Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications

Pattern-oriented calibration and validation of urban growth models: Case studies of Dublin, Milan and Warsaw

Urban growth models are established to simulate complex dynamic processes of urban development, such as urban sprawl. According to the pattern-oriented modelling (POM) paradigm, recently gaining weight in ecology as a strategy for modelling complex systems, patterns at multiple scales should be considered to reflect the underlying processes of a complex system. Yet, calibration and validation of urban growth models is typically performed with a goal function of locational (cell-by-cell) agreement only, thus not in line with POM. We therefore examined POM as an approach to calibrate and validate (constrained) cellular automata for the European cities Warsaw, Milan, and Dublin. For Milan and Warsaw, the model structures identified with POM outperformed reference solutions calibrated on a single pattern with improvements up to 25% and 30%, respectively. For Dublin, no good model structure was found, but POM did help to recognize this problem, while locational agreement only failed to do so. Furthermore, the model structures identified with POM were more diverse, i.e. including more driving factors. In these diverse structures, the importance of the neighborhood effect relative to the infrastructure and land use effects reflected the polycentricity of the city as well as its type of sprawl: from monocentric edge expansion in Dublin to in-between ribbon sprawl in Warsaw to polycentric infill development in Milan. We conclude that POM improves the robustness of urban growth model calibration and validation, and obtains more dependable information about the processes driving urban sprawl that may serve the design of instruments to limit it

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This study shows how bioenergy potential and total greenhouse gas (GHG) balances of land-use change and agricultural intensification can be modeled in an integrated way. The modeling framework is demonstrated for first- and second-generation ethanol production in Ukraine for the timeframe 2010-2030 for two scenarios: a business as usual (BAU) scenario in which current trends in agricultural productivity are continued; and a progressive scenario, which projects a convergence of yield levels in Ukraine with Western Europe. The spatiotemporal development in land for food production is analyzed making use of the PCRaster Land Use Change (PLUC) model. The land-use projections serve as input for the analysis of the CO2, N2O, and CH4 emissions related to changes in land use and agricultural management, as well as the abatement of GHG emissions by replacing fossil fuels with bioethanol production from wheat and switchgrass. This results in annual maps (1 km2 resolution) of the different GHG emissions for the modeled timeframe. In the BAU scenario, the GHG emissions increase over time, whereas in the progressive scenario, a total cumulative GHG emission reduction of 0.8 Gt CO2-eq for wheat and 3.8 Gt CO2-eq for switchgrass could be achieved in 2030. When the available land is used for the re-growth of natural vegetation, 3.5 Gt CO2-eq could be accumulated. These emission reductions could increase when appropriate measures are taken. The spatiotemporal PLUC model + GHG module allows for spatiotemporal and integrated modeling of total GHG emissions of bioenergy production and intensification of the agricultural sector

Empirical characterisation of agents’ spatial behaviour in pedestrian movement simulation

Route choice behaviour is a key factor in determining pedestrian movement flows throughout the urban space. Agent-based modelling, a simulation paradigm that allows modelling individual behaviour mechanisms to observe the emergence of macro-level patterns, has not employed empirical data regarding route choice behaviour in cities or accommodated heterogeneity. The aim of this paper is to present an empirically based Agent-Based Model (ABM) that accounts for behavioural heterogeneity in pedestrian route choice strategies, to simulate the movement of pedestrians in cities. We designed a questionnaire to observe to what degree people employ salient urban elements (local and global landmarks, regions, and barriers) and road costs (road distance, cumulative angular change) and to empirically characterise the agent behaviour in our ABM. We hypothesised that a heterogeneous ABM configuration based on the construction of agent typologies from empirical data would portray a more plausible picture of pedestrian movement flows than a homogeneous configuration, based on the same data, or a random configuration. The city of Münster (DE) was used as a case study. From a sample of 301 subjects, we obtained six clusters that differed in relation to the role of global elements (distant landmarks, barriers, and regions) and meaningful local elements along the route. The random configuration directed the agents towards natural elements and the streets of the historical centre. The empirically based configurations resulted in lower pedestrian volumes along roads designed for cars (25% decrease) but higher concentrations along the city Promenade and the lake (40% increase); based on our knowledge, we deem these results more plausible. Minor differences were identified between the heterogeneous and homogeneous configurations. These findings indicate that the inclusion of heterogeneity does not make a difference in terms of global patterns. Yet, we demonstrated that simulation models of pedestrian movement in cities should be at least based on empirical data at the average sample-level to inform urban planners about areas prone to high volumes of pedestrians

Systemic change in the Rhine-Meuse basin: Quantifying and explaining parameters trends in the PCR-GLOBWB global hydrological model

