Читать книгу Spatial Regression Models for the Social Sciences - Jun Zhu - Страница 11

 Understand the current status of spatial social science research.

 Understand basic concepts and terminologies related to spatial effects.

 Familiarize yourself with the primary data example of population change that is used throughout this book.

This is a book for social scientists who want to learn spatial regression models with relative ease. But first of all, why should social scientists care about studying spatial regression models? Many statistical methods could be useful to social science research; learning any of them could take one considerable effort and also sometimes involve a steep learning curve.

We believe that quantitative social scientists, especially those who deal with aggregated quantitative data, can benefit from learning and using spatial regression models from at least three perspectives—theoretical, methodological, and practical. First, theoretically, many phenomena of the social sciences exhibit spatial effects. This has been explained both explicitly and implicitly by many theories and examined in many empirical studies. Second, methodologically, the standard linear regression analysis may not be reliable when the independence assumption of the model is not adequately met; if spatial effects are not properly accounted for, estimation and statistical inference may be unreliable (e.g., the effects of explanatory variables may be overstated or understated). Third, practically, the past twenty years have experienced a rapid increase in the use of spatial regression models for social science research—first human geography and regional science and then other social science disciplines, including anthropology, criminology, demography, economics, political science, urban studies and urban planning, and sociology. The bar for quantitative social science research has been raised, and conducting spatial regression analysis can be methodologically more rigorous in many research areas.

Why has there been a rapid increase in the use of spatial regression models for social science research? Because in addition to the recognition of their usefulness in social science research, spatial regression models have also become more accessible for social scientists to explore (Chi & Zhu, 2008). Multiple factors are associated with this increased accessibility, such as the following:

 the upsurge in the availability of geographically referenced data,

 the development of user-friendly spatial data analysis software packages, and

 increased computing power combined with affordable computers.

Geographically referenced data from geographic information system (GIS) sources and remote sensing images are often useful for social scientists and can be easily added to geo-referenced social science databases. Three resources are especially useful for researchers conducting spatial demographic analysis: the topologically integrated geographic encoding and reference (TIGER) system products; census summary files of 1980, 1990, 2000, and 2010, as well as the American Community Survey data; and sociological and demographic survey databases companioned with the geocoding technique. Moreover, development of spatial statistical software packages has been rapid in the past two decades, via products such as ArcGIS, GeoDa, GWR, MATLAB, Python, SAS, SpaceStat, S-plus, SPSS, Stata, and WinBugs. Improved computing power together with inexpensive personal computers has made conducting spatial regression analysis more affordable for social scientists. Furthermore, greater opportunities for studying spatial regression have arisen due to the larger number of textbooks, journal articles, and conference presentations advancing or using spatial data analytical tools (Entwisle, 2007; Florax & Van der Vlist, 2003; Logan, 2012). And this number is only growing.

Considering the opportunities for and development in spatial regression methods, we assert that it is an opportune time for social scientists to make better use of spatial regression methods and apply them to social science research. We believe that spatial regression models and methods can be learned with relative ease, and this book is intended to help readers do just that.