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Urban Growth Model

Prof Ni-Bin Chang talked about the urban growth model to be adopted in the "Flood impact assessment in mega cities under urban sprawl and climate change" project.

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Urban Growth Model

  1. 1. Flood Impact Assessment in Mega Cities under Urban Sprawl and Climate Change (Part I) Ni-Bin Chang, Ph.D., P.E. Director, Stormwater Management Academy University of Central Florida July 6, 2015
  2. 2. Current Relevant Research At UCF • “Coupling Risk and Resilience Assessment for Networked Sustainable Drainage Systems in a Coastal City under Climate Change Impact” funded by NOAA Florida Sea Grant. • “Developing a Sustainable Hong Kong through Low Impact Development: from Science to Innovation Policy.” funded by Hong Kong Research Council. • “Flood impact assessment in mega cities under urban sprawl and climate change” submitted to British Council, Global Innovation Initiatives Grant Program.
  3. 3. Historical satellite imagery analysis Urban growth model Detailed hydraulic model Fast hydraulic model Urban extents Impervious areas Future land cover classification Detailed hydraulic model Fast hydraulic model Future climate scenarios Future flood impact Adaptation strategies Future urban extents Land cover classification Future impervious areas Flood impact Current climate scenarios Correlation analysis between r modelling results at regional and city scales Historical social- economic data Trend analysis using Big Data Future social- economic state AI for pattern recognition Trend analysis using Big Data The Framework for Analysing Future Flood Impact under Urban Growth and Climate Change in Mega Cities
  4. 4. Modelling Framework and Planning Scenarios
  5. 5. Four Types of Flooding in Coastal Cities : Three Mega Cities • Coastal flooding : It affects areas along the ocean, bays, rivers, streams, or estuaries of tidal influence of tidal influence and storm surge. • Tidal flooding: Sea level fluctuates daily due to gravitational forces and the orbital cycle of moon, sun and earth. Flooding from high tide in low lying area is an issue. • Riverine flooding: Flooding occurs when freshwater rivers and streams exceed local flow capacity and water spills over their banks. • Inland flooding: Flash floods can be caused by short-term, high- density rainfall, often associated with sudden thunder storms, hurricanes, or large scale storms.
  6. 6. Planning Framework for Vulnerability Assessment Source: Climate Risks and Adaptation in Asian Coastal Mega Cities, World Bank, 2010
  7. 7. Types of Urban Growth Models • Land Use Transportation Models – Top down models : They dealing with location and interaction, transport and the urban economy, represented at a level of abstraction involving administrative rather than physical subdivisions of the city. • Cellular Automata Models (CA) – Bottom up models : They dealing with urban growth sprawl, land development and land cover, represented at finer spatial scales defined by or detecting physical morphology, do not deal with explicit transportation; dynamic in time.
  8. 8. Types of Urban Growth Models • Land Cover Models (LUCC) : They simulate vegetation cover, ecosystem properties, agriculture, as well as some urban dynamics. • Agent‐Based Models (ABM) : They are a generic style of representation for individual‐based dynamics processes, such as movement of individuals and objects.
  9. 9. Three Generalizations of Urban Structure • Upper Left: Burgess' Concentric Zone Model; • Upper Right: Hoyt's Sector Model; • Bottom Left: Harris and Ullman Multiple Nuclei Model. Sources: Graphic repared by Department of Geography and Earth Sciences, University of North Carolina at Charlotte. Beijing New York Longdon
  10. 10. The Cellular Automata Approach: Urban Growth and Complexity Theory • These CA models have found favour in rapidly growing systems which are characterised by urban sprawl, like Phoenix, Las Vegas, Taipei and Beijing. • They have been quite inappropriately applied to non‐rapid growth cities where the focus is on redistribution. 1 (A) (B)
  11. 11. Logistic Cellular Automata (CA) Models for Urban Planning • Such models view cities as complex systems based on the principle of self-organization. • Cell, state, neighborhood, and the transition rule are the primary components in CA models. • The state variation of a cell depends on its previous state and those of its neighbors. • The change of state for each cell is controlled by a set of transitional rules (functions) that are assessed at each time step. • Transitional functions can be either deterministic or stochastic, and time is in discrete steps.
