Spatial Modeling Approaches & Considerations Class #12. Major Elements: Complexity Theory. Complexity Theory: Theoretical Context

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1 Spatial Modeling Approaches & Considerations Class #12 Complexity theory Non linear system dynamics Emergence Complex adaptive systems Spatial simulations What if scenarios Extend in space & time and conditions of change Cellular automata Initial conditions, neighbor associations, transition rules Agent based models Dynamic actors, environment, and interactions Major Elements: Complexity Theory Nonlinearity of mechanisms Heterogeneity of constituents Self-organizing and order creations or emergence Emergence Across scale interactions Sensitivity to initial conditions and historic contingency Complexity Theory: Theoretical Context Conceives the world as consisting of self-organized systems, either reproducing their state (or stable state) through negative feedbacks with their environment or moving along trajectories from one state to another (or state cycle) as a result of positive feedbacks. The goal is to understand how simple, fundamental processes can be combined to produce complex holistic systems. Change and Organization State cycle is a system phase that embodies all the possible states for a system in a given environment. Environmental change can cause perturbations to the system, which can alter its behavior by a negative feedback or develop into a chaotic state through a positive feedback, thereby generating change along a trajectory within the system s phase space. More Complexity Theory Non-equilibrium systems with feedbacks can lead to non-linearity and may evolve into systems that exhibit criticality. The emergence of global properties can be generated from local interactions. Some theoretical approaches have bridged the gap between Complexity theory and modeling, among them is Cellular Automata, Agent Based Models, Complex Adaptive Systems. Complexity Theory is destined to be the dominant scientific trend of the 's... this revolutionary technique can explain any kind of complex system - multinational corporations, mass extinctions, ecosystems such as rainforests, or human consciousness. All are built on the same few rules. Complexity (Levin ) 1

2 PREY by Michael Crichton () Harper Collins Publishing In the Nevada desert, an experiment has gone horribly wrong. A cloud of nano-particles (microrobots) has escaped from the laboratory. This cloud is self-sustaining and self-reproducing. It is intelligent and learns from experience. For all practical purposes, it is alive. It has been programmed as a predator. It is evolving swiftly, becoming more deadly with each passing hour. Every attempt to destroy it has failed. And we are the prey! CA: How it Works Dynamic, discrete space-time systems. Regular grid of cells each in a finite state. Iteratively updated via discrete time steps. A cell state is determined by the states of the neighboring cells in the previous time step. Ability to grow, vary rates, or reverse direction. Regional Context of the Study Area Central Places in the Northern Ecuadorian Amazon Lago Agrio - the region s central city Puerto Bolivar - forest clearing and settlement patterns in remote areas 2

3 IKONOS Multi-Spectral (1999) & Survey Farms Integrated GISc Database Satellite Imagery Digital Line Graphs Data Integration Aerial Photography Digital Elevation Models Cadastral Data GPS Data Link Join Chris Betz 1757 Millbrook Ln Y 2 Christian Carl 1761 Millbrook Ln Y 1 Chris McAfee 1765 Millbrook Ln Y 2 Dale Legere 1776 Millbrook Ln N 6 Donna Black 1780 Millbrook Ln Y 2 Survey Collection Longitudinal survey: & In, 64 sectors sampled; 6% probability sample; 2-stage sample contiguous plots selected randomly from each sector. 418 settler plots on 405 fincas surveyed. In 1999, 767 farms on the same settler plots (land subdivision), plus 109 solares created by parcelization near several major towns. Social, Economic,Demographic, Health, and Environmental Data Differential GPS Settlement Patterns Affecting Analysis Design Sample Households & Survey Sectors & 1999 The Ecuadorian fishbone or piano key settlement pattern is characterized by on-premise management and a distinct linear pattern 3

