Exploring Spatial Process Dynamics Using Irregular Cellular Automaton Models

Cellular automaton models have enjoyed popularity in recent years as easily constructed models of many complex spatial processes, particularly in the natural sciences, and more recently in geography also. Most such models adopt a regular lattice (often a grid) as the basis for the spatial relations of adjacency that govern evolution of the model. A number of variations on the cellular automaton formalism have been introduced in geography but the impact of such variations on the likely behavior of the models has not been explored. This paper proposes a method for beginning to explore these issues and suggests that this is a new approach to the investigation of the relationships between spatial structure and dynamics of spatial processes. A framework for this exploration is suggested, and details of the required methods and measures are provided. In particular, a measure of spatial pattern—spatial information—based on entropy concepts is introduced. Initial results from investigation along the proposed lines are reported, which suggest that a distinction can he made between spatially robust and fragile processes. Some implications of this result and the methodology presented are briefly discussed.     

Spatial Competition and Bounded Rationality: Retailing at the Edge of Chaos

The spatial dimension of competition among retail outlets is well researched and typically captured with spatial interaction models. A stream of theoretical research has studied the consequences of incorporating various types of dynamics into these models. We build on this research by incorporating a behavioral decision process based on bounded rationality, and by allowing for unexpected adversity in the environment in the form of exogenous shocks. Given these characteristics—spatial competition, boundedly rational decision making, and environmental adversity—we study the long-run dynamics of a model retail industry. The model reaches a stochastic steady state which is “poised,” in the sense that a shock may—or may not—trigger a wave of innovation which sweeps the entire system. Detailed investigation of this steady state shows that it has the characteristics of a general type of organization, known as self-organized criti-cality, that has been described in both theoretical biology and statistical physics.     

LUTI模型原理、实现及应用综述

经济全球化背景下可持续发展已成为当今世界发展的主题。快速城镇化下的中国,城市空间的可持续性发展提上日程。为此,期待有力的、可操作的工具用于定量模拟城市空间演化过程、预测不同政策下城市空间的发展趋势,为城市化持续健康发展提供决策支持。本文分析了LUTI模型的基本原理、构建思路、实现框架和建立中国城市LUTI模型面临的问题,并回顾了国内外利用LUTI模型模拟城市空间演化过程,以辅助表明城市空间决策的研究现状,希望能为中国的城市空间可持续发展模拟分析研究提供参考。     

不同模型在镇域空间相互作用中的应用与比较分析——以湖北省大冶市为例

镇域尺度的空间相互作用研究对促进城乡统筹和城镇体系规划具有重要意义。本文借鉴最新研究成果,从理论及实践层面对镇域空间相互作用研究进行了探索,提出一种基于空间可达性的辐射模型,并与传统重力模型进行比较分析。以大冶市16个乡镇为例进行实例研究,结果表明:1辐射模型与实测数据的相似度较高,可以更好地反映镇域空间相互作用的客观规律;2两种模拟方法在体现空间相互作用的方向性以及结构特征方面具有显著差异;3辐射模型中影响范围的引入以及空间可达性对区域异质性的体现是其与重力模型的主要区别。     

Multi‐Type,Multi‐Zone Facility Location

The placement of facilities according to spatial and/or geographic requirements is a popular problem within the domain of location science. Objectives that are typically considered in this class of problems include dispersion, median, center, and covering objectives—and are generally defined in terms of distance or service‐related criteria. With few exceptions, the existing models in the literature for these problems only accommodate one type of facility. Furthermore, the literature on these problems does not allow for the possibility of multiple placement zones within which facilities may be placed. Due to the unique placement requirements of different facility types—such as suitable terrain that may be considered for placement and specific placement objectives for each facility type—it is expected that different suitable placement zones for each facility type, or groups of facility types, may differ. In this article, we introduce a novel mathematical treatment for multi‐type, multi‐zone facility location problems. We derive multi‐type, multi‐zone extensions to the classical integer‐linear programming formulations involving dispersion, centering and maximal covering. The complexity of these formulations leads us to follow a heuristic solution approach, for which a novel multi‐type, multi‐zone variation of the non‐dominated sorting genetic algorithm‐II algorithm is proposed and employed to solve practical examples of multi‐type, multi‐zone facility location problems.     

The Impact of Telecommuting on the Activity Spaces of Participants

A spatial analysis of the activity spaces of telecommuters, based on the geocoded travel diary data from the State of California Telecommuting Pilot Project, is performed to analyze the impacts of telecommuting. The study analyzes the spatial location, orientation, and extent of the activity locations within the “activity spaces” of telecommuters and a nontelecommuting control group. To be able to quantitatively compare and contrast the travel patterns and the distribution of trip ends within the activity space, several spatial indicators have been defined. Several hypotheses concerning the selection of activity locations by individuals are presented and the impact of telecommuting on the selection of locations for activity is analyzed. Key findings include: on telecommuting days, 86 percent of telecommuters' activities are performed closer to home than to work, compared to 56 percent on normal commuting days; and destinations on telecommuting days are more evenly distributed in all directions around the home, whereas a majority of destinations on commuting days are oriented toward the work location. To be able to understand the influence of the contributing factors toward the selection of nonwork activity locations, potential causal relationships between the influencing factors and the activity location choice are investigated. Several plausible log-linear model structures for cross-classified data provide a good fit to these relationships. Key results include: interaction effects of activity location with commute distance and with trip purpose are present in all the best-fitting models, confirming the importance of these two variables in the selection of activity location; the interaction of activity location and income is also significant; and day status (telecommuting or not) of the employee influences the trip purpose, which in turn affects location.     

A GENERAL FRAMEWORK FOR THE INTEGRATION OF A LAND-USE MODEL WITH A TRANSPORTATION MODEL COMPONENT

Integrated land-use—transportation models are characterized as models in which the redistributive effects of one subsystem upon the other are explicitly defined and incorporated into the model's structure. At the core of integrated models is a linking procedure whose function is to transform the outputs of one model component into inputs for the other. Despite major efforts made in the past to build operational large scale integrated models, the structural properties of such models remain largely unexplored. This paper describes a general framework for an integrated model consisting of prototype model components. These are an iterative activity allocation model of the Garin-Lowry type, an equilibrium-assignment transportation network model, and a linking procedure. Given the level of analysis, this framework is shown useful for exploring the analytics of integrated models and, in particular, their equilibrium properties. By means of many simulation experiments based on an hypothetical numerical example, the operation of the model is demonstrated with an emphasis on the locational interpretation of the integration procedure. On the basis of the empirical results and considering the model's intrinsic assumptions, the following major findings can be cited. First, the effects of nontravel factors (such as basic employment and zonal attractions) upon activity distribution are stronger than effects caused by changes in the transportation system. The latter effects were found to yield nonlinear and, spatially, nonuniform changes in activity location which also tended to be larger in peripheral regions. Second, implicit in the specification of the integration procedure are behavioral assumptions regarding time-lags in locational adjustments made by activities in reaction to rising cost of travel. The present formulation implies that once located, activities do not revise their locational decisions despite substantial increases in travel costs. At the other extreme, all activities are permitted to readjust their locational preferences after the final interzonal travel costs are derived. The effect upon activity distribution of the latter specification is, of course, larger than that of the former, although less than the effect yielded by changes in nontravel factors. Regarding the equilibrium properties of the integrated system, both the theoretical and empirical analyses show that the entire model will converge into an equilibrium solution and that the corresponding trip patterns are also at equilibrium. These results will hold as long as the operation of the integrated models is completely controlled by the generation functions of the land-use model and the transportation model component only affects the spatial distribution of activities. Finally, the results from the simulation experiments indicate that the computed mean travel cost parameter tends to stabilize around a certain value as the level of demand for travel, within the system, rises. There is evidence that compensating changes in the location and composition of this demand are the main causes of this phenomenon. In light of these findings, it is possible to point to three key problems whose resolution could largely improve the predictive power of integrated models. First, it would be useful to define activity models in which the generation of activities is, among other things, a function of travel conditions. Second, currently formulated integrated models do not contain trip demand functions and, thus, demand for travel by an activity unit is regarded as completely inelastic. Third, different locating activities respond differently—over time and space—to changíng travel conditions, and models should, therefore, reflect explicitly such differences in activity behavior. A recent paper by Los [14], is an important contribution to the analysis of this issue.     

