Timothy J. Fik

Spatial Flows and Competing Central Places: Towards a General Theory of Hierarchical Interaction

This paper presents a variation of Fotheringhams competing destinations model using categorized information on spatial flows in a central place system. A competing central place model is developed using spatially defined choice sets for origin — destination pairs by threshold distance and central place order. The competing central place generalization is empirically tested by undertaking a comparative analysis of 1980 domestic airline passenger traffic amongst selected cities in the continental United States. A host of modeling strategies are contrasted and the effects of mass, separation, and competitive forces noted in the presence or absence of hierarchical data. The production-constrained competing central place specification is shown to exhibit not only greater explanatory power than Fotheringhams competing destinations model, but also significant reductions in potential multicollinear relations between regressors. The model is later extended to incorporate an intervening opportunities filter. Competitive flow patterns between paired origins and destinations are then delimited by geographic range, compatibility, and the impeding effects of substitute and/or intervening flows within a hierarchical network.

Functional Form and Spatial Interaction Models

This paper is an examination of functional form and functional misspecification in regression-based spatial interaction models. Box–Cox transformations are used to model US state-to-state labor migration. Attention is given to the production-constrained gravity model, the competing and intervening destinations (CID) model, and an extension of the CID model which uses dummy variables and an iterative transformation procedure. Statistical evidence supports the argument that the use of highly restrictive log-linear specifications may be inappropriate and problematic. Implications for spatial interaction modeling using general nonrestrictive functional forms are discussed.

The high-tech potential : economic development in rural America

Rural America is at a crossroads in its economic development. Like regions of other First World nations, the traditional economic base of rural communities in the United States is rapidly deteriorating. Natural resources, including agriculture, show little prospect for generating future job growth, and manufacturing has become a new source of instability. Faced with these changes and an increasing vulnerability to international economic events, rural communities have begun to seek high-technology industries and advanced services as candidates for job growth and economic stability. What is the potential for high-tech growth outside the largest cities? What is the role of high-tech industry in the economic development of non-metropolitan America? This book provides a hard-nosed look at the high-tech potential in rural economic development. Some of the questions Glasmeier addresses include: Are rural areas attractive to high tech? Will high tech follow earlier patterns and filter down the lowest-paid jobs to rural areas? Will rural communities be bypassed completely for even lower-wage Third World locations? Glasmeier answers in a sober analysis that separates fact from myth. Empirical data reveals the kinds of high-tech jobs that locate in rural areas, and the kinds of rural areas that attract high-tech jobs. This analysis leads to a highly critical evaluation of state and local economic development policy and recommendations for its improvement. This book is a must for policymakers, practitioners, scholars, and an informed public interested in the promise of high tech and the future of US economic development.

Labor migration amongst hierarchically competing and intervening origins and destinations.

A spatial interaction methodology is developed for modeling flows in a hierarchical system. A competing and intervening destinations framework is employed to model and predict US state-to-state labor migration. This analysis is used to assess the importance of geographic variables in explaining variations in regional labor flows. Empirical findings suggest that US labor migration is largely explained by Newtonian and systemic forces—size, distance, locational accessibility, and intervening opportunities in a spatial hierarchy. It is also suggested that lagged migration or migrant stock is a product of the combined effect of these forces.

Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination

BackgroundIdentifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.MethodsNational census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region.ResultsThe spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations.ConclusionThis was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

Modeling Spatial Patterns of Recreational Boaters: Vessel, Behavioral, and Geographic Considerations

Abstract A regression-based framework for modeling recreational boating patterns and estimating preferred on-water destinations was introduced. A survey of 500 boaters provided model input regarding vessel, behavioral, and geographic characteristics. This information was used to construct a travel network within a Geographic Information System to identify major network intersections (pivots) and to calculate the distance traveled along network segments between intersections, as derived model input. Model estimates of preferred destinations and use intensity were compared to mail survey results for validation. The average error between reported and estimated boating destinations was 4.3 miles for a regional application and 3.0 miles for a sub-regional application. In addition to vessel and behavioral considerations, the results highlighted the significance of geographic and network variables in modeling the spatial patterns of recreational boaters and destination choice.

Price patterns in competitively clustered markets

In this paper, numerical analyses of price competition in spatial markets are presented. How equilibrium conditions are affected by the geographic distribution of firms and their pricing behavior is illustrated. Simulated and empirical findings suggest that clustering of competitors in space tends to promote lower overall price levels. It is posited that competitive market clusters may be responsible for the adoption of lower price conjectures. In theory, this suggests that the spatial structure of a market can create context-specific price-reaction functions that are sensitive to the relative locations of firms and distance to nearest and next-nearest rivals. Point-pattern and nearest-neighbor analyses of store locations for rival food chains in a major metropolitan market show that rivals tend to cluster in space, and that chain outlets in competitive market clusters demonstrate a tendency to support lower prices.

CONDUCT IN SPATIAL MARKETS: AN EMPIRICAL ANALYSIS OF SPATIAL PRICING BEHAVIOR

An empirical procedure for differentiating between organized and competitive spatial oligopoly (or oligopsony) is proposed. The procedure evaluates pricing behavior using a time series of short-term price data from spatially dispersed locations. Cooperation is implied when price changes at one location are matched instantly at others. Unidirectional price matching arises under price leadership. On the other hand, competitive spatial oligopoly involves lags and feedbacks, as well as stronger relationships between prices in contiguous marketing areas than between prices in spatially separated marketing areas. The procedure is applied in the context of a recent Competition Act case against Canadian meat packers.

Designing a Tourism Spillover Index Based on Multidestination Travel A Two-Stage Distance-Based Modeling Approach

The proliferation of multidestination trips provides valuable opportunities for regions to benefit from spillover effects generated by other regions nearby. To better understand the spatial patterns of multidestination travel, we propose a two-stage distance-based model. Results suggest that long-haul tourists (i.e., those who travel far from home) tend to choose a subsequent destination that is closer to the previous destination but farther away from their residences. In addition to sociodemographic and tripographic factors, we recognize the importance of spatial structure effects in determining the travel distance for a multidestination trip. Based on the model estimates, we propose a tourism spillover index to reflect a region’s potential to receive spillover benefits from multidestination tourists leaving a particular region. Finally, we discuss implications for marketing strategies to enhance the attraction potential of specific destinations.