Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality

Cellular Automata (CA) models at present do not adequately take into account the relationship and interactions between variables. However, land use change is influenced by multiple variables and their relationships. The objective of this study is to develop a novel CA model within a geographic information system (GIS) that consists of Bayesian Network (BN) and Influence Diagram (ID) sub‐models. Further, the proposed model is intended to simplify the definition of parameter values, transition rules and model structure. Multiple GIS layers provide inputs and the CA defines the transition rules by running the two sub‐models. In the BN sub‐model, land use drivers are encoded with conditional probabilities extracted from historical data to represent inter‐dependencies between the drivers. Using the ID sub‐model, the decision of changing from one land use state to another is made based on utility theory. The model was applied to simulate future land use changes in the Greater Vancouver Regional District (GVRD), Canada from 2001 to 2031. The results indicate that the model is able to detect spatio‐temporal drivers and generate various scenarios of land use change making it a useful tool for exploring complex planning scenarios.     

Accessibility Futures

This study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. The concept of accessibility dates to the 1950s, but this type of application to transportation planning is new. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility‐based performance measures in the Twin Cities Metropolitan Area. This choice of performance measure also allows for transit and highway networks to be compared side‐by‐side. For roadway modeling, zone‐to‐zone travel time matrix was computed using stochastic user equilibrium (SUE) assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each traffic analysis zone (TAZ) within specified time ranges. Tables of person‐weighted accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. Maps of accessibility by zone were produced to show the spatial distribution of accessibility across the region. The results show that a scenario where population and employment growth are concentrated in the center of the metropolitan area would produce the highest accessibility no matter which transportation network changes are made. However, another scenario which concentrates population growth in the center of the metropolitan area and shifts employment growth to the periphery consistently outperforms the scenario representing the projected 2030 land use without any growth management strategy.     

Dynamic modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of the Xingu National Park,Brazilian Amazon

The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform – Dinamica EGO – consists in a cellular automata environment that embodies neighborhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further applicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide.     

Stylized environments and ABMs: educational tools for examining the causes and consequences of land use/land cover change

A challenge in land change science is to assess the causes and consequences of LULC change and associated pattern–process relations. Increasingly, land change organizations are examining land use at local to global scales for historical, contemporary and future periods through scenarios that assess population–environment interactions. Spatial analytical tools in GIScience are being used to link people and environment and to search for the distal and proximate factors that affect local to global land use patterns. Spatial simulation models that rely upon complexity theory as the framework and agent-based models as the analytical approach offer the capability to inform through experimentation about land issues important to science and society. Using a stylized landscape where a selected set of key social, geographical and ecological elements are spatially organized, we describe how land dynamics can be examined through agent-based models as educational tools that are useful in the classroom, boardroom and public forums.     

Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata—Markov modelling

There have been rapid population and accelerating urban growth with associated changes in land use and soil degradation in northeast China, an important grain-producing region. The development of integrated use of remote sensing, geographic information systems, and combined cellular automata– Markov models has provided new means of assessing changes in land use and land cover, and has enabled projection of trajectories into the future. We applied such techniques to the prefecture-level city of Harbin, the tenth largest city in China. We found that there had been significant losses of the land uses termed “cropland”, “grassland”, “wetland”, and “floodplain” in favour of “built-up land” and lesser transformations from “floodplain” to “forestland” and “water body” over the 18-year period. However, the transition was not a simple process but a complex network of changes, interchanges, and multiple transitions. In the absence of effective land use policies, projection of past trajectories into a balance state in the future would result in the decline of cropland from 65.6% to 46.9% and the increase of built-up area from 7.7% to 23.0% relative to the total area of the prefecture in 1989. It also led to the virtual elimination of land use types such as unused wetland and floodplain.     

Visualisation techniques to support public interpretation of future climate change and land-use choices: a case study from N-E Scotland

Mitigating and adapting to climate change includes a requirement to evaluate the role of future land uses in delivering robust integrated responses that are sensitive to local landscape contexts. In practice, this emphasises the need for community engagement, planning and inclusive decision-making. Community engagement may be potentially facilitated by the use of spatially explicit quantitative scenarios of land-use change in combination with interactive visualisation. This requires a coherent framework to integrate spatial data modelling, analytical capabilities and visualisation tools in a format that will also engage diverse public audiences. These challenges were explored with a case study of virtual landscapes from N-E Scotland that was used to test preferences for scenarios of future land use. Visualisations employed texture-based rendering rather than full photo-realistic rendering to facilitate interactivity and this provided additional scope for audiences to explore multiple future scenarios compared to the present landscape. Interactive voting in a virtual landscape theatre suggested preferences for visual diversity, good stewardship and perceived naturalness that should be considered in developing planned responses to change. Further investigation of preferences was conducted using interactive 3D features located within the landscape. Study findings are reviewed against objectives for inclusive engagement in the Digital Earth agenda and used to make further recommendations on the use of scenarios and visualisation tools. In particular, technical advances in user engagement need to be developed in conjunction with emerging good practice that addresses ethical, behavioural and inclusion issues so that the content is presented in as transparent and unbiased format as possible.     

