2 edition of application of a spatial regression model to the analysis and mapping of poverty found in the catalog.
application of a spatial regression model to the analysis and mapping of poverty
by Food and Agriculture Organization of the United Nations in Rome
Written in English
|Statement||by Alessandra Petrucci, Nicola Salvati, Chiara Seghieri.|
|Series||Environment and natural resources series -- no. 7.|
|Contributions||Salvati, Nicola., Seghieri, Chiara., Food and Agriculture Organization of the United Nations. Sustainable Development Dept.|
|The Physical Object|
|Pagination||vii, 54 p. :|
|Number of Pages||54|
Spatial Analysis and Modeling in Geographical Transformation Process: GIS-based Applications Yuji Murayama, Rajesh B. Thapa (auth.), Yuji Murayama, Rajesh Bahadur Thapa (eds.) Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote. Geospatial Analysis (6th edition) provides a compehensive guide to concepts, methods and tools, with many examples being provided using a variety of software tools such as ArcGIS, Idrisi, Grass, Surfer and many others to clarify the concepts discussed/5(5).
Spatial Regression Models By Michael Ward and Kristian Gleditsch. This book illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including. Regression analysis issues. OLS regression is a straightforward method, has well-developed theory behind it, and has a number of effective diagnostics to assist with interpretation and troubleshooting. OLS is only effective and reliable, however, if your data and regression model meet/satisfy all the assumptions inherently required by this method (see the table below).
This study investigates an approach based on the spatial regression model, for map- ping poverty in Ecuador. 2 Generalized spatial linear models. This study applies a spatial analysis to determine those variables that aﬀect house- hold poverty and to estimate the number of poor people in the target areas. This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis.
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Therefore, methods which use spatial analysis tools are required to explore such spatial dimensions of poverty and its linkages with the environmental conditions.
This study investigates an approach based on the spatial regression model, for mapping poverty in Ecuador. The application of a spatial regression model to the analysis and mapping of poverty Author: Alessandra Petrucci ; Nicola Salvati ; Chiara Seghieri ; Food and Agriculture Organization of.
The Application of a Spatial Regression Model to the Analysis and Mapping of Poverty. Alessandra Petrucci Professor of Statistics, University of Florence. Nicola Salvati Project Assistant University of Florence. Chiara Seghieri Project Assistant, University of Florence. The application of a spatial regression model to the analysis and mapping of poverty (E) Availability: May Ar Arabic Muitil Multilingual C Chinese Out of print E English In preparation F French P Portuguese S Spanish The FAO Technical Papers are available through the authorized FAO Sales Agents or directly from Sales and Marketing Group, FAO, Viale delle Terme di Caracalla.
Application of Spatial Regression Models to Income Poverty Ratios in Middle Delta Contiguous Counties in Available via license: CC BY Content may be subject to copyright. Pattern Analysis and cluster mapping made easy About This Book Analyze patterns, clusters, and spatial relationships using ArcGIS tools Get up to speed in R programming to create custom tools for analysis Sift through tons of crime and real estate data and analyze it using the tools built in the book Who This Book Is For This book is for ArcGIS developers who want to perform complex geographic analysis through the use of spatial statistics tools.
There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics.
Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method. Regression analysis allows you to Model, examine, and explore spatial relationships Predict Coefficients for percent rural and low-weight births T-scores show where this relationship is significant.
Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis.
The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with.
Taking advantage of the analytical capability of GIS, the study incorporated spatial variables in the multiple regression analysis, namely: agro-climatic conditions, access to road infrastructure, and proximity to major markets; together with the influence of land distribution program, fiscal decentralization policy, and population growth, in Cited by: The application of a spatial regression model to the analysis and mapping poverty By Alessandra Petrucci, Nicola Salvati, Chiara Seghieri University of Florence Poverty mapping in developing countries has become an important tool in identifying ways to improve living standards.
Residual spatial autocorrelation undermines other forms of regression analysis including Poisson regression used to model small area count data (e.g., numbers of cases of a disease by area). A solution to this problem may entail the use of a spatial random effects term in the specification of the Poisson mean (Griffith and Haining, ; Law.
Spatial Analysis, GIS, and Remote Sensing Applications in the Health Sciences Editors Donald Wilbert Barbara Levergood Ann Arbor Press Chelsea, Michigan This edition published in the Taylor & Francis e-Library, “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others.
This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis.
The release of the Community Health Status Indicators (CHSI) database provides ready access to a rich compilation of data for researchers and individuals interested in the health of communities (1). CHSI data cover a wide range of county-level attributes that describe the sociodemographic context in which people by: Michael D.
Ward is Professor of Political Science at Duke University. He is an affiliate of the Duke Network Analysis Center. His primary interests are in international relations (spanning democratization, globalization, international commerce, military spending, as well as international conflict and cooperation), political geography, as well as mathematical and statistical methods.5/5(1).
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and.
Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.
economic growth, inequality and poverty. The growth model is the Solow-Swan () model as used by Barro and Sala-i-martin (). Solow-Swan growth model is a widely used growth model and is the starting point for almost all analyses of growth.
Using of spatial econometrics is the value added of this paper. The case of the study isFile Size: KB. Just adjacent to the coordinate display is the scale display.
This shows the scale of the map at the view setting you have chosen (it will change as you zoom in or out). Take note of the scale selector that allows you to choose between predefined scales that range from to ,Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical ng on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics.Scale, aggregations, and distance are two key concepts in spatial data analysis that can be tricky to come to grips with.
This chapter ﬁrst discusses scale and related concepts resolution, aggregation and zonation. The second part of the chapter discusses distance and adjacency.