Reimagining GIScience for relational spaces

A mismatch between largely absolute Newtonian models of space in GIScience and the relational spaces of critical human geography has contributed to mutual disinterest between the fields. Critical GIS has offered an intellectual critique of GIScience without substantially altering how particular key geographical concepts are expressed in data structures. Although keystone ideas in GIScience such as Tobler's “First Law” and the modifiable areal unit problem speak to enduring concerns of human geography, they have drawn little interest from that field. Here, we suggest one way to reformulate the computational approach to the region for relational space, so that regions emerge not through proximity in an absolute space or similarities in intensive properties, but according to their similarities in relations. We show how this might operate theoretically and empirically, working through three illustrative examples. Our approach gestures toward reformulating key terms in GIScience like distance, proximity, networks, and spatial building blocks such as the polygon. Re‐engaging the challenges of representing geographical concepts computationally can yield new kinds of GIS and GIScience resonant with theoretical ideas in human geography, and also lead to critical human geographic practices less antagonistic to computation.     

Contiguity Constraints for Single‐Region Site Search Problems

This paper proposes an explicit set of constraints as a general approach to the contiguity problem in site search modeling. Site search models address the challenging problem of identifying the best area in a study region for a particular land use, given that there are no candidate sites. Criteria that commonly arise in a search include a site's area, suitability, cost, shape, and proximity to surrounding geographic features. An unsolved problem in this modeling arena is the identification of a general set of mathematical programming constraints that can guarantee a contiguous solution (site) for any 0–1 integer‐programming site search formulation. The constraints proposed herein address this problem, and we evaluate their efficacy and efficiency in the context of a regular and irregular tessellation of geographic space. An especially efficient constraint form is derived from a more general form and similarly evaluated. The results demonstrate that the proposed constraints represent a viable, general approach to the contiguity problem.     

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.     

Geographical and Temporal Weighted Regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19‐year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.     

Chaos,Complexity, and a Revitalization of Four-Field Anthropology?

Four-field anthropology has always struggled with the problem of how to holistically study human cultural systems that are the products of environment, process, and history. Complexity science offers a set of tools calibrated to the analysis of complex systems like those of human societies, and has the potential to allow us to scientifically understand how history and process affect the physical, cultural, and linguistic components that are entangled in the whole of human societies. Application of complexity science to anthropological problems thus far has favored engagement on a conceptual level rather than one that harnesses the full power of the approach.     

Improving Distance Estimates between Areal Units in Migration Models

There are many methods of modeling migrant flows within a set of areal units, but it is common in most to incorporate some measure of distance as an explanatory variable. These distances are effectively meant to represent the typical distance between pairs of areas that would be traveled by potential migrants. They are usually calculated between population-weighted centroids derived for each zone. It is argued here that this method of calculating distance is biased and that the zonal system used will influence the final model parameters that are intended to describe the underlying migration process. The distances between nearby zones will be particularly poorly specified using this approach, but other problems arise which relate to the shape of the zones and the position of the zones in relation to each other. This paper describes an alternative method of calculating these distances which reduces this bias. It is shown that the resulting models fit the data far more satisfactorily and that the residuals from models incorporating this approach are significantly different from those identified from models that use the standard method of specifying distance.     

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.     

Linguistic boundaries as barriers to exchange

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) data are used to explore the structure of obsidian acquisition for groups in the King Range National Conservation Area of northern California. The data indicate that, contrary to a distance-decay model, obsidian from more distant sources in the Medicine Lake Highlands, dominates the obsidian found at sites in the area. Furthermore, metric data indicate a positive relationship between size and distance to source with more distant sources represented by larger flakes. It is suggested that this pattern is the result of culturally mediated exchange relations under which Athabaskan speakers concentrated trade relations with one another to the exclusion of trade with linguistic groups to the south, with longer histories in the area, such as the Pomo and Yuki. Additionally, the use of redwood dugouts on rivers in northern California and the importance of northeastern Californian obsidian in ritual practices of California Athabaskan groups may have further encouraged the use of more distant, northeastern Californian, sources.     

An Empirically Based Implementation and Evaluation of a Hierarchical Model for Commuting Flows. 一种基于经验的层状通勤流模型实现与评估

This article provides an empirical evaluation of a hierarchical approach to modeling commuting flows. As the gravity family of spatial interaction models represents a benchmark for empirical evaluation, we begin by reviewing basic aspects of these models. The hierarchical modeling framework is the same that Thorsen, Ubøe, and Nævdal (1999) used. However, because some modifications are required to construct a more workable model, we undertake a relatively detailed presentation of the model, rather than merely referring to the presentation in Thorsen, Ubøe, and Nævdal (1999) . The model uses a hierarchical specification of a transportation network and the individual search procedure. Journeys to work are determined by the effects of distance deterrence and of intervening opportunities, and by the location of potential destinations relative to alternatives at subsequent levels in a transportation network. The model calibration uses commuting data from a region in western Norway. The estimated parameter values are reasonable, and the explanatory power is very satisfactory when compared with the results of a competing destinations approach.     