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Much effort has been applied to the study of land use multi‐objective optimization. However, most of these studies have focused on the final land use scenarios in the projected year, without considering how to reach the final optimized land use scenario. To fill this gap, a spatio‐temporal land use multi‐objective optimization (STLU‐MOO) model is innovatively proposed in this research to determine possible spatial land use solutions over time. The STLU‐MOO is an extension of a genetic land use multi‐objective optimization model (LU‐MOO) in which the LU‐MOO is generally carried out in different years, and the solutions at year T will affect the solutions at year T + 1. We used the Wuhan agglomeration (WHA) as our case study area. The STLU‐MOO model was employed separately for the nine cities in the WHA, and social, economic, and environmental objectives have been considered. The success of the experiments in the case study demonstrated the value and novelty of our proposed STLU‐MOO model. In addition, the results also indicated that the objectives considered in the case study were in conflict. According to the results, the optimal land use plan in 2050 can be traced back to 2040, 2030, and 2020, providing a series of Pareto solutions over the years which can provide spatio‐temporal land use multi‐objective optimization solutions to support the land use planning process.     

Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.     

Calibration of cellular automata models using differential evolution to simulate present and future land use

A key issue in cellular automata (CA) modeling is the minimization of the differences between the actual and simulated patterns, which can be mathematically formulated as an objective function. We develop a new hybrid model (termed DE‐CA) by integrating differential evolution (DE) into CA to solve the objective function and retrieve the optimal CA parameters. Constrained relations among factors were applied in DE to generate different sets of CA parameters for prediction of future scenarios. The DE‐CA model was calibrated using historical spatial data to simulate 2016 land use in Kunming and predict multiple scenarios to the year 2026. Assessment of quantitative accuracy shows that DE‐CA yields 92.4% overall accuracy, where 6.8% is the correctly captured urban growth; further, the model reported only 5.0% false alarms and 2.6% misses. Regarding the simulation ability, our new CA model performs as well as the widely applied genetic algorithm‐based CA model, and outperforms both the logistic regression‐based CA model and a no‐change NULL model. We projected three possible scenarios for the year 2026 using DE‐CA to adequately address the baseline urban growth, environmental protection and urban planning to show the strong prediction ability of the new model.     

A web‐based visualization tool for exploring stakeholder conflicts in land‐use planning

This article describes an open source web‐based visualization tool for exploring stakeholder conflicts in land‐use planning. It implements a multi‐criteria, decision‐analytic framework which solves a conflict‐constrained knapsack problem in order to find Pareto efficient combinations of actions which maximize public value, given the conflict constraint. The user interface is centered around an interactive map in which geographical subareas are color‐shaded in proportion to the mean strength of preference for a particular chosen action within the population in the selected area. The aim of this work is to investigate the use of web technologies and formal problem‐solving methods for improving public participation in land‐use planning and support local governments in the pursuit of improved decision‐making. The tool was developed using the free statistical programming language R and the web application framework Shiny. Its usefulness is demonstrated through a case study in Upplands Väsby municipality in Stockholm County, Sweden. The article ends with a summary together with some ideas for future research and development.     

Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng,China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.     

Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment,Zimbabwe

The land use and land cover pattern of a region is a consequence of natural and socio-economic factors and their utilization by man in time and space. In this study, we hypothesized that land use and land cover change patterns in the Lake Chivero catchment, Zimbabwe, were related to its human population dynamics. Using nonparametric correlation coefficients (Spearman’s rho, ρ), we found that bareland, cropland and built-up land had positive relations with human population growth of ρ = 0.7, ρ = 0.9 and ρ = 1, respectively. Grassland/shrubland, water and forest, on the other hand, had a negative relationship with human population growth of ρ = ?0.9, ρ = ?0.7 and ρ = ?0.667, respectively. However, these relationships were only significant (p < 0.05) for cropland, grassland/shrubland and built-up land. Human population dynamics in the Lake Chivero catchment could be one of the major drivers of land use and land cover change in the catchment between 1986 and 2014.     

A systems approach to assess climate change mitigation options in landscapes of the United States forest sector

Background

United States forests can contribute to national strategies for greenhouse gas reductions. The objective of this work was to evaluate forest sector climate change mitigation scenarios from 2018 to 2050 by applying a systems-based approach that accounts for net emissions across four interdependent components: (1) forest ecosystem, (2) land-use change, (3) harvested wood products, and (4) substitution benefits from using wood products and bioenergy. We assessed a range of land management and harvested wood product scenarios for two case studies in the U.S: coastal South Carolina and Northern Wisconsin. We integrated forest inventory and remotely-sensed disturbance data within a modelling framework consisting of a growth-and-yield driven ecosystem carbon model; a harvested wood products model that estimates emissions from commodity production, use and post-consumer treatment; and displacement factors to estimate avoided fossil fuel emissions. We estimated biophysical mitigation potential by comparing net emissions from land management and harvested wood products scenarios with a baseline (‘business as usual’) scenario.