Evolving Location Analytics for Service Coverage Modeling

Among the wide range of geographic analysis methods are service coverage models. These location analytic approaches have been invaluable for better understanding existing (and past) conditions, deriving creative plans, and developing policy for addressing associated societal problems. Increasingly sophisticated spatial information and better knowledge about interrelated mechanisms of process and behavior have made it possible to enhance coverage models, enabling more nuanced formalizations to be devised. This has proven both important and essential in many ways. Mathematical details associated with service coverage models are derived to highlight evolving capabilities. Empirical results are presented in order to illustrate the significance and substantive importance of advanced location analytics capabilities.     

Investigating the Influence of Spatial and Temporal Granularities on Agent‐Based Modeling

Epidemic agent‐based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease‐related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time—termed spatial and temporal granularities (STGs)—in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space–time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.     

The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India

In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models—Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.     

人-地行为的主要模式(型)论

行为地理学将心理因素引入人-地关系的研究中,形成了很多人与环境的行为模式(型),这些规律从人的认识论上揭示人与环境的空间关系实质。本文力图用六类将主要的行为模式(型)进行总结和归纳,并且简要地给予论述。     

交通区位演化与空间开发强度的时空协同关系及其弹性识别——重庆市案例实证研究

交通区位条件是反映区域发展潜力的重要指标,分析其与空间开发强度之间的时空协同关系对揭示区域发展的动力机制具有重要启示意义。以重庆市为例,通过指数建模、回归拟合及弹性系数等方法,揭示其间互动关系。研究发现：①研究区交通区位指数处于快速跃升阶段,区域差距有所缩小,空间集聚格局基本稳定;②空间开发强度指数数值整体偏低,增速差异较大,空间集聚格局呈单集聚中心向多集聚中心演进趋势;③不同类型的交通区位指数（内部、外部和综合）与空间开发强度指数均呈显著正相关,且关联性表现出不同状态;④交通区位演化与空间开发强度互动敏感程度较高,各弹性系数空间集聚格局基本一致,但其形成机理差异较大。     

Dynamic Central Place Theory: Results of a Simulation Approach

A dynamic central place theory is formulated as a simulation model in which retail activities, described by cost equations, and consumers, described by spatial interaction equations, interact to generate a central place system. The behavior of the model is then examined. Simulation results show that the basic character of the system—whether it is agglomerated or dispersed—depends primarily on a single parameter in the interaction equation, and only secondarily on the specification of the cost function. The results are highly robust in that they are largely independent of the initial sizes and locations of centers, and even independent of the type of interaction equation used. The patterns generated appear plausible.     

Where Do Cities Form? A Geographical Agglomeration Model for Europe*

Abstract. In the recent literature on spatial agglomeration models, substantial progress has been made in modeling urban structures in terms of number and size of cities, but the question where cities arise remains unanswered. This paper illustrates that if a spatial agglomeration model is extended with a true geographical dimension, the location of cities can also be endogenized. A geographical agglomeration model for Europe shows that the size and place of cities can be simultaneously determined. The empirical results suggest that elementary economic forces such as agglomeration economies and transportation costs might be able to explain place and size of cities in the long run to a substantial degree. In addition, some new statistical measures of fit are discussed that are needed to evaluate simulations results of this kind.     

DRS. MUTH AND MILLS MEET DR. TIEBOUT: INTEGRATING LOCATION‐SPECIFIC AMENITIES INTO MULTI‐COMMUNITY EQUILIBRIUM MODELS*

ABSTRACT We consider the problem of integrating spatial amenities into locational equilibrium models with multiple jurisdictions. We provide sufficient conditions under which models that assume a single housing price in each community continue to apply in the presence of location‐specific amenities that vary both within and across communities. If these conditions are satisfied, the models, estimation methods, and results in Epple and Sieg (1999) are valid in the presence of (potentially unobserved) location‐specific amenities. We also show how to construct sufficient statistics that capture location specific spatial heterogeneity. We apply these techniques using data from the Pittsburgh metropolitan area. We find that these amenity measures capture proximity to important local employment centers as well as heterogeneity in school quality within a given school district.     