  12. 12. Formulation of a Logistic CA Model • A reference set of cells, usually a raster grid of pixels covering an urban area; • A set of states associated with the cells at any given time, which can be in the detailed land uses such as {urban, forest, agricultural, wildland, wetlands, water}; • A set of rules that govern state changes over time; • An update mechanism, in which rules are applied to the state at one time period to yield the states of the same cells in the next time period; and • An initial condition of the framework is required and a boundary condiiton may be present.
  13. 13. Assumptions of Traditional Logistic CA Models for Urban Planning • The underlying plane is homogeneous Cells don't have intrinsic properties. • Transition rules must be uniform, and they must apply to every cell, state, and neighborhood. • Every change in state must be local, which in turn implies that there is no action-at-a-distance effect. • All these features operate uniformly and universally (i.e. each cell is an automata).
  14. 14. Cellular Automata (CA) Models for Urban Planning: the 1990s and before • Before the 1990s, CA models mainly have two featurers: 1. Land use allocation and other geographic factors with the dynamic approach of the CA model are considered in this period. 2. Lacking of software to deal with extensive lattices. • In the 1990s, Dynamic Urban Evolutionary Modeling (DUEM): • Developed with the application of other software such as GIS to demonstrate the hypothesis. • To maximize the use of GIS approach to visualize urban simulations. • Language: C++, CDL • Xie/Batty– Ypsilanti/London, US/UK–DUEM Batty et al., 1999
  15. 15. Case Studies in the 1990s • In 1992, Dublin was selected as a case study urban city that is simulated 30 years from 1968 to 1998 using a GIS-based CA software prototype. • Calibration is included by means of the fractal dimension and the comparison matrix methods. • The simulation results are relatively accurate. Jose et al., 2003
  16. 16. The Fractal Dimension and Urban Growth In fact in mathematics, a function is scaling if it can be shown to be scalable under a simple transformation – i.e. if we can scale a distance by multiplying it by 2 and the function does not change qualitatively, then this is scaling – so power laws – functions like f(y)=x‐1 scale because if we multiply by 2, say, we get f(2y)= 2x‐1 =2‐1x‐1~f(y) Berlin, 1875 Berlin, 1920 Berlin, 1945 Hern, 2008
  17. 17. Cellular Automata (CA) Models for Urban Planning: at the end of the 1990s • Cellular automata models were used to simulate urban dynamics through GIS-based approach. • AUGH model (generalised urban automata with the help on-line) and other GIS-based models were developed around this time. • Calibration and prediction results were achieved. • The model was expected to simulate the urban growth process and provide long-term predictions for urban planning. • Language: C, PERL • Data: historical digital maps • Testing region: Marseilles region (Meaille & Wald, 1990), Cincinnati (White & Engelen, 1993), the Bay Area (Clarke et al., 1997), the Washington/Baltimore corridor (Clarke & Gaydos, 1998), Guandong (Yeh & Li, 1998), and Guanzhou (Wu, 1998),
  18. 18. • At the beginning of 2000’s, different types of computer languages and tools were applied to build CA models, such as C language, Java, matlab, and so forth. • Models: • CLUE (Conversion of Land Use and its Effects) Model – University of Amsterdam, The Netehrlands • SLEUTH Model – UC Santa Babara, USA • ANN - SLEUTH CA Model – UC Santa Babara, USA • Metronamica Model – Research Institute for Knowledge Systems (RIKS), The Netehrlands • JCASim Model – Technical University Braunschweig, Germany Cellular Automata (CA) Models for Urban Planning: the 2000s
  19. 19. Overview of the CLUE Modelling Procedure The model is sub-divided into two distinct modules, namely a non-spatial demand module and a spatially explicit allocation procedure. Curtesy of Peter Verburg
  20. 20. Illustration of the translation of a hypothetical land use change sequence into a land use conversion matrix Overview of the Information Flow in the CLUE-S Model Curtesy of Peter Verburg Two sets of parameters are needed to characterize the individual land use types: conversion elasticities and land use transition sequences.