Results

Baseline scenario results showed that the strength of the ecosystem carbon sink has been decreasing in the two sites due to age-related productivity declines and deforestation. Mitigation activities have the potential to lessen or delay the further reduction in the carbon sink. Results of the mitigation analysis indicated that scenarios reducing net forest area loss were most effective in South Carolina, while extending harvest rotations and increasing longer-lived wood products were most effective in Wisconsin. Scenarios aimed at increasing bioenergy use either increased or reduced net emissions within the 32-year analysis timeframe.

Conclusions

It is critical to apply a systems approach to comprehensively assess net emissions from forest sector climate change mitigation scenarios. Although some scenarios produced a benefit by displacing emissions from fossil fuel energy or by substituting wood products for other materials, these benefits can be outweighed by increased carbon emissions in the forest or product systems. Maintaining forests as forests, extending rotations, and shifting commodities to longer-lived products had the strongest mitigation benefits over several decades. Carbon cycle impacts of bioenergy depend on timeframe, feedstocks, and alternative uses of biomass, and cannot be assumed carbon neutral.

     

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Social connections between villages can represent farmers’ interests and thus benefit participatory rural relocation planning. With rural development, however, these connections will change and may weaken the adaptation of relocation plans to future rural systems. As yet, most studies still use empirical social connections to guide relocation planning, while a few have incorporated predicted connections in the context of rural development into relocation decisions. Meanwhile, spatial optimization approaches have seldom been adopted to solve this geographical decision issue. Accordingly, our study proposes a novel spatial relocation framework that incorporates changed inter‐village social connections under future rural development scenarios. Empirical inter‐village connections and their policy‐induced changes in central China were explored using social network analysis. An integration of particle swarm optimization and geographic information systems was adopted to identify the relocation solutions with maximum inter‐village connections and maximum spatial land use compactness, and to examine how connection changes under different policy scenarios influenced relocation outcomes. The results demonstrate the significance of incorporating policy‐induced social connections into relocation plans, and most importantly, show the negative relations between changed social connections and the migration distance/direction of relocated settlements. Our study is expected to improve the adaptation of relocation plans to future rural development.     

Using Land Use Data to Estimate the Population Distribution of China in 2000

To control for the defects found in remote sensing-derived estimates of population distributions, this study created a new method for estimating the population density of China at a 1 × 1 km spatial resolution by combining remote sensing-derived land use with China residence polygon data. As a result, we obtained three sets of land use data (i.e., remote sensing-derived, China residence polygon, and a combination of the two) to estimate population. On the basis of these data, we developed both urban and rural population distribution models. The results demonstrated that this new method could improve the accuracy of population estimation.     

Similarity Weighted Instance‐based Learning for the Generation of Transition Potentials in Land Use Change Modeling

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user‐friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance‐based learning algorithm based on the logic of the K‐Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi‐Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform.     

集成地理探测器与随机森林模型的城市人口分布格网模拟

精细尺度的城市人口分布信息是城市资源配置和综合管理的重要依据。本文以广州市越秀区、荔湾区、天河区、海珠区、白云区及黄埔区作为研究区域,基于人口统计、夜间灯光、兴趣点及土地利用等多源数据,利用地理探测器识别人口分布的影响因子,运用随机森林模型开展人口分布空间格网模拟研究。研究结果表明,与传统的相关分析相比,地理探测器能够更为准确地识别人口空间分布的重要影响因子。基于随机森林模型的人口分布格网模拟结果与街道（镇）实际人口的相关系数为0.774,平均相对误差约为30%。相比基于线性回归模型的模拟结果,随机森林模型的精度有明显提高。     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

Utilization of Landsat data to quantify land-use and land-cover changes related to oil and gas activities in West-Central Alberta from 2005 to 2013

Land use and land cover (LULC) change detection associated with oil and gas activities plays an important role in effective sustainable management practices, compliance monitoring, and reclamation assessment. In this study, a mapping methodology is presented for quantifying pre- and post-disturbance LULC types with annual Landsat Best-Available-Pixel multispectral data from 2005 to 2013. Annual LULC and land disturbance maps were produced for one of the major conventional oil and gas production areas in West-Central Alberta with an accuracy of 78% and 87%, respectively. The highest rate of vegetation loss (178 km²/year) was observed in coniferous forest compared to broadleaf forest, mixed forest, and native vegetation. Integration of ancillary oil and gas geospatial data with annual land disturbances indicated that less than 20% of the total land disturbances were attributable to oil and gas activities. In 2013, approximately 44% of oil and gas disturbances from 2005 to 2013 showed evidence of vegetation recovery. In the future, geospatial data related to wildfire, logging activities, insect defoliation, and other natural and anthropogenic factors can be integrated to quantify other causes of land disturbances.