1,720,994 research outputs found
A distance correlation index of spatial dependence for compositional data
Full text linkGeographical data in economic, social or environmental sciences are usually recorded as compositions, i.e. relative frequencies, and a common inquiring problem concerns the analysis of these data over different geographical regions. In the present paper we define a new statistical test to verify spatial dependence of such geographical distributions based on distance correlation, a recently introduced measure of dependence between random vectors. The proposed index computes the non-linear spatial distance between distributions and can be applied on compositional frequency distributions. Simulations and an application on Italian electoral data are presented, to illustrate the capabilities of the proposed test to detect spatial dependence. © 2019 The Author(s). Papers in Regional Science © 2019 RSA
Spatial econometric methods in agricultural economics using R
No full textThe collection of spatial agricultural data and the spatial analysis of agriculture represent two issues of primary relevance for a large number of people. This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data. Hence, the book represents a comprehensive guide in methodological and empirical advanced techniques for practitioners.
This volume can also be considered as a primary tool for users from less developed countries, where agriculture is still the prevalent economic sector. Therefore, different contributions may guide one through the application of spatial survey methods, technologies developed in the past decades, such as remote sensing and GIS, and appropriate methods to analyse spatial agricultural data. Applied spatial analysts might also benefit from this work. In particular, a part of the book is devoted to the integration techniques used to merge agricultural data from different sources. Finally, both people from Academic institutions and National Statistical Offices may appreciate the occasion of deepening their knowledge of spatial techniques for agriculture.
Although the book could also represent a valued support on spatial methodologies in agriculture for graduate classes, the primary audience is mainly composed by researchers with some prior background in econometrics and spatial statistics.
The main objective of this book is to introduce agricultural economists to statistical approaches for the analysis of spatial data. The aim is to illustrate, for the main typologies of agricultural data, the most appropriate methods for the analysis, together with a description of available data sources and collection methods.
Spatial econometrics methods for different types of data are described and adopted with reference to typical analyses of agricultural economics. Topics such as spatial interpolation, point patterns, spatial autocorrelation, survey data analysis, small area estimation, regional data modelling, and spatial econometrics techniques are covered jointly with issues arising from the integration of several data types. Besides, the different phases of agricultural data collection, analysis, and integration are described in a simple way. The joint use of statistical methods, new technologies, and economic theory is treated considering the peculiarities of spatial data for a proper and efficient analysis of agricultural data.
Theoretical aspects of each model are described and complemented by examples on real data that are developed by using the open-source R software. The codes are available in the text, explained with details and in an intuitive way so that the readers can replicate these analyses on their own data. Moreover, any prior knowledge of the R programming environment is not assumed throughout the book.
The volume is organized in a number of review chapters on several specific themes. In particular, this book contains 13 Chapters, of which the first one can be considered as an introductory chapter, reviewing the main underlying concepts and presenting each contribution.
We would like to thank Alfredo Cartone for reading some parts of this book and for his support in the implementation of some R codes. Thanks also to Vijay Primlani of Science Publishers, CRC Press, for his continuous encouragement to complete this book. Finally, we are grateful to the individual chapter authors for their diligence in writing the documents. We are confident that their work will lead to new insights in the application of spatial econometric methods to agricultural data
Handling Out-of-Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy
No full textUnemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out-of-sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in-sample as well as out-of-sample areas. Extensive simulation experiments are used to assess the impact of the auto-regression parameter and of the rate of out-of-sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed
Spatial network sampling in small area estimation
Full text linkThe spatial distribution of a population represents an important in sampling designs where that use the network of the contiguities between units as auxiliary information in the frame. Its use is increased in the last decades as the GIS and GPS technologies made more and more cheap to add information regarding the exact or estimated position for each record in the frame. These data may represent a source of auxiliaries that can be helpful to design effective sampling strategies, which, assuming that the observed phenomenon is related with the spatial features of the population, could gather a considerable gain in their efficiency by a proper use of this particular information. This assumption is particularly relevant if we are dealing with not planned geographical domains or, in other terms, if we want that the design will be efficient for a future use within a small area estimation context. A method for selecting samples from a spatial finite population that are well spread over the population in every dimension should guarantee that the variability of the expected sampling ratio should be smaller than that obtained by using a simple random sampling. Some algorithms of sample selection are presented such that a set of units with higher within distance will be selected with higher probability than a set of nearby units. Some examples on real data show that the RMSE of the EBLUP estimates applied to samples selected with these network methods are lower than those obtained by using a classical solution as the Generalized Random Tessellation Stratified (GRTS). The proposed algorithm, even if in its nature it is computationally intensive, seems to be a feasible solution even when dealing with frames relevant to large finite network populations
Identification of spatially constrained homogeneous clusters of COVID-19 transmission in Italy
No full textThe paper introduces an approach to identify a set of spatially constrained homogeneous areas maximally homogeneous in terms of epidemic trends. The proposed hierarchical algorithm is based on the Dynamic Time Warping distances between epidemic time trends where units are constrained by a spatial proximity graph. The paper includes two different applications of this approach to Italy, based on different data (number of positive test and number of differential deaths, with respect to the previous years) and on different observational units (provinces and Labour Market Areas). Both applications, above all the one related to Labour Market Areas, show the existence of well-defined areas, where the dynamics of growth of the infection have been strongly differentiated. The adoption of the same lock-down policy throughout the entire national territory has been therefore sub-optimal, showing once again the urgent need for local data-driven policies
Sampling and modelling issues using big data in now-casting
No full textThe use of Big Data and, more specifically, Google Trends data in nowand forecasting, has become common practice nowadays, even by Institutes and
Organizations producing official statistics worldwide. However, the use of Big Data
has many neglected implications in terms of model estimation, testing and forecasting, with a significant impact on final results and their interpretation. Using a
MIDAS model with Google Trends covariates, we analyse sampling error issues and
time-domain effects triggered by these digital economy new data sources
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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