"}],"short_description":"A practical guide to including embeddings in the most common spatial analytics workflows","flexible_content":[{"acf_fc_layout":"content","content":"Embeddings are rapidly becoming a powerful tool in a data scientist\u2019s toolkit, but many GIS professionals are still exploring how to incorporate them into spatial analysis. If you\u2019re curious about enriching your spatial workflows with embeddings, this guide will help you get started and highlight best practices, common pitfalls, and practical tips.<\/p>\n
<\/p>\n
Embeddings are useful representations of complex data that capture semantic meaning in a way that traditional variables cannot. You can create embeddings yourself, but some data providers have done the heavy lifting for you! There are many types of embeddings out there, from text to satellite imagery to geodemographic data. In spatial analysis, embeddings can add rich information to your geographic features, opening new doors for prediction, clustering, and similarity search.<\/p>\n
For a comprehensive introduction to embeddings, check out this<\/a> blog.<\/p>\n <\/p>\n How do you tie embeddings to your spatial problems?<\/p>\n For example, if you\u2019re working with census tracts, use tract-level embeddings and join them using a common identifier (like FIPS codes) or a one-to-one spatial join.<\/p>\n If you only have embeddings at a finer scale (e.g., H3 hexagons) but need them at a larger polygon level, you can try to summarize the embeddings within your polygons of interest. Aggregation methods may include average, median, standard deviation, min, max, range, or even a combination of these. However, this is still an open area of research, aggregate with caution, as you may lose important information.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2982166,"id":2982166,"title":"embdoc1","filename":"embdoc1.png","filesize":399992,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/including-embeddings-in-your-spatial-analysis-workflows\/embdoc1","alt":"On the left are H3 Level 7 hexagons containing embeddings, symbolized by a randomly selected dimension, with voting precincts overlaid. On the right are the precincts with the hexagon level embeddings aggregated to the larger polygon using an intersection and an average, symbolized by the same dimension as on the left. Aggregating embeddings to other scales is an ongoing area of research with unknown implications.","author":"437350","description":"","caption":"On the left are H3 Level 7 hexagons containing embeddings, symbolized by a randomly selected dimension, with voting precincts overlaid. On the right are the precincts with the hexagon level embeddings aggregated to the larger polygon using an intersection and an average, symbolized by the same dimension as on the left. Aggregating embeddings to other scales is an ongoing area of research with unknown implications.","name":"embdoc1","status":"inherit","uploaded_to":2982164,"date":"2026-09-14 22:39:11","modified":"2026-09-14 22:39:43","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":780,"height":581,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","medium-width":350,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","medium_large-width":768,"medium_large-height":572,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","large-width":780,"large-height":581,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","1536x1536-width":780,"1536x1536-height":581,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","2048x2048-width":780,"2048x2048-height":581,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1-624x465.png","card_image-width":624,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc1.png","wide_image-width":780,"wide_image-height":581}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" If your geometries are smaller than the embedding scale (e.g., using points with polygon embeddings), you may see repeated values and therefore little variation. This is related to the ecological fallacy<\/a>, which is the problem created by applying aggregate-level statistics to individual observations. To address this, consider using embeddings at a more appropriate scale, trying spatial thinning of your points, interpolating embedding values to a surface, or accepting repeated values (proceed with caution when altering either embedding data or your input data).<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2982167,"id":2982167,"title":"embdoc2","filename":"embdoc2.png","filesize":143852,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/including-embeddings-in-your-spatial-analysis-workflows\/embdoc2","alt":"grey wolf sightings from iNaturalist layered over H3 Level-7 hexagons containing embeddings. There are multiple points in many of the hexagons, which would lead to repeated enriched values.","author":"437350","description":"","caption":"Grey wolf sightings from iNaturalist layered over H3 Level-7 hexagons containing embeddings. There are multiple points in many of the hexagons, which would lead to repeated enriched values.","name":"embdoc2","status":"inherit","uploaded_to":2982164,"date":"2026-09-14 22:40:18","modified":"2026-09-14 22:40:30","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":624,"height":377,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","medium-width":432,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","medium_large-width":624,"medium_large-height":377,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","large-width":624,"large-height":377,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","1536x1536-width":624,"1536x1536-height":377,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","2048x2048-width":624,"2048x2048-height":377,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","card_image-width":624,"card_image-height":377,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc2.png","wide_image-width":624,"wide_image-height":377}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" <\/p>\n When embeddings are added to models like Generalized Linear Regression<\/a>, coefficients on traditional variables may change. Interpret these coefficients with caution. Ask yourself: \u201cWhat am I controlling for when including embeddings as predictors?