The European Enlargement Process and Regional Convergence Revisited: Spatial Effects Still Matter.

This paper has two main goals. First, it reconsiders regional growth and convergence processes in the context of the enlargement of the European Union to new member states. We show that spatial autocorrelation and heterogeneity still matter in a sample of 237 regions over the period 1993-2002. Spatial convergence clubs are defined using exploratory spatial data analysis and a spatial autoregressive model is estimated. We find strong evidence that the growth rate of per capita GDP for a given region is positively affected by the growth rate of neighbouring regions. The second objective is to test the robustness of the results with respect to non-normality, outliers and heteroskedasticity using two other methods: The quasi maximum Likelihood and the Bayesian estimation methods.

Residential sorting across Auckland neighbourhoods

This paper addresses the extent to which people in Auckland exhibit residential location patterns that differ between groups, i.e. the extent to which they are spatially sorted. To measure patterns of residential location, the paper uses the index of segregation, an isolation index, Gini coefficients, Ellison & Glaeser and Maurel & Sédillot concentration measures, Moran’s I and Getis and Ord’s G*. Results are presented based on a classification of the population in different ways: ethnicity, income, education, age and country of birth. Both city-wide and local measures are considered. We find that ethnic-based sorting is the strongest indicator of residential sorting patterns, but soring by income, education and age is also present. Sorting by income and qualifications is strongest at the top and, to lesser extent, at the bottom of the income and qualifications range. Age segregation is most pronounced for older residents. Clustering is strongest within a range of up to one kilometre and declines significantly over greater distances. Local analysis by means of Getis and Ord’s G* calculations suggest significant ethnic clustering. Apart from Maori and Pacific Islanders, ethnic groups tend to locate way from each other, as confirmed with cross-Moran’s I calculations. When considering interactions between ethnicity and income we find that the location of ethnicity income subgroups is more strongly related to neighbourhood income

Computation-free Nonparametric testing for Local and Global Spatial Autocorrelation with application to the Canadian Electorate

Measures of local and global spatial association are key tools for exploratory spatial data analysis. Many such measures exist including Moran's $I$, Geary's $C$, and the Getis-Ord $G$ and $G^*$ statistics. A parametric approach to testing for significance relies on strong assumptions, which are often not met by real world data. Alternatively, the most popular nonparametric approach, the permutation test, imposes a large computational burden especially for massive graphical networks. Hence, we propose a computation-free approach to nonparametric permutation testing for local and global measures of spatial autocorrelation stemming from generalizations of the Khintchine inequality from functional analysis and the theory of $L^p$ spaces. Our methodology is demonstrated on the results of the 2019 federal Canadian election in the province of Alberta. We recorded the percentage of the vote gained by the conservative candidate in each riding. This data is not normal, and the sample size is fixed at $n=34$ ridings making the parametric approach invalid. In contrast, running a classic permutation test for every riding, for multiple test statistics, with various neighbourhood structures, and multiple testing correction would require the simulation of millions of permutations. We are able to achieve similar statistical power on this dataset to the permutation test without the need for tedious simulation. We also consider data simulated across the entire electoral map of Canada.Comment: 22 pages, 7 figure

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in network, rather than spatial, data, motivated by applications in social network data. We demonstrate that existing tests for autocorrelation in spatial data for continuous variables and our new test for categorical variables can both be used in the network setting

Recent Advances in Spatial Data Analysis

This article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heart of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, the ecological fallacy problem, the modifiable areal unit problem, boundary and frame effects, and the spatial dependence problem. The problem of spatial dependence is at the core of modern spatial analysis and requires the use of specialised techniques and models in the data analysis. The discussion focuses on exploratory techniques and model-driven [confirmatory] modes of analysing spatial point patterns and area data. In closing, prospects are given towards a new style of data-driven spatial analysis characterized by computational intelligence techniques such as evolutionary computation and neural network modeling to meet the challenges of huge quantities of spatial data characteristic in remote sensing, geodemographics and marketing. (author's abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Comparing Three Spatial Cluster Tests from Rare to Common Spatial Events

In the past few years, several new tests for spatial clustering have been proposed. With ever increasing capability of GIS and wider availability of spatial statistic functions, spatial analysts are likely to face challenge of properly using these tests. Seemingly gaps also exist between the development of new tests and follow up evaluations against various assumptions. In this research note, I compare three cluster tests along a range of distribution from rare to common spatial events. The results not only revealed sensitive data feature that each test is designed to detect, but also clarified the interpretation based on the nature of the test

Does Geography Explain Differences in Economic Growth in Peru?

In Peru, a country with an astonishing variety of different ecological areas, including 84 different climate zones and landscapes, with rainforests, high mountain ranges and dry deserts, the geographical context may not be all that matters, but it could be very significant in explaining regional variations in income and welfare. The major question this paper tries to answer is: what role do geographic variables, both natural and manmade, play in explaining per capita expenditure differentials across regions within Peru? How have these influences changed over time, through what channels have they been transmitted, and has access to private and public assets compensated for the effects of an adverse geography? We have shown that what seem to be sizable geographic differences in living standards in Peru can be almost fully explained when one takes into account the spatial concentration of households with readily observable non-geographic characteristics, in particular public and private assets. In other words, the same observationally equivalent household has a similar expenditure level in one place as another with different geographic characteristics such as altitude or temperature. This does not mean, however that geography is not important but that its influence on expenditure level and growth differential comes about through a spatially uneven provision of public infrastructure. Furthermore, when we measured the expected gain (or loss) in consumption from living in one geographic region (i. e. , coast) as opposed to living in another (i. e. , highlands), we found that most of the difference in log per-capita expenditure between the highland and the coast can be accounted for by the differences in infrastructure endowments and private assets. This could be an indication that the availability of infrastructure could be limited by the geography and therefore the more adverse geographic regions are the ones with less access to public infrastructure. It is important to note that there appear to be non-geographic, spatially correlated, omitted variables that need to be taken into account in our expenditure growth model. Therefore policy programs that use regional targeting do have a rationale even if geographic variables do not explain the bulk of the difference in regional growth, once we have taken into account differentials in access to private and public assets.

Adverse Geography and Differences in Welfare in Peru

regional economics, spatial distribution, welfare, poverty, Peru