4 vs. 1999: Land Use Household Demographics Characteristics 5% 1% 17% 55% Mean Area = 43.6 Ha 6% Perennials Annuals / Food Crops Pasture Forest 16% Fallow Swamp 33% 9% 4% 18% 1999 Mean Area = 23.3 Ha 24% 12% (N = 418 HH s) 1999 (N = 763 HH s) Average people per HH Population (farms only)* Average distance to market 28.2 km 20.4 km Road distance 24.2 km (n=394) 19.1 km (n=682) Walking to road 5.3 km (n=214) 2.6 km (n=333) Canoe 34.1 km (n=35) 18.7 km (n=30) Head born in Oriente 4.6 % 8 % Electricity in dwelling 14 % 32 % Own chainsaw 30 % 20 % Have full legal title 50 % 34 % Gini Coefficients for Landholdings *Excludes population living in solares (housing lots) Ground Control Approaches LULC Dynamics Panel Data & Bi-Directional Change NISA NISA Red - Long Term Ag Green - New Forest Yellow & Beige - New Ag 4

5 Cell Suitability Population Density DEM START Landsat TM landcover Income Class Suitability Year = 1986 Accessibility Compute cell suitability derived from static & dynamic GIS inputs GIS inputs Class Growth: stochastic + diffusive Urban Resolve class competition Flux classes Separate classes Agriculture Pasture For. Succession Class transition probabilities (sat. time series) based on suitabilities Merge classes Year +1 No Final model year? Modeled landcover Yes END Forest to Non-Forest Vegetation CA Process: Deforestation in the Ecuadorian Amazon Starts with 1986 LULC classification Stochastic parameter: cells randomly selected to be turned on as new cells of the class Threshold value is set to determine whether the focal cell of the kernel will change based upon the other cells and a suitability scoring of the input layers Input layers are processed using GIS functions; greater values indicate a greater likelihood of a cell changing from its initial state to possible outcome state (s) Suitability scores indicate areas of greater/lesser likelihood of change based on multiple criteria A masking parameter is applied to this score to regulate change/no-change cells Agent-Based Models South ISA: Simulation Forest Agriculture/Pasture Urban/Barren Water Travel distance to nearest of 3 major communities; lower, greater change probability; computed as Euclidean distance to the nearest road and then simple distance along network to the community Euclidean distance to nearest road; lower, greater change probability Sector population; higher, greater change probability Slope angle; lower, greater change probability Soil moisture index; lower, greater change probability Parameters: stochastic (0.06), kernel threshold (4 cells), masking threshold (0.4) ABMs consist of autonomous decisionmaking entities (agents), an environment through which agents interact, rules that define the relationship between agents and their environment, and rules defining the sequence of actions in the model. 5

6 Agent Based Models & Complexity Agent-Based Models & Complexity Agent based models examine the basic characteristics and activities of individual agents as the basic building blocks. Agents differ in important characteristics and their interactions are dynamic in that the characteristics of the agents change over time as the agents adapt to their environment, learn from experiences through feedbacks, or die as they fail to alter behavior relative to new conditions and/or factors. Individual agents may be organized into groups or hierarchies that may influence how the underlying system evolves over time. Complex adaptive systems are self-organized systems that combine local processes to produce holistic systems; emergent in that macro-level behaviors emerge from the actions of individual agents as agents learn through experiences and change and develop feedbacks with finer scale building blocks. Agent based models capture the building blocks or processes of an emergent hierarchical system. Agent-Based Model Agent-Based Model of LULC (ABM): represent the co-evolution of human-environment systems. It is an effective tool for the exploration of how human decision-making produces land use and land cover change outputs ABM consists of three key elements: Agent: decision-making entity, is able to act flexibly and autonomously Environment: where agent interact Rules: define the relationship between agents and their environment The relationship of agent and environment in the Ecuador model Layer 1 Individual agents Layer 2 LULC Layer 3 Parcels Layer 4 Farms 6

7 The snapshot of Ecuador Model output: Forest change (%) Pasture change (%) Color Type Forest House Succession Pasture Cash crop % Year $ 2018 % Year Subsistence Assets change Person change Number of person Year Year Selected Findings Human frontier settlements exhibit self-organized complexity; feedbacks exist between spatial pattern and process. Emergent behavior of farmers is seen at macro-level development fronts. Changes in land tenancy and the implementation of protective buffers around and within protected areas can increase deforestation and land fragmentation. Forest succession and fallow are related to OFE, household assets, male adults, & legal title. Spatial structure of LULCC are related to household demographics, labor, change in pop density, year of farm establishment, & farm size. 7

8 Model Calibration & Validation Summary correlation of composition Spatial pattern Rule stability Scaling of rules in space & time Scenario building & policy implications 8