Kor de Jong

A method to analyse neighbourhood characteristics of land use patterns

Abstract Neighbourhood interactions between land use types are often included in the spatially explicit analysis of land use change. Especially in the context of urban growth, neighbourhood interactions are often addressed both in theories for urban development and in dynamic models of (urban) land use change. Neighbourhood interactions are one of the main driving factors in a large group of land use change models based on cellular automata (CA). This paper introduces a method to analyse the neighbourhood characteristics of land use. For every location in a rectangular grid the enrichment of the neighbourhood by specific land use types is studied. An application of the method for the Netherlands indicates that different land use types have clearly distinct neighbourhood characteristics. Land use conversions can be explained, for a large part, by the occurrence of land uses in the neighbourhood. The neighbourhood characterization introduced in this paper can help to further unravel the processes of land use change allocation and assist in the definition of transition rules for cellular automata and other land use change models.

The PCRaster Software and Course Materials for Teaching Numerical Modelling in the Environmental Sciences

Teaching numerical modelling in the environmental sciences not only needs good software and course material but also an understanding of how to program the models in the computer. Conventional environmental modelling procedures require computer science and programming skills, which may detract from the important understanding of the environmental processes involved. An alternative strategy is to build a generic toolkit or modelling language that operates with concepts and operations that are familiar to the environmental scientist. PCRaster is such a spatio-temporal environmental modelling language developed at Utrecht University, the Netherlands. It is used for teaching modelling in classrooms and over the Web (distance learning) at three levels: (1) explaining environmental processes and models, where models with a fixed structure of model equations are evaluated by changing model parameters, (2) teaching model construction, where students learn to program spatial and temporal models with the language, and (3) teaching all phases of scientific modelling related to field research. So far, we have received positive responses to these courses, largely because the software provides a set of easily learned functions matching the conceptual thought processes of a geoscientist that can be used at all levels of teaching.

Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: an air quality example

This paper introduces a method for visually exploring spatio‐temporal data or predictions that come as probability density functions, e.g. output of statistical models or Monte Carlo simulations, under different scenarios. For a given moment in time, we can explore the probability dimension by looking at maps with cumulative or exceedance probability while varying the attribute level that is exceeded, or by looking at maps with quantiles while varying the probability value. Scenario comparison is done by arranging the maps in a lattice with each panel reacting identically to legend modification, zooming, panning, or map querying. The method is illustrated by comparing different modelling scenarios for yearly NO2 levels in 2001 across the European Union.

Map algebra and model algebra for integrated model building

Computer models are important tools for the assessment of environmental systems. A seamless workflow of construction and coupling of model components is essential for environmental scientists. However, currently available software packages are often tailored either to the construction of model components, or to the coupling of existing components. Combining both objectives is not straightforward, because it requires merging concepts for model component building and model component coupling. Also, software packages should be usable for domain experts such as hydrologists or ecologists who do not necessarily have expert knowledge in programming. We propose an integrated modelling framework that provides descriptive means to specify (1) model components with conventional map algebra, and (2) interactions between model components with model algebra. A prototype implementation in a high-level scripting language supports the building of integrated spatio-temporal models. For a seamless coupling of model components with different temporal and spatial discretisation, we introduce the use of accumulators. These handle the temporal aggregation of model component outputs. The framework provides templates for the custom construction of model components and accumulators, and a management layer arranges the schedule for the execution of the integrated model. We use the prototype implementation of the framework in an illustrative case study to build an integrated model that couples model components simulating the interaction between biomass growth, wildfire, and human impacts with different temporal discretisations. The high-level Python language is used as model building environment to allow domain experts without in-depth knowledge of software development practices to conduct exploratory model construction and analysis.

The stochastic dimension in a dynamic GIS

Coping with random fields in a time-dynamic geographic information system (GIs) increases the computational burden and storage requirements with a large amount, and calls for a number of custom functions to enable easy analysis of the resulting random components, as well as specialised output reporting functions. This paper addresses the computational and implementation issues when a Monte Carlo approach is taken, and shows some results from a rainfall-runoff model running within a GIS.

Design and demonstration of a data model to integrate agent-based and field-based modelling

Dynamic environmental modelling of spatio-temporal systems often requires the representation of both fields and agents. Fields are continuous with values in the whole spatio-temporal domain of a model, while agents are bounded in space and often mobile. It is currently difficult for environmental modellers with limited software engineering background to construct such field-agent models, as modelling frameworks mostly do not support the integration of fields and agents. To overcome this issue, we describe a data model combining fields and agents in a single concept. This data model represents fields, agents and relations by grouping items sharing properties into a phenomenon. The concepts domain, property set and value handle spatio-temporal attribute representations. The data model is implemented in a software prototype that shows how data on fields and agents is stored and manipulated.