{"id":9548,"date":"2020-10-30T06:01:54","date_gmt":"2020-10-30T13:01:54","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=9548"},"modified":"2025-06-13T22:45:07","modified_gmt":"2025-06-14T05:45:07","slug":"geospatial-search-how-do-i-use-thee-let-me-count-the-ways","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/geospatial-search-how-do-i-use-thee-let-me-count-the-ways\/","title":{"rendered":"Using NoSQL for Geospatial Search Without a Spatial Database"},"content":{"rendered":"

Introduction<\/span><\/h2>\n

Couchbase Full Text Search<\/a> (FTS) is a great tool for indexing and querying geospatial data<\/a>.\u00a0 In this article, I\u2019ll present a geospatial search use case and demonstrate the various ways that we can perform a search of location data using the Couchbase Full Text Search service.\u00a0 I\u2019ll be using Couchbase Server Enterprise Edition 6.6 (running <\/span>locally in Docker<\/span><\/a>) to create an FTS index on my sample geospatial dataset and then run geospatial queries against the index.<\/span><\/p>\n

What makes this case interesting is that we aren’t using a spatial database. What is a spatial database?<\/a> Unlike Couchbase Server, which is a NoSQL document database, spatial databases are specially optimized for data that describes geometric spaces such as lines, points of interest, or even 3-D topology in advanced instances. As we’ll see, Couchbase’s Full Text Search capabilities make it just as useful for handling and querying geospatial data as anything we might expect from a more specialized solution.<\/p>\n

Use Case<\/span><\/h3>\n

My family has always enjoyed visiting and exploring Great Smoky Mountains National Park<\/a> (or GRSM, the National Park Service\u2019s abbreviation), and one day we might be interested in relocating there. But you can\u2019t live in the national park, so we need to consider the various cities and towns near the park and make a short list of the ones to evaluate and possibly visit.\u00a0 <\/span><\/p>\n

The main objective is to be within close proximity to the national park, but we\u2019ll consider other factors like the size (population) of the towns, too. <\/span><\/p>\n

Sample Dataset<\/span><\/h3>\n

To support my GRSM use case, I\u2019ve decided to use a public dataset from <\/span>GeoNames<\/span><\/a> that includes states, cities, towns, and other landmarks from various nations around the world.\u00a0 I downloaded their <\/span>United States data file<\/span><\/a> and imported into Couchbase only the \u201cpopulated places\u201d data (records with <\/span>feature codes<\/span><\/a> of ‘PPL’, ‘PPLA’, ‘PPLA2’, ‘PPLA3′,’PPLA4’, and ‘PPLC’) for cities\/towns with a non-zero population. The result is a Couchbase bucket \u201ccities\u201d with 30,734 documents.\u00a0\u00a0<\/span><\/p>\n

Each city\u2019s document data model includes some attributes of interest for my GRSM use case: the name, state, population, elevation, and, most importantly, the latitude and longitude.\u00a0 Here are a couple of sample JSON documents:<\/span><\/p>\n

city::4699066<\/b><\/p>\n

{\r\n  \"geonameid\": 4699066,\r\n  \"featureclass\": \"P\",\r\n  \"featurecode\": \"PPLA2\",\r\n  \"name\": \"Houston\",\r\n  \"state\": \"TX\",\r\n  \"population\": 2296224,\r\n  \"elevation\": 12,\r\n  \"geo\": {\r\n    \"lat\": 29.76328,\r\n    \"lon\": -95.36327\r\n  }\r\n}\r\n<\/pre>\n
 <\/p>\n
city::4649251<\/b><\/p>\n
{\r\n  \"geonameid\": 4649251,\r\n  \"featureclass\": \"P\",\r\n  \"featurecode\": \"PPL\",\r\n  \"name\": \"Pigeon Forge\",\r\n  \"state\": \"TN\",\r\n  \"population\": 6171,\r\n  \"elevation\": 305,\r\n  \"geo\": {\r\n    \"lat\": 35.78842,\r\n    \"lon\": -83.55433\r\n  }\r\n}\r\n<\/pre>\n
Creating the Index<\/span><\/h2>\n
With the city data loaded into the cities bucket in Couchbase, we can build an FTS index that suits the \u201clive near GRSM\u201d use case at hand.\u00a0 I\u2019ll briefly cover the highlights of creating the index required here, and the full index definition is below in the appendix of the post. (For a more detailed explanation of creating FTS indexes, please refer to <\/span>my blog post on the topic<\/span><\/a>.)<\/span><\/p>\n
Index creation<\/a> key points:\u00a0<\/span><\/p>\n