Spatial econometrics is a cross-disciplinary field of study that crosses into statistics, economics, regional science and econometrics. The field originally evolved out of econometrics, which mixes statistics and math with economics. The spatial aspect of econometrics leads to a study of how spatial relationships, or geography, affects the study of analysis of statistical data, providing a geographically centered interpretation of a set of data.
Regression analysis is used by practitioners of spatial econometrics, who use such an approach for building statistical models and tests. With regression analysis, researchers look at one dependent variable, and one or more independent variables. When the value of the dependent variable changes, researchers try to predict how the change will affect the values of the independent variables. These variables involve spatial relationships, such as what part of a city different people included in a statistical model call home, or how the proximity of certain geographical features affect the prices of homes in different parts of a country.
Those who use spatial econometrics to study relationships between various variables do so in a way that is different from spatial statistics. Instead of focusing just on the relationships between the data collected by researchers, researchers concentrate on a theoretical model to understand how changes in data through regression analysis are affected by geography. This focus on theoretical models also determines what variables in a set of data interest someone using spatial econometrics. The limits of a theoretical model are estimated by researchers using regression analysis.
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The ultimate goal of spatial econometrics is to not only understand what has occurred, but to also make educated predictions about future events. Researchers concentrate on how spatial relationships between these variables affect the ultimate outcomes of various events. Using the outcomes of such predictions, various organizations can prepare more fully for what would otherwise be unpredictable spatially-driven events.
The application of spatial econometrics crosses into a wide variety of fields. House appraisers, real estate investors and other professions that need to measure and predict pricing trends in real estate depend on spatial econometrics. Political scientists use data to study the effect of the neighborhood a person lives in and how that person votes in elections. Public health administrators use the demographic data gathered by spatial econometrists to understand better how disease is spread throughout a population. Criminologists might use similar information to study factors that contribute to specific types of criminal activity in different geographical areas.