  21. 21. Flow Chart of the Allocation Module of the CLUE-S Model
  22. 22. How Does SLEUTH Simulate Urban Growth and Land Cover Change? • Coefficients : Five coefficient, or parameter, values effect how the growth rules are applied. These values are calibrated by comparing simulated land cover change to a study area's historical data. • Growth rules : SLEUTH begins with a set of inital conditions which is the input data configuration of the landscape. A set of decision, or growth, rules is then applied to the data to simulate urban driven land cover change. • Self modification: The coefficients do not necessarily remain static throughout an application. In response to rapid or depressed growth rates, the coefficients may increase or decrease to further encourage growth rate trends.
  23. 23. The Existing Coefficients of SLEUTH Model • The calibration process is automated, so SLEUTH “learns” the best set for any given application from the data (slope, land use, exclusion, urban extent, transportation, hillshade). • The parameters were chosen after extensive testing by trial and error. They include • parameters that control the random likelihood of any pixel turning urban (dispersion), • the likelihood of cells starting their own independent growth trajectory (breed), • the regular outward expansion of existing urban areas and infill (spread), • the degree of resistance of urbanization to growing up steep slopes (slope) and • the attraction of new development toward roads (road gravity).
  24. 24. • Markov-CA model: • Goal: • Analyze temporal change and spatial distribution of land use influenced by the natural and socioeconomic factors • forecast the future land use changes • Approach: GIS • Calibration: included • Parameters: agriculture land, forestland areas, and upward trend in built-up areas • Restriction: the land use dynamics changes of the social and environmental interactions among people are not considered in this model. Cellular Automata (CA) Models for Urban Planning: from 2010 to the Present
  25. 25. Case Studies in 2010 and after • Markov-CA model • Case study city: • Saga, Japan • Fangshan, a district of Beijing, China Guan et al., 2011
  26. 26. Cellular Automata (CA) Models for Urban Planning: from 2010 to the Present • AIS-Based CA model: • Self-adaptive CA model (an artificial immune system) • Goal: simulate the rural-urban land conversion • Parameters are allowed to be self-modified • Can be used to retrieve the changing urban dynamic evolution rules over time. • Data: Landsat TM satellite image from 1995 to 2012 • Case study city: Guangzhou, China • Comparison between the AIS-based model and a Logistic CA model: The results indicate that the AIS-based CA model can perform better. • Advantage: • Perform better and higher is precision in simulating urban growth • The simulated spatial pattern is more close to the real development situation.He et al., 2015
  27. 27. Case Studies in 2010 and after: AIS-based Model Urban evolution process of Guangzhou city during the period 1990-2012 Simulation results of Guangzhou city during the period 1990-2012 with the AIs-based CA He et al., 2015
  28. 28. Case Studies in 2010 and after: Urban Growth in Beijing City • This study applied the model to assess the general urban development plan entitled "disperse polycentric urban development plan" of Beijing City and found that the plan failed to meet its objectives.
  29. 29. CA-based Urban Growth Model in Beijing: 1975-1997 Source: Chen Jin, Gong Peng, He Chunyang, Luo Wei, Tamura Masayuki, and Shi Peijun, Assessment of the Urban Development Plan of Beijing by Using a CA-Based Urban Growth Model, Photogrammetric Engineering & Remote Sensing, October 2002, 1063- 1071.
