A GIS-based spatial analysis on neighborhood effects and voter turn-out:: a case study in College Station,Texas

This paper examines individual voter turn-out and its putative relationship with voting outcomes at the voting precinct level. Via a GIS-based address matching procedure, we were able to georeference individual voters (registered voters who casted their votes) and non-voters (those registered voters who did not cast their votes) for three recent local referenda in College Station, Texas. We then conducted a scale-sensitive, second-order spatial analysis for the spatial distribution of voter turn-outs, followed by a spatial clustering analysis of the voting results using Getis–Ord’s G_i statistic. We found that the extent of neighborhood effects in local elections is heavily influenced by the voter turn-out. If voter turn-out is clustered at intermediate and large scale, voting results tend to be clustered and also exhibit a sharp polarization between high and low values. If voter turn-out tends to be uniform/regular at intermediate scales but randomly distributed at both small and large scales, there appears to be less clustering in the voting results and thus lack of the neighborhood effect. If the voter turn-out pattern is mixed-uniform/regular at the small scale, random at the intermediate scale, but clustered at the large scale, the voting results show a stronger neighborhood effect.     

ECONOMIC IMPACTS OF CULTURAL DIVERSITY IN THE NETHERLANDS: PRODUCTIVITY,UTILITY, AND SORTING*

ABSTRACT This paper identifies the impact of cultural diversity on local economies, by explaining spatial disparities in wages and housing prices across Dutch cities using unique individual panel data of homeowners during the period 1999 and 2008. We distinguish between the effects of spatial sorting based on individual heterogeneity, interactions‐based productivity effects, and consumer amenities while controlling for interactions between the labor and housing market. In line with previous literature, we find a positive effect of cultural diversity on average housing prices. After controlling for spatial sorting, the effect of cultural diversity on housing prices is negative. The negative impact of cultural diversity on local housing markets is likely driven by a causal effect between the presence of immigrants and neighborhood quality that outweighs a positive effect of immigrant‐induced diversity in consumption goods.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques

Geographically weighted regression (GWR) is a technique that explores spatial nonstationarity in data‐generating processes by allowing regression coefficients to vary spatially. It is a widely applied technique across domains because it is intuitive and conforms to the well‐understood framework of regression. An alternative method to GWR that has been suggested is spatial filtering, which it has been argued provides a superior alternative to GWR by producing spatially varying regression coefficients that are not correlated with each other and which display less spatial autocorrelation. It is, therefore, worthwhile to examine these claims by comparing the output from both methods. We do this by using simulated data that represent two sets of spatially varying processes and examining how well both techniques replicate the known local parameter values. The article finds no support that spatial filtering produces local parameter estimates with superior properties. The results indicate that the original spatial filtering specification is prone to overfitting and is generally inferior to GWR, while an alternative specification that minimizes the mean square error (MSE) of coefficient estimates produces results that are similar to GWR. However, since we generally do not know the true coefficients, the MSE minimizing specification is impractical for applied research.     

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.     

A Simulation Study on Specifying a Regression Model for Spatial Data: Choosing between Autocorrelation and Heterogeneity Effects

In this simulation study, regressions specified with autocorrelation effects are compared against those with relationship heterogeneity effects, and in doing so, provides guidance on their use. Regressions investigated are: (1) multiple linear regression, (2) a simultaneous autoregressive error model, and (3) geographically weighted regression. The first is nonspatial and acts as a control, the second accounts for stationary spatial autocorrelation via the error term, while the third captures spatial heterogeneity through the modeling of nonstationary relationships between the response and predictor variables. The geostatistical‐based simulation experiment generates data and coefficients with known multivariate spatial properties, all within an area‐unit spatial setting. Spatial autocorrelation and spatial heterogeneity effects are varied and accounted for. On fitting the regressions, that each have different assumptions and objectives, to very different geographical processes, valuable insights to their likely performance are uncovered. Results objectively confirm an inherent interrelationship between autocorrelation and heterogeneity, that results in an identification problem when choosing one regression over another. Given this, recommendations on the use and implementation of these spatial regressions are suggested, where knowledge of the properties of real study data and the analytical questions being posed are paramount.     

