![]() This means that, even for aspatial, “non-geographic” data, you can use spatial feature engineering to create useful, highly relevant features for your analysis.Īt its core, spatial feature engineering is the process of developing additional information from raw data using geographic knowledge. Thus, space is the ultimate linkage key, allowing us to connect different datasets together in order to improve our models and our predictions. And, while things that happen near one another in time do not necessarily have a fundamental relationship, things that are near one another are often related. Geography is one of the most high-quality, ubiquitous ways to introduce domain knowledge into a problem: everything has a position in both space and time. While feature engineering always relies on this implicit domain knowledge, it is an extremely important part of adapting general-purpose algorithms to unique or distinctive problems facing the every-day data scientist. ![]() Often, this involves some sort of transformation of the original dataset, which is a well-studied concept in both classical statistics and remains so in machine learning methods. In traditional machine learning, feature engineering involves applying additional domain knowledge to raw information in order to structure it in a manner that is meaningful for a model. ![]() Where data is available, but not yet directly usable, feature engineering helps to construct representations or transformations of the original data that are more useful for modeling a given phenomenon of interest. However, we often need to be able to construct useful features from this rich and deep sea of data. Indeed, given the constellation of packages to query data services, free and open source data-sets, and the rapid and persistent collection of geographical data, there is simply too much data to even represent coherently in a single, tidy fashion. In machine learning and data science, we are often equipped with tons of data. ![]()
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