A.K. Bregt

Assessing the worldwide developments of national spatial data clearinghouses

Many countries have spent considerable resources over the past few years debating optimal national spatial data infrastructures. One of the (main) elements of these infrastructures is the national spatial data clearinghouse, which facilitates access to required spatial data and provides complementary services. With this in mind, in April 2000, 2001, 2002 and December 2000, 2001, 2002, a web survey was carried out to assess systematically the developments of these national clearinghouses worldwide. Regarding the development in the number of implementations, it can be considered a worldwide success. However, of concern are the declining trends in use, management and content. One of the main reasons for these negative trends could be the dissatisfaction of the spatial data community with the functional capability of current clearinghouses. The functional capabilities of clearinghouses should likely be changed from a data-oriented to a user and application-oriented focus. This is in accord with the objectives of the second generation of spatial data infrastructures. The main factors, therefore, that will have positive impacts on developments in this field are the inclusion of web services, stability of funding and creation of user-friendly interfaces.

An agent-based approach to model land-use change at a regional scale

Land-use/cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the conceptual framework is represented by combining different concepts including agent typologies, farm trajectories and probabilistic decision-making processes. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.

Multi-actor-based land use modelling: spatial planning using agents

This paper describes a spatial planning model combining a multi-agent simulation (MAS) approach with cellular automata (CA). The model includes individual actor behaviour according to a bottom-up modelling concept. Spatial planning intentions and related decision making of planning actors is defined by agents. CA is used to infer the knowledge needed by the agents to make decisions about the future of a spatial organisation in a certain area. The innovative item of this approach offers a framework for modelling complex land use planning process by extending CA approach with MAS. The modelling approach is demonstrated by the implementation of a pilot model using JAVA and the SWARM agent modelling toolkit. The pilot model itself is applied to a study area near the city of Nijmegen, The Netherlands.

Assessing fitness for use: the expected value of spatial data sets

This paper proposes and illustrates a decision analytical approach to compare the value of alternative spatial data sets. In contrast to other work addressing value of information, its focus is on value of control. This is a useful concept when choosing the best data set for decision making under uncertainty due to error in the reported data. Application of the concept requires probabilistic accuracy measures and a loss function representing the cost of incorrect judgement about some target property. This is illustrated by an assessment of the suitability of two digital elevation models (DEMs) for determining the volume of sand required for building a container port. To demonstrate flexibility of the approach, accuracy assessment was based on both a random and a systematic sample of error data, using design-based estimation and model-based prediction, that is geostatistics. Analysis results included the expected loss for each combination of DEM and sampling strategy. These indicated that both DEMs were equally suitable for the intended use. Operational practicability of the method is highly dependent on the willingness of database producers to give access to sample information similar to the quick looks provided to potential users of remote sensing imagery.

A review of current calibration and validation practices in land-change modeling

Land-change models are increasingly used to explore land-change dynamics, as well as for policy analyses and scenario studies. In this paper we review calibration and validation approaches adopted for recently published applications of land-change models. We found that statistical analyses and automated procedures are the two most common calibration approaches, while expert knowledge, manual calibration, and transfer of parameters from other applications are less frequently used. Validation of model results is predominantly based on locational accuracy assessment, while a small fraction of the applications assessed the accuracy of the generated land-use or land-cover patterns. Of the reviewed model applications, thirty-one percent did not report any validation. We argue that to mature as a scientific tool, and to gain credibility for scenario studies and policy assessments, the validation of land-change models requires consideration of challenges posed by uncertainty, complexity, and non-stationarity of land-change processes, and equifinality and multifinality of land-change models.