  30. 30. CA-based Urban Growth Model in Beijing: 1975-1997 • The transitional function is the core of CA models. • There are two groups of factors in the transitional function. • The first group includes local factors, such as interactions between adjacent land uses. • The second group includes broad-scale factors such as regional interactions based on transportation networks. • The following modifications to the formal CA framework to reflect the realistic situation: • External land demand control • Transition potential from non-urban land to urban land based on land suitability and neighborhood effect • Definition of neighborhood effect was relaxed to involve the more distant influence of neighbors
  31. 31. Unique Features • An adaptive Monte-Carlo method was used to automate the calibration of factor weights used in the CA transitional rules. • This study used one scene of Landsat MSS imagery from 1975 and three scenes of Landsat TM imageryfiom 1984, 1991, and 1997 to classify the land-use patterns.
  32. 32. Unique Features • Constrained Condition: w𝑚 𝑘=1 k=100 • Objective function: Max F(w,, w2, ..., wm,) where wk > 0, and F is a fitness function between simulation results and the actual situation • The objective is to find optimal weights so that a fitness index reaches its maximum. This inverse problem can be solved using an adaptive Monte Carlo method
  33. 33. An Artificial-Neural-Network-based, Constrained CA Model for Simulating Urban Growth
  34. 34. Tietenberg Model • According to Tietenberg (1992), land resource can be treated as a depletable, non-recyclable resource. • Its demand and supply are influenced by price. • Thus, the optimal allocation of land resources is to maximize the net benefit. • The maximum net benefit can be obtained when the marginal benefit function is equal to the marginal cost function.
  35. 35. Tietenberg Model
  36. 36. From Theory to Practice • Because the marginal benefit falls as land consumption or land consumption per capita increases, the marginal benefit function in year t can be given by assuming the land demand curve is linear and stable over time (Tietenberg, 1992). • Population and economic growth driven by the development of the tertiary industry and infrastructure construction propelled urbanization as a whole. • Factors such as traffic condition, distance to central city, slope, and so on determined the spatial distribution of urban growth.
  37. 37. Tietenberg Model • To execute the Tietenberg Model, increased population in the future is needed. • By using the Logistic regression, based on the population data in the history, the increasing curve of population can be calculated as follows
  38. 38. Modelling Structural Change in Spatial System Dynamics • System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. • This is especially true for models on structural change (e.g. LULC modeling). • A Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). • The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. • Practical applications of structural change models in agriculture and disaster management are proposed in a spatial system dynamics (SSD) environment. Neuwirth et al., 2014
  39. 39. Association of Process (time) and Structure (space) in a Structural Change Model
  40. 40. Model Formulation
  41. 41. Schematic Representation of Synchronized operations between SD and GIS
  42. 42. Modifications of Traditional Logistic CA Models for Urban Planning • CA models are often relaxed to adapt to real problems at hand. • Common relaxations include • adopting heterogeneous underlying planes; • extending the immediate neighborhood definition from a Moore or Neumann neighborhood to a larger extent; • incorporating action-at-a-distance effects, or broad-scale factors, etc. • Use of Adaptive Monte Carlo Simualtion or ANN/AIS model to determine the paratemetrs. • These modified CA models are easy for integration with GIS and remote sensing algorithms also facilitates their implementation. • The structure dynamic change in a spatial system dynamic environment was developed.
  43. 43. Unsolved Issues and Problems • Almost all variance captured and measured in Monte Carlo simulation is contained in the first few iterations, and that increasing the number of iterations quickly has diminishing returns in terms of model fit. • Modelers lack of attention in spatial modeling to the idiosyncrasies of pseudo-random number generators - the lack of repetitive cycling in the random numbers, and the ability to replicate sequences across computational platforms. • Memory effect - the persistence is both of type (i.e. which land use transition changed to which) and time, since changes are spatially autocorrelated in time and space
  44. 44. Unsolved Issues and Problems • The fourth behavior type of SLUETH simulated is “road gravity”, in which new growth is attracted to and allowed to travel along the road network. It would be of interest to determine is the value changes over time, over space, or with transportation technology. • The remaining constants in SLEUTH all determine how the model implements self-modification. Self-modification is macro-scale behavior. It lacks sensitivity when tuning them one by one in sequence. • Load balance in parallele computing when more models/tools need to be integrated • Big data analytics may need to be in place in support of the urban growth model.
