Land Use-Transportation Interaction: Lessons Learned from an Experimental Model using Cellular Automata and Artificial Neural Networks

Land use and transportation interact to produce large urban concentrations in most major cities that create tremendous sprawl, noise, congestion, and environmental concerns. The desire to better understand this relationship has led to the development of land use–transport (LUT) models as an extension of more general urban models. The difficulties encountered in developing such models are many as local actions sum to form global patterns of land use change, producing complex interrelationships. Cellular automata (CA) simplify LUT model structure, promise resolution improvement, and effectively handle the dynamics of emergent growth. Artificial Neural Networks (ANN) can be used to quantify the complex relationships present in historical land use data as a means of calibrating a CA-LUT model. This study uses an ANN, slope, historical land use, and road data to calibrate a CA-LUT model for the I-140 corridor of Knoxville, TN. The resulting model was found to require a complex ANN, produce realistic emergent growth patterns, and shows promising simulation performance in several significant land classes such as single-family residential. Problems were encountered as the model was iterated due to the lack of a mechanism to extend the road network. The presence of local roads in the model’s configuration strengthened ability of the model to simulate historical development patterns. Shortcomings in certain aspects of the simulation performance point to the need for the addition of a socio-economic sub-model to assess demand for urban area and/or an equilibrium mechanism to arbitrate the supply of developable land. The model constructed in this study was found to hold considerable potential for local-scale simulation and scenario testing given suitable modification to its structure and input parameters

An integrated computational and collaborative approach for city resilience planning

Given the rise in climate change-related extreme events, there is an urgent need for cities and regions to implement resilience plans based on data and evidence and developed in collaboration with key stakeholders. However, current planning and decision-making processes rely on limited data and modelling. Moreover, stakeholder engagement is significantly inhibited by social, political, and technological barriers. The research presented in this thesis aims to enhance resilience planning practice through the development and evaluation of an integrated computational and collaborative scenario planning approach. The scenario planning approach is tested within a geodesign framework and supported by several planning support systems (PSS), including urban growth models. These PSS tools are made accessible to key stakeholders through dedicated planning support theatres, enabling participants to collaborate both in-person and online. Through two empirical case studies conducted in Australian regions, this research integrates data-driven modelling (computational) with people-led geodesign (collaborative) approaches for scenario forecasting and planning. The first case study explores anticipatory/normative scenarios, while the second focuses on exploratory scenario planning, with both aiming to enhance city and regional resilience. This thesis examines the roles played by both simple digital tools and purpose-built planning support theatres in scenario planning processes with key stakeholders. The research investigates the utility of data-driven models in supporting collaborative scenario planning. Both integration experiments received positive feedback from most participants. However, to truly improve the process, there is a need for widely available high-quality spatial and temporal datasets, including localised climate change impact data. In summary, an integrated computational and collaborative approach, augmented by data and technology, can provide an evidence base for decision-making towards a resilient future, fostering deeper engagement of the local community and across-government collaboration in scenario planning

THE EFFECTS OF CHANGES IN LAND COVER AND LAND USE ON NUTRIENT LOADINGS TO THE CHESAPEAKE BAY USING FORECASTS OF URBANIZATION

This dissertation examined the effects of land cover and land use (LC/LU) change on nutrient loadings (mass for a specified time) to the Chesapeake Bay, after future projections of urbanization were applied. This was accomplished by quantifying the comprehensive impacts of landscape on nutrients throughout the watershed. In order to quantify forecasted impacts of future development and LC/LU change, the current (2000) effects of landscape composition and configuration on total nitrogen (TN) and total phosphorus (TP) were examined. The effects of cover types were examined not only at catchment scales, but within riparian stream buffer to quantify the effects of spatial arrangement. Using the SPAtially Referenced Regressions On Watershed Attributes (SPARROW) model, several compositional and configurational metrics at both scales were significantly correlated to nutrient genesis and transport and helped estimate loadings to the Chesapeake Bay with slightly better accuracy and precision. Remotely sensed forecasts of future (2030) urbanization were integrated into SPARROW using these metrics to project TN and TP loadings into the future. After estimation of these metrics and other LC/LU-based sources, it was found that overall nutrient transport to the Chesapeake Bay will decrease due to agricultural land losses and fertilizer reductions. Although point and non-point source urban loadings increased in the watershed, these gains were not enough to negate decreased agricultural impacts. In catchments forecasted to undergo urban sprawl conditions by 2030, the response of TN locally generated within catchments varied. The forecasted placement of smaller patches of development within agricultural lands of higher nutrient production was correlated to projected losses. However, shifting forecasted growth onto or adjacent to existing development, not agricultural lands, resulted in projected gains. This indicated the importance of forecasted spatial arrangement to projected TN runoff from the watershed. In conclusion, comprehensive landscape analysis resulted in differences in simulations of current and future nutrient loadings to the Chesapeake Bay, as a result of urbanization and LC/LU change. With eutrophication from excess nutrients being the primary challenge to the estuary, information gained from the estimation of these effects could improve the future management and regulation of the Chesapeake Bay

