban Anal City Sci 47(9):1605–1621. https://doi.org/10.1177/2399808319830971
Marceau DJ (2014) The scale issue in the social and natural sciences. Can J Remote Sens 25(4):347–356. https://doi.org/10.1080/07038992.1999.10874734
Ménard A, Marceau DJ (2016) Exploration of spatial scale sensitivity in geographic cellular automata. Environ Plan B Urban Analyt City Sci 32(5):693–714. https://doi.org/10.1068/B31163
Moreno N, Ménard A, Marceau DJ (2008) VecGCA: a vector-based geographic cellular automata model allowing geometric transformations of objects. Environ Plan B Urban Analyt City Sci 35(4):647–665. https://doi.org/10.1068/B33093
Mustafa A, Saadi I, Cools M, Teller J (2014) Measuring the effect of stochastic perturbation component in cellular automata urban growth model. Procedia Environ Sci 22:156–168. https://doi.org/10.1016/J.PROENV.2014.11.016
Mustafa A, Saadi I, Cools M, Teller J (2018a) Understanding urban development types and drivers in Wallonia: a multi-density approach. Int J Bus Intell Data Mining 13(1–3):309–330. https://doi.org/10.1504/IJBIDM.2018.088434
Mustafa A, van Rompaey A, Cools M, Saadi I, Teller J (2018c) Addressing the determinants of built-up expansion and densification processes at the regional scale. Urban Stud 55(15):3279–3298. https://doi.org/10.1177/0042098017749176
Mustafa A, Ebaid A, Omrani H, McPhearson T (2021) A multi-objective Markov Chain Monte Carlo cellular automata model: simulating multi-density urban expansion in NYC. Comput Environ Urban Syst 87:101602. https://doi.org/10.1016/J.COMPENVURBSYS.2021.101602
Mustafa A, Saadi I, Cools M, Teller J (2018b) A Time Monte Carlo method for addressing uncertainty in land-use change models. 32(11), 2317–2333. https://doi.org/10.1080/13658816.2018.1503275
Pontius GR, Malanson J (2007) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19(2):243–265. https://doi.org/10.1080/13658810410001713434
Pontlus RG (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66(8):1011–1016
Saltelli A, Tarantola S, Chan KPS (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41(1):39–56. https://doi.org/10.1080/00401706.1999.10485594
Samat N (2006) Characterizing the scale sensitivity of the cellular automata simulated urban growth: a case study of the Seberang Perai Region, Penang State, Malaysia. Comput Environ Urban Syst 30(6):905–920. https://doi.org/10.1016/J.COMPENVURBSYS.2005.11.002
Sensitivity and Uncertainty–Center for Systems Reliability. (n.d.). Available at 22 Oct 2022 https://www.sandia.gov/csr/center-for-systems-reliability/capabilities/sensitivity-and-uncertainty/
Shan J, Alkheder S, Wang J (2008) Genetic algorithms for the calibration of cellular automata urban growth modeling. Photogramm Eng Remote Sens 74(10):1267–1277. https://doi.org/10.14358/PERS.74.10.1267
Stevens D, Dragicevic S, Rothley K (2007) iCity: a GIS–CA modelling tool for urban planning and decision making. Environ Model Softw 22(6):761–773. https://doi.org/10.1016/J.ENVSOFT.2006.02.004
Torrens PM, O’Sullivan D (2022) Cities, cells, and complexity: developing a research agenda for urban geocomputation. In: Abrahart RJ, Carlisle BH (eds) International conference on geocomputation. Available at 24 Oct 2022 http://www.geocomputation.org/2000/GC044/Gc044.htm
Vardoulakis S, Fisher BEA, Gonzalez-Flesca N, Pericleous K (2002) Model sensitivity and uncertainty analysis using roadside air quality measurements. Atmos Environ 36(13):2121–2134. https://doi.org/10.1016/S1352-2310(02)00201-7
Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SSA (2014) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30(3):391–405. https://doi.org/10.1007/S00267-002-2630-X
Ward DP, Murray AT, Phinn SR (2003) Integrating spatial optimization and cellular automata for evaluating urban change. Ann Reg Sci 37(1):131–148. https://doi.org/10.1007/S001680200113
White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 24(5):383–400. https://doi.org/10.1016/S0198-9715(00)00012-0
Wolfram S (2002) Stephen Wolfram: A new kind of science. Wolfram Media Inc. https://www.wolframscience.com/nks/
Wu F (2010) Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci 16(8):795–818. https://doi.org/10.1080/13658810210157769
Wu F (2016) An experiment on the generic polycentricity of urban growth in a cellular automatic city. Environ Plan B Urban Analyt City Sci 25(5):731–752. https://doi.org/10.1068/B250731
Wu H, Li Z, Clarke KC, Shi W, Fang L, Lin A, Zhou J (2019) Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. Int J Geogr Inf Sci 33(5):1040–1061. https://doi.org/10.1080/13658816.2019.1568441
Yeh AGO, Li X (2006) Errors and uncertainties in urban cellular automata. Comput Environ Urban Syst 30(1):10–28. https://doi.org/10.1016/J.COMPENVURBSYS.2004.05.007
Yen AGO, Li X (2016) A constrained CA model for the simulation and planning of sustainable urban forms by using GIS environment and planning b: urban analytics and city science 28(5):733–753. https://doi.org/10.1068/B2740
Young SG, Datta J, Kar B, Huang X, Williamson MD, Tullis JA, Cothren J (2021) Challenges and limitations of geospatial data and analyses in the context of COVID-19. In: Mapping COVID-19 in space and time. Springer, Cham, pp 137–167. https://doi.org/10.1007/978-3-030-72808-3_8
Zhang B, Xu G, Jiao L, Liu J, Dong T, Li Z, Liu X, Liu Y (2019) The scale effects of the spatial autocorrelation measurement: aggregation level and spatial resolution. Int J Geograph Inform Sci 33(5):945–966. https://doi.org/10.1080/13658816.2018.1564316
Zhang H, Zeng Y, Bian L, Yu X (2010) Modelling urban expansion using a multi agent-based model in the city of Changsha. J Geograph Sci 20(4):540–556. https://doi.org/10.1007/S11442-010-0540-Z
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This research was funded by the INTER program and co-funded by the Fond National de la Recherche, Luxembourg (FNR) and the Fund for Scientific Research-FNRS, Belgium (F.R.S—FNRS), T.0233.20,—‘Sustainable Residential Densification’ project (SusDens, 2020–2023).
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Chakraborty, A., Mustafa, A., Omrani, H., Teller, J. (2023). A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium. In: Goodspeed, R., Sengupta, R., Kyttä, M., Pettit, C. (eds) Intelligence for Future Cities. CUPUM 2023. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-031-31746-0_17
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