Inge Uljee

The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics

A cellular automaton is specified to give a spatially detailed representation of the evolution of urban land-use patterns. Cell states represent land uses, and transition rules express the likelihood of a change from one state to another as a function both of existing land use in the 113-cell neighbourhood of the cell and of the inherent suitability of the cell for each possible use. The model is used to simulate the land-use pattern of Cincinnati, Ohio. The simulation results are realistic and sensitivity analysis shows that the predictions of the model are relatively accurate and reproducible, thus suggesting that cellular ast;automata-based models may be useful in a planning context.

Using cellular automata for integrated modelling of socio-environmental systems

Cellular automata provide the key to a dynamic modelling and simulation framework that integrates socio-economic with environmental models, and that operates at both micro and macro geographical scales. An application to the problem of forecasting the effect of climate change on a small island state suggests that such modelling techniques could help planners and policy makers design more effective policies — policies better tuned both to specific local needs and to overall socio-economic and environmental constraints.

Further developments of a fuzzy set map comparison approach

Fuzzy set map comparison offers a novel approach to map comparison.The approach is specifically aimed at categorical raster maps and applies fuzzy set techniques, accounting for fuzziness of location and fuzziness of category, to create a similarity map as well as an overall similarity statistic: the Fuzzy Kappa. To date, the calculation of the Fuzzy Kappa (or K-fuzzy) has not been formally derived, and the documented procedure was only valid for cases without fuzziness of category. Furthermore, it required an infinitely large, edgeless map. This paper presents the full derivation of the Fuzzy Kappa; the method is now valid for comparisons considering fuzziness of both location and category and does not require further assumptions. This theoretical completion opens opportunities for use of the technique that surpass the original intentions. In particular, the categorical similarity matrix can be applied to highlight or disregard differences pertaining to selected categories or groups of categories and to distinguish between differences due to omission and commission.

Integrating constrained cellular automata models, GIS and decision support tools for urban planning and policy-making

In this paper we present a Decision Support System developed to assist urban planners and policy makers to simulate and analyse alternative urban layouts, land uses, and growth patterns. The core of the system is a modelli ng and simulation shell allowing the user to specify Constrained Cellular Automata models of urban and regional systems. Unlike conventional Cellular Automata, these are defined with a relatively large neighbourhood and a large number of states, representing both human and natural land-uses. They forecast changes in land-use for small parcels on the basis of both the activities present in the local neighbourhood and the specific characteristics of the parcels themselves. Since each parcel affects every other within its neighbourhood, complex dynamics emerge. More traditional dynamic models --ideally spatial interaction based models-control the overall dynamics of the cellular automata. In the DSS, a custom-built GIS is integrated in the modelli ng shell . It stores the detailed geographical characteristics and feeds them into the model at the cellular level as required. Thus the approach permits the straightforward integration of physical, environmental, technical and institutional factors into a single dynamic socio-economic model at a high level of spatial detail . By way of ill ustration, we report applications of the models to both intra-urban and inter-urban cases, and examine specifically the case where the cellular model is forced by means of a regionalised macro-model.

Integrated modelling of population, employment and land-use change with a multiple activity-based variable grid cellular automaton

A constrained cellular automaton (CA) framework is used to model both land use and, at the same resolution, densities of population and economic activity. The multi-scale processes determining the location of population, economic activity and land use are captured in a variable grid CA, in which the neighbourhood of each cell includes the entire modelled area. The transition rules generating the spatial dynamics incorporate both the land use and the activities, and because they cover the entire modelled area, they represent interaction effects at all spatial scales; effectively, they are distance decay functions. In general, any particular cell hosts a number of activities (population, employment in various sectors) regardless of its land use, so in effect multiple land uses are modelled as multiple activities, although activity levels are normally highest on cells of the corresponding land use. The model is applied to both the urban-centred Greater Dublin Region and the country of Belgium, which consists of the entire polycentric urban system and its rural matrix. Results for both applications are good, as measured by the errors of both predicted populations and fractal dimensions, and the model outperforms the best existing models by these measures.

