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In urban systems modeling, there are many elaborate dynamic models based on intricate decision processes whose simulation must be based on customized software if their space±time properties are to be explored eectively. In this paper we present a class of urban models whose dynamics are based on theories of development associated with cellular automata (CA), whose data is ®ne-grained, and whose simulation requires software which can handle an enormous array of spatial and temporal model outputs. We ®rst introduce the generic problem of modeling within GIS, noting relevant CA models before outlining a generalized model based on Xie's (1996, A general model for cellular urban dynamics. Geographical Analysis, 28, 350±373)``dynamic urban evolutionary modeling'' (DUEM) approach. We present ways in which land uses are structured through their life cycles, and ways in which existing urban activities spawn locations for new activities. We de®ne various decision rules that embed distance and direction, density thresholds, and transition or mutation probabilities into the model's dynamics, and we then outline the software designed to generate eective urban simulations consistent with GIS data inputs, outputs and related functionality. Finally, we present a range of hypothetical urban simulations that illustrate the diversity of model types that can be handled within the framework as a prelude to more realistic applications which will be reported in later papers. # 1999 Published by Elsevier Science Ltd. All rights reserved.

Since mathematical models came to be applied to problems of architectural and urban form, new concepts based on predicting large-scale structure from local rules have emerged through insights originating in computation and biology. The clearest of these are computer models based on cellular automata (CA) and their recent generalization in evolutionary biology and artificial life. Here we show how such models can be used to simulate urban growth and form, thus linking our exposition to the longer tradition of ideas in studies of built form emanating from the ‘Cambridge School’. We first review developments of CA in general and then in urban systems in particular. We propose a general class of CA models for urban simulation and illustrate two simple applications, the first a simulation of the development of the historical ‘cell’ city of Savannah, Georgia, the second, a generic hypothetical application. We then show how this generic model can be used to simulate the growth dynamics of a suburban area of a mid-sized North American city, thus illustrating how this approach provides insights into the way microprocesses lead to aggregate development patterns.

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Belowground communities exert major controls over the carbon and nitrogen balances of terrestrial ecosystems by regulating decomposition and nutrient availability for plants. Yet little is known about the patterns of belowground communities and their relationships with environmental factors, particularly at the regional scale where multiple environmental gradients co‐vary. Here, we describe the patterns of belowground communities (microbes and nematodes) and their relationships with environmental factors based on two parallel studies: a field survey with two regional‐scale transects across the Mongolia plateau and a water‐addition experiment in a typical steppe. In the field survey, soils and plants were collected across two large‐scale transects (a 2000‐km east–west transect and a 900‐km south–north transect). At the regional‐scale, the variations in soil microbes (e.g. bacterial PLFA, fungal PLFA, and F/B ratio) were mainly explained by precipitation and soil factors. In contrast, the variation in soil nematodes (e.g. density of trophic groups and the bacterial‐feeding/fungal‐feeding nematode ratio) were primarily explained by precipitation. These variations of microbe or nematode variables explained by environmental factors at regional scale were derived from different vegetation types. Along the gradient from nutrient‐poor to nutrient‐rich vegetation types, the total variation in soil microbes explained by precipitation increased and that explained by plant and soil decreased, while the opposite was true for soil nematodes. Experimental water addition, which increased rainfall by 30% during the growing season, increased biomass or density of belowground communities, with the nematodes being more responsive than the microbes. The different responses of soil microbial and nematode communities to environmental gradients at the regional scale likely reflect their different adaptations to climate, soil nutrients, and plants. Our findings suggest that the soil nematode and microbial communities are strongly controlled by bottom‐up effects of precipitation alone or in combination with soil conditions.

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In this paper, we argue that the geometry of urban residential development is fractal. Both the degree to which space is filled and the rate at which it is filled follow scaling laws which imply invariance of function, and self-similarity of urban form across scale. These characteristics are captured in population density functions based on inverse power laws whose parameters are fractal dimensions. First we outline the relevant elements of the theory in terms of scaling relations and then we introduce two methods for estimating fractal dimension based on varying the size of cities and the scale at which their form is detected. Exact and statistical estimation techniques are applied to each method respectively generating dimensions which measure the extent and the rate of space filling. These methods are then applied to residential development patterns in six industrial cities in the northeastern United States, with an innovative data source from the TIGER/Line files. The results support the theory of the fractal city outlined in books by Batty and Longley and Frankhauser, but with the clear conclusion that different scale and estimation techniques generate different types of fractal dimension.

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