Globalisation Lived Locally: A Labour Geography Perspective on Control,Conflict and Response among Workers in Kerala

Abstract: Supported by the labour geography framework, I analyse how spatial practices of labour shape the economic geography of capitalism, by looking into a model not at a global but at a very local scale of organisation and showing its effectiveness while confronting social actors organised at global or extra‐local scales. Questioning global stereotypes on economic responses to globalisation, I argue that labour becomes actively involved in the very process of globalisation and the expansion of capital, empirically demonstrating the relevance of this in the globalisation literature. I deal with one region—Kerala—and processes in its labour markets, taking the case of apparel workers in an export‐promoting industrial park.     

Who are the People in my Neighborhood?: The “Contextual Fallacy” of Measuring Individual Context with Census Geographies

Scholars frequently use counts of populations aggregated into geographic units like census tracts to represent measures of neighborhood context. Decades of research confirm that variation in how individuals are aggregated into geographic units can dramatically alter analyses conducted with these units. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude or its capacity to affect analytical results obtained using these contextual measures. Using confidential access to the complete 2010 U.S. Decennial Census, we can construct—for all persons in the U.S.—individual-specific contexts, which we group according to Census-assigned block, block group, and tract. We compare these individual-specific measures to the published statistics at each scale, and we then determine the degree to which published measures could be affected by how boundaries are drawn using a simple statistic, the standard deviation of individual context (SDIC). For three key measures (percent Black, percent Hispanic, and Entropy—a measure of ethno-racial diversity), we find that block-level Census statistics frequently contain a high degree of uncertainty meaning that they may not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the uncertainty associated with contextual variables at all three scales.     

BLOCK,NEIGHBOURHOOD OR DISTRICT? THE IMPORTANCE OF GEOGRAPHICAL SCALE FOR AREA EFFECTS ON EDUCATIONAL ATTAINMENT

Researchers show an increasing interest in the question of how a neighbourhood influences its residents. The crucial question is whether place‐related factors have an independent effect on individual life chances. This study examines adolescent development, with educational attainment as the dependent variable. It further addresses contextual effects that emerge at different intra‐urban geographical scales by exploring spatial effects at block, neighbourhood and district level in Oslo, Norway. How does the population composition at the three scales affect the level of future educational attainment for adolescents? What are the most important aspects of the population? Is the impact of various population indicators similar or different across the three scales? A number of causal mechanisms, which operate at different geographical scales, such as social interaction, shared social spaces, stigmatization and institutional resources are discussed. The study has a longitudinal approach, and includes register‐based information about the whole population of Oslo and a young target population. The analysis is based on two‐, three‐ and four‐level modelling. The results reveal significant effects on the youth's future educational attainment at all geographical levels and for all tested measures of social and demographic area composition. The share of low‐educated neighbours seems to have the strongest impact. Contradictory to most other studies, the results show that the highest geographical level (district) has the strongest effect. This surprising result is tentatively interpreted to emerge from a combination of three interwoven mechanisms: the youths' extended activity spaces and social interactions, the institutional aspects (schools), and place stigmatization.     

Distance and social interaction in a Victorian city

The paper examines patterns of marriage in a small industrial city—Huddersfield—between 1850 and 1880, when the residential structure of the city was becoming more modern in its spatial organization. The need to interpret distance-decay patterns of interaction in their social context is stressed. The apparently unchanging relationship between physical distance and frequency of interaction is related to the balance between changing patterns of individual mobility, class consciousness and scales of residential segregation. The more extensive interaction fields of the rich are attributed to their greater mobility, but also their lower population density. The close-knit patterns of the poor reflect their higher population density and more particularly the segregation of the Irish community. Finally, the differences between normative (within-class) and non-normative (between-class) marriage distances are considered. It is suggested that physical distance operated independently of social distance, although this conclusion requires further testing at different scales of analysis, and using information on forms of interaction other than marriage.     

An Initial Analysis of River Discharge and Rainfall in Coastal New South Wales,Australia Using Wavelet Transforms

In many coastal catchments of south eastern New South Wales, Australia, changes in river morphology are a response to human impact superimposed on spatial and temporal patterns of variability in precipitation and discharge. Understanding, and preferably quantifying, spatial and temporal patterns of hydrologic variability are essential to understanding natural changes, and to separate these from artificial changes in river systems. Prediction and management of water resources are also dependent upon this understanding. We assess the variability in precipitation and discharge using the wavelet transform which projects the time series of data into a three dimensional surface of frequency, amplitude and time. The analysis reveals that changes across time often reflect changes in individual seasons and may be linked to changes in particular seasonal atmospheric circulation systems. Strong perturbations in the analysis of one catchment are consistent with documented, geomorphically‐effective, flooding sequences. The characteristics of the series in the transformed data reveal interesting differences at certain times and scales which may be a reflection of changes in larger scale atmospheric processes.     

A Route Map for Successful Applications of Geographically Weighted Regression

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.     

The Local Implications of Major Job Transformations in the City: A Spatial Microsimulation Approach

The changing dynamics of regional and local labor markets during the last decades have led to an increasing labor market segmentation and socioeconomic polarization and to a rise of income inequalities at the regional, urban, and intraurban level. These problems call for effective social and local labor market policies. However, there is also a growing need for methods and techniques capable of efficiently estimating the likely impact of social and economic change at the local level. For example, the common methodologies for estimating the impacts of large firm openings or closures operate at the regional level. The best of these models disaggregate the region to the city (Armstrong 1993; Batey and Madden 1983). This paper demonstrates how spatial microsimulation modeling techniques can be used for local labor market analysis and policy evaluation to assess these impacts (and their multiplier effects) at the local level‐to measure the effects on individuals and their neighborhood services. First, we review these traditional macroscale and mesoscale regional modeling approaches to urban and regional policy analysis and we illustrate their merits and limitations. Then, we examine the potential of spatial microsimulation modeling to create a new framework for the formulation, analysis and evaluation of social and local labor market policies at the individual or household level. Outputs from a local labor market microsimulation model for Leeds are presented. We show how first it is possible to investigate the interdependencies between individual's or households labor market attributes at the microscale and to model their accessibilities to job opportunities in different localities. From this base we show how detailed what‐if microspatial analysis can be performed to estimate the impact of major changes in the local labor market through job losses or gains, including local multiplier effects.     

PERSISTENCE OF REGIONAL UNEMPLOYMENT: APPLICATION OF A SPATIAL FILTERING APPROACH TO LOCAL LABOR MARKETS IN GERMANY*

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region‐specific information (e.g., the endowment of natural resources, or the size of the “home market”) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region‐specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time‐stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS‐3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.     

Absent Regions: Spaces of Financialisation in the Arab World

This paper examines processes of financialisation in the Arab world, a region that has been almost completely absent from the wider financial literature. The paper shows that financialisation is much more than simply the expansion of financial markets within neatly bounded sets of social relations operating at the national scale. In the Arab world, financialisation has been marked by the growing weight of regional finance capital—most specifically, those capital groups based in the Gulf Cooperation Council—in circuits of capital operating at all scales. This has important implications for processes of class and state formation. Approaching financialisation in this manner—moving away from methodologically nationalist assumptions and the literature's largely singular focus on the advanced capitalist core—brings into focus the significance of cross‐scalar accumulation patterns, their spatial hierarchies, and geographic unevenness. The paper thus reaffirms the need for a more spatially sensitive approach to financialisation.     

