Exploration of Spatial Scale Sensitivity in Geographic Cellular Automata

Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning

Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that has been imposed by the neighboring country Myanmar which has forced the government to shelter almost a million Rohingya refugees in less than 3 years (2017-2020). The government had no other option but to acquire almost 24.1 km2 of forest areas only to construct refugee camps for the Rohingyas which has led to catastrophic environmental outcomes. This study has analyzed the land use and land surface temperature pattern change of the Rohingya camp area for the course of 1997 to 2022 with a 5-year interval rate. Future prediction of the land use and temperature of Teknaf and Ukhiya was also done in this process using a machine learning algorithm for the years 2028 and 2034. The analysis says that in the camp area, from 1997 to 2017, percentage of settlements increased from 5.28 to 11.91% but in 2022, it reached 70.09%. The same drastically changing trend has also been observed in the land surface temperature analysis. In the month of January, the average temperature increased from 18.86 to 21.31 °C between 1997 and 2017. But in 2022. it was found that the average temperature had increased up to 25.94 °C in only a blink of an eye. The future prediction of land use also does not have anything pleasing in store.

The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh

Groundwater recharge is affected by various anthropogenic activities, land use and land cover (LULC) change among these. The long-term temporal and seasonal changes in LULC have a substantial influence on groundwater flow dynamics. Therefore, assessment of the impacts of LULC changes on recharge is necessary for the sustainable management of groundwater resources. The objective of this study is to examine the effects of LULC changes on groundwater recharge in the northwestern part of Bangladesh. Spatially distributed monthly groundwater recharge was simulated using a semi-physically based water balance model. Long-term temporal LULC change analysis was conducted using LULC maps from 2006 to 2016, while wet and dry LULC maps were used to examine seasonal changes. The results show that the impervious built-up area has increased by 80.3%, whereas vegetated land cover has decreased by 16.4% over the study period. As a result, groundwater recharge in 2016 has decreased compared to the level seen in 2006. However, the decrease in recharge due to long-term temporal LULC changes is very small at the basin scale (2.6 mm/year), although the impact on regional level is larger (17.1 mm/year) due to urbanization. Seasonal LULC variations also affect recharge due to the higher potential for dry seasonal LULC compared to the wet seasonal LULC, a substantial difference (20.6 mm/year). The results reveal important information about the groundwater system and its response to land cover changes in northwestern Bangladesh.

An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models

Cellular automata (CA) is a bottom-up self-organizing modeling tool for simulating contagion-like phenomena such as complex land-use change and urban growth. It is not known how CA modeling responds to changes in spatial observation scale when a larger-scale study area is partitioned into subregions, each with its own CA model. We examined the impact of changing observation scale on a model of urban growth at UA-Shanghai (a region within a one-hour high-speed rail distance from Shanghai) using particle swarm optimization-based CA (PSO-CA) modeling. Our models were calibrated with data from 1995 to 2005 and validated with data from 2005 to 2015 on spatial scales: (1) Regional-scale: UA-Shanghai was considered as a single study area; (2) meso-scale: UA-Shanghai was partitioned into three terrain-based subregions; and (3) city-scale: UA-Shanghai was partitioned into six cities based on administrative boundaries. All three scales yielded simulations averaging about 87% accuracy with an average Figure-of-Merit (FOM) of about 32%. Overall accuracy was reduced from calibration and validation. The regional-scale model yielded less accurate simulations as compared with the meso- and city-scales for both calibration and validation. Simulation success in different subregions is independent at the city-scale, when compared with regional- and meso-scale. Our observations indicate that observation scale is important in CA modeling and that smaller scales probably lead to more accurate simulations. We suggest smaller partitions, smaller observation scales and the construction of one CA model for each subregion to better reflect spatial variability and to produce more reliable simulations. This approach should be especially useful for large-scale areas such as huge urban agglomerations and entire nations.

Spatially Explicit Landscape-Level Ecological Risks Induced by Land Use and Land Cover Change in a National Ecologically Representative Region in China

Land use and land cover change is driven by multiple influential factors from environmental and social dimensions in a land system. Land use practices of human decision-makers modify the landscape of the land system, possibly leading to landscape fragmentation, biodiversity loss, or environmental pollution—severe environmental or ecological impacts. While landscape-level ecological risk assessment supports the evaluation of these impacts, investigations on how these ecological risks induced by land use practices change over space and time in response to alternative policy intervention remain inadequate. In this article, we conducted spatially explicit landscape ecological risk analysis in Ezhou City, China. Our study area is a national ecologically representative region experiencing drastic land use and land cover change, and is regulated by multiple policies represented by farmland protection, ecological conservation, and urban development. We employed landscape metrics to consider the influence of potential landscape-level disturbance for the evaluation of landscape ecological risks. Using spatiotemporal simulation, we designed scenarios to examine spatiotemporal patterns in landscape ecological risks in response to policy intervention. Our study demonstrated that spatially explicit landscape ecological risk analysis combined with simulation-driven scenario analysis is of particular importance for guiding the sustainable development of ecologically vulnerable land systems.