Spatial data infrastructures as complex adaptive systems

Many researchers throughout the world have been struggling to better understand and describe spatial data infrastructures (SDIs). Our knowledge of the real forces and mechanisms behind SDIs is still very limited. The reason for this difficulty might lie in the complex, dynamic and multifaceted nature of SDIs. To evaluate the functioning and effects of SDIs we must have a proper theory and understanding of their nature. This article describes a new approach to understanding SDIs by looking at them through the lens of complex adaptive systems (CASs). CASs are frequently described by the following features and behaviours: complexity, components, self-organization, openness, unpredictability, nonlinearity and adaptability, scale-independence, existence of feedback loop mechanism and sensitivity to initial conditions. In this article both CAS and SDI features are presented, examined and compared using three National SDI case studies from the Netherlands, Australia and Poland. These three National SDIs were carefully analysed to identify CAS features and behaviours. In addition, an Internet survey of SDI experts was carried out to gauge the degree to which they consider SDIs and CASs to be similar. This explorative study provides evidence that to a certain extent SDIs can be viewed as CASs because they have many features in common and behave in a similar way. Studying SDIs as CASs has significant implications for our understanding of SDIs. It will help us to identify and better understand the key factors and conditions for SDI functioning. Assuming that SDIs behave much like CASs, this also has implications for their assessment: assessment techniques typical for linear and predictable systems may not be valid for complex and adaptive systems. This implies that future studies on the development of an SDI assessment framework must consider the complex and adaptive nature of SDIs.

Validation of an agent-based model for spatial planning: A role-playing approach

This paper discusses the validation of an agent-based model for simulating a multi-actor spatial planning process. After a general discussion about the pitfalls of validating agent-based models that simulate complex spatial systems, we briefly present a validation method based on role playing. The method is designed to generate insights that can improve our understanding of the behaviour of socio-spatial systems in a planning context. The method was tested by carrying out an experimental role play to validate individual agent tasks, focusing on the ability of agents to generate beliefs and preferences about their environment. We conclude that using role play as a validation technique can provide valuable information about the performance of the model. The insights gained aid identification and understanding of those parts of a multi-actor spatial planning system that are currently poorly understood and poorly represented by the agents.

Measuring the neighbourhood effect to calibrate land use models

Abstract Many spatially explicit land use models include the neighbourhood effect as a driver of land use changes. The neighbourhood effect includes the inertia of land uses over time, the conversion from one land use to another, and the attraction or repulsion of surrounding land uses. The neighbourhood effect is expressed in the neighbourhood rules, but calibration of the neighbourhood rules is not straightforward. This paper aims to characterise the neighbourhood effect of observed land use changes and use this information to improve the calibration of land use models. We measured the over- and underrepresentation of land uses in the neighbourhood of observed land use changes using a modified version of the enrichment factor. Enrichment factors of observed land use changes in Germany between 1990 and 2000 indicate that the neighbourhood effect exists. This suggests that it is appropriate to use neighbourhood rules to simulate urban land use changes. Observed enrichment factors were used to calibrate a land use model for Germany from 1990 to 2000 and the obtained neighbourhood rules were validated independently from 2000 to 2006. The results show that both the allocation accuracy and the pattern accuracy of the land use model improved for the calibration period, as well as for the independent validation period. This indicates that enrichment factors can be used to improve the calibration of the neighbourhood rules in land use models.

Cross-agency coordination in the shadow of hierarchy: 'joining up' government geospatial information systems

Government agencies striving to make geospatial information systems interoperable and cost‐effective often appear to function as a self‐regulating network shaped only by internal trust and reciprocity. However, recent public management research suggests that external steering of a network, exercised by authoritative bodies through hierarchical means, may invigorate cross‐agency coordination. The two case studies of federal geospatial coordination in Canada and the USA confirm this emerging theory of network–hierarchy dynamics. In these countries, the central budget agency (CBA) is influencing resource flows and accountabilities within a federal geospatial network of government agencies, which in turn affects how these agencies deliver ‘joined up’ services. The CBA relies upon three types of tools: the shaping of network governing structures, promotion of uptake of new management information systems, and the use of evaluation (scrutiny) to solidify accountabilities of the network. Since these tools cast a shadow of hierarchy upon the network, they may be viewed as counter to the voluntary ethos of networks. However, the case studies suggest that the CBAs actions appear to confer legitimacy to the network—resulting in a seeming contradiction—greater central control, more vigorous, distributed geospatial coordination.

Scaling dimensions in spectroscopy of soil and vegetation

Abstract The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration. Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce (Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed. We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities. Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping. All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.