GRAVITY AND SPATIAL STRUCTURE: THE CASE OF INTERSTATE MIGRATION IN MEXICO*

ABSTRACT The estimation of gravity models of internal (aggregate) place‐to‐place migration is plagued with endogeneity (omitted‐variable) biases if the unobserved effects of spatial structure are not accounted for. To address this econometric problem, this paper presents a more general specification of the gravity model, which allows for (bilateral) parameter heterogeneity across individual migration paths—along with (unilateral) origin‐ and destination‐specific effects. The resultant “three‐way fixed‐effects” (3FE) model is applied for an analysis of interstate migration in Mexico based on cross‐sectional data. To overcome parameter‐dimensionality problems (due to limited or incomplete information), the 3FE model is estimated using the Generalized Maximum Entropy (GME) estimator. The empirical implications of this new modeling strategy are illustrated by contrasting the 3FE‐GME estimates with those for the traditional and two‐way fixed‐effects (2FE) models. The former are far more plausible and intuitively interpretable than their traditional and 2FE counterparts, with parameter estimates changing in expected directions. The (average) effect of the migrant stock is markedly smaller than usually estimated, providing a more realistic measure of network‐induced migration. Migration outflows from centrally located origins have significantly steeper distance decay. Path‐specific distance effects exhibit directional asymmetries and spatial similarities.     

A Cost-Based Ripley’s <Emphasis Type="Italic">K</Emphasis> Function to Assess Social Strategies in Settlement Patterning

Most quantitative approaches to distributional analysis in archaeology assume a homogeneous study surface that is amenable to easy generalisations. This framework has been widely used to describe settlement processes, disregarding the spatial heterogeneity inherent to geographic reality. In other words, researchers have often assumed that the correlation between the elements of a spatial distribution is a function of the Euclidean distance (i.e. straight line distance) between them. Other archaeological studies have tested alternative measures to Euclidean distances, such as cost-based ones, both to describe optimal routes and to assess spatial autocorrelation in a point pattern. Nevertheless, until now there has been no suitable model to introduce these measures into spatial statistical equations. In order to overcome this obstacle, we approach the implementation problem inversely by embedding the spatial pattern under study into a Euclidean frame of reference based on its cost-distance pairwise matrix. This paper describes the application of this methodology on one of the main tools used by archaeologists to assess settlement patterns: Ripley’s K function. We present two case studies, covering both macroscale and mesoscale, with significant variations in the results depending on the use of the Euclidean or cost-based approach. Data, functions and results have been R-packaged for the sake of reproducibility and reusability, allowing other researchers to build upon our methods.     

ATTENUATION RELATION FOR WEST EURASIA DETERMINED WITH RECENT NEAR-FAULT RECORDS FROM CALIFORNIA,JAPAN AND TURKEY

Strong ground motion close to a fault can be expected to be very large, so its estimation is essential for human safetv. Although a few strong-motion data exist for the west Eurasian region, we proposed in a previous work [Berge-Thierry et al., 2003] an attenuation relation for spectral acceleration using strong-motion data recorded in west Eurasia (mainly in Europe) and some in the western United States: this relationship was derived for the French Safety Rule, which is applied for seismic hazard assessment at nuclear power plants. In this study, we propose a constraining of the amplitude saturation term related to the proximity of the fault, and an adding of an amplitude saturation term in the regression model. We add, to the data-set previously used to derive the west Eurasian attenuation relationship strong-motions recorded during recent large earth-quakes: the 1995 Hyogo-ken Nanbu (Kobe) event in Japan and the 1999 Kocaeli (Izmit) event in Turkey. The regression analysis, adopted from Fukushima and Tanaka [1990], is non-linear, so an iterative procedure is applied. The determined regression coefficients lead to a prediction of a peak ground acceleration of about 0.7 g for soil site conditions at a fault distance of 0.5 km. The Q coefficient deduced, from the distance coefficient is in agreement with scattering Q models. The introduction of the saturation term leads to significantly lower predictions of average spectral accelerations at short distances as compared with using the Berge-Thierry et al. [2003] empirical model.     

Exploring Spatial Patterns Using an Expanded Spatial Autocorrelation Framework

Spatial autocorrelation (SA) is regarded as an important dimension of spatial pattern. SA measures usually consist of two components: measuring the similarity of attribute values and defining the spatial relationships among observations. The latter component is often represented by a spatial weights matrix that predefines spatial relationship between observations in most measures. Therefore, SA measures, in essence, are measures of attribute similarity, conditioned by spatial relationship. Another dimension of spatial pattern can be explored by controlling observations to be compared based upon the degree of attribute similarity. The resulting measures are spatial proximity measures of observations, meeting predefined attribute similarity criteria. Proposed measures reflect degrees of clustering or dispersion for observations meeting certain levels of attribute similarity. An existing spatial autocorrelation framework is expanded to a general framework to evaluate spatial patterns and can accommodate the proposed approach measuring proximity. Analogous to the concept of variogram, clustergram is proposed to show the levels of spatial clustering over a range of attribute similarity, or attribute lags. Specific measures based on the proposed approach are formulated and applied to a hypothetical landscape and an empirical example, showing that these new measures capture spatial pattern information not reflected by traditional spatial autocorrelation measures.     

