Understanding the Socio-Economic Distribution and Consequences of Patterns of Multiple Deprivation: An Application of Self-Organising Maps. ESRI WP302. June 2009

In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial

Tools for spatial density estimation

The purpose of this talk is to illustrate the main features and applications of two new Stata programs for spatial density estimation: spgrid and spkde. The spgrid program generates two-dimensional arrays of evenly spaced points spanning across any regular or irregular study region specified by the user. In turn, the spkde program carries out spatial kernel density estimation based on reference points generated by spgrid.

Largo teso: The Seven Studies for guitar by Maurizio Pisati

In this contribution, composer and interpreter talk about the Seven Studies from their respective points of view. Maurizio Pisati explains how he developed a new guitar, departing from a single study and arriving at the overall formal conception through timbres, techniques and articulations; and how the soloistic studies led him to a guitarled ensemble piece. Elena Càsoli deals with issues such as the score\u27s indications and the instrumental techniques

Understanding the Socio-Economic Distribution and Consequences of Patterns of Multiple Deprivation: An Application of Self-Organising Maps

In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial.

Determinants of international tourist choices in Italian provinces: a joint demand-supply approach with spatial effects

Research trying to explain tourism flows and expenditures for different destinations has so far adopted either a tourism-demand or a tourism-supply approach. Whereas on the one hand the former ignores the product specificities (Papatheodorou, 2001), the latter, on the other, fails to take into account the characteristics of the tourist origin markets. In recent years attempts to merge the two views have come from scholars using spatial econometric techniques, i.e. origin-destination models (O-D), which have been able to consider both effects simultaneously (Marrocu and Paci, 2013; Massidda and Etzo, 2012). This paper contributes to this literature by investigating the determinants of the expenditures of foreign tourists in 103 Italian provinces (NUTS 3). We depart from the previous literature in that our dependent variable is not tourist flows but foreign tourist expenditures. This variable, recently made available by the Bank of Italy for the years 1997-2012, is more informative than tourist flows in that it captures not only the number of arrivals but also their contribution to a destination's GDP. The observations of our cross-section database reflect the tourist expenditure for each Italian province from each of the 20 highest spending countries of origin, accounting for 85% of total receipts. Without having to use O-D models, we will disentangle the effects of both demand and supply variables on a province's tourism exports. Among the former ones, per capita GDP levels at origin and a measure of relative price will be considered. Among the latter ones: per capita GDP levels at destination and supply variables such as capacity constraints of tourist accomodations; tourism and transport infrastructures; crime, cultural and environmental capital, climate, settlement structure typology, etc. Moreover, we will take into account the role of the distance between origin and destination, which is also a proxy of transportation costs, and the possible spillover effects originating by the supply variables in contiguous provinces. Following Halleck Vega and Elhorst (2013), spatial effects will be analysed using the spatial lag of some of the independent variables and by parameterizing the spatial matrix W. Moreover, we will use a Poisson pseudo-maximum-likelihood method as suggested by Santos Silva and Tenreyro (2006) since this method is robust to different patterns of heteroskedasticity and provides a natural way to deal with zeros in trade data

Packet Classification via Improved Space Decomposition Techniques

P ack et Classification is a common task in moder n Inter net r outers. The goal is to classify pack ets into "classes" or "flo ws" according to some ruleset that looks at multiple fields of each pack et. Differ entiated actions can then be applied to the traffic depending on the r esult of the classification. Ev en though rulesets can be expr essed in a r elati v ely compact way by using high le v el languages, the r esulting decision tr ees can partition the sear ch space (the set of possible attrib ute v alues) in a potentially v ery lar ge ( and mor e) number of r egions. This calls f or methods that scale to such lar ge pr oblem sizes, though the only scalable pr oposal in the literatur e so far is the one based on a F at In v erted Segment T r ee [1 ]. In this paper we pr opose a new geometric technique called G-filter f or pack et classification on dimensions. G-filter is based on an impr o v ed space decomposition technique. In addition to a theor etical analysis sho wing that classification in G-filter has time complexity and slightly super -linear space in the number of rules, we pr o vide thor ough experiments sho wing that the constants in v olv ed ar e extr emely small on a wide range of pr oblem sizes, and that G-filter impr o v e the best r esults in the literatur e f or lar ge pr oblem sizes, and is competiti v e f or small sizes as well

A Scalable Algorithm for Metric High-Quality Clustering in Information Retrieval Tasks

We consider the problem of finding efficiently a high quality k-clustering of n points in a (possibly discrete) metric space. Many methods are known when the point are vectors in a real vector space, and the distance function is a standard geometric distance such as L1, L2 (Euclidean) or L2 2 (squared Euclidean distance). In such cases efficiency is often sought via sophisticated multidimensional search structures for speeding up nearest neighbor queries (e.g. variants of kd-trees). Such techniques usually work well in spaces of moderately high dimension say up to 6 or 8). Our target is a scenario in which either the metric space cannot be mapped into a vector space, or, if this mapping is possible, the dimension of such a space is so high as to rule out the use of the above mentioned techniques. This setting is rather typical in Information Retrieval applications. We augment the well known furthest-point-first algorithm for kcenter clustering in metric spaces with a filtering step based on the triangular inequality and we compare this algorithm with some recent fast variants of the classical k-means iterative algorithm augmented with an analogous filtering schemes. We extensively tested the two solutions on synthetic geometric data and real data from Information Retrieval applications. The main conclusion we draw is that our modified furthest-point-first method attains solutions of better or comparable quality within a fraction of the time used by the fast k-means algorithm. Thus our algorithm is valuable when either real time constraints or the large amount of data highlight the poor scalability of traditional clustering methods

Mapping Patterns of Multiple Deprivation Using Self-Organising Maps: An Application to EU-SILC Data for Ireland. ESRI WP286. March 2009

The development of conceptual frameworks for the analysis of social exclusion has somewhat out-stripped related methodological developments. This paper seeks to contribute to this process through the application of self-organising maps (SOMs) to the analysis of a detailed set of material deprivation indicators relating to the Irish case. The SOM approach allows us to offer a differentiated and interpretable picture of the structure of multiple deprivation in contemporary Ireland. Employing this approach, we identify 16 clusters characterised by distinct profiles across 42 deprivation indicators. Exploratory analyses demonstrate that position in the income distribution varies systematically by cluster membership. Moreover, in comparison with an analogous latent class approach, the SOM analysis offers considerable additional discriminatory power in relation to individuals’ experience of their economic circumstances. The results suggest that the SOM approach could prove a valuable addition to a ‘methodological platform’ for analysing the shape and form of social exclusion

Understanding the socio-economic distribution and consequences of patterns of multiple deprivation: An application of self-organising maps

In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial