Ecological Inference and Spatial Variation of Individual Behavior: National Divide and Elections in Catalonia

When in geography one reconstructs individual behavior starting from aggregated data through ecological inference, a crucial aspect is the spatial variation of individual behavior. Basic ecological inference methods treat areas as if they were all exchangeable, which in geographical applications is questionable due to the existence of contextual effects that relate to area location and induce spatial dependence. Here that assumption is avoided by basing ecological inference on a model that simultaneously does a cluster analysis, grouping together areas with similar individual behavior, and an ecological inference analysis in each cluster, estimating the individual behavior in the areas of each group. That allows one to capture most of the spatial dependence and summarize the individual behavior at a local level through the behavior estimated for each cluster. This approach is used to investigate vote switching in Catalonia, where voters split across a national allegiance divide on top of the ideological divide. That leads to Catalans having a lot of options to choose from, and to them voting differently depending on whether the election is for the Catalan parliament or for the Spanish parliament. To investigate that, the results in the two most recent pairs of such elections are analyzed by simultaneously clustering areas based on the similarity of their vote and vote switch patterns, and estimating one vote switch pattern for each cluster. As a result, Catalonia is partitioned into four clusters that have a strong spatial structure, with all the areas in the same cluster having similar demographic composition. The estimated vote switch patterns are quite different across clusters but very similar across pairs of elections, and they help assess how the differential voter turnout and the strategic dual vote effects vary in space.     

Local indicators for categorical data: impacts of scaling decisions

When the geographic distribution of landscape pattern varies, global indices fail to capture the spatial nonstationarity within the dataset. Methods that measure landscape pattern at a spatially local scale are advantageous, as an index is computed at each point in the dataset. The geographic distribution of local indices is used to discover spatial trends. Local indicators for categorical data (LICD) can be used to statistically quantify local spatial patterns in binary geographic datasets. LICD, like other spatially local methods, are impacted by decisions relating to the spatial scale of the data, such as the data grain (p), and analysis parameters such as the size of the local neighbourhood (m). The goal of this article is to demonstrate how the choice of the m and p parameters impacts LICD analysis. We also briefly discuss the impacts spatial extent can have on analysis; specifically the local composition measure. An example using 2006 forest cover data for a region in British Columbia, Canada where mountain pine beetle mitigation and salvage harvesting has occurred is used to show the impacts of changing m and p. Selection of local window size (m = 3,5,7) impacts the prevalence and interpretation of significant results. Increasing data grain (p) had varying effects on significant LICD results. When implementing LICD the choice of m and p impacts results. Exploring multiple combinations of m and p will provide insight into selection of ideal parameters for analysis.     

Settlement patterns,social complexity and agricultural strategies during the Chalcolithic period in the Northern Negev,Israel

Previous studies have reported a Chalcolithic site distribution pattern in the Northern Negev as clustered, exhibiting a number of characteristics that imply a chiefdom level of organization. However, a recent spatial analysis suggests that in some areas of the Northern Negev, settlement patterns were essentially random and that there is no evidence for regional chiefdom organization. We examine this controversy by closely inspecting the methods of spatial analyses employed by previous researchers, by introducing an additional multiscalar spatial technique, namely Ripley's K-function, and by using updated and modified data. Our results indicate that settlement distributions were essentially clustered, even in small areas along the wadis. Examining the relation between these spatial-dependent distributions and the landscape surroundings revealed that particular physiographic characteristics of the wadis contribute to increscent in site clusters. Furthermore, a general linear model analysis suggests that the distribution of Chalcolithic sites is determined primarily by environmental factors rather than factors related to political organization.     

基于加权平均旅行时间的浙江省交通可达性时空格局研究

以浙江省69个县(市)为研究对象,利用1996、2004、2012年路网数据,借助ArcGIS网络分析功能,采用基于城镇综合发展水平的加权平均旅行时间指标,探讨浙江省综合交通网络中节点城镇可达性及其时空演变特征。研究结果表明:①1996-2012年,各城镇可达性均出现不同程度提高,彼此间联系趋于紧密;②城镇可达性存在空间分异,且提升幅度各异,均衡化趋势明显;③城镇可达性空间格局变化大,逐渐向以"四大都市区"为中心向四周递增的"∏型"多级空间结构转变。总体上来看,城镇可达性变化及空间格局演变体现了区域统筹发展的战略要求。     

