Spatial econometrics uses regression analysis to study how geography influences statistical data. It focuses on theoretical models to predict future events and is used in various fields such as real estate, politics, public health, and criminology.
Spatial econometrics is an interdisciplinary field of study spanning statistics, economics, regional science and econometrics. The field originally evolved from econometrics, which mixes statistics and mathematics with economics. The spatial aspect of econometrics leads to a study of how spatial relationships, or geography, influence the study of statistical data analysis, providing a geographically centered interpretation of a data set.
Regression analysis is used by spatial econometrics professionals, who use this approach to build 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 which part of a city different people in a statistical model call home, or how the proximity of certain geographic features affects house prices in different parts of a country.
Those who use spatial econometrics to study the relationships between various variables do so differently from spatial statistics. Instead of focusing only on the relationships between the data researchers have collected, the researchers focus on a theoretical model to understand how changes in data through regression analysis are affected by geography. This focus on theoretical models also determines which variables in a data set are of interest to someone using spatial econometrics. The limits of a theoretical model are estimated by researchers using regression analysis.
The ultimate goal of spatial econometrics is not only to understand what happened, but also to make educated predictions about future events. Researchers focus on how the spatial relationships between these variables influence the final outcomes of various events. Using the results of such forecasts, various organizations can prepare more fully for otherwise unpredictable space-driven events.
The application of spatial econometrics cuts across a wide range of fields. Home appraisers, real estate investors, and other professions that need to measure and predict price trends in real estate depend on spatial econometrics. Political scientists use the data to study the effect of which neighborhood a person lives in and how that person votes in elections. Public health administrators use demographic data gathered by spatial economists to better understand how disease spreads in a population. Criminologists could use similar information to study factors that contribute to specific types of criminal activity in different geographic areas.
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