\u201d<\/p>\n <\/p>\n Embeddings are ideal for similarity search<\/a> and clustering<\/a>, but there are important considerations:<\/p>\n Embeddings offer exciting new possibilities for spatial analysis, but they require thoughtful integration into your workflows. Start with best practices, experiment carefully, and always validate your results. As the research evolves, so will the techniques for making the most of embeddings in GIS.<\/p>\n Have you tried using embeddings in your spatial analyses? Share your experiences and tips in the comments below!<\/p>\n <\/p>\n Introductory Embeddings Blog<\/a><\/p>\n Esri Geodemographic Embeddings<\/a><\/p>\nEnriching Embeddings to Your Geometry<\/strong><\/h2>\n
As a best practice, always use embeddings generated at the same scale as your analysis.<\/h3>\n
Any aggregation can dilute the information contained in embeddings. Use these techniques judiciously.<\/h3>\n
Predicting with Embeddings<\/strong><\/h2>\n
Embeddings can be powerful predictors when combined with traditional variables, often improving model performance over using either alone. This is true for models ranging from linear regression to machine learning and deep learning.<\/h3>\n"},{"acf_fc_layout":"image","image":{"ID":2982271,"id":2982271,"title":"embdoc3again","filename":"embdoc3again.png","filesize":34460,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/including-embeddings-in-your-spatial-analysis-workflows\/embdoc3again","alt":"Example R\u00b2 chart comparing models using traditional variables only, embedding dimensions only, and combining the two. The optimal performance metrics come with the combination. This success is dependent on the traditional variables and the embeddings being properly selected for the use case.","author":"437350","description":"","caption":"Example R\u00b2 chart comparing models using traditional variables only, embedding dimensions only, and combining the two. The optimal performance metrics come with the combination. This success is dependent on the traditional variables and the embeddings being properly selected for the use case.","name":"embdoc3again","status":"inherit","uploaded_to":2982164,"date":"2026-09-16 15:55:25","modified":"2026-09-16 15:55:45","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2148,"height":1294,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again.png","medium-width":433,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again.png","medium_large-width":768,"medium_large-height":463,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again.png","large-width":1793,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again-1536x925.png","1536x1536-width":1536,"1536x1536-height":925,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again-2048x1234.png","2048x2048-width":2048,"2048x2048-height":1234,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again-772x465.png","card_image-width":772,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc3again-1793x1080.png","wide_image-width":1793,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"
Embeddings are not easily interpretable as individual vectors.<\/h3>\n
More explanatory variables require more observations, especially in linear models. A good rule of thumb: have at least 10 observations per explanatory variable.<\/h3>\n
Similarity Search & Clustering with Embeddings<\/strong><\/h2>\n
Embeddings are already on an ideal scale of values for analysis. Standardizing may remove meaningful semantic information.<\/h3>\n
Mixing embeddings with traditional variables in similarity metrics is not recommended.<\/h3>\n
Similarity and clustering algorithms (like k-means or nearest neighbor) weight all embedding dimensions equally. The \u201cmost similar\u201d results reflect this equal weighting.<\/h3>\n"},{"acf_fc_layout":"image","image":{"ID":2982169,"id":2982169,"title":"embdoc4","filename":"embdoc4.png","filesize":406110,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/including-embeddings-in-your-spatial-analysis-workflows\/embdoc4","alt":"multivariate clusters calculated on aggregated embeddings at the voter precinct level","author":"437350","description":"","caption":"Multivariate clusters calculated on aggregated embeddings at the voter precinct level","name":"embdoc4","status":"inherit","uploaded_to":2982164,"date":"2026-09-14 22:43:04","modified":"2026-09-14 22:43:18","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":780,"height":430,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","medium-width":464,"medium-height":256,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","medium_large-width":768,"medium_large-height":423,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","large-width":780,"large-height":430,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","1536x1536-width":780,"1536x1536-height":430,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","2048x2048-width":780,"2048x2048-height":430,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","card_image-width":780,"card_image-height":430,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/embdoc4.png","wide_image-width":780,"wide_image-height":430}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"
<\/h2>\n
Final Thoughts<\/strong><\/h2>\n
Further Reading<\/h2>\n