Weighted Spatial Adaptive Filtering: Monte Carlo Studies and Application to Illicit Drug Market Modeling

This paper introduces a pattern recognizer, similar to weighting schemes used in combining time series forecasts, for use in spatial adaptive filtering applied to estimating multivariate cross-sectional models. The pattern recognizer enhances the ability to automatically detect and estimate parameters with discontinuous or sharp gradient changes over geographic contexts. Results from Monte Carlo studies suggest that the weighted spatial adaptive filter is at least as accurate as the unweighted filter for cases having smoothly changing parameters, but superior for cases having discontinuous, step-jump parameters. A case study on illicit drug-market activities using census tract-level data from Pittsburgh, Pennsylvania, further illustrates the advantages of the weighted filter.     

TRENDS IN NEIGHBORHOOD INCOME INEQUALITY IN THE U.S.: 1980–2000*

ABSTRACT This paper reports evidence on the geographic pattern of income inequality, both within and between neighborhoods, across a sample of 359 U.S. metropolitan areas between 1980 and 2000. The results indicate that overall income inequality within a metro area tends to be driven by variation within neighborhoods, not between them, although we find that between‐neighborhood differences rose dramatically during the 1980s and subsided somewhat during the 1990s. While this trend is similar to what existing research has found, our findings reveal potentially important differences in the magnitudes of the changes depending on whether neighborhoods are defined by block groups or tracts.     

A Local Model for Multivariate Analysis: Extending Wartenberg’s Multivariate Spatial Correlation

Local analysis can provide specific information about individual observations that is often useful in understanding nonstationary interactions among variables. This paper extends the application of Wartenberg’s Multivariate Spatial Correlation (MSC) method to a local setting. The original MSC can be considered as an adaptation of Principal Component Analysis for spatial effects with respect to spatial autocorrelation. The extended MSC method described in this paper, however, further incorporates another spatial effect, spatial heterogeneity, by the addition of geographic weights in standardizing the data and in calculating the spatial association weight matrix. The extension allows more local analysis and facilitates additional visualization of the results. The geographically weighted MSC is illustrated and justified using the classic dataset collected by André-Michel Guerry on moral statistics in 1830s France.     

失地农民就业空间特征及行为模式研究——以安徽省芜湖市为例

以芜湖市失地农民为研究对象,利用2014年的实地调研数据,运用GIS空间分析、地理集中指数和空间使用曲线等方法,探讨芜湖市失地农民就业空间特征、行为模式及影响因素。研究表明,芜湖市失地农民就业以本地就业为主,空间呈大集中、小分散的格局,为单中心的社区圈层模式。在市域尺度上本地就业在空间上呈现郊区为就业热区,城区相对较少;社区尺度上,就业地主要集中在社区范围内,短距离就业为主,主要集中在1-5km距离段,其中2km处出现就业人数高峰。就业空间行为模式类型多样,本地-就业型占主导地位。失地农民的自身属性特征及思维定式影响和制约着其对就业空间行为的决策;城市化过程中制度转型与空间重构实现了郊区产业集聚格局以及社区周边的城市就业环境,为失地农民就近就业创造机会。     

University-led innovation in and for peripheral urban areas: new approaches in Naples,Italy and Newark,NJ, US

This paper focuses on the spatial development problem of university-led innovation in peripheral urban areas. Highlighting issues of proximity, uneven geographic development, and multi-scalar urban governance as weaknesses of the regional innovation systems literature, we provide a novel synthesis of regional economics, innovation policy, and critical urban studies to assess the development roles of universities in concrete contexts. A comparative investigation of Naples and Newark, NJ captures the functional operation of regional innovation and urban development as a contested product of discourses, technologies (material and governance), and territorial arrangements. Our analysis demonstrates the significance of multi-scalar relationships in structuring innovation policy and practice in peripheral urban areas. The architecture of innovation is not simply rolled out into pre-determined spatial containers in places lacking established ‘institutional thickness’ or urban centrality. The spatial development of university-led innovation is a social product: material and governance infrastructures are essential components of the urban fabric and are essential to its co-constitution. Universities are shown to contribute differing resources dependent on their institutional strategic goals and the capacities and spatial imaginaries afforded to them by their situation in broader territorial governance regimes. We conclude by drawing comparative lessons and identifying directions for future research.     