  45. 45. Planning Framework of the AI-based CA Model (UGM) in This Project
  46. 46. Systems Analysis of UGM in This Project Yin et al., 2008
  47. 47. Multi-temporal Change Detection of Land Use Using Remote Sensing • The location of the study area and the corresponding SPOT-5 images in 2003 and 2007. Ground truth Database Training Dataset Testing Dataset PL-ELM Classifier Feature Extraction Field Trips LULC Class Definition The PL-ELM Classifier 1 2 2 3 3 4 4 Major experimental steps: 1. Extract multiple features from the original remote sensing images; 2. Construct the training data set and testing data set based on the ground truth data base; 3. Train the classifier with the training data set, and test its performance with the testing data set; 4. Classify the full scale image of the study area using the PL-ELM classifier.
  48. 48. Multi-temporal Change Detection of Land Use Using Remote Sensing Source: NASA
  49. 49. The Porposed UGM Flow Chart Yin et al., 2008
  50. 50. Novelty of This Study • Strict CA are models whose rules work on neighbourhoods defined by nearest neighbours and exhibit emergence – i.e. their operation is local giving rise to global pattern. • Neighbourhoods can be wider or they could be formed as fields – like interaction fields around a cell - like interaction fields around a cell. • Cells are irregular and not necessarily spatially adjacent. • Structure dynamic changes may be explored by using Stella.
  51. 51. Modeling the Spatial Trasition Rules by Gravity Theory • According to the gravity theory proposed by Newton in 1687, the attraction Fik between two objects i and k can be briefly formulated by their masses and the distance between them and expressed as thefollowing equation: in which Mi and Mk are the mass of object i and k, respectively; Dik is the distance between object i and object k; G stands for gravity factor.
  52. 52. Modelling the Spatial Trasition Rules by Gravity Theory • It is determined by the distance (Dij) between jth cell of major land use change (Aj) and and ith cell tat may be influenced by the changeover time. • Following the gravity theory, this study assumes that the decay rate of a crowd due to such a land use change follows the Inverse Square Law. • Concerning the diffidence among various types of land use changes, the preference are clustered into four groups. Gij = f (Dij, Aj)
  53. 53. UGM Calibration and Validation Using Remote Sensing Hindcasting Nowcasting Forecasting Yeh and Li, 2002 Current Trend Managed Growth Ecologically Sustainable
  54. 54. How Do Low Impact Development (LID) Technologies Come to Help? # Introduce a spatially-explicit approach to assist landscape architects, urban planners, and water managers in identifying priority sites for LID. # Examine the current flood proofing facilities to public utility department in identifying priority sites in response to sea level rise, storm surge, and storm tides.
  55. 55. Risk & Resilience: A Systems Approach for Water Security • Sustainable stormwater management mimics nature by integrating management of stormwater runoff into the surrounding terrain, using systems like landscaped medians, swales and interchange areas to store and treat runoff.
  56. 56. Software Availability • Dynamic Urban Evolutionary Modeling (DUEM): • CLUE - • SLEUTH - • JCASim -
  57. 57. Science Questions • How can neighbourhood interactions and inherent constraining and enhancing factors for urban development be extracted and related to actual changes in land use patterns? • How can scenarios of planned and unplanned growth be created and used for evaluating policy options? • How to connect data driven model with knowledge drivenr model to closely capture the spatial an dtemporal dynamics?
  58. 58. Challenges in Synergistic Research • Integration between socioeconomical development, smart growth, and urban growth model for different mega-cities. • Integration between the CA-based urban growth model (UCF) and the CA-based flood impact assessment model (Exeter).
  59. 59. Thank you Questions ? Acknolwedgement: We are grateful for the funding support from the British Council in this research.
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Prof Ni-Bin Chang talked about the urban growth model to be adopted in the "Flood impact assessment in mega cities under urban sprawl and climate change" project.


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