In hydrological modelling, traditionally one calibration was performed over a certain calibration period before the model is used to study the hydrological system. This implies that a constant model structure and parameterization are assumed. However, if the catchment system is subject to changes that are not incorporated in the model, the parameter values found in a calibration period may not be optimal for other periods, which is called systemic change. The aim of this study was to identify systemic change and its possible causes with the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. Systemic change was studied for the main model components, by selecting a key parameter from each component (minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor, Manning's n). These parameters were calibrated for 10-year rolling periods between 1901-2010. The results showed that at the downstream locations, the changes in optimal parameter values were small, while at the upstream locations, the optimal values of most parameters changed considerably over the different rolling calibration periods, signifying systemic change. Especially the degree day factor showed large variations, varying over time between 0.5 and 2.5 times its default value at Basel and Maxau (upstream and middle part of the Rhine basin). Based on correlation analysis, it was found that climate change as well as changes in land use and river structure are possible causes of changes in optimal parameter values through time

Environmental vulnerability assessment of Brazilian Amazon Indigenous Lands

Amazonian Indigenous Lands (ILs) are human-environment systems facing a multitude of environmental threats. Yet, the resulting vulnerability of these systems are to date unknown. We adopt the theoretical vulnerability framework of the IPCC to assess the environmental vulnerability of Brazilian Amazon ILs for two periods (2001–2010 and 2011–2019) and overall (2001–2019). Vulnerability is deemed a function of exposure (EX), sensitivity (SE) and adaptive capacity (AC) of a system to threats. Sensitivity (threats within IL) and exposure (threats in IL's buffer zones) indicators are changes in forest cover, economic activities, and road access, quantified using data of deforestation, forest degradation, land-use, fire, roads and mining. Adaptive capacity indicators represent Indigenous self-organization, education and access to knowledge, land ownership, external incomes, and institutional arrangement. We find a concentration of ILs with high vulnerability in the Arc of Deforestation and South, and advancing in Pará and Roraima states. A strong relationship (Spearman r = 0.79) between EX and SE indicates the strong pressure exerted by external processes. An increase in EX (73.9% of the ILs) and in SE (64.8% of the ILs) in 2011–2019 compared to 2001–2010 signals a worrying rise in vulnerability recently. We advise the adoption of policies by the State, such as combating illegal activities, and strengthening National Policy for Environmental and Territorial Management of ILs. Herein, our vulnerability quantification can prioritize help to certain ILs, and the understanding of the contribution of the underlying dimensions can direct these policies, possibly according to the vulnerability profile of each IL

The potential and limitations of intrahepatic cholangiocyte organoids to study inborn errors of metabolism

Inborn errors of metabolism (IEMs) comprise a diverse group of individually rare monogenic disorders that affect metabolic pathways. Mutations lead to enzymatic deficiency or dysfunction, which results in intermediate metabolite accumulation or deficit leading to disease phenotypes. Currently, treatment options for many IEMs are insufficient. Rarity of individual IEMs hampers therapy development and phenotypic and genetic heterogeneity suggest beneficial effects of personalized approaches. Recently, cultures of patient-own liver-derived intrahepatic cholangiocyte organoids (ICOs) have been established. Since most metabolic genes are expressed in the liver, patient-derived ICOs represent exciting possibilities for in vitro modeling and personalized drug testing for IEMs. However, the exact application range of ICOs remains unclear. To address this, we examined which metabolic pathways can be studied with ICOs and what the potential and limitations of patient-derived ICOs are to model metabolic functions. We present functional assays in patient ICOs with defects in branched-chain amino acid metabolism (methylmalonic acidemia), copper metabolism (Wilson disease), and transporter defects (cystic fibrosis). We discuss the broad range of functional assays that can be applied to ICOs, but also address the limitations of these patient-specific cell models. In doing so, we aim to guide the selection of the appropriate cell model for studies of a specific disease or metabolic process

Terrain Prickliness: Theoretical Grounds for High Complexity Viewsheds

An important task in terrain analysis is computing viewsheds. A viewshed is the union of all the parts of the terrain that are visible from a given viewpoint or set of viewpoints. The complexity of a viewshed can vary significantly depending on the terrain topography and the viewpoint position. In this work we study a new topographic attribute, the prickliness, that measures the number of local maxima in a terrain from all possible angles of view. We show that the prickliness effectively captures the potential of terrains to have high complexity viewsheds. We present near-optimal algorithms to compute it for TIN terrains, and efficient approximate algorithms for raster DEMs. We validate the usefulness of the prickliness attribute with experiments in a large set of real terrains

LIPIcs, Volume 177, GIScience 2021, Complete Volume

LIPIcs, Volume 177, GIScience 2021, Complete Volum