Addressing data limitations and uncertainties in broad-scale coastal flood-risk assessments

Coastal flooding constitutes a major risk to all low-lying coastal areas around the world. This risk is expected to increase during the 21st century with rising sea-levels and future societal development. Broad-scale coastal flood risk assessments are essential for identifying regions most at risk and evaluating the effectiveness of coastal adaptation responses in reducing future coastal impacts. Despite recent advances in coastal flood risk modelling research, there are a number of methodological and data related constraints and limitations inherent in broad-scale studies that affect the accuracy of assessment findings. Understanding and communicating these uncertainties is necessary for effectively supporting decision-makers in developing long-term robust and flexible adaptation plans. However, most uncertainties involved in broad-scale assessments are not fully quantified and their relative importance often remain unexplored. This thesis contributes to improving our understanding of data uncertainties and addresses data limitations in broad-scale coastal flood risk assessments. In particular, this thesis (1) addresses data availability, consistency and reproducibility constraints, (2) extends existing data models and increase the level of detail of assessments and (3) explores and quantifies data uncertainties in broad-scale coastal flood risk studies. For this purpose, Chapter 1 summarizes the main data limitations and uncertainties inherent in each coastal flood risk component (coastal hazard, exposure and vulnerability) and its implications for broad-scale coastal flood risk assessments. Chapter 2 assesses sea-level rise related coastal flood impacts for Emilia-Romagna (Italy) using the Dynamic Interactive Vulnerability Assessment (DIVA) modelling framework and investigates the sensitivity of model results to four uncertainty dimensions, namely (1) elevation, (2) population, (3) vertical land movement, (4) scale and resolution of assessment. Results show that by the end of the century coastal flood impacts are most sensitive to variations in elevation input data, followed by vertical land movement data and population data. The choice of one digital elevation model over another can lead up to 45% differences in the total extent of the coastal flood plain. Further, the inclusion of human-induced subsidence rates in the input data increases the relative sea-level rise on average by 60cm in 2100, resulting in coastal flood impacts that are up to 25% higher, highlighting that the non-consideration of human-induced subsidence in broad-scale studies underestimates coastal flood impacts. Chapter 3 describes the development of the open-access, spatially-explicit Mediterranean Coastal Database (MCD) that contains consistent information in terms of resolution, quality, accuracy and format of around 160 parameters on characteristics of the natural and socio-economic coastal subsystems for the entire region. The MCD, as well as the code for all data processing steps, is publicly available in an online repository. Chapter 4 illustrates the development of a new set of spatially-explicit projections of urban extent for ten countries in the Mediterranean, with a high spatial (100m) and temporal resolution (5-year time steps). These future urban projections indicate that accounting for the spatial patterns of urban development can lead to significant differences in the assessment of future coastal urban exposure. Depending on the urban development scenario chosen, the exposure of certain coastal regions can vary by up to 104 percent until 2100. The urban extent projections spanning from 2025 to 2100 and the python code to set up the urban change model are available from a public repository. Finally, Chapter 5 summarizes the main findings and lessons learned from this thesis and highlights some key challenges related to data that require further research targeting

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Geosimulation and Multicriteria Modelling of Residential Land Development in the City of Tehran: A Comparative Analysis of Global and Local Models

Conventional models for simulating land-use patterns are insufficient in addressing complex dynamics of urban systems. A new generation of urban models, inspired by research on cellular automata and multi-agent systems, has been proposed to address the drawbacks of conventional modelling. This new generation of urban models is called geosimulation. Geosimulation attempts to model macro-scale patterns using micro-scale urban entities such as vehicles, homeowners, and households. The urban entities are represented by agents in the geosimulation modelling. Each type of agents has different preferences and priorities and shows different behaviours. In the land-use modelling context, the behaviour of agents is their ability to evaluate the suitability of parcels of land using a number of factors (criteria and constraints), and choose the best land(s) for a specific purpose. Multicriteria analysis provides a set of methods and procedures that can be used in the geosimulation modelling to describe the behaviours of agents. There are three main objectives of this research. First, a framework for integrating multicriteria models into geosimulation procedures is developed to simulate residential development in the City of Tehran. Specifically, the local form of multicriteria models is used as a method for modelling agents’ behaviours. Second, the framework is tested in the context of residential land development in Tehran between 1996 and 2006. The empirical research is focused on identifying the spatial patterns of land suitability for residential development taking into account the preferences of three groups of actors (agents): households, developers, and local authorities. Third, a comparative analysis of the results of the geosimulation-multicriteria models is performed. A number of global and local geosimulation-multicriteria models (scenarios) of residential development in Tehran are defined and then the results obtained by the scenarios are evaluated and examined. The output of each geosimulation-multicriteria model is compared to the results of other models and to the actual pattern of land-use in Tehran. The analysis is focused on comparing the results of the local and global geosimulation-multicriteria models. Accuracy measures and spatial metrics are used in the comparative analysis. The results suggest that, in general, the local geosimulation-multicriteria models perform better than the global methods