A NEW SPATIAL MULTIPLE DISCRETE‐CONTINUOUS MODELING APPROACH TO LAND USE CHANGE ANALYSIS

This paper formulates a multiple discrete‐continuous probit (MDCP) land use model within a spatially explicit economic structural framework for land use change decisions. The spatial MDCP model is capable of predicting both the type and intensity of urban development patterns over large geographic areas, while also explicitly acknowledging geographic proximity‐based spatial dependencies in these patterns. At a methodological level, the paper focuses on specifying and estimating a spatial MDCP model that allows the dependent variable to exist in multiple discrete states with an intensity associated with each discrete state. The formulation also accommodates spatial dependencies, as well as spatial heterogeneity and heteroskedasticity, in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decision agents (or observation units) lead to spillover effects in multiple discrete‐continuous choices (or states). A simulation exercise is undertaken to evaluate the ability of the proposed maximum approximate composite marginal likelihood (MACML) approach to recover parameters from a cross‐sectional spatial MDCP model. The results show that the MACML approach does well in recovering parameters. An empirical demonstration of the approach is undertaken using the city of Austin parcel level land use data.     

On the Performance of the Subtour Elimination Constraints Approach for the p‐Regions Problem: A Computational Study

The p‐regions is a mixed integer programming (MIP) model for the exhaustive clustering of a set of n geographic areas into p spatially contiguous regions while minimizing measures of intraregional heterogeneity. This is an NP‐hard problem that requires a constant research of strategies to increase the size of instances that can be solved using exact optimization techniques. In this article, we explore the benefits of an iterative process that begins by solving the relaxed version of the p‐regions that removes the constraints that guarantee the spatial contiguity of the regions. Then, additional constraints are incorporated iteratively to solve spatial discontinuities in the regions. In particular we explore the relationship between the level of spatial autocorrelation of the aggregation variable and the benefits obtained from this iterative process. The results show that high levels of spatial autocorrelation reduce computational times because the spatial patterns tend to create spatially contiguous regions. However, we found that the greatest benefits are obtained in two situations: (1) when ; and (2) when the parameter p is close to the number of clusters in the spatial pattern of the aggregation variable.     

Determining Market Areas Captured by Competitive Facilities: A Continuous Equilibrium Modeling Approach

Many existing models concerning locations and market areas of competitive facilities assume that customers patronize a facility based on distance to that facility, or perhaps on a function of distances between the customer and the different facilities available. Customers are generally assumed to be located at certain discrete demand points in a two-dimensional space, or continuously distributed over a one-dimensional line segment. In this paper these assumptions are relaxed by employment of a continuum optimization model to characterize the equilibrium choice behavior of customers for a given set of competitive facilities over a heterogeneous two-dimensional space. Customers are assumed to be scattered continuously over the space and each customer is assumed to choose a facility based on both congested travel time to the facility and on the attributes of the facility. The model is formulated as a calculus of variations problem and its optimality conditions are shown to be equivalent to the spatial customer-choice equilibrium conditions. An efficient numerical method using finite element technique is proposed and illustrated with a numerical example.     

The Changing Geography of Violence in Russia's North Caucasus, 1999-2011: Regional Trends and Local Dynamics in Dagestan,Ingushetia, and Kabardino-Balkaria

Three U.S. geographers analyze the temporal and spatial trends of 17,438 violent events in Russia's North Caucasus region from August 1999 to July 2011, demonstrating that the diffusion of conflict away from Chechnya intensified during the period 2007-2011, as levels of violence rose in neighboring republics. An increasing number of casualties are civilians in Dagestan, Ingushetia, and Kabardino-Balkaria, the three republics that are the focus of the paper. Employing multiple methods of spatial pattern analysis and geographically sensitive regression models, the authors examine the spatial fragmentation of violence from the perspective of rebel groups operating in the three republics. The analysis documents how the incidence of violence varies dramatically over space (i.e., reflecting the influence of urbanization, strategic location, and physical geographic factors such as elevation and extent of forest cover). Although violence in the North Caucasus region as a whole has declined in absolute terms over the past four years, the authors show how new geographies of violence are developing in the region, underscoring the emergence of republic-based insurgent operations against the various organs of the Russian state. Journal of Economic Literature, Classification Numbers: H560, H770, O180. 9 figures, 3 tables, 1 appendix, 103 references:     

Spatial Analysis of Employment and Population Density: The Case of the Agglomeration of Dijon 1999

The aim of this paper is to analyze the intraurban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut‐offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline‐exponential, and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity, and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments, and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.     

Designs for Detecting Spatial Dependence

The aim of this article is to find optimal or nearly optimal designs for experiments to detect spatial dependence that might be in the data. The questions to be answered are: how to optimally select predictor values to detect the spatial structure (if it is existent) and how to avoid to spuriously detect spatial dependence if there is no such structure. The starting point of this analysis involves two different linear regression models: (1) an ordinary linear regression model with i.i.d. error terms—the nonspatial case and (2) a regression model with a spatially autocorrelated error term, a so-called simultaneous spatial autoregressive error model. The procedure can be divided into two main parts: The first is use of an exchange algorithm to find the optimal design for the respective data collection process; for its evaluation an artificial data set was generated and used. The second is estimation of the parameters of the regression model and calculation of Moran's I , which is used as an indicator for spatial dependence in the data set. The method is illustrated by applying it to a well-known case study in spatial analysis.     

Redundancy as an Index of Change in Point Pattern Analysis

Point pattern analysis based on concepts from information theory can go beyond existing techniques. Direct measurement of spatial form is achieved when Thiessen polygons are constructed around the points; in this scheme, each point's proportion of total area may be treated like a probability. Three information-theoretic indices are available for analysis of a distribution of such probabilities. Entropy is density dependent. Redundancy, defined as the difference between observed and maximum entropy, seems to avoid this problem when the number of individuals in a pattern exceeds twenty. Comparisons of prior and posterior redundancy provide an indication of change in overall pattern form. An information gain expectation reflects changes for each individual in a pattern. Here, point-area redundancy parameters are determined for Poisson-generated patterns, using a gamma distribution of polygon areas and a computer-generated set. An application to an urban crime problem illustrates the use of these parameters in the analysis of pattern change.     

THE GEOGRAPHY OF OCEANS AND ITS BASIC PROBLEMS

The study of oceans as a subfield of geography has gained acceptance in the Soviet Union. Some universities have introduced courses in marine geography, and geographers have participated in oceanographic research voyages. An effort is made here to define the place of a marine geography within the geographic discipline as a whole, to set the spatial limits for geographical investigations of the oceans and to suggest problem areas suitable for geographical analysis. In keeping with the Soviet dichotomy, physical and economic geographic problems are distinguished. Physical-geographic problem areas would include study of oceanic water masses; large-scale interaction between oceans and atmosphere; study of island environments, and the biogeography of oceans. Economic geographic problems would focus both on theoretical aspects, such as spatial regularities in human activities related to oceans, and on applied aspects, providing a sound basis for economic development of ocean areas.     

DECOMPOSING REGIONAL EFFICIENCY*

ABSTRACT Applying an outlier robust extension of the data envelopment analysis (DEA) followed by a geoadditive regression analysis, this study identifies and decomposes the efficiency of 439 German regions in using infrastructure and human capital. The findings show that the regions’ efficiency is driven by a spatial and a nonspatial, arguably structural factor. As a consequence, concrete regional funding schemes, shaped by best practice results, might not be appropriate for all regions. Instead, a more differentiated funding scheme that accounts for both spatial and structural factors seems more promising.     

On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

Monitoring population characteristics and their patterns of spatial evolution are fundamental components for urban management and policy decision‐making. Societal issues such as health, transport, or crime are often explored using a range of models describing the urban dynamics of population attributes at specific scales that can be seen as complementary. Using and simulating data at different scales of aggregation asks for the need to analyze and compare spatiotemporal variations in order to better understand the model behaviors and emerging properties of the geosimulation. This article analyzes the uses of the entropy measure in the literature and constraining factors needed for its potential extension to explore the variations in geographic and time scales. In particular, the article discusses the need for a truly spatial entropy that takes into account the spatial contiguities of the observations usually aggregated within a zoning system of areal units. Two generic solutions are exposed for the various geometries and attribute structures used for census‐related analyses; they are based on existing measures for point data using (i) co‐occurrences of observations and (ii) discriminant ratios of distances between groups of observations. Their extensions to areal compositional data are articulated around their conceptual changes and geocomputational challenges. A revisited and new version of the entropy decomposition theorem, encompassing a spatiality concept semantically related to correlation, is also presented as efficiently reusing the constrained hierarchical zoning system of administrative units to enable discovery of emerging spatial pattern features from the geosimulation. A comparison of the results between the classical use of entropy and the spatial entropy framework devised shows the flexibility and added capabilities of the approach for new types of analyses, thus allowing new insight into studies of population dynamics.