Governance, scale and the environment: the importance of recognizing knowledge claims in transdisciplinary arenas

Any present day approach of the world’s most pressing environmental problems involves both scale and governance issues. After all, current local events might have long-term global consequences (the scale issue) and solving complex environmental problems requires policy makers to think and govern beyond generally used time-space scales (the governance issue). To an increasing extent, the various scientists in these fields have used concepts like social-ecological systems, hierarchies, scales and levels to understand and explain the “complex cross-scale dynamics” of issues like climate change. A large part of this work manifests a realist paradigm: the scales and levels, either in ecological processes or in governance systems, are considered as “real”. However, various scholars question this position and claim that scales and levels are continuously (re)constructed in the interfaces of science, society, politics and nature. Some of these critics even prefer to adopt a non-scalar approach, doing away with notions such as hierarchy, scale and level. Here we take another route, however. We try to overcome the realist-constructionist dualism by advocating a dialogue between them on the basis of exchanging and reflecting on different knowledge claims in transdisciplinary arenas. We describe two important developments, one in the ecological scaling literature and the other in the governance literature, which we consider to provide a basis for such a dialogue. We will argue that scale issues, governance practices as well as their mutual interdependencies should be considered as human constructs, although dialectically related to nature’s materiality, and therefore as contested processes, requiring intensive and continuous dialogue and cooperation among natural scientists, social scientists, policy makers and citizens alike. They also require critical reflection on scientists’ roles and on academic practices in general. Acknowledging knowledge claims provides a common ground and point of departure for such cooperation, something we think is not yet sufficiently happening, but which is essential in addressing today’s environmental problems

Predicting Growth of City\u27s Built-up Land Based on Scenario Planning

In this paper, method of scenario planning is applied to the study of land use planning, putting forward a new approach to analyze future growth of city\u27s built-up land in the context of future uncertainty. By introducing economic and policy factors into land use system, a calculation model of urban built-up land is built based on the correlation between industries and land use. And using Chongqing Municipality from China as an example, we establish 6 different scenarios and simulate future development of city\u27s land use from 2015 to 2020 under each scenario. The results indicate that Chongqing will meet fast urban expansion according to current trend and is in urgent need to improve its land use efficiency which shows strongest effect in controlling city size

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models

A travel time-based variable grid approach for an activity-based cellular automata model

Urban growth and population growth are used in numerous models to determine their potential impacts on both the natural and the socio-economic systems. Cellular automata (CA) land-use models became popular for urban growth modelling since they predict spatial interactions between different land uses in an explicit and straightforward manner. A common deficiency of land-use models is that they only deal with abstract categories, while in reality, several activities are often hosted at one location (e.g. population, employment, agricultural yield, nature…). Recently, a multiple activity-based variable grid CA model was proposed to represent several urban activities (population and economic activities) within single model cells. The distance-decay influence rules of the model included both short- and long-distance interactions, but all distances between cells were simply Euclidean distances. The geometry of the real transportation system, as well as its interrelations with the evolving activities, were therefore not taken into account. To improve this particular model, we make the influence rules functions of time travelled on the transportation system. Specifically, the new algorithm computes and stores all travel times needed for the variable grid CA. This approach provides fast run times, and it has a higher resolution and more easily modified parameters than the alternative approach of coupling the activity-based CA model to an external transportation model. This paper presents results from one Euclidean scenario and four different transport network scenarios to show the effects on land-use and activity change in an application to Belgium. The approach can add value to urban scenario analysis and the development of transport- and activity-related spatial indicators, and constitutes a general improvement of the activity-based CA model

Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Urban growth and land use change models, supported by Geographic Information Systems (GIS) software and increased digital data availability, have the potential to become important tools for monitoring and guiding urban spatial planning and development. Five broad categories of urban models are utilised internationally, that is, land use transportation models, cellular automata, system dynamics, agent-based models and spatial economics/econometric models. This paper provides a broad overview of South African modelling projects that monitor or simulate urban spatial change. The review identified a variety of government and academic urban modelling initiatives. These initiatives mostly track trends, rather than simulating future scenarios, and analyse historical land cover change using GIS and remote sensing software. There is a risk within Gauteng, however, that out-dated data, different population projections, duplicated tools, limited spatial data infrastructure (SDI) and a lack of resources; could compromise urban spatial change modelling efforts within government institutions. As such, the paper discusses key challenges and opportunities for modelling urban spatial change, with specific reference to the Gauteng City-Region – the heartland of the South African economy and the Southern African region