Integrating Local and Regional in Pre-Contact Hawaiian Agriculture at Kahuku,Hawai‘i Island

ABSTRACT

The agricultural systems of the Hawaiian archipelago were some of the most intensive in the Pacific and this scale of intensity is well illustrated by the large agricultural landscapes of leeward Hawai‘i Island. Previous research in the area has centred on understanding the relationship between agriculture, political process, and large-scale environmental conditions. Much of this research has been oriented at the regional level, privileging discussion of elite management and oversight, with only limited investigation exploring farmer-centric adaptation at local scales. In this paper, we assess the integration of local and regional processes in Hawaiian agriculture using recent paired archaeological and ecological data from the Ka‘ū Field System as a case study. We demonstrate the presence of both general patterns previously identified in the archipelago and particular adaptations to the local environment of Kahuku ahupua‘a. In particular, we highlight targeted infrastructural developments that allowed for cultivation of what would otherwise be a difficult cultivation medium within the confines of a larger, likely regionally organised, field system constrained by general soil biogeochemical thresholds. We argue that such investigations provide an increased understanding on how these large-scale agricultural landscapes were formed by integration at multiple social and spatial scales.     

Une histoire de résidus : à propos des facteurs généraux et locaux de croissance régionale au Canada,de 1971 à 2001

Starting from an econometric model of local employment growth, applied to Canada (1971–2001), residuals—relative to model predictions—are analyzed over time and over space, in turn allowing us to draw a distinction between general explanatory variables and factors of a more local, cyclical or accidental nature. The model's explanatory power grows over time, founded on variables such as urban size, market access and industrial structure, allowing us to conclude that local employment growth in Canada follows an increasingly geographically predictable pattern. However, an examination of the residuals reveals more localized processes. Growth volatility is most manifest in Alberta and British Columbia, home to the most erratic local economies. Emerging patterns are visible in the last period, most notably the underperformance of Northern Ontario and of non‐metropolitan communities between Windsor and Québec City, lying along the Great Lakes and the Saint Lawrence. The over‐performance—compared to model predictions — of small and mid‐sized towns in south‐eastern Québec can, on the other hand, be interpreted as a sign of truly local social processes, generally associated with a particularly dynamic local entrepreneurial class.     

Combining Geostatistical Methods and Hierarchical Scene Models for Analysis of Multiscale Variation in Spatial Data

Concepts from Hierarchical Analysis of Variance (ANOVA) can be combined with ideas from geostatistics to describe the multiscale structure of spatial data. Hierarchical ANOVA involves modeling spatial data as the sum of effects associated with processes acting at different spatial scales. These effects can be modeled as stationary regionalized variables, whose spatial structure can be described using the variogram. According to this model, the variogram of the spatial data is the sum of variograms and cross‐variograms of the effects. Whereas hierarchical ANOVA reveals the relationship between scale and variability, the hierarchical decomposition of the variogram relates scale with spatial structure. This analysis method can reveal otherwise undetected features of spatial data, and can guide further analysis.     

The Role of Spatial Scale in Geomorphic Systems

Geomorphic systems are characterized by numerous, complex interrelationships between system components, and by processes and controls which may operate over different spatial scales. Factors operating at any given spatial scale can be viewed as an abstracted subset of all relationships operating at all scales. The theory that relationships which operate over spatial scales an order of magnitude different are effectively independent of each other is formally stated in terms of abstracted systems. An example is given to illustrate the use of spatial statistics to determine what constitutes a significant spatial scale difference in controls over hydraulic geometry of a desert wash.     

A Shape‐Based Local Spatial Association Measure (LISShA): A Case Study in Maritime Anomaly Detection

The explicit consideration of the shape of geographic features has been largely ignored in existing spatial association measures. The primary contribution of this work is the development of a new local spatial association measure—a Local Indicator of Spatial and Shape Association (LISShA). The LISShA measure is modeled after local Geary's Spatial Autocorrelation measure with distance between shapes, calculated using the Small–Le metric, replacing difference between attribute values and the spatial neighborhood defined by Fréchet distance. We provide some explanation of these metrics and show, in detail, how the LISShA and proposed moments are calculated in a one‐dimensional context in a case study of maritime anomaly detection.