Systems and methods of establishing correlative relationships between geospatial data features in feature vectors representing property locations

A system for automatically appraising property value in view of features located within a predetermined vicinity of a property, the system comprising:

• a non-transitory computer readable storage region configured to store a plurality of feature vectors;
• a plurality of machine learning data models, each machine learning data model of the plurality of machine learning data models trained with a respective data set corresponding to at least a portion of a plurality of types of geospatial information system (GIS) features, wherein
• the respective data set corresponding to one or more types of GIS features of the plurality of types of GIS features comprises a set of amplifying data obtained from at least one respective external data source of one or more external data sources;
• and
• at least one processor configured to determine a predetermined boundary surrounding a property location,
• obtain, from a GIS information data set, a plurality of sets of feature data comprising a respective set of feature data for each respective GIS feature of a plurality of GIS features located within or overlapping with the predetermined boundary surrounding the property location, wherein
• the plurality of GIS features comprises one or more of a set of amenity features, a set of transportation features, a set of land use features, a set of economic features, or a set of public utility features,
• each GIS feature of the plurality of GIS features comprises a respective location identified using respective geographic coordinates representing a line, a point, or a polygon, and
• each set of feature data comprises a respective type of the respective GIS feature, and a respective identifier of the respective GIS feature,
• store, to the non-transitory computer readable storage region, the plurality of sets of feature data in a respective feature vector of the plurality of feature vectors, wherein the respective feature vector is associated with the property location,
• access a plurality of amplifying data features collected from at least one of the one or more external data sources, each amplifying data feature related to at least one respective GIS feature of the plurality of GIS features, wherein
• each amplifying data feature of the plurality of amplifying data features expands a knowledge scope of the at least one respective GIS feature, and
• each amplifying data feature of the plurality of amplifying data features is accessed using at least one of the identifier of the at least one respective GIS feature or the geographic coordinates of the at least one respective GIS feature,
• add each of the plurality of amplifying data features to the corresponding set of feature data in the feature vector of the property location, and
• for each respective set of feature data of the plurality of sets of feature data of the feature vector, identify, using at least one machine learning data model of the plurality of machine learning data models, an amount of correlation between the respective set of feature data and a property value of the property location, wherein
• the at least one machine learning data model comprises, for each respective GIS feature type of a subset of the plurality of types of GIS features, one or more machine learning data models trained with the respective GIS feature type, wherein
• the subset of the plurality of types of GIS features is represented in the feature vector.