Accounting for Temporal Demand Variations in Retail Location Models

This article develops and calibrates a spatial interaction model (SIM) incorporating additional temporal characteristics of consumer demand for the U.K. grocery market. SIMs have been routinely used by the retail sector for location modeling and revenue prediction and have a good record of success, especially in the supermarket/hypermarket sector. However, greater planning controls and a more competitive trading environment in recent years has forced retailers to look to new markets. This has meant a greater focus on the convenience market which creates new challenges for retail location models. In this article, we present a custom built SIM for the grocery market in West Yorkshire incorporating trading and consumer data provided by a major U.K. retailer. We show that this model works well for supermarkets and hypermarkets but poorly for convenience stores. We then build a series of new demand layers taking into account the spatial distributions of demand at the time of day that consumers are likely to use grocery stores. These new demand layers include workplace populations, university student populations and secondary school children. When these demand layers are added to the models, we see a very promising increase in the accuracy of the revenue forecasts.     

Theoretical Underpinnings of the Gravity Hypothesis

Various theoretical rationalizations of the gravity hypothesis for spatial interaction are examined. Deterministic utility theory cannot account for variability in individual behavior. Choice models based on Luce's axiom contain some inconsistencies. Random utility theory provides a probabilistic formulation that is internally consistent, and also solves the aggregation problem. However, investigation reveals that, although this provides a theoretical justification for gravity-like behavior, the assumptions made are sufficiently unrealistic to cast doubt on the empirical validity of the gravity hypothesis.     

Location and Growth in the Brazilian Northeast

Abstract Clustering of economic performance across jurisdictions has generated considerable research on the spillovers and linkages among geographical neighbors. Much of this work, however, has been aspatial, implying that the influence of location attributes on growth has been largely ignored. In this paper, we examine the contribution of location to regional economic growth using municipio‐level data for the Brazilian Northeast—a historically lagging region of the country. We test if productivity among northeastern municipios is converging to a steady state and whether spatial externalities are linked to productivity growth in individual municipios. We find that, conditional on structural characteristics, productivity among municipios is converging at about 3 percent per year. Further, productivity in individual municipios is positively associated with own‐structural characteristics but negatively associated with productivity growth in neighboring municipios. This means that there are negative spatial externalities coming from productivity improvements in neighboring regions.     

Destination Choice of Athenians: An Application of Geographically Weighted Versions of Standard and Zero Inflated Poisson Spatial Interaction Models

The main aim of this article is to combine recent developments in spatial interaction modeling to better model and explain spatial decisions. The empirical study refers to migration decisions made by internal migrants from Athens, Greece. To achieve this, geographically weighted versions of standard and zero inflated Poisson (ZIP) spatial interaction models are defined and fit. In the absence of empirical studies for the effect of potential determinants on internal migration decisions in Greece and the presence of an excessive number of zero migration flows among municipalities in Greece, this article provides empirical evidence for the power of the proposed Geographically Weighted ZIP regression method to better explain destination choices of Athenian internal migrants. We also discuss statistical inference issues in relation to the application of the proposed regression techniques.     

LOCALIZATION AND INDUSTRY CLUSTERING ECONOMETRICS: AN ASSESSMENT OF GIBBS MODELS FOR SPATIAL POINT PROCESSES

The objective of this paper is to assess an approach to statistical modeling of point referenced establishment data that permit inclusion of “environmental” or establishment‐specific covariates and specific forms of interestablishment interaction. Gibbs models are used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend is composed of access measures (primarily different classes of roads) and three different interaction processes are tested: Geyer, area interaction, and Strauss hard core. While the models used have proved to be useful in ecology, we are unaware of any applications to establishment or firm data. In empirical application, the models yield intuitively appealing results for the trend component, and the ability to specify the interaction component gives deeper insights into interestablishment spatial dynamics than any previously published methods.