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

Modeling environmental impacts of urban expansion: a systematic method for dealing with uncertainties

In a rapidly transitioning China, urban land use has changed dramatically, both spatially and in terms of magnitude; these changes have significantly affected the natural environment. This paper reports the development of an Integrated Environmental Assessment of Urban Land Use Change (IEA-ULUC) model, which combines cellular automata, scenario analysis, and stochastic spatial sampling with the goal of exploring urban land-use change, related environmental impacts, and various uncertainties. By applying the IEA-ULUC model to a new urban development area in Dalian in northeastern China, the evolution of spatial patterns from 1986 to 2005 was examined to identify key driving forces affecting the changing trajectories of local land use. Using these results, future urban land use in the period 2005-2020 was projected for four scenarios of economic development and land-use planning regulation. A stochastic sampling process was implemented to generate industrial land distributions for each land expansion scenario. Finally, domestic and industrial water pollution loads to the ocean were estimated, and the environmental impacts of each scenario are discussed. The results showed that the four urban expansion scenarios could lead to considerable differences in environmental responses. In principle, urban expansion scenarios along the intercity transportation rail/roadways could have higher negative environmental impacts than cluster-developing scenarios, while faster economic growth could more intensely aggravate the environment than in the moderate growth scenarios.

An Activity-Based Cellular Automaton Model to Simulate Land-Use Dynamics

In recent decades several methods have been proposed to simulate land-use changes in a spatially explicit way. In these models land is generally represented on a lattice with cell states indicating the predominant land use. Since a cell can have only one state, mixed land uses and different densities of one land use can only be introduced superficially, as separate cell states. In this paper we describe a cellular automata model that simulates dynamics in both land uses and activities, where activities represent quantitative information, such as the number of inhabitants at a location. Therefore each cell has associated with it (1) a value representing one of a finite set of land-use classes, and (2) a vector of numerical values representing the quantity of each modelled activity that is present at that location. This allows simulation of incremental changes as well as mixed land uses. The proposed model is tested with a synthetic application that uses two activities: population and jobs. It simulates the emergence of human settlements over time from local interactions between activities and land uses. Assessment of results indicates that the model generates realistic urbanization patterns.

Retrieving Spatial Policy Parameters from an Alternative Plan Using Constrained Cellular Automata and Regionalized Sensitivity Analysis

In this paper we propose an approach to identify the spatial policy parameters (termed the implementation intensity reflecting planning controls on corresponding spatial constraint) associated with a predefined alternative plan, namely, a predefined-binary urban form. During plan implementation, the alternative plan cannot be fully realized in some cases due to practical urban growth driven by both institutional forces and market incentives, which are comprehensive and complex. Few researchers have investigated spatial policies appropriate for an alternative plan. We aim to propose a novel approach incorporating constrained cellular automata and regionalized sensitivity analysis, a method for global sensitivity analysis to calculate the realization possibility and identify the spatial policy parameters for an alternative plan. This approach is first tested in a virtual space with four predefined urban forms and various point, line, and polygon spatial constraints, with both positive and negative impacts on urban growth. Finally, the approach is also tested in the Beijing Metropolitan Area to identify the required spatial policy parameters for four alternative plans with seven spatial constraints.

VecGCA: A Vector-Based Geographic Cellular Automata Model Allowing Geometric Transformations of Objects

Cellular automata (CA) can reproduce global patterns and behavior from local interactions of cells and they are used increasingly to simulate complex natural and human systems. Among their attributes are their computational simplicity and their explicit representation of space and time. However, the classic definition of CA limits their application to problems that involve a discrete space, and similar rules and neighborhoods for all cells. In addition, the standard raster-based CA model is sensitive to spatial scale. This paper presents a new vector-based geographic cellular automata model, called the VecGCA model, which defines space as a collection of irregular geographic objects. Each object has a geometric representation (a polygon) that evolves through time according to a transition function that depends on the influence of neighboring polygons. In this model, the neighborhood is defined as the region of influence on each geographic object, and the neighbors are all geographic objects located within the region of influence. An innovative aspect of the VecGCA model is that the procedure allows geometric transformation of objects. The area of a polygon (representing an object) is reduced in the region that is nearest to the neighbor that exerts an influence on it, and the area of that neighbor is increased accordingly. The proposed model was tested with real data and compared with a raster-based CA model to simulate land-use changes in an agroforested area in southern Quebec, Canada. The model was validated using two land-use maps, produced from satellite Landsat Thematic Mapper imagery, which were acquired in 1999 and 2002. The results obtained show that VecGCA can represent well the dynamics in the study area through an adequate evolution of the geometry of the geographic objects which are independent of the cell size, whereas, to generate similar outcomes in the raster-based CA model, a sensitivity analysis must be conducted to determine which cell size is needed. The geometric transformation procedure introduced in the VecGCA model executes the change of shape of a geographic object by changing its state in a portion of its surface, allowing a more realistic representation of the evolution of the landscape.