Inferring urban land use using the optimised spatial reclassification kernel

In the 1990s, promising results in land-use classification were obtained by kernel-based contextual classification algorithms. Soon, however, it was recognised that kernel-based reclassifiers have important shortcomings and research instead focused on object-based image analysis. This study proposes a solution to two of the most important drawbacks of kernel-based reclassifiers: (1) the use of kernels tends to smooth boundaries between discrete land-use/land-cover parcels, and (2) it is difficult to determine a priori the optimum kernel size of the classifier. The Spatial Reclassification Kernel (SPARK) algorithm has been adapted in order to automatically optimise the kernel size depending on the spatial variation found in the neighbourhood of a pixel to be classified, resulting in the Optimised SPARK (OSPARK) algorithm. The performance of SPARK and OSPARK for land-use classification has been evaluated for the Dublin urban area (Ireland), using a Landsat TM image. The MOLAND land-use map of 1990 was used as a reference. Results show that the use of optimal kernel sizes instead of fixed kernel sizes reduces the omission and commission errors for most land-use classes.

Using remote sensing derived spatial metrics for the calibration of land-use change models

More than ever before, planners and policy makers need tools to anticipate and assess the impact of their decisions on the spatial system that they are to manage. A growing number of high resolution models is currently being developed for this purpose. The calibration of these models remains a major challenge. Typically the required time series of land-use maps based on identical and consistent mapping methodologies, legends and scales are missing. The availability of images from earth observation satellites is much larger. However, conventional remote sensing based land-use classifications result in land cover maps, based on reflective properties of the surface, rather than land-use maps representing the functional classes needed for urban land-use change modeling. Recently, landscape metrics or spatial metrics have been introduced in the field of urban land-use mapping and modeling to characterize the spatial dynamics of such systems. The question raised in the study presented is whether spatial metrics directly applied to remote sensing images can be used to calibrate and validate land-use models of urban systems. The underlying hypothesis is that a methodology can be developed which enables to calculate metrics on both the remote sensing image and the predicted land-use map, which quantify the same distinguishing spatial structures at some level of abstraction. The study demonstrates the potential of spatial metrics to simplify and speed up the calibration procedures in so far that the development of land-use maps could be avoided.

The importance of uncertainties in scenario analyses – A study on future ecosystem service delivery in Flanders

Land use is rapidly changing and is significantly affecting ecosystem service delivery all around the world. The socio-economic context and political choices largely determine land use change. This land use change, driven by socio-economic pressures, will impact diverse elements of the environment including, for example, air quality, soil properties, water infiltration and food and wood production, impacts that can be linked to the provisioning of ecosystem services. To gain more insight into the effects of alternative socio-economic developments on ecosystem service delivery, land use change models are being coupled to ecosystem service delivery models to perform scenario analyses. Although the uncertainty of the results of these kind of scenario analyses are generally far from negligible, studies rarely take them into account. In this study, a cellular automaton land use change model is coupled to Bayesian belief network ecosystem service delivery models to facilitate the study of error propagation in scenario analysis. The proposed approach is applied to model the impact of alternative socio-economic developments on ecosystem service delivery in Flanders, Belgium and to assess the impact of land use allocation uncertainty on the uncertainty associated to future ecosystem service delivery. Results suggest that taking into account uncertainties may have an effect on policy recommendations that come out of the scenario analysis. However, in this study, uncertainties in the applied ecosystem service models were dominant, reducing the importance of accounting for land use allocation uncertainty.

A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin

Land-use change models are useful tools for assessing and comparing the environmental impact of alternative policy scenarios. Their increasing popularity as spatial planning instruments also poses new scientific challenges, such as correctly calibrating the model. The challenge in model calibration is twofold: obtaining a reliable and consistent time series of land-use information and finding suitable measures to compare model output to reality. Both of these issues are addressed in this paper. The authors propose a model calibration framework that is supported by information on urban form and function derived from medium-resolution remote sensing data through newly developed spatial metrics. The remote sensing derived maps are compared to model output of the same date for two model scenarios using well-known spatial metrics. Results demonstrate a good resemblance between the simulation output and the remote sensing derived maps.

Computer systems that enhance the productivity of public-sector planners

Policy Support Systems (PSS) for regional and national planning of public policy are intelligent software systems, that can be viewed as special Decision Support Systems (DSS). They are tailored to the practical needs of policy-makers and serve to enhance the quality of their work and their productivity. In this article a systematic analysis is made of the endogenous and exogenous constraints on the Policy Making Process (PMP), as also of the endogenous and exogenous threats to the PMP. The rationale for PSS is they will enhance the opportunities for a PMP that is more rational and more verifiable, by taking into account such constraints and threats. Thus they will bring about a higher quality and productivity of public policies, which means that these will better cater for the interests of Society. In this article we focus on the following issues: What should intelligent DSS be like? Why is there a need for PSS? In which respects are PSS different from other DSS? What types of decision support should the...