Assessing clustering methods for exploratory spatial data analysis

Exploratory spatial data analysis continues to be an important area of research. The use and application of clustering methods for the analysis of spatially referenced data is beginning to show some promise. However, a variety of clustering methods does exist. It is essential that a better understanding of these approaches in the geographic domain be pursued in terms of data requirements, computational efficiencies and inherent biases. This paper presents an initial attempt to demonstrate strengths and weaknesses of various clustering approaches for exploratory spatial data analysis.

Exploratory spatial data analysis with GEOXP

GEOX is a computer package of Splus and Matlab routines implementing interactive graphics methods for exploring spatial data. We analyse a large data basis from the regional public health insurance agency concerning physicians'' activity in the Midi-Pyrénées region. We evaluate in particular heterogeneity and outliers in the density of physicians, their prescriptions per patient, salaries, number of visits per patient, etc.. We examine spatial dependencies of the main variables and thus locate spatial clusters. We attempt to explain the patterns of the prescription by some characteristics of the physicians together with the socio-economic characteristics of the counties using a spatial regression model with autocorrelated errors involving a hierarchical structure since these two sets of variables are known at a different level: physician level or county level.

Exploratory spatial data analysis with GEOXP

GEOX is a computer package of Splus and Matlab routines implementing interactive graphics methods for exploring spatial data. We analyse a large data basis from the regional public health insurance agency concerning physicians' activity in the Midi-Pyrénées region. We evaluate in particular heterogeneity and outliers in the density of physicians, their prescriptions per patient, salaries, number of visits per patient, etc.. We examine spatial dependencies of the main variables and thus locate spatial clusters. We attempt to explain the patterns of the prescription by some characteristics of the physicians together with the socio-economic characteristics of the counties using a spatial regression model with autocorrelated errors involving a hierarchical structure since these two sets of variables are known at a different level: physician level or county level

SPACE-TIME LAGS: SPECIFICATION STRATEGY IN SPATIAL REGRESSION MODELS

he purpose of this article is to analyse the dynamic trend of spatial dependence, which is not only contemporary but time-lagged in many socio-economic phenomena. Firstly, we show some of the commonly used exploratory spatial data analysis (ESDA) techniques and we propose other new ones, the exploratory space-time data analysis (ESTDA) that evaluates the instantaneity of spatial dependence. We also propose the space-time correlogram as an instrument for a better specification of spatial lag models, which should include both kind of spatial dependence. Some applications with economic data for Spanish provinces shed some light upon these issues.Spatial dependence, spatial diffusion, ESDA, correlogram, Spanish provinces

The evolution of the spatial and sectoral patterns in Ile-de-France over 1978-1997

The aim of this paper is to analyze the intra-urban spatial distribution of employment in the agglomeration of Ile-de-France in 1978 and 1997. In that purpose, exploratory spatial data analysis is used in order to identify employment centers and a sectoral analysis of the CBD and the subcenters is performed. Our results highlight a suburbanization process of employment between 1978 and 1997 in Ile-de-France. A more polarized space emerges in 1997 compared to 1978 with several employment centers specialized in different activities. Moreover, even if the spatial influence of the CBD is diminishing during the study period, the CBD preserves its economic leadership by concentrating a large variety of high-order producer services. Keywords: exploratory spatial data analysis; employment centers; spatial autocorrelation; suburbanization JEL Classification: C12, R12, R14

Geography and economic performance: exploratory spatial data analysis for Great Britain

This paper uses the techniques of exploratory spatial data analysis to analyse patterns of spatial association for different indicators of economic performance, and in so doing identify and describe the spatial structure of economic performance for Great Britain. This approach enables us to identify a number of significant local regimes – clusters of areas in which income per worker differs significantly from the global average – and investigate whether these come about primarily through spatial association in occupational composition or in productivity. Our results show that the contributions of occupational composition and productivity vary significantly across local regimes. The ‘winner’s circle’ of areas in the south and east of England benefits from both above average levels of productivity and better than average occupational composition, while the low income regime in the north of England suffers particularly from poor occupational composition. Keywords; regional disparities, income per worker, productivity, occupational composition, spatial autocorrelation JEL Classification: O18, O4, R11, R12

The geography of French creative class: An exploratory spatial data analysis

This paper analyses the creative class geography in France, in 2006. This geography is seen here through the lens of Explanatory Spatial Data Analysis (ESDA). This method brings originality to the question of creative people geography in addition to the spatial context, France, where this question hasn’t been deepened yet. Methodology allows measurement of spatial agglomeration degree and identification of creative people location patterns. First, by computing locational Gini index and Moran’s I statistic of global spatial autocorrelation. These measures provide an overview of the spatial distribution of creative people among French districts and the existence of some hotspot regions with strong dynamic of creative people accumulation. Second, Exploratory Spatial Data Analysis (ESDA) tools, such as Moran scatterplot and LISA statistics, allow to identify district clusters of creative people. It leads to evidence that creative people are unevenly geographically distributed across French districts. District clusters of creative occupations result from spreading of French largest cities influence.Creative class, ESDA, location patterns, spatial autocorrelation, French districts

French creative clusters: An exploratory spatial data analysis

Since Florida (2002b) revealed the importance of creative skills on economic growth, the notion of "creative class" has been deepened to analyze factors that shape the geography of talent of creative people. In spite of some strong correlations between artistic or cultural vibrancy, Third places, diversity, and the presence of huge constellations of creative people in cities (Chantelot, 2009), urban size and economic opportunities appears to be the real reasons that encourage its mobility in Europe (Andersen and Lorenzen, 2009 ; Hansen and al., 2009 ; Grossetti, 2009). However, if a bulk of literature emerged in Northern Europe on these questions, the geography of the creative class in France really needs to be investigated. That's why we propose here an original approach lying on Exploratory Spatial Data Analysis. This paper analyses the agglomeration patterns for creative class in France in 1999 and 2006. We adopt a methodology allowing the measurement of the degree of spatial agglomeration and the identification of location patterns (Guillain and Le Gallo, 2008) of creative people. First, we compute the locational Gini coefficient and Moran's I statistics of global spatial autocorrelation. We show that these measures provide different but complementary information about the spatial agglomeration of the creative class. Second, we use the tools of Exploratory Spatial Data Analysis. Moran scatterplots and LISA statistics allow us to shed light on clusters of French creative cities and to see how the different groups of creative people (bohemians - creative pro - creative core) affect the cluster's structures. First, we find that creative class is highly concentrated in French cities, more than employment and population. Second, we identify and analyze several clusters of French creative cities that show virtuous path of creative people accumulation since 1990

Quality of life in the regions: An exploratory spatial data analysis for West German labor markets

Which of Germanys regions is the most attractive? Where is it best to live and work - on objective grounds? These questions are summed up in the concept quality of life. This paper uses recent research projects that determine this parameter to examine the spatial distribution of quality of life in Germany. For this purpose, an Exploratory Spatial Data Analysis is conducted which focuses on identifying statistically significant (dis-)similarities in space. An initial result of this research is that it is important to choose the aggregation level of administrative units carefully when considering a spatial analysis. The level plays a crucial role in the strength and impact of spatial effects. In concentrating on various labor market areas, this paper identifies a significant spatial autocorrelation in the quality of life, which seems to be characterized by a North-Mid-South divide. In addition, the ESDA results are used to augment the regression specifications, which helps to avoid the occurrence of spatial dependencies in the residuals. --Quality of Life,Exploratory Spatial Data Analysis,Functional Economic Areas,Spatial Econometrics,LISA Dummies