A Statistical Toolbox For Mining And Modeling Spatial Data

Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis

On the Stability of the German Beveridge Curve. A Spatial Econometric Perspective

In this paper, we use the Beveridge relationship to address the effectiveness of the matching process, that brings workers searching for jobs together with employers searching for workers. For a fixed matching technology, the curve yields a negative relation between the unemployment rate and the rate of vacancies. Movements along a curve reflect adjustments over the business cycle. In a recession vacancies are closed, and workers enter the unemployed. Shifts of the curve are more important here, as they point to structural change. For example, an outward shift of the curve indicates an in-creased mismatch, perhaps due to a deterioration in human capital of the unemployed or changes in the unemployment benefit system, which affects the willingness of the un-employed to fill out vacancies. Empirical estimates rely on panel data. A sample of 180 regional labour markets is em-ployed, and the sample period runs from 1993 to 2004. The regional labour markets are seperated on the base of flows of the job commuters and correspond to travel-to-work areas. Due to common or idiosyncratic shocks, however, the cross sections are not inde-pendent. Instead, they are tied together to some extent, and the spillovers account for spatial effects. As these patterns can have an impact on the correlation between unem-ployment and vacancy rates, the results of OLS regressions are eventually biased. Thus the Beveridge curve is efficiently estimated by a spatial procedure, where regional de-pendencies are taken into account. No previous paper has investigated a similar broad regional dataset so far. The eigenfunction decomposition approach suggested by Griffith (1996, 2000) is used to identify spatial and non-spatial components in regression analysis. As the spatial pat-tern may vary over time, inference is conducted on the base of a spatial seemingly unre-lated regressions (spatial SUR) model. Due to this setup, efficient estimates for the Beveridge relationship are obtained. Time dummies are used to control for shifts in the curve. The empirical results provide some indication that the degree of job mismatch has increased over the recent periods.

Modeling Spatial Autocorrelation in Spatial Interaction Data: A Comparison of Spatial Econometric and Spatial Filtering Specifications

The need to account for spatial autocorrelation is well known in spatial analysis. Many spatial statistics and spatial econometric texts detail the way spatial autocorrelation can be identified and modelled in the case of object and field data. The literature on spatial autocorrelation is much less developed in the case of spatial interaction data. The focus of interest in this paper is on the problem of spatial autocorrelation in a spatial interaction context. The paper aims to illustrate that eigenfunction-based spatial filtering offers a powerful methodology that can efficiently account for spatial autocorrelation effects within a Poisson spatial interaction model context that serves the purpose to identify and measure spatial separation effects to interregional knowledge spillovers as captured by patent citations among high-technology-firms in Europe.

Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida

Social media services generate enormous amounts of spatiotemporal data that can be used to characterize and analyse user activities and social behaviour. Although crowdsourced data have the advantage of comprehensive spatial and temporal coverage compared to data collected in more traditional ways, the various social media platforms target different user groups, which leads to user selection bias. Since data from social media platforms are used for a variety of geospatial applications, understanding such differences and their implications for analysis results is important for geoscientists. Therefore, this research analyses differences in spatial and temporal contribution patterns to three online platforms, namely Flickr, Twitter and Snapchat, over a six-week period in Florida. For the comparison of spatial contribution patterns, a set of negative binomial regression models are estimated to identify which socio-economic factors and characteristics of the built and natural environments are associated with contribution activities. The contribution differences observed are discussed in light of the targeted user groups and different purposes of the three platforms

Knowledge Production in Nanomaterials: An Application of Spatial Filtering to Regional Systems of Innovation

Nanomaterials are seen as a key technology for the 21st Century, and much is expected of them in terms of innovation and economic growth. They could open the way to many radically new applications, which would form the basis of innovative products. In this context, it seems all the more important for regions to put their own innovation systems in place, and to ensure that they offer a suitable location for such activities in order to benefit from the expected growth. Many regions have already done so by establishing ?science parks? and ?nanoclusters?. As nanomaterials are still in their infancy, both public research institutes and private businesses could play a vital role in the process. This paper investigates what conditions and configurations allow a regional innovation system to be competitive in a cutting-edge technology like nanomaterials. We analyse European Patent Office data at the German district level (NUTS-3) on applications for nanomaterial patents, in order to chart the effects of localised research and development (R&D) in the public and private sector. We estimate two negative binomial models in a knowledge production function framework and include a spatial filtering approach to adjust for spatial effects. Our results indicate that there is a significant positive effect of both public and private R&D on the production of nanomaterial patents. Moreover, we find a positive interaction between them which hints at the importance of their co-location for realising the full potential of an emerging technology like nanomaterials. --nanotechnology,innovation,patents,Germany,spatial autocorrelation,spatial filtering

Regional Unemployment in the EU before and after the Global Crisis

In this paper, we have empirically assessed the evolution of European regions in terms of both employment and unemployment during the recent financial crisis and Global Recession. Our specific research questions were as follows: (i) has there been a reversal in employment and unemployment dynamics at a regional level, during the crisis (2007–10) compared to the previous period (2004–07)? (ii) have the western regions in ‘old’ EU states behaved differently in response to the crisis compared with the eastern regions of the NMS? Finally, (iii) are the differences between the two groups of regions related to structural or institutional variables? After a review of the literature on the key determinants of regional unemployment, we have summarized our main findings concerning the Global Crisis’ impact on the labour market. Our econometric investigation aimed to answer the questions we have posed. Structural characteristics have been considered in terms of sector specialization of regional economies. In addition, we have considered certain institutional characteristics, by including indicators of the share of temporary workers and of long-term unemployed. Our analysis has then been targeted at the sub-samples of western- and eastern-European regions: we show that the critical factors for labour market performance during the crisis in these two groups differs greatly. From a methodological viewpoint, we have exploited a spatial filtering technique which allowed us to greatly reduce any unobserved variable bias – a significant problem in cross-sectional models – by accounting for latent unobserved spatial patterns.crisis, employment, unemployment, European Union, NUTS-2, spatial filtering, sectoral composition, spatially heterogeneous parameters

Persistent Disparities in Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany

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 analysing 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. In the paper 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.unemployment persistence, dynamic panel, hysteresis, spatial filtering, fixed effects

Persistence of Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany

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 analysing 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 parameters. 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 parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.unemployment persistence, dynamic panel, hysteresis, spatial filtering, fixed effects