BigQuery와 같은 데이터 웨어하우스에서 위치 정보는 일반적이며 중요한 비즈니스 결정에 영향을 줄 수 있습니다. 지리정보 분석을 사용하면 GEOGRAPHY 데이터 유형과 GoogleSQL 지리 함수를 사용하여 BigQuery에서 지리공간 데이터를 분석하고 시각화할 수 있습니다.
예를 들면 시간의 경과에 따라 배송 차량 또는 패키지의 위도와 경도를 기록할 수 있습니다. 고객 트랜잭션을 기록하고 데이터를 점포 위치 데이터가 있는 다른 테이블에 조인할 수도 있습니다. 이러한 유형의 위치 데이터를 사용하여 다음을 수행할 수 있습니다.
Python용 BigQuery 클라이언트 라이브러리에서만 GEOGRAPHY 데이터 유형이 지원됩니다. 다른 클라이언트 라이브러리의 경우 ST_ASTEXT 또는 ST_ASGEOJSON 함수를 사용하여 GEOGRAPHY 값을 문자열로 변환합니다.
ST_ASTEXT를 사용하여 텍스트로 변환하면 하나의 값만 저장되며, WKT로 변환하면 데이터가 GEOGRAPHY 유형 대신 STRING 유형으로 주석 처리됩니다.
할당량
지리정보 분석의 할당량 및 한도는 지리정보 데이터가 포함된 테이블에 실행할 수 있는 여러 작업 유형에 적용되며, 여기에는 다음 작업 유형이 포함됩니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[[["\u003cp\u003eGeospatial analytics in BigQuery allows for the analysis and visualization of location data, utilizing geography data types and GoogleSQL geography functions.\u003c/p\u003e\n"],["\u003cp\u003eLocation data, such as latitude and longitude, is commonly used in data warehouses to inform critical business decisions, like delivery times or targeted marketing.\u003c/p\u003e\n"],["\u003cp\u003eGeospatial analytics has some limitations, including being exclusively available in GoogleSQL and with the BigQuery client library for Python being the only one to directly support the \u003ccode\u003eGEOGRAPHY\u003c/code\u003e data type.\u003c/p\u003e\n"],["\u003cp\u003eThe use of geospatial analytics in BigQuery incurs costs based on data storage and query execution, with certain operations like loading, copying, and exporting data being free, but still subject to quotas and limits.\u003c/p\u003e\n"],["\u003cp\u003eSeveral resources are available for those wishing to learn more, including getting started guides, visualization options, and information on working with geospatial data and GoogleSQL functions.\u003c/p\u003e\n"]]],[],null,["# Introduction to geospatial analytics\n====================================\n\nIn a data warehouse like BigQuery, location information is\ncommon and can influence critical business decisions. You can use geospatial\nanalytics to analyze and visualize geospatial data in BigQuery\nby using the\n[`GEOGRAPHY` data type](/bigquery/docs/reference/standard-sql/data-types#geography_type)\nand\n[GoogleSQL geography functions](/bigquery/docs/reference/standard-sql/geography_functions).\n\nFor example, you might record the latitude and longitude of your delivery\nvehicles or packages over time. You might also record customer transactions and\njoin the data to another table with store location data. You can use this type\nof location data to do the following:\n\n- Estimate when a package is likely to arrive.\n- Determine which customers should receive a mailer for a particular store location.\n- Combine your data with percent tree cover from satellite imagery to decide if delivery by aerial drone is feasible.\n\nLimitations\n-----------\n\nGeospatial analytics is subject to the following limitations:\n\n- [Geography functions](/bigquery/docs/reference/standard-sql/geography_functions) are available only in GoogleSQL.\n- Only the BigQuery client library for Python supports the `GEOGRAPHY` data type. For other client libraries, convert `GEOGRAPHY` values to strings by using the `ST_ASTEXT` or `ST_ASGEOJSON` function. Converting to text using `ST_ASTEXT` stores only one value, and converting to WKT means that the data is annotated as a `STRING` type instead of a `GEOGRAPHY` type.\n\nQuotas\n------\n\nQuotas and limits on geospatial analytics apply to the different types of\njobs you can run against tables that contain geospatial data, including the\nfollowing job types:\n\n- [Loading data](/bigquery/quotas#load_jobs) (load jobs)\n- [Exporting data](/bigquery/quotas#export_jobs) (export jobs)\n- [Querying data](/bigquery/quotas#query_jobs) (query jobs)\n- [Copying tables](/bigquery/quotas#copy_jobs) (copy jobs)\n\nFor more information on all quotas and limits, see [Quotas and limits](/bigquery/quotas).\n\nPricing\n-------\n\nWhen you use geospatial analytics, your charges are based on the\nfollowing factors:\n\n- How much data is stored in the tables that contain geospatial data\n- The queries you run against the data\n\nFor information on storage pricing, see [Storage pricing](/bigquery/pricing#storage).\n\nFor information on query pricing, see [Analysis pricing models](/bigquery/pricing#analysis_pricing_models).\n\nMany table operations are free, including loading data, copying tables, and\nexporting data. Though free, these operations are subject to\nBigQuery's [Quotas and limits](/bigquery/quotas). For information\non all free operations, see [Free operations](/bigquery/pricing#free) on the\npricing page.\n\nWhat's next\n-----------\n\n- To get started with geospatial analytics, see [Get started with geospatial analytics](/bigquery/docs/geospatial-get-started).\n- To learn more about visualization options for geospatial analytics, see [Visualize geospatial data](/bigquery/docs/geospatial-visualize).\n- To learn more about working with geospatial data, see [Work with geospatial data](/bigquery/docs/geospatial-data).\n- To learn more about working with raster data, see [Work with raster data](/bigquery/docs/raster-data).\n- To learn more about incorporating Google Earth Engine geospatial data into BigQuery, see [Load Google Earth Engine geospatial data](/bigquery/docs/geospatial-data#load-ee).\n- For documentation on GoogleSQL functions in geospatial analytics, see [Geography functions in GoogleSQL](/bigquery/docs/reference/standard-sql/geography_functions).\n- To learn about different grid systems, see [Grid systems for spatial analysis](/bigquery/docs/grid-systems-spatial-analysis).\n- To learn more about geospatial datasets and geospatial analytics and AI, see [Geospatial Analytics](/solutions/geospatial)."]]