IMPLEMENTING REPRESENTATIONS OF SPACE IN ECONOMIC GEOGRAPHY*

ABSTRACT Since spatial issues have become more important in economic theory, it is of interest to examine how space is represented. In empirical terms, this concerns at least the representation of distance and proximity. Both of these may be represented and operationalized in a number of ways, with possible consequences for inferences when modeling. This question is related to the representation of spatial entities, and itself plays into the treatment of spatial dependence as substantive and/or just a nuisance. Because other disciplines face similar questions—for example ecology or epidemiology, their conceptualizations and operationalizations of space will also be used for comparative purposes.     

Spatial Proximity and Intercompany Communication: Myths and Realities

Abstract

Spatial proximity is credited with numerous virtues in the economic literature. In particular, for a company to be located near other companies is seen as conducive to the development of business relations. Spatial proximity is also considered to contribute to the quality and efficiency of these relations by facilitating face-to-face meetings that foster the exchange of complex knowledge and, in particular, the emergence of innovation. This article explores the notion of spatial proximity in intercompany relations, its capacity to facilitate exchange, as well as the link with the methods of communication employed (information and communication technologies and face-to-face). It is based on a distinction between real proximity (the spatial distance between firms), perceived proximity (the spatial distance as evaluated by the firm itself) and active proximity (spatial proximity that facilitates the exchanges). The data come from a survey conducted in 2008 with more than 2000 firms located in the Brittany region (France). The findings emphasize the relative nature of the notion of spatial proximity and the distinction in some cases between real and active proximities, and show that the positive perception of the role of spatial proximity is sustained by the increased face-to-face contact it entails.     

Fighting over friends and neighbors: The effect of inter-candidate geographic distance on intra-party competition

Previous studies on intra-party competition have largely neglected the role played by geographic distance between co-partisan candidates. In this study, we argue that candidates who live further away from intra-party competitors on the same party list benefit electorally from their remoteness. Moreover, we contend that the electoral effectiveness of exhibiting local personal vote attributes – a theoretically and empirically well-established candidate strategy to cultivate personal votes – also depends on the geographical proximity of localized co-partisan candidates. Using a unique and untapped dataset of more than 5,000 Finnish election candidates' home address coordinates over four consecutive parliamentary elections (1999–2011), we run beta regression models to examine the effects of candidate remoteness and nearest candidates' local characteristics on intra-party vote shares. To measure the remoteness of a particular candidate, we develop a novel index based on the distribution of co-partisans over concentric circles around that candidate. The empirical analyses show that the effect of geographic remoteness depends on local party strength and the degree of urbanization: candidates particularly benefit from more distant co-partisans in party strongholds and rural and suburban municipalities. Moreover, all models confirm that nearby located localized co-partisans decrease a candidate's own vote share. These findings have important implications for politicians' careers, party nomination strategies and future empirical research on intra-party competition.     

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.     

Innovation Processes and Proximity: The Case of IDEON Firms in Lund,Sweden

Increasing pressure on innovativeness and collaboration is supposed to strengthen the importance of geographical proximity in innovation systems. But simultaneously there are 'globalization forces' and information and communication technology (ICT) developments that stress wider contexts and different dependencies to place and distance. With case studies of innovative firms at IDEON Science Park in Lund, the article presents analyses of the importance of proximity, related to the kind of knowledge and collaborative arrangements involved in innovation processes. Scope for 'untraded interdependencies' is discussed as well as the possible ICT impact of these collaborative relations. Argument for a three tier innovation system is put forward and its implications for regional innovation strategies are, concludingly, discussed.     

Programming Models for Facility Dispersion: The p-Dispersion and Maxisum Dispersion Problems

The p-dispersion problem is to locate p facilities on a network so that the minimum separation distance between any pair of open facilities is maximized. This problem is applicable to facilities that pose a threat to each other and to systems of retail or service franchises. In both of these applications, facilities should be as far away from the closest other facility as possible. A mixed-integer program is formulated that relies on reversing the value of the 0–1 location variables in the distance constraints so that only the distance between pairs of open facilities constrain the maximization. A related problem, the maxisum dispersion problem, which aims to maximize the average separation distance between open facilities, is also formulated and solved. Computational results for both models for locating 5 and 10 facilities on a network of 25 nodes are presented, along with a multicriteria approach combining the dispersion and maxisum problems. The p -dispersion problem has a weak duality relationship with the (p-1)-center problem in that one-half the maximin distance in the p-dispersion problem is a lower bound for the minimax distance in the center problem for (p-1) facilities. Since the p-center problem is often solved via a series of set-covering problems, the p-dispersion problem may prove useful for finding a starting distance for the series of covering problems.