Provided herein are systems and methods for an iterative approach to topic modeling and the use of web mapping technology to implement advanced spatial operators for interactive high-dimensional visualization and inference.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of constructing a base map, the method comprising: processing data items to create modified data items; removing stop topics from the modified data items; processing of modified data item to create topic model usable data format to form a topic model; processing the topic model into a self organizing map (SOM) to form a geometric data structure; and providing the geometric data structure into a geographic information system (GIS) to form a base map. 2. The method of claim 1, wherein the processing data to create a topic model usable data format comprises: receiving a plurality of text documents; and selecting a portion from each of the plurality of text documents to create a plurality of modified text documents. 3. The method of claim 2, further comprising: converting the modified text documents into a topic model usable format. 4. The method of claim 3, wherein the topic model usable format comprises a plurality of individual modified text documents as plain text files or a single plain text file including line-separated text content of a plurality of modified text documents. 5. The method of claim 3, further comprising: reformatting the topic model usable format into an ingestable topic model format. 6. The method of claim 1, wherein the topic model comprises a Latent Dirichlet allocation (LDA) topic model. 7. The method of claim 1, wherein the removing stop topics from the modified data items to form a topic model comprises: receiving topic model usable data; and identifying a number of topics to use in the topic model. 8. The method of claim 7, further comprising: determining a topic model output including a plurality of top words and top phrases for each topic. 9. The method of claim 8, wherein the topic model output further comprises: a list of data items each having weighted values for topic model topics; and a topic model inferencer configured to provide the weighted values for the topic model topics. 10. The method of claim 8, further comprising: identifying stop topics based on top words and top phrases for the topic model topics. 11. The method of claim 10, further comprising: identifying stop phrases and stop terms based on top terms and top phrases of the identified stop topics; and removing stop phrases and stop terms from the topic model usable data format to form a topic model. 12. The method of claim 1, wherein the base map comprises a plurality of layered geometric structures, symbolization and labeling. 13. The method of claim 12, wherein the layered geometric structures comprise: SOM neurons structured as polygons and point centroids of those polygons, with associated attributes including topic weights for all topics, row-normalized weights for all topics, and rank order of each topic based on the row-normalized weights; raster structure as interpolated from row-normalized topic weights for all neurons; or clusters of SOM neurons as polygon features, with different clustering solutions represented in separate polygon layers. 14. The method of claim 12, wherein the symbolization comprises: contour line symbolization and hill shading of an interpolated raster structure; or delineation and coloring of boundaries of neuron clusters, or coloring of cluster areas. 15. The method of claim 12, wherein the labeling comprises: within a cluster layer, top words and phrases of the highest ranked topics for each cluster being used as label text and placed such that the shape and extent of each cluster in the two-dimensional space is approximated; and for a separate polygon layer representing a cluster solution, a separate label layer is generated and is displayed within specified zoom levels. 16. The method of claim 1, wherein removing stop topics from the modified data items to be processed to form a topic model comprises an automated or manual iterative loop. 17. The method of claim 16, wherein the automated iterative loop comprises comparing stop topics to a stored library of stop topics. 18. The method of claim 1, further comprising: providing a query text; receiving an inference and mapping the inference onto the base map based on the similarity of an inferred query topic vector to SOM neuron topic vectors; and storing the query text and the inferred query topic vector. 19. The method of claim 18, wherein the query topic vector is determined as the weighted distribution of topic model topics for the query text. 20. The method of claim 18, further comprising: providing a discrete overlay or continuous overlay onto the base map to form an inference layer. 21. The method of claim 20, further comprising: storing the inference layer as a new base map. 22. A computer system for constructing a base map, the computer system comprising: a data store configured to store data; a computer-readable tangible medium comprising software instructions; and a processor configured to access the computer-readable tangible medium to load and execute the software instructions to: process data items from the data store to create modified data items; remove stop topics from the modified data items; process modified data items into a topic model usable data format to form a topic model; process the topic model into a self organizing map (SOM) to form a geometric data structure; and provide the geometric data structure into a geographic information system (GIS) to form a base map.