In Search of Causality: A Regional Approach to Urban Growth in Eighteenth-century England

The simple equation of industrialisation and urbanisation contains much truth: recent national surveys confirm that rapid coal-based industrial growth spawned massive urban development in Britain. And yet at the local level a wide diversity of growth experiences reflect a complex and overlapping nexus of growth stimuli which defy attempts at generalisation. In taking a regional perspective, this paper seeks to occupy a productive middle ground in this ongoing debate. Placing urban growth in north-west England into its real economic and geographical context, it recognises the complexities of urban growth but transcends the narrowness of the particular. In so doing, it challenges some of the certainties of industrial urbanisation, emphasising instead the importance of rural industry, urban commercial functions and inter-place linkages. Most fundamentally, it argues for a more nuanced understanding of the geography of causality.     

Modeling Residential and Business Telecommunication Flows: A Regional Point-to-Point Approach

Regional telecommunication flows, measured in terms of numbers of messages and conversation minutes, are analyzed with a systematic random sample of toll calls characterized by their timing, duration, cost, and origin-destination (O-D) locations. Point-to-point models are econometrically estimated, with such independent variables as destination market size, O-D distance, and average and time-of-day (TOD) prices, for the residential and business sectors separately. The results indicate that (1) the demands for calls and conversation minutes are price-inelastic and slightly elastic, respectively, (2) business demand is relatively more price-elastic than residential demand, (3) distance is a strong determinant of telephone demand, and (4) most TOD demand substitutions resulting from TOD price changes would take place between the daily and evening rate periods. Several areas for further research are outlined.     

Knowledge Spillovers and Total Factor Productivity: Evidence Using a Spatial Panel Data Model

This article investigates the impact of knowledge capital stocks on total factor productivity (TFP) through the lens of the knowledge capital model proposed by Griliches (1979) , augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on TFP in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen, and Diewert (1982) . This index describes how efficiently each region transforms physical capital and labor into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy annual regional knowledge capital stocks for N =203 regions during 1997–2002. In estimating the effects, we implement a spatial panel data model that controls for spatial autocorrelation as well as individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions and add an important spatial dimension to discussions in the literature by showing that productivity effects of knowledge spillovers increase with geographic proximity.     

Re-investing in the Built Environment: A Spatial Network Approach

Before launching ambitious and expensive development programmes to induce new regional technology corridors and clusters, it is critical to appreciate existing spatial economic patterns in a region. Initial economic conditions drive location decisions of firms and a labour force such that any changes must intercede onto an existing landscape built for current economic conditions. This work adopts a simple regional economic model to integrate and review traditional and modern urban location theories in order to illustrate the power of initial conditions to determine a final result. A simple spatial dynamic simulation model captures many of the pertinent effects of real estate pricing patterns to frame both opportunities and constraints to re-shape an urban landscape. Attention to 'ground up' spatially correlated location patterns revealed in price data that suggests close attention to strategic zoning can have profound impacts on the success or failure of economic development. Relatively modest policy interventions that carefully utilize existing preferences for urban amenities and concurrent real property investments involve fewer policy risks with potentially more powerful stimulative economic consequences than promised in more ambitious programmes.     

A Surface-Based Approach to Measuring Spatial Segregation

Quantitative indices of residential segregation have been with us for half a century, but suffer significant limitations. While useful for comparison among regions, summary indices fail to reveal spatial aspects of segregation. Such measures generally consider only the population mix within zones, not between them. Zone boundaries are treated as impenetrable barriers to interaction between population subgroups, so that measurement of segregation is constrained by the zoning system, which bears no necessary relation to interaction among population subgroups. A segregation measurement approach less constrained by the chosen zoning system, which enables visualization of segregation levels at the local scale and accounts for the spatial dimension of segregation, is required. We propose a kernel density estimation approach to model spatial aspects of segregation. This provides an explicitly geographical framework for modeling and visualizing local spatial segregation. The density estimation approach lends itself to development of an index of spatial segregation with the advantage of functional compatibility with the most widely used index of segregation (the dissimilarity index D ). We provide a short review of the literature on measuring segregation, briefly describe the kernel density estimation method, and illustrate how the method can be used for measuring segregation. Examples using a simulated landscape and two empirical cases in Washington, DC and Philadelphia, PA are presented.     

A Programming Approach to Minimizing and Maximizing Spatial Autocorrelation Statistics

A programming approach is presented for identifying the form of the weights matrix W which either minimizes or maximizes the value of Moran's spatial autocorrelation statistic for a specified vector of data values. Both nonlinear and linear programming solutions are presented. The former are necessary when the sum of the links in W is unspecified while the latter can be used if this sum is fixed. The approach is illustrated using data examined in previous studies for two variables measured for the counties of Eire. While programming solutions involving different sets of constraints are derived, all yield solutions in which the number of nonzero elements in W is considerably smaller than that in W defined using the contiguity relationships between the counties. In graph theory terms, all of the Ws derived define multicomponent graphs. Other characteristics of the derived Ws are also presented.     

Cognitive Maps and Spatial Behavior Of Consumers

Two multidimensional scaling algorithms are used to externally measure cognitive maps of supermarket locations for a sample of sixty-one supermarket shoppers. Issues raised in the construction of the cognitive maps are discussed. The relationship between the derived cognitive maps and consumer behavior is compared to the relationship between physical maps and consumer behavior. Behavior is shown to be more closely related to cognitive maps. Differences in cognitive maps are defined and canonically correlated with consumers' socioeconomic and locational attributes. While aggregate cognitive maps are closely associated with physical maps, there is substantial variation between individual maps. This variation is related to shopping behavior and selected socioeconomic and locational characteristics of consumers.     

A Structural Equation Approach to Models with Spatial Dependence

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it possible to obtain a closer correspondence between theory and empirics, to explicitly account for measurement errors, and to reduce multicollinearity. We extend the standard SEM maximum likelihood estimator to allow for spatial dependence and propose easily accessible SEM software like LISREL 8 and Mx. We present an illustration based on Anselin's Columbus, OH, crime data set. Furthermore, we combine the spatial lag model with the latent multiple-indicators–multiple-causes model and discuss estimation of this latent spatial lag model. We present an illustration based on the Anselin crime data set again.     

A Cognitive Approach to Extant Boat Structure in Goa,India

This article presents information derived from unfunded fieldwork undertaken between 2008 and 2014 in Goa, India. Traditional boat structure is understood in the context of the use of local materials in response to climatic, geophysical, and cultural factors. Expanded and unexpanded logboats are shown to affect differently the pirogues based on them. The outrigger is not used as a sailing aid. Small sailed boats with sewn strakes on a keel‐plank are compared with 19th‐century records. Simple techniques are used on larger sewn and metal‐fastened vessels still being built. Today some factors combine to compromise traditional construction, while others are bringing about the demise of the vessels themselves.     

Equilibrium and Economic Growth: Spatial Econometric Models and Simulations

Neoclassical theory assumes diminishing returns to capital and spatially constant exogenously-determined technological progress, although it is questionable whether these are realistic assumptions for modeling manufacturing productivity growth variations across European Union (E.U.) regions. In contrast, the model developed in this paper assumes increasing returns and spatially varying technical progress, and is linked to endogenous growth theory and particularly to 'new economic geography' theory. Simulations, involving 178 E.U.regions, show that productivity levels and growth rates are higher in all E.U. regions when the financially assisted (Objective 1) regions have faster output growth. This also reduces inequalities in levels of technology. Allowing the core regions to grow faster has a similar effect of raising productivity growth rates across the E.U., although inequality increases. Thus, the simulations are seen as an attempt to develop a type of 'computable geographical equilibrium' model which, as suggested by Fujita, Krugman, and Venables (1999), is the way theoretical economic geography needs to evolve in order to become a predictive discipline.     

A Generalized Randomization Approach to Local Measures of Spatial Association

This article establishes a unified randomization significance testing framework upon which various local measures of spatial association are commonly predicated. The generalized randomization approach presented is composed of two testing procedures, the extended Mantel test and the generalized vector randomization test. These two procedures employ different randomization assumptions, namely total and conditional randomization, according to the way in which they incorporate local measures. By properly specifying necessary matrices and vectors for a particular local measure of spatial association under a particular randomization assumption, the generalized randomization approach as a whole yields a reliable set of equations for expected values and variances, which then is confirmed by a Monte Carlo simulation utilizing random permutations.     

Policy Coalitions in the U.S. Congress: A Spatial Duration Modeling Approach

This paper applies spatial duration models to the analysis of cosponsorship coalitions in the U.S. House of Representatives. This approach provides a unique and simultaneous statistical analysis of ideological space (specifically, coalition formation) and geographical space. Typically, duration models are associated with temporal longitudinal data, but recently have been adapted to the spatial domain (Pellegrini and Reader 1996). In this paper, spatial duration models are further adapted to examine ideological space including a consideration of unobserved sources of spatial variation (or omitted variable bias). We examine two features of cosponsorship coalitions, breadth and clustering. Breadth is defined as the ideological distance between the two most extreme members of the coalition which is an important “signal” to the rest of Congress regarding the scope and broad appeal of the proposed legislation. In contrast, clustering refers to the distance between individual members of a coalition and reveals the tendency, or not, of ideologically similar members of Congress to support various bills. To examine breadth and clustering, we employ spatial duration models of cosponsorship that permit a multivariate analysis incorporating both the characteristics of members of Congress and the geographical regions they represent. Results indicate that cosponsorship coalition patterns are primarily determined by the content of the legislation, not the actions of the coalition leadership. While the leadership characteristics of sponsors have a limited effect on cosponsorship breadth, the size of the coalition is the primary determinent. Leadership characteristics also have little effect on cosponsorship clustering. Rather, clustering is due to members' policy preferences, as measured by distance to the coalition leader. In addition, the duration analysis results suggest that geographical proximity between members of Congress “overcomes” ideological distance. Finally, the spatial duration approach is noted as a fruitful methodology for examining explicitly spatial patterns in both ideological or geographical space.     

The Network Approach: Dutch Spatial Planning between Substratum and Infrastructure Networks

This paper investigates the substratum and infrastructure networks as relevant components of spatial planning. Since 2001, in Dutch government documents on spatial planning the Layers Approach has been presented, with three layers: substratum; networks; occupation patterns. The Layers Approach assumes that the characteristics of the substratum provide conditions for infrastructure networks and that these infrastructure networks then condition the occupation patterns, including the locations for housing, business activities and related services. These more or less hierarchical relationships are supposed to produce clear ground rules for the spatial planner. The Layers Approach seems to offer a robust methodology for spatial planning. This contribution presents the background of the Layers Approach, adds some critical notes and proposes an amendment: the Network Approach, connecting different spatial scales. After the presentation of a typology of networks a preliminary overview of the dynamics of transport networks is given. The spatial relevance of information and communication technology (ICT) networks, that support transport networks, is discussed. Nodes are presented as links between infrastructure networks and occupation patterns. The paper concludes that actors, dealing with regional spatial plans in a decentralized planning system, have to acquire an in-depth knowledge of the technology, economy and governance of current networks and the qualities of the substratum, which form a conditioning and stimulating framework for the spatial planning of urban and regional areas.