Five innovative methods of establishing the population characteristics of small areas are introduced and evaluated in this book. Changes in communities can be slow, but recent history has seen huge growth in some areas and depopulation of others. As a result, population estimating has grown up under pressure from legislators and administrators who place a high premium on validity. The contributors to this volume provide ideas that have been tested in practice, anticipate the usual types of error, and are suitable for different purposes.

Providing a unified and comprehensive treatment of the theory and techniques of sub-national population estimation, this much-needed publication does more than collate disparate source material. It examines hitherto unexplored methodological links between differing types of estimation from both the demographic and sample-survey traditions and is a self-contained primer that combines academic rigor with a wealth of real-world examples that are useful models for demographers. Between censuses, which are expensive, administratively complex, and thus infrequent, demographers and government officials must estimate population using either demographic modeling techniques or statistical surveys that sample a fraction of residents. These estimates play a central role in vital decisions that range from funding allocations and rate-setting to education, health and housing provision. They also provide important data to companies undertaking market research. However, mastering small-area and sub-national population estimation is complicated by scattered, incomplete and outdated academic sources—an issue this volume tackles head-on. Rapidly increasing population mobility is making inter-census estimation ever more important to strategic planners. This book will make the theory and techniques involved more accessible to anyone with an interest in developing or using population estimates.

Excerpt from Small-Area Population Estimates-Methods and Their Accuracy and New Metropolitan Area Definitions and Their Impact on the Private and Public Sector: Papers Presented at the Conference on Small-Area Statistics, American Statistical Association, Houston, Texas, August 11, 1980 Although Ericksen (1973, 1974) argues that regression models incorporating sample data are useful in estimating county populations, this approach is not often found in actual practice. Consequently, the discussion of the regression models and procedures in this report is directed